{ "cells": [ { "cell_type": "markdown", "id": "88f84c11", "metadata": {}, "source": [ "# LIANA x Tensor-cell2cell Quickstart (R)" ] }, { "cell_type": "markdown", "id": "aa297f63", "metadata": {}, "source": [ "This tutorial provides an abbreviated version of Tutorials 01-06 in order to give a quick overview of the pipeline to go from a raw counts matrix to downstream cell-cell communication (CCC) analyses. By combining LIANA and Tensor-cell2cell, we get a general framework that can robustly incorporate many existing CCC inference tools, ultimately retriving consensus communication scores for any sample and analyzing all those samples together to identify context-dependent communication programs. " ] }, { "cell_type": "markdown", "id": "ae4d56f2", "metadata": {}, "source": [ "## Initial Setups" ] }, { "cell_type": "markdown", "id": "f36813f6", "metadata": {}, "source": [ "### Enabling GPU use\n", "\n", "First, if you are using a NVIDIA GPU with CUDA cores, set `use_gpu=TRUE` and enable PyTorch with the following code block. Otherwise, set `use_gpu=FALSE` or skip this part" ] }, { "cell_type": "code", "execution_count": 1, "id": "0a2e08ca", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "use_gpu <- TRUE\n", "library(reticulate, quietly = TRUE)" ] }, { "cell_type": "code", "execution_count": 2, "id": "5149d25d", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "reticulate::use_condaenv(\"ccc_protocols\", required = TRUE)" ] }, { "cell_type": "code", "execution_count": 3, "id": "3f871c55", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "\n", "if (use_gpu){\n", " device <- 'cuda:0'\n", " tensorly <- reticulate::import('tensorly')\n", " tensorly$set_backend('pytorch')\n", "}else{\n", " device <- NULL\n", "}" ] }, { "cell_type": "markdown", "id": "8c731c0e", "metadata": {}, "source": [ "### Libraries\n", "Then, import all the packages we will use in this tutorial:" ] }, { "cell_type": "code", "execution_count": 4, "id": "ecb8e089", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "suppressMessages({\n", " library(dplyr, quietly = TRUE)\n", " library(tidyr, quietly = TRUE)\n", " library(purrr, quietly = TRUE)\n", " library(forcats, quietly = TRUE)\n", " library(textshape, quietly = TRUE)\n", " library(ggpubr, quietly = TRUE)\n", " library(rstatix, quietly = TRUE)\n", " library(reshape2, quietly = TRUE)\n", " library(tibble, quietly = TRUE)\n", " \n", " library(scater, quietly = TRUE)\n", " library(scuttle, quietly = TRUE)\n", " \n", " library(ggplot2, quietly = TRUE)\n", "\n", " library(liana, quietly = TRUE)\n", " library(decoupleR, quietly = TRUE)\n", "# library(\"ExperimentHub\")\n", " c2c <- reticulate::import(module = \"cell2cell\")\n", "})" ] }, { "cell_type": "code", "execution_count": 5, "id": "95a5fcbd", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# library(showtext)\n", "# showtext_auto()" ] }, { "cell_type": "markdown", "id": "cabd426f", "metadata": {}, "source": [ "### Directories\n", "\n", "Afterwards, specify the data and output directories:" ] }, { "cell_type": "code", "execution_count": 6, "id": "a8bc9c6b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "data_folder = '../../data/quickstart_pbmc/'\n", "output_folder = '../../data/quickstart_pbmc/outputs'\n", "\n", "for (folder_ in c(data_folder, output_folder)){\n", " if (!dir.exists(folder_)){\n", " dir.create(folder_)\n", " }\n", "}" ] }, { "cell_type": "markdown", "id": "c26b6554", "metadata": {}, "source": [ "### Loading Data\n", "\n", "We begin by loading the single-cell transcriptomics data. For this tutorial, we will use a dataset of ~25k PBMCs from 8 pooled patient lupus samples, each before and after IFN-beta stimulation ([Kang et al., 2018; GSE96583](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96583)). \n", "Originally preprocessed for [pertpy](https://github.com/theislab/pertpy).\n", "\n", "We can download the data and store it in the SingleCellExperiment format:" ] }, { "cell_type": "code", "execution_count": 7, "id": "a4a5f16d", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "sce <- readRDS(url('https://zenodo.org/records/10069528/files/kang_counts_25k.RDS?download=1'))" ] }, { "cell_type": "markdown", "id": "67bbb31a", "metadata": {}, "source": [ "## Preprocess Expression\n", "\n", "Note, we do not include a batch correction step as Tensor-cell2cell can get robust decomposition results without this; however, we have extensive analysis and discuss regarding this topic in [Supplementary Tutorial 01](./S1_Batch_Correction.ipynb)" ] }, { "cell_type": "markdown", "id": "5ddff77b", "metadata": {}, "source": [ "### Quality-Control Filtering\n", "\n", "The loaded data has already been pre-processed to a degree and comes with cell annotations. Nevertheless, let’s highlight some of the key steps. To ensure that any noisy cells or features are removed, we filter any non-informative cells and genes:" ] }, { "cell_type": "code", "execution_count": 8, "id": "c7a023f5", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# basic filters\n", "sce <- scater::addPerCellQC(sce)\n", "min.features <- 300\n", "min.cells <- 5" ] }, { "cell_type": "code", "execution_count": 9, "id": "67c660b3", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# basic outlier filtering\n", "qc <- scater::perCellQCMetrics(sce)\n", "\n", "# basic feature filtering\n", "sce <- sce[rowSums(counts(sce) >= 1) >= min.cells, ]\n", "\n", "# basic cells filtering\n", "sce <- sce[, colSums(counts(sce) >= 1) >= min.features]\n" ] }, { "cell_type": "markdown", "id": "62cc7303", "metadata": {}, "source": [ "We additionally remove high mitochondrial content:" ] }, { "cell_type": "code", "execution_count": 10, "id": "d3f04040", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "class: SingleCellExperiment \n", "dim: 14658 24297 \n", "metadata(0):\n", "assays(1): counts\n", "rownames(14658): AL627309.1 RP11-206L10.2 ... S100B PRMT2\n", "rowData names(1): name\n", "colnames(24297): AAACATACATTTCC-1 AAACATACCAGAAA-1 ... TTTGCATGGGACGA-2\n", " TTTGCATGTCTTAC-2\n", "colData names(18): nCount_RNA nFeature_RNA ... total\n", " subsets_Mito_percent\n", "reducedDimNames(2): X_pca X_umap\n", "mainExpName: NULL\n", "altExpNames(0):" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "is.mito <- grep(\"MT-\", rownames(sce))\n", "per.cell <- scuttle::perCellQCMetrics(sce, subsets=list(Mito=is.mito))\n", "colData(sce)[['subsets_Mito_percent']] <- per.cell$subsets_Mito_percent\n", "sce <- sce[, colData(sce)$subsets_Mito_percent < 15] \n", "sce" ] }, { "cell_type": "markdown", "id": "91a589ae", "metadata": {}, "source": [ "### Normalization" ] }, { "cell_type": "markdown", "id": "9c520a41", "metadata": {}, "source": [ "Normalized counts are usually obtained in two essential steps, the first being count depth scaling which ensures that the measured count depths are comparable across cells. This is then usually followed up with log1p transformation, which essentially stabilizes the variance of the counts and enables the use of linear metrics downstream:" ] }, { "cell_type": "code", "execution_count": 11, "id": "4cc7d7be", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "scale.factor <- 1e4\n", "lib.sizes <- colSums(counts(sce)) / scale.factor\n", "assay(sce, 'logcounts') <- scuttle::normalizeCounts(sce, size.factors=lib.sizes, log=FALSE, center.size.factors=FALSE)\n", "assay(sce, 'logcounts') <- log1p(assay(sce, 'logcounts'))" ] }, { "cell_type": "markdown", "id": "af583cf3", "metadata": {}, "source": [ "Let's see what our data looks like:" ] }, { "cell_type": "code", "execution_count": 12, "id": "9b0e451c", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "DataFrame with 6 rows and 18 columns\n", " nCount_RNA nFeature_RNA tsne1 tsne2 condition\n", " \n", "AAACATACATTTCC-1 3017 877 -27.640373 14.96663 ctrl\n", "AAACATACCAGAAA-1 2481 713 -27.493646 28.92489 ctrl\n", "AAACATACCATGCA-1 703 337 -10.468194 -5.98439 ctrl\n", "AAACATACCTCGCT-1 3420 850 -24.367997 20.42928 ctrl\n", "AAACATACCTGGTA-1 3158 1111 27.952170 24.15974 ctrl\n", "AAACATACGATGAA-1 1869 635 -0.470236 -25.39871 ctrl\n", " cluster cell_type patient nCount_SCT nFeature_SCT\n", " \n", "AAACATACATTTCC-1 9 CD14+ Monocytes patient_1016 1704 711\n", "AAACATACCAGAAA-1 9 CD14+ Monocytes patient_1256 1614 662\n", "AAACATACCATGCA-1 3 CD4 T cells patient_1488 908 337\n", "AAACATACCTCGCT-1 9 CD14+ Monocytes patient_1256 1738 653\n", "AAACATACCTGGTA-1 4 Dendritic cells patient_1039 1857 928\n", "AAACATACGATGAA-1 5 CD4 T cells patient_1488 1525 634\n", " integrated_snn_res.0.4 seurat_clusters sample cell_abbr\n", " \n", "AAACATACATTTCC-1 1 1 ctrl&1016 CD14\n", "AAACATACCAGAAA-1 1 1 ctrl&1256 CD14\n", "AAACATACCATGCA-1 6 6 ctrl&1488 CD4T\n", "AAACATACCTCGCT-1 1 1 ctrl&1256 CD14\n", "AAACATACCTGGTA-1 12 12 ctrl&1039 DCs \n", "AAACATACGATGAA-1 2 2 ctrl&1488 CD4T\n", " sum detected total subsets_Mito_percent\n", " \n", "AAACATACATTTCC-1 3017 877 3017 0\n", "AAACATACCAGAAA-1 2481 713 2481 0\n", "AAACATACCATGCA-1 703 337 703 0\n", "AAACATACCTCGCT-1 3420 850 3420 0\n", "AAACATACCTGGTA-1 3158 1111 3158 0\n", "AAACATACGATGAA-1 1869 635 1869 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(colData(sce))" ] }, { "cell_type": "markdown", "id": "5886a5c1", "metadata": {}, "source": [ "Define columns to use for downstream analysis:" ] }, { "cell_type": "code", "execution_count": 13, "id": "ca30ce60", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "sample_col <- 'sample'\n", "condition_col <- 'condition'\n", "idents_col <- 'cell_abbr' # cell types column from SCE metadata" ] }, { "cell_type": "markdown", "id": "27aea60f", "metadata": {}, "source": [ "## Deciphering Cell-Cell Communication\n", "\n", "we will use LIANA to infer the ligand-receptor interactions for each sample. LIANA is highly modularized and it natively implements the formulations of a number of methods, including CellPhoneDBv2, Connectome, log2FC, NATMI, SingleCellSignalR, CellChat, a geometric mean, as well as a consensus in the form of a rank aggregate from any combination of methods" ] }, { "cell_type": "code", "execution_count": 14, "id": "4cb6f37a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 'connectome'
  2. 'logfc'
  3. 'natmi'
  4. 'sca'
  5. 'cellphonedb'
  6. 'cytotalk'
  7. 'call_squidpy'
  8. 'call_cellchat'
  9. 'call_connectome'
  10. 'call_sca'
  11. 'call_italk'
  12. 'call_natmi'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'connectome'\n", "\\item 'logfc'\n", "\\item 'natmi'\n", "\\item 'sca'\n", "\\item 'cellphonedb'\n", "\\item 'cytotalk'\n", "\\item 'call\\_squidpy'\n", "\\item 'call\\_cellchat'\n", "\\item 'call\\_connectome'\n", "\\item 'call\\_sca'\n", "\\item 'call\\_italk'\n", "\\item 'call\\_natmi'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'connectome'\n", "2. 'logfc'\n", "3. 'natmi'\n", "4. 'sca'\n", "5. 'cellphonedb'\n", "6. 'cytotalk'\n", "7. 'call_squidpy'\n", "8. 'call_cellchat'\n", "9. 'call_connectome'\n", "10. 'call_sca'\n", "11. 'call_italk'\n", "12. 'call_natmi'\n", "\n", "\n" ], "text/plain": [ " [1] \"connectome\" \"logfc\" \"natmi\" \"sca\" \n", " [5] \"cellphonedb\" \"cytotalk\" \"call_squidpy\" \"call_cellchat\" \n", " [9] \"call_connectome\" \"call_sca\" \"call_italk\" \"call_natmi\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "liana::show_methods()" ] }, { "cell_type": "markdown", "id": "8bdaea49", "metadata": {}, "source": [ "LIANA classifies the scoring functions from the different methods into two categories: those that infer the “Magnitude” and “Specificity” of interactions. We define the “Magnitude” of an interaction as a measure of the strength of the interaction's expression, and the “Specificity” of an interaction is a measure of how specific an interaction is to a given pair of clusters. Generally, these categories are complementary, and the magnitude of the interaction is often a proxy of the specificity of the interaction. For example, a ligand-receptor interaction with a high magnitude score in a given pair of cell types is likely to also be specific, and vice versa. \n" ] }, { "cell_type": "markdown", "id": "553f8659", "metadata": {}, "source": [ "For clarity, here we map each output score (column names in the above dataframe) to the scoring method and scoring type:\n", "\n", "| Method Name | Magnitude Score | Specificity Score |\n", "| :- | -: | :-: |\n", "| CellPhoneDB | lr.mean | cellphonedb.pvalue\n", "| Connectome | prod_weight | weight_sc\n", "| log2FC | None | logfc_comb\n", "| NATMI | prod_weight | edge_specificity\n", "| SingleCellSignalR (sca) | LRscore | None" ] }, { "cell_type": "markdown", "id": "d56c861b", "metadata": {}, "source": [ "When considering ligand-receptor prior knowledge resources, a common theme is the trade-off between coverage and quality, and similarly each resource comes with its own biases. In this regard, LIANA builds on OmniPath29 as any of the resources in LIANA are obtained via OmniPath. These include the expert-curated resources of CellPhoneDBv223, CellChat27, ICELLNET30, connectomeDB202025, CellTalkDB31, as well as 10 others. LIANA further provides a consensus expert-curated resource from the aforementioned five resources, along with some curated interactions from SignaLink. In this protocol, we will use the consensus resource from liana, though any of the other resources are available via LIANA, and one can also use liana with their own custom resource." ] }, { "cell_type": "code", "execution_count": 15, "id": "348c57a9", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 'Default'
  2. 'Consensus'
  3. 'Baccin2019'
  4. 'CellCall'
  5. 'CellChatDB'
  6. 'Cellinker'
  7. 'CellPhoneDB'
  8. 'CellTalkDB'
  9. 'connectomeDB2020'
  10. 'EMBRACE'
  11. 'Guide2Pharma'
  12. 'HPMR'
  13. 'ICELLNET'
  14. 'iTALK'
  15. 'Kirouac2010'
  16. 'LRdb'
  17. 'Ramilowski2015'
  18. 'OmniPath'
  19. 'MouseConsensus'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'Default'\n", "\\item 'Consensus'\n", "\\item 'Baccin2019'\n", "\\item 'CellCall'\n", "\\item 'CellChatDB'\n", "\\item 'Cellinker'\n", "\\item 'CellPhoneDB'\n", "\\item 'CellTalkDB'\n", "\\item 'connectomeDB2020'\n", "\\item 'EMBRACE'\n", "\\item 'Guide2Pharma'\n", "\\item 'HPMR'\n", "\\item 'ICELLNET'\n", "\\item 'iTALK'\n", "\\item 'Kirouac2010'\n", "\\item 'LRdb'\n", "\\item 'Ramilowski2015'\n", "\\item 'OmniPath'\n", "\\item 'MouseConsensus'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'Default'\n", "2. 'Consensus'\n", "3. 'Baccin2019'\n", "4. 'CellCall'\n", "5. 'CellChatDB'\n", "6. 'Cellinker'\n", "7. 'CellPhoneDB'\n", "8. 'CellTalkDB'\n", "9. 'connectomeDB2020'\n", "10. 'EMBRACE'\n", "11. 'Guide2Pharma'\n", "12. 'HPMR'\n", "13. 'ICELLNET'\n", "14. 'iTALK'\n", "15. 'Kirouac2010'\n", "16. 'LRdb'\n", "17. 'Ramilowski2015'\n", "18. 'OmniPath'\n", "19. 'MouseConsensus'\n", "\n", "\n" ], "text/plain": [ " [1] \"Default\" \"Consensus\" \"Baccin2019\" \"CellCall\" \n", " [5] \"CellChatDB\" \"Cellinker\" \"CellPhoneDB\" \"CellTalkDB\" \n", " [9] \"connectomeDB2020\" \"EMBRACE\" \"Guide2Pharma\" \"HPMR\" \n", "[13] \"ICELLNET\" \"iTALK\" \"Kirouac2010\" \"LRdb\" \n", "[17] \"Ramilowski2015\" \"OmniPath\" \"MouseConsensus\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "liana::show_resources()" ] }, { "cell_type": "markdown", "id": "a6e4d9c6", "metadata": {}, "source": [ "Selecting any of the lists of ligand-receptor pairs in LIANA can be done through the following command (here we select the aforementioned \"consensus\" resource:" ] }, { "cell_type": "code", "execution_count": 16, "id": "27616d8a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "lr_pairs <- liana::select_resource('Consensus')" ] }, { "cell_type": "markdown", "id": "c2a468e2", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 17, "id": "50ea23fc", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# # check if ITGAD is any of the receptor.complex strings\n", "# df %>% filter(stringr::str_detect(receptor.complex, 'ITGAD'))" ] }, { "cell_type": "markdown", "id": "7ce5a810", "metadata": {}, "source": [ "Next, we can run LIANA on each sample across the available methods. By default, LIANA calculates an aggregate rank across these mtehods using a re-implementation of the [RobustRankAggregate]('https://doi.org/10.1093/bioinformatics/btr709') method, and generates a probability distribution for ligand-receptors that are ranked consistently better than expected under a null hypothesis (See Appendix 2). The consensus of ligand-receptor interactions across methods can therefore be treated as a p-value. " ] }, { "cell_type": "code", "execution_count": 18, "id": "9b2a85df", "metadata": { "scrolled": true, "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Current sample: ctrl&101\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“2414 genes and/or 0 cells were removed as they had no counts!”\n", "Warning message:\n", "“\u001b[1m\u001b[22m`invoke()` is deprecated as of rlang 0.4.0.\n", "Please use `exec()` or `inject()` instead.\n", "\u001b[90mThis warning is displayed once every 8 hours.\u001b[39m”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Warning message:\n", "“\u001b[1m\u001b[22m`progress_estimated()` was deprecated in dplyr 1.0.0.\n", "\u001b[36mℹ\u001b[39m The deprecated feature was likely used in the \u001b[34mliana\u001b[39m package.\n", " Please report the issue at \u001b[3m\u001b[34m\u001b[39m\u001b[23m.”\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: ctrl&107\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Warning message in exec(output, ...):\n", "“3320 genes and/or 0 cells were removed as they had no counts!”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD4T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD14”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and B”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and NK”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD8T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and FGR3”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and DCs”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: ctrl&1015\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“1038 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: ctrl&1016\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“1657 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: ctrl&1039\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Warning message in exec(output, ...):\n", "“3495 genes and/or 0 cells were removed as they had no counts!”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD4T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD14”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and B”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and NK”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD8T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and FGR3”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and DCs”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: ctrl&1244\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“1464 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: ctrl&1256\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“1313 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: ctrl&1488\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“1383 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&101\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Warning message in exec(output, ...):\n", "“2216 genes and/or 0 cells were removed as they had no counts!”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD4T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD14”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and B”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and NK”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD8T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and FGR3”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and DCs”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&107\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“3606 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&1015\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“1392 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&1016\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Warning message in exec(output, ...):\n", "“1920 genes and/or 0 cells were removed as they had no counts!”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD4T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD14”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and B”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and NK”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD8T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and FGR3”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and DCs”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&1039\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“3196 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&1244\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“2000 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&1256\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Cell identities with less than 5 cells: Mega were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“1471 genes and/or 0 cells were removed as they had no counts!”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Current sample: stim&1488\n", "\n", "Running LIANA with `cell_abbr` as labels!\n", "\n", "Warning message in exec(output, ...):\n", "“1227 genes and/or 0 cells were removed as they had no counts!”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD4T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD14”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and B”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and NK”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and CD8T”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and FGR3”\n", "Warning message in FUN(...):\n", "“no within-block comparison between Mega and DCs”\n", "LIANA: LR summary stats calculated!\n", "\n", "Now Running: Natmi\n", "\n", "Now Running: Connectome\n", "\n", "Now Running: Logfc\n", "\n", "Now Running: Sca\n", "\n", "Now Running: Cellphonedb\n", "\n", "Now aggregating natmi\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: connectome”\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: logfc”\n", "Now aggregating sca\n", "\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n", "Now aggregating natmi\n", "\n", "Now aggregating connectome\n", "\n", "Now aggregating logfc\n", "\n", "Warning message in exec(output, ...):\n", "“Unknown method name or missing specifics for: sca”\n", "Now aggregating cellphonedb\n", "\n", "Aggregating Ranks\n", "\n" ] } ], "source": [ "sce <- liana_bysample(sce = sce,\n", " sample_col = sample_col, # context dimension column from SCE metadata\n", " idents_col = idents_col, # cell types column from SCE metadata\n", " resource = 'Consensus', \n", " expr_prop = 0.1, # must be expressed in expr_prop fraction of cells\n", " min_cells = 5,\n", " assay.type = 'logcounts', # run on log- and library-normalized counts\n", " aggregate_how = 'both', # aggregate magnitude and specificity\n", " verbose = TRUE,\n", " inplace = TRUE,\n", " permutation.params=list(nperms=100),\n", " return_all=TRUE\n", " )" ] }, { "cell_type": "markdown", "id": "bd3756e7", "metadata": {}, "source": [ "The parameterrs used here are as follows:\n", " \n", "- `sce` stands for SingleCellExperiment, and we pass here with an object with a single sample/context.\n", "- `resource` is the name of any of the resources available in liana\n", "- `idents_col` corresponds to the cell group label stored in `adata.obs`.\n", "- `assay.type` indicates which counts assys in the SCE object to use, here the log-normalized counts are assigned to `logcounts`, other options include the raw UMI counts stored in `counts`\n", "- `expr_prop` is the expression proportion threshold (in terms of cells per cell type expressing the protein)threshold of expression for any protein subunit involved in the interaction, according to which we keep or discard the interactions.\n", "- `min_cells` is the minimum number of cells per cell type required for a cell type to be considered in the analysis\n", "- `aggregate_how` specifies whether to aggregate on magnitude score types, specificity score types, or both\n", "- `verbose` is a boolean that indicates whether to print the progress of the function\n", "- `inplace` is a boolean that indicates whether storing the results in place, i.e. to `adata.uns[“liana_res”]`." ] }, { "cell_type": "markdown", "id": "8df92e32", "metadata": {}, "source": [ "Let's see what the results look like:" ] }, { "cell_type": "code", "execution_count": 19, "id": "6e9b56b2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 6 × 16
samplesourcetargetligand.complexreceptor.complexmagnitude_rankspecificity_rankmagnitude_mean_ranknatmi.prod_weightsca.LRscorecellphonedb.lr.meanspecificity_mean_ranknatmi.edge_specificityconnectome.weight_sclogfc.logfc_combcellphonedb.pvalue
<fct><chr><chr><chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
ctrl&101FGR3CD14TIMP1CD63 1.789712e-133.292801e-071.00000020.355290.97504674.603019192.3750.100781791.41006542.5507520
ctrl&101CD14CD14TIMP1CD63 1.431769e-123.292801e-072.00000017.403470.97306814.203108221.1250.086166901.23653632.3731060
ctrl&101FGR3DCs TIMP1CD63 4.832222e-126.258644e-073.00000015.745800.97172504.185146250.6250.077959561.15981581.7133550
ctrl&101DCs CD14TIMP1CD63 1.145416e-117.412884e-074.00000015.382910.97140293.929365257.1250.076162841.11775371.8055000
ctrl&101NK NK B2M KLRD16.138712e-113.292801e-076.33333310.833000.96610833.916777219.3750.092865141.69623401.2426240
ctrl&101CD14DCs TIMP1CD63 9.163325e-111.395631e-066.00000013.462420.96949133.785236294.6250.066654240.98628681.5357080
\n" ], "text/latex": [ "A tibble: 6 × 16\n", "\\begin{tabular}{llllllllllllllll}\n", " sample & source & target & ligand.complex & receptor.complex & magnitude\\_rank & specificity\\_rank & magnitude\\_mean\\_rank & natmi.prod\\_weight & sca.LRscore & cellphonedb.lr.mean & specificity\\_mean\\_rank & natmi.edge\\_specificity & connectome.weight\\_sc & logfc.logfc\\_comb & cellphonedb.pvalue\\\\\n", " & & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t ctrl\\&101 & FGR3 & CD14 & TIMP1 & CD63 & 1.789712e-13 & 3.292801e-07 & 1.000000 & 20.35529 & 0.9750467 & 4.603019 & 192.375 & 0.10078179 & 1.4100654 & 2.550752 & 0\\\\\n", "\t ctrl\\&101 & CD14 & CD14 & TIMP1 & CD63 & 1.431769e-12 & 3.292801e-07 & 2.000000 & 17.40347 & 0.9730681 & 4.203108 & 221.125 & 0.08616690 & 1.2365363 & 2.373106 & 0\\\\\n", "\t ctrl\\&101 & FGR3 & DCs & TIMP1 & CD63 & 4.832222e-12 & 6.258644e-07 & 3.000000 & 15.74580 & 0.9717250 & 4.185146 & 250.625 & 0.07795956 & 1.1598158 & 1.713355 & 0\\\\\n", "\t ctrl\\&101 & DCs & CD14 & TIMP1 & CD63 & 1.145416e-11 & 7.412884e-07 & 4.000000 & 15.38291 & 0.9714029 & 3.929365 & 257.125 & 0.07616284 & 1.1177537 & 1.805500 & 0\\\\\n", "\t ctrl\\&101 & NK & NK & B2M & KLRD1 & 6.138712e-11 & 3.292801e-07 & 6.333333 & 10.83300 & 0.9661083 & 3.916777 & 219.375 & 0.09286514 & 1.6962340 & 1.242624 & 0\\\\\n", "\t ctrl\\&101 & CD14 & DCs & TIMP1 & CD63 & 9.163325e-11 & 1.395631e-06 & 6.000000 & 13.46242 & 0.9694913 & 3.785236 & 294.625 & 0.06665424 & 0.9862868 & 1.535708 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 16\n", "\n", "| sample <fct> | source <chr> | target <chr> | ligand.complex <chr> | receptor.complex <chr> | magnitude_rank <dbl> | specificity_rank <dbl> | magnitude_mean_rank <dbl> | natmi.prod_weight <dbl> | sca.LRscore <dbl> | cellphonedb.lr.mean <dbl> | specificity_mean_rank <dbl> | natmi.edge_specificity <dbl> | connectome.weight_sc <dbl> | logfc.logfc_comb <dbl> | cellphonedb.pvalue <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| ctrl&101 | FGR3 | CD14 | TIMP1 | CD63 | 1.789712e-13 | 3.292801e-07 | 1.000000 | 20.35529 | 0.9750467 | 4.603019 | 192.375 | 0.10078179 | 1.4100654 | 2.550752 | 0 |\n", "| ctrl&101 | CD14 | CD14 | TIMP1 | CD63 | 1.431769e-12 | 3.292801e-07 | 2.000000 | 17.40347 | 0.9730681 | 4.203108 | 221.125 | 0.08616690 | 1.2365363 | 2.373106 | 0 |\n", "| ctrl&101 | FGR3 | DCs | TIMP1 | CD63 | 4.832222e-12 | 6.258644e-07 | 3.000000 | 15.74580 | 0.9717250 | 4.185146 | 250.625 | 0.07795956 | 1.1598158 | 1.713355 | 0 |\n", "| ctrl&101 | DCs | CD14 | TIMP1 | CD63 | 1.145416e-11 | 7.412884e-07 | 4.000000 | 15.38291 | 0.9714029 | 3.929365 | 257.125 | 0.07616284 | 1.1177537 | 1.805500 | 0 |\n", "| ctrl&101 | NK | NK | B2M | KLRD1 | 6.138712e-11 | 3.292801e-07 | 6.333333 | 10.83300 | 0.9661083 | 3.916777 | 219.375 | 0.09286514 | 1.6962340 | 1.242624 | 0 |\n", "| ctrl&101 | CD14 | DCs | TIMP1 | CD63 | 9.163325e-11 | 1.395631e-06 | 6.000000 | 13.46242 | 0.9694913 | 3.785236 | 294.625 | 0.06665424 | 0.9862868 | 1.535708 | 0 |\n", "\n" ], "text/plain": [ " sample source target ligand.complex receptor.complex magnitude_rank\n", "1 ctrl&101 FGR3 CD14 TIMP1 CD63 1.789712e-13 \n", "2 ctrl&101 CD14 CD14 TIMP1 CD63 1.431769e-12 \n", "3 ctrl&101 FGR3 DCs TIMP1 CD63 4.832222e-12 \n", "4 ctrl&101 DCs CD14 TIMP1 CD63 1.145416e-11 \n", "5 ctrl&101 NK NK B2M KLRD1 6.138712e-11 \n", "6 ctrl&101 CD14 DCs TIMP1 CD63 9.163325e-11 \n", " specificity_rank magnitude_mean_rank natmi.prod_weight sca.LRscore\n", "1 3.292801e-07 1.000000 20.35529 0.9750467 \n", "2 3.292801e-07 2.000000 17.40347 0.9730681 \n", "3 6.258644e-07 3.000000 15.74580 0.9717250 \n", "4 7.412884e-07 4.000000 15.38291 0.9714029 \n", "5 3.292801e-07 6.333333 10.83300 0.9661083 \n", "6 1.395631e-06 6.000000 13.46242 0.9694913 \n", " cellphonedb.lr.mean specificity_mean_rank natmi.edge_specificity\n", "1 4.603019 192.375 0.10078179 \n", "2 4.203108 221.125 0.08616690 \n", "3 4.185146 250.625 0.07795956 \n", "4 3.929365 257.125 0.07616284 \n", "5 3.916777 219.375 0.09286514 \n", "6 3.785236 294.625 0.06665424 \n", " connectome.weight_sc logfc.logfc_comb cellphonedb.pvalue\n", "1 1.4100654 2.550752 0 \n", "2 1.2365363 2.373106 0 \n", "3 1.1598158 1.713355 0 \n", "4 1.1177537 1.805500 0 \n", "5 1.6962340 1.242624 0 \n", "6 0.9862868 1.535708 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "liana_res <- sce@metadata$liana_res %>% \n", " bind_rows(.id = sample_col) %>%\n", " mutate(!!sample_col := factor(.[[sample_col]], levels = unique(.[[sample_col]])))\n", "head(liana_res)" ] }, { "cell_type": "markdown", "id": "ad75df23", "metadata": {}, "source": [ "This dataframe provides the results from running each method, as well as the consensus scores across the methods. In our case, we are interested in the magnitude consensus scored denoted by the `'magnitude_rank'` column.\n", "\n", "We can visualize the output as a dotplot across multiple samples:" ] }, { "cell_type": "code", "execution_count": 20, "id": "54b8afcb", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "ligand_complex <- 'B2M'\n", "receptor_complex <- c(\"KLRD1\", \"LILRB2\", \"CD3D\")\n", "source_labels <- c(\"CD4T\", \"B\", \"FGR3\")\n", "target_labels <- c(\"CD8T\", 'DCs', 'CD14')\n", "colour <- 'magnitude_rank'\n", "size <- \"specificity_rank\"\n", "\n", "lv <- liana_res %>% \n", " filter(source %in% source_labels) %>%\n", " filter(target %in% target_labels) %>%\n", " filter(ligand.complex %in% ligand_complex) %>%\n", " filter(receptor.complex %in% receptor_complex) %>%\n", " unite(lr.pairs, ligand.complex, receptor.complex, sep = ' -> ') %>%\n", " mutate(lr.pairs = factor(lr.pairs, levels = unique(lr.pairs))) %>%\n", " # inverse ranks\n", " mutate_at(vars(specificity_rank, magnitude_rank), ~-log10(. + 1e-10))\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "494bbce1", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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UpO5/NFW3Yf/HdntMFgjBg+8M5bpqgE6yxLuCc2m3Em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WAUv9kUc9UdqJokCPT1vzHj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\nhe3Og6NjGJG9V8F22R4a65hFQ23t3om9vN209sYNBcZ8PXT3jG8H71rGaO6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IcG2b1YuPnBK865LTBnb7JZufmcNy6v9Ow8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\ncy1Fu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fMUcE6F\nZUZ3R6ixh6zCMvnF0a4D4TXzRGDbQw4P6dtN5p7TsuW2aivV2vSsHLtbd9FqpOJo1/5wrfam\nWpkgCNv3Hh7Uu6uxu6oupdCKyEbOHohI5JRTps3PL5ezk7yC4stX46t/won2HDqWl5cnYQhj\nIxVHuw5EVJ8nXRTFwuLSQ0dPjhxkdOWhuvxyE0wQZJjLQubdt+tAuFZ306wIjLFtew/17xFk\n7K5knoL0AlmXk0FBmSovL19mk/zO/XXkyfaQsH7G50kNWQWl8gPiCWk5rta3nMii/rMAFqix\nA6MlKZcvlfx3yyms+t4/xI+UFUqys/uv5lty/Kc3fo698b/2D/3w/UOBREQkXvzzjYvV9+c6\n4Mklw6uPs9dmXIw4cU1doHZs56YgLm1mnPp+QK0ZUjjxYhPN4qTWcS4jPKPRGb2lWiN1iDSR\nWmO2dRWLS+t4ANT5oQSVWr3UNjdeqTVNntTxWzgvMtHsPMbS6PTE2K1X5L0NxkmjM0HbeElZ\nrR4rnJtqAjWNVi9nYE6l1jTTRRmrjjwx0Y2gNr4wqYFz0olk5NweVKnRSRgpozZT/huUlJZV\nhSGqyH8oqPUkfUzUDZxIft6U1FEcsTo+bBKynztMrTFFcdRoNQGN7BNmrtuhrkuCyc8TrWiC\nWhwnknDapeQkI43OnMVRcWk5r1UcyX9QSl52qQb5lYHaTz3GmKlqAkbRi6JeRiONgWmKo9p5\nQsw0NQFTFCZqrb6ewGgjqStPqLhMbp7I7rx+Yz96ucVanfN1qlSVOp3e2tpM0+LVUlRSVvsW\nkV950OhNcxrUsvdT+7cwZoLqnwQmec/Si6JeFBXyZsmrXTtijJlkelm11gQn3lTv6WAhGrsw\n7fHgh2+MrnXHBQQEUG5GppaCrImIyHPG5ztmEBFRzj8vPlFtIJbVuGXbXhpq+LeuOPH83jXf\nrlq+ofvq+VXNEA5DFiwfQsSL9r375Cd/dPrz2QFVD5+lS5eKNzr1JCUlZWRkODkZvYz74H61\n+oNwGtgnWMKuavNxLeep0scr+Xs6OzkZN1tKgG/t2Vsbqp2v121/tZVVfZUhKysrKysrCVnX\nv1f3MxdrTo06pH8v05wFd+eUnGJpSx77eriYJA21LzPO+cDeUi6z+k8BY+y2p8DHw1lOGx0n\n8vVwlp8tg/rVnKaKc26yW8/DRc4qN+18POpPRiPdCIP61uyKxUXRJHni5y4Qyaq82lkzLzej\n57ry83KVEAv083JtyE+u/ywIgiDtLAzoHXzlakKNGObQAb1lnoU2TmVpxRKX9ajCiNo428hM\nyaB+tS4zkniZyT8F3m5O2cVKybnCBPJ2dzBBcVTr1hNFPqhvD/l79vf2kLkHHzfHZlIcmSRP\nfBnJGEhzncDIy8HoH+Xt7kzcuOlxuMgbWA1otOKoe2xiKokmLo583ByJTDAapq2n3MrAwD41\nawJ6qZeZ/FPgZGddJmnF9usYebuZoDga2Ce4xid6URzU1wQ1AV+vW89l1jAKgbVt41FPH7RG\nK45qXyfioD5yiyMfLTdJceTpKMhMSe8eXR3s7Sor1VV1V0EQunfp4O5u9Nzu1JjFUVJadvVq\nDGNM/suam70iv1zGfXcjJd7OdnJrR317EG2p/oko8sH9ejZ9ceTPTRDAcba3cXOVe8sP6tuT\n/thW/RNRFAf3M0HtqI2bY1aRUs7LGt3ufa3+swAWyDytTPbdurenTftD80dPvTlOp7wanUpU\n93hEhVunIQ/NGr7t3fj4MgqqCP12ZcLgFxaOMDwRmHtwd5+yY7EZNCDgxgYzZ86s2njz5s3Z\n2dnVe6k20NABvefMmLRp5wHGGBEnTl4erksWzZOwq9raeTpI3pYRdfB2trMzbohHtw5tBUEQ\njV/dTxCE7h39b/urGxKVk5B1Lz05d3tIWG5+ITFGxDgX75s2fsTgvsbup06d2nqdvCpljjCR\neKe2Xia5EkYM7nvftPFbdh9iTCASiZO3l8dLT86VsHP5pyDQx72evzZEex93+dkyakj/WXfe\nsW3vYSYw4kSce3t5vLhQSp7UFuTfRvK2VoLQyd+7/luvITUeCT9kzLABM6eM3b4v/HqeEPfx\n8nzhiYfl50mAvOAMY+TrbCMhGX06+0s4XN8u7RpyrEYqjl5ZNG/H/ojC4lLDEAXO+cOzpg6s\n9VZmLG9nW87lzusncvJxsZN5PdwxcvD0iaN2HTxadZn5eXs99/iDZimOAtq4XEnOk9yVVuQ8\n0NtN/g0yftSQaRNG7Q79L0/8fb0XPyYlT2ro0NZTRgd9EgTW7nY/sJGKo4ljht45fuTeQ8du\nDOTk/r5tnl3wgMw88bMmgSll9szjnNo4WhlbQerZ0W/Pqdjbf+9mvYPamrE4Wvrs/D2hx0rL\nKjjnTBBEUZw/Z0afHrKGDxNRW08TNEASkb+Hk8zrYcq44ZPGDDsQcdJwmXHOA/19npk/xyzF\nkb+nc2xGofR2Gsb8vZzlFxp3jh85YfSQ0COnBcY4Eee8fYDf04/Mlr/nQG8pUbb/MPL1cLK3\nN0NxdNeEUeNHDjp07KzhZY1z6tDOb9Ej98stjlw5kdwpMjkxb0crOzu5iyIuX/r0i+98ZRir\nLggCcf7J689K+4GNVBy98dyCfWEnK5QqzokJJIp84dxZ3bt0kpDC6nxd7Qoq5DYbc+I+rnJr\nR9Mmjhk3fGDYiXM3LjPWKbDtk/Pua/riyM/G1kYhyOk3yoj5e5igOJoxeeyYYbsjTl64UURT\nUPu2T8y91wQvJp7Ol5PkTqEeWO8LKQKjUIOZut+3u/vRySHv/vHVxnavzenper1lUZN18Lu1\nZ7XUvp4NHeztqUJZQeRhU35p/4Gzs0dMNARxCiOjsqx8fKT3h7y175e/Mqhv8NY9oaVlyqED\ner2yaJ6Xh7yqww3DOrn9GJYqoaRnRJ28HLycjJ74xsXRflBw+7NXk43qICkIrF+Xdm7O0sO4\nMnm6u4ZtWfX5T+tOnot0cXacOXXc/AdmmGrnY/sF/XXwvJQtOY3pK3cKlSorPnptcN8e20PC\nSssqhg/svWTRPE93V1Pt3CjDu7etPkmQsZjAhnU3zRLJP32ydEi/nv/uM32ejO7TUVowgjE2\nsHuAo73cOackW/nZG4P799y5P6K0rGLEoD5LFs3zcJPb3ktEHT3t3B0URUqJQ044p8GBUl6k\nB3Vr5+RgW6HSNLDKyxhzd7bvE+Qn4Vim4u3lEb5l9ec/rTtzMdrVxfG+aRPm3T9N/m77Bjju\niTbBshX9AkwQ0fj5izd/+Wv7rgNHyiuUIwb1XbJonrmWvh3ePWD36WvSt+c0okfA7b/WAL98\n+ebP67fvPnikvEI5cnDfl5+a5+psgqx2d7IPDvSOScuTNpWqKPIRPQPlJ0Oa3756a/Wf23Yd\nOKJUqUyVJ7ZW1M1DiC0wvv22Gk7U19voF57x/YO+3Bhu1LPPxtpqRK8Oxh7IhNr6tInY9vNn\nP647d/mqu6vz7BmT5s66U/5uB7R3l7X8OhEROdhadfWT+3hijP3+7bur/9yy++BRVaV65JB+\nS56a5+xknuroiO5tY2QsLsRFPrybCR5ejLE/vnt/1R9b9oQeVVWqRw3p//JT/3NyNEGeDO7q\npxAEnYybb1TPdvKTIQFj7M/vP1j5x5a9h46pKtWjh/Z/+an/OTrYy9ytlwPzcxSy5fVa45z3\nMb44qm3efXf5+3qvXPdPelZet6DAlxbO7R1sgiXFTSjQ3/fI9l8+XbH2YlSch6vLgzOnPHjP\nZPm77d/OJTJD7tBszqlfO7nFkSCwv1Z8+NO6f/YePq5Wa8YMG/Diwocd6m0JaCSCwIZ0a3s8\nOkPypcmJj+ghpWtC7ZT8vWL5T+u2hIQdV6s1Y4cPNFWeDOvuv+tMguTNGWNBfm6eznILAbAo\n5pqXxG7A/KWPFn227o2nj/Tt26Ojr50yK+bcZVX/Rx8Zvjqsnu3c3NypMC9PT77DZ8/a+OrK\npR8kju/pJeZcDD0QE/jwZ2Mao3CyEoQFD95998QRROTs7GxrK7fRr4q3s00vf+eorDJjKyGc\naFIPT2kHfXjK0NNRSUZtIop87tSh0g5nKm083T945amysjIi8vIyZQB8ULd2bb1csgvKjHq6\nCIx8PFwGdzdZFVBhZfX4wzNnTh5Fpr7MjOXpbN+3k/fFxFwJzbMCY/07+bg7meZGVFhZPTF3\n5qwpps+TNm6OQ4Pbn76aamyVgnN+zwi5fQPlUFhZLZw7676pY8ikecKIRndy3hEpfYXKUZ2k\nBM6sFVYL7hz83ZaGLjHMOX9i+lA5y5SbhK+350dLnzZtcTQo0FkhMJ2cSAQje4VVH3/j5lep\nk7VC8dS8+2bfNY7MXRyN6OFva20lbeY7RszJwbp/kK9JUmKtUCz6v/vmTBtHRC4uLjY2Jmsd\nmTwwKDpFyipkxJiNlTCmd32tyY3KWqF4+pH7H5h+B5k0Twb7Ka4WyFoSQWFFfbyNnjfNz9Nl\n6tBue0/GNryH8kMT+jsa2S/V5Nr6tPnk9WfKy8sZY56eEmuGNbjYW/fyd41ML5ZcIAmMRnT2\nMnY5vjrZWCuemT/nwRkTyNS3nrHG9Qr47WCktG0ZkZUgjAw2QSSCrufJ7IfuNnGeONnZDO7m\ndyomU2K0hdMdfczWTmNjY/3so3MevmcimTRPhrS12hEvazS9IFB/H9N0TBs/ctDAXl20Wq2d\nndxR4Y0kwM/7s2WLKyoqTFgcDQ9y/+NkhsydWFsJgzqYoGuFjY314gUPzJ05iYhcXV2tra3l\n71OaO3oHHo2SvqIyEY3tZZp3WFtbm+cee+B/s0ycJyOC/W0UglZqtZhzPr6P2WpH0ELJmnD3\nNrt279C7d3u3W1WKHLvOevO7r5c+NCrQt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T/jMWG4+j1VXPmb80PjHN9GOFtmrB4hX7j8izpENsbpWEHgg7lVsl4m37Tqcb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\nd+0ExoZa7opARH17dv1x5WshwQGMMRuNeva08V+/+4LEqCgRhfo42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87H0M6Kd2jhyMqW0BAW1OIaB20SYi3Y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\n932hWp6uTi+i48nbvf/WHi7cW6GFOaezL4qmr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qvnzDtz1ro19ZSy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\n5tta0LCdvDzcPnnz+Qt5otNpX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j3ZXywi1HdVSIEELV1EXbjj94hc0txdA0bd5Xq+cvXtXQ2DhyyICH7pzp59tuS/V/tfvM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\nq+9qzS5vVL3HyXlii+1avqFpZVKKnrHhBmPOERERKd0TT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n/foKacBK9fdI/53n3+RKdhi1bssEy6SDo7bEmU/z3eTZQbQN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\nTH8Rc7RNTeVDExLK3bs1aRTk0xKSmIa9T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veQB4AxDtITFoVHpo2aZVb1LPB4vO6MswfGUyUAYITdtslbQx6P2jFI4f/+/7ignP4pPqMr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\nlRJgraNKOr0IaBnFvcfN+0e6KCHy/J75n0uDDo1ULfx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CieyzrWeFYln98/PPq7FHLkc3aqma1qW4wj/v59b/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\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HCOEZb4wanD6f9sWiFU89NMPUpJrJrRA7/a2eJ/kVBjcTK6QTF7Pr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dO/fV8soqaZmsl13qhCm3ZptYKLs5Onwi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\n4Rfm51l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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 960, "width": 900 } }, "output_type": "display_data" } ], "source": [ "h_ = 16\n", "w_ = 15\n", "options(repr.plot.height=h_, repr.plot.width=w_)\n", "\n", "ggplot(lv, aes(x = target, y = source, color = .data[[colour]], size=.data[[size]])) +\n", " geom_point() + \n", " facet_wrap(sample ~ lr.pairs, ncol = 12, scales='free_x') + \n", " theme_bw() +\n", " theme(axis.text.x = element_text(angle = 90))" ] }, { "cell_type": "markdown", "id": "bc8981d5", "metadata": {}, "source": [ "Let's export it in case we want to access it later:" ] }, { "cell_type": "code", "execution_count": 22, "id": "4b783951", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "write.csv(liana_res, file.path(output_folder, '/rLIANA_by_sample.csv'))" ] }, { "cell_type": "markdown", "id": "43c508e8", "metadata": {}, "source": [ "Alternatively, one could just export the whole SingleCellExperiment object, together with the ligand-receptor results stored:" ] }, { "cell_type": "code", "execution_count": 23, "id": "382bdeb2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "saveRDS(sce, file.path(output_folder, '/sce_processed.rds'))" ] }, { "cell_type": "markdown", "id": "6b42bc63", "metadata": {}, "source": [ "## Comparing cell-cell communication across multiple samples\n", "\n", "We can use Tensor-cell2cell to infer context-dependent CCC patterns from multiple samples simultaneously. To do so, we must first restructure the communication scores (LIANA's output) into a 4D-Communication Tensor. \n", "\n", "The tensor is built as follows: we create matrices with the communication scores for each of the ligand-receptor pairs within the same sample, then generate a 3D tensor for each sample, and finally concatenate them to form the 4D tensor:\n", "\n", "![ccc-scores](https://github.com/earmingol/cell2cell/blob/master/docs/tutorials/ASD/figures/4d-tensor.png?raw=true)" ] }, { "cell_type": "markdown", "id": "e68222a3", "metadata": {}, "source": [ "First, we generate a list containing all samples from our SingleCellExperiment object. Here, we can find the names of each of the samples in the `sample` column of the adata.obs information:" ] }, { "cell_type": "code", "execution_count": 24, "id": "0abc3dbe", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "sorted_samples <- sort(names(sce@metadata$liana_res))" ] }, { "cell_type": "markdown", "id": "d89bcd40", "metadata": {}, "source": [ "Then we can directly pass the communication scores from LIANA to build the 3D tensors for each sample (panel c in last figure), and concatenate them, with the following function:" ] }, { "cell_type": "code", "execution_count": 25, "id": "73c818d0", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n", "Inverting `magnitude_rank`!\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[1] 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Loading `ccc_protocols` Conda Environment\n", "\n", "Building the tensor using magnitude_rank...\n", "\n" ] } ], "source": [ "# build the tensor\n", "context_df_dict <- liana:::preprocess_scores(context_df_dict = sce@metadata[['liana_res']], \n", " invert = TRUE, # transform the scores\n", " invert_fun = function(x) 1-x, # Transformation function\n", " non_negative = TRUE, # fills negative values\n", " non_negative_fill = 0 # set negative values to 0\n", " )\n", "\n", "tensor <- liana_tensor_c2c(context_df_dict = context_df_dict,\n", " sender_col = \"source\", # Column name of the sender cells\n", " receiver_col = \"target\", # Column name of the receiver cells\n", " ligand_col = \"ligand.complex\", # Column name of the ligands\n", " receptor_col = \"receptor.complex\", # Column name of the receptors\n", " score_col = 'magnitude_rank', # Column name of the communication scores to use\n", " how='outer_cells', # What to include across all samples\n", " outer_fraction=1/3, # Fraction of samples as threshold to include cells and LR pairs.\n", " context_order=sorted_samples, # Order to store the contexts in the tensor\n", " conda_env = 'ccc_protocols', # used to pass an existing conda env with cell2cell\n", " build_only = TRUE, # set this to FALSE to combine the downstream rank selection and decomposition steps all here \n", " device = device # Device to use when backend is pytorch.\n", " )" ] }, { "cell_type": "markdown", "id": "f35e2542", "metadata": {}, "source": [ "The key parameters when building a tensor are:\n", "\n", "- `context_df_dict` is the dataframe containing the results from LIANA, usually located in `sce@metadata[['liana_res']]`. \n", "\n", "- `sender_col`, `receiver_col`, `ligand_col`, `receptor_col`, and `score_col` are the column names in the dataframe containing the samples, sender cells, receiver cells, ligands, receptors, and communication scores, respectively. Each row of the dataframe contains a unique combination of these elements.\n", "\n", "- `invert` and `invert_fun` control the function we use to convert the communication score before using it to build the tensor. In this case, the 'magnitude_rank' score generated by LIANA considers low values as the most important ones, ranging from 0 to 1. In contrast, Tensor-cell2cell requires higher values to be the most important scores, so here we pass a function (`function(x) 1-x`) to adapt LIANA's magnitude-rank scores (subtracts the LIANA's score from 1).\n", "\n", "- `how` controls which ligand-receptor pairs and cell types to include when building the tensor. This decision depends on whether the missing values across a number of samples for both ligand-receptor interactions and sender-receiver cell pairs are considered to be biologically-relevant. Options are:\n", " - `'inner'` is the more strict option since it only considers only cell types and LR pairs that are present in all contexts (intersection).\n", " - `'outer'` considers all cell types and LR pairs that are present across contexts (union).\n", " - `'outer_lrs'` considers only cell types that are present in all contexts (intersection), while all LR pairs that are present across contexts (union).\n", " - `'outer_cells'` considers only LR pairs that are present in all contexts (intersection), while all cell types that are present across contexts (union).\n", "\n", "- `outer_frac` controls the elements to include in the union scenario of the `how` options. Only elements that are present at least in this fraction of samples/contexts will be included. When this value is 0, the tensor includes all elements across the samples. When this value is 1, it acts as using `how='inner'`.\n", "\n", "- `context_order` is a list specifying the order of the samples. The order of samples does not affect the results, but it is useful for posterior visualizations." ] }, { "cell_type": "markdown", "id": "3ac31972", "metadata": {}, "source": [ "We can check the shape of this tensor to verify the number of samples, LR pairs, sender cells, adn receiver cells, respectively:" ] }, { "cell_type": "code", "execution_count": 26, "id": "fda5853f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "torch.Size([16, 467, 7, 7])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tensor$shape" ] }, { "cell_type": "markdown", "id": "91bb46ca", "metadata": {}, "source": [ "We can export our tensor:" ] }, { "cell_type": "code", "execution_count": 27, "id": "19a1af6b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "reticulate::py_save_object(object = tensor, \n", " filename = file.path(output_folder, 'pbmc.pkl'))" ] }, { "cell_type": "markdown", "id": "04ba074b", "metadata": {}, "source": [ "Then, we can load it with:" ] }, { "cell_type": "code", "execution_count": 28, "id": "993ad1a2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "tensor <- reticulate::py_load_object(filename = file.path(output_folder, 'pbmc.pkl'))" ] }, { "cell_type": "markdown", "id": "80e3c357", "metadata": {}, "source": [ "## Perform Tensor Factorization" ] }, { "cell_type": "markdown", "id": "26fa855f", "metadata": {}, "source": [ "Now that we have built the tensor and its metadata, we can run Tensor Component Analysis via Tensor-cell2cell with one simple command that we implemented for our unified framework:" ] }, { "cell_type": "code", "execution_count": 29, "id": "7ac0fa7e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Decomposing the tensor...\n", "\n" ] } ], "source": [ "tensor <- liana::decompose_tensor(tensor = tensor, \n", " rank = 7, # Number of factors to perform the factorization. If NULL, it is automatically determined by an elbow analysis\n", " tf_optimization = 'regular', # To define how robust we want the analysis to be.\n", " seed = 0, # Random seed for reproducibility\n", " factors_only = FALSE, \n", " )" ] }, { "cell_type": "markdown", "id": "90094f4c", "metadata": {}, "source": [ "**Key parameters are:**\n", "\n", "- `rank` is the number of factors or latent patterns we want to obtain from the analysis. You can either indicate a specific number or leave it as `None` to perform the decomposition with a suggested number from an elbow analysis.\n", "\n", "- `tf_optimization` indicates whether running the analysis in the `'regular'` or the `'robust'` way. The `'regular'` way runs the tensor decomposition less number of times than the robust way to select an optimal result. Additionally, the former employs less strict convergence parameters to obtain optimal results than the latter, which is also translated into a faster generation of results. **Important**: When using `tf_optimization='robust'` the analysis takes much longer to run than using `tf_optimization='regular'`. However, the latter may generate less robust results.\n", "\n", "- `seed` is the seed for randomization. It controls the randomization used when initializing the optimization algorithm that performs the tensor decomposition. It is useful for reproducing the same result every time that the analysis is run. If `NULL`, a different randomization will be used each time." ] }, { "cell_type": "code", "execution_count": 30, "id": "84e7d2f8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"The estimated tensor rank is: 7\"\n" ] } ], "source": [ "print(paste0('The estimated tensor rank is: ', tensor$rank))" ] }, { "cell_type": "code", "execution_count": 31, "id": "4aea532a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# # Estimate standard error\n", "# error_average <- tensor$elbow_metric_raw %>%\n", "# t() %>%\n", "# as.data.frame() %>%\n", "# mutate(rank=row_number()) %>% \n", "# pivot_longer(-rank, names_to = \"run_no\", values_to = \"error\") %>%\n", "# group_by(rank) %>%\n", "# summarize(average = mean(error),\n", "# N = n(),\n", "# SE.low = average - (sd(error)/sqrt(N)),\n", "# SE.high = average + (sd(error)/sqrt(N))\n", "# )\n", "\n", "# # plot\n", "# error_average %>%\n", "# ggplot(aes(x=rank, y=average), group=1) +\n", "# geom_line(col='red') + \n", "# geom_ribbon(aes(ymin = SE.low, ymax = SE.high), alpha = 0.1) +\n", "# geom_vline(xintercept = tensor$rank, colour='darkblue') + # rank of interest\n", "# theme_bw() +\n", "# labs(y=\"Error\", x=\"Rank\")" ] }, { "cell_type": "code", "execution_count": 32, "id": "e3e8b127", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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6GQsGPWolApIGk8dkxwRYeoteszgKZvT25lBqT89fHMY5mFaioifnh/8terVn1/\nWuGpvbPSkz8nTNjyBCLfd39d9IpN3Q9/ndvUFKrwb96ce1JvjkL7Gc2zL69btmzZsg+X7kgs\nXE+kUMgBoEaNYubaEtGzVfEQUYTcjZxNLSwK3yqluLf2qy1PCz15wOtj21kAOaeXzdmRXLB+\nzo2ffz2mBNC6a5eCt7wXHLwezzbS2iWd+nrZsmXLPvr2gE4wVCoUKgCoUUPPXrNEVLyGo8e2\nMAXwdPOEkV+eTVDpqyO/sXLssgsARJ6vD8vdnc1q0OQ3qwG48sXk1VGFw0Pqf3MnzVq1atWf\nT2zLt0md5auT3qwBIGnTzNlHC8WptFPz5m0pfN5vwIhqE3Nw2bJlyxbO+CVMWfgxhSIHACxq\n1DDUbC0iMjCFQp0QzcrSHDCUMV6Ju419sxaAx+vmrrxVaIP3xJAf/34CwKVr1yAA8Bo1uY8d\nID8y9/0thc/AhCd/fjJx6apVq3anuZd4CuY/bHgzAJm7tm7cuOlgDuAz+s32he6bZaAjquIE\nIkEQZNe/7Z63nZ+5b99p32wJvXg7KuZh+MVjezd/P61Hrdx17cxbLr2Wo91Qfnaa+kYsSY2e\nC7afu5cgVcmSIi/8s6RfLfXvjeek0Kz86pfn1AEAvxmX9Qxi62AAQN91GfllCas7AoCo20/x\nBcZ7epqvCACsAgZ/sfvyw2SZICgyYs5tntuzhhiAqMbwrdoNYtb1cwAg8p5yJK9z+ZVPg0wB\niAM+OKU1Qr3PmLZ1mHqfJvs2H20+F5ulFARBJU97GLZ5btdqIgCiWu+fVpXtLSeq4rYOU8eI\nwVvzy+JWtgMAh3H79TQIfdcFAJp9GVWgFwBA0OI7Bb6BZQsRir8GAAAGbJQWftL0DX1MAMD+\nlU+PxGSry1TZ0cd//bizhyZVGvj+f09luQ1yzs+qJwIA09oDP99zKSpZLiizE27s/rx/bRMA\n8BixMyGv8yLim544WcZIKxya6AAArVbG6L6PCas6AoDNG7tzC2JXq28CELn3WLTz6lOpShAE\nlSw54uja91o5AIBJ42W39fxBiKgksnOfNc2dlSSyazh07upNOw+fvREV8zD8ypnQPRuWjutY\nS/2wedCnZ7W/w4/X9LQBALFru/fXnQx/kqkUVLKk2we/fzPIBgAsmyy+rMivXqavvCDE/Nxd\n/bRm3gOWhZy+8yRLmnz//N5vXw+0AMQ2NlYAXCYeyn8ZZTvoKiaS35jfQOzprMkAACAASURB\nVAwAktqvrTx4J1muEgRBmR1/88B3bzS0BACLnmseV+ANJ6JyiVmpXu7De1pYcdWuz68DAAhe\nEq5VWsZ49fTP/ursokPzqb8fvx2XqRRy0h6eXju5pT0AWLRaejXvZFN1a1kLEwAwrdVjzpZz\nkQnZKkGZ/eTqrs8H+5sCgH23n+7l91x0JIz6qrUIgLW9vQRA/YU39bw4BjqiqoyJUcoju7N+\nbGOHYtbxN/fqNWv3Q2XhdmnnvxvgY66pZWKafwHO1LPXF6eStCuXOTGq3DNKPbfKxL5WgJ/n\na+tScx9IP//dYF+tpzU3z5/PZf/K51ey8/tQPVjTzQ4Aar9zSKtrQXZ+Tn0JAHHgh8ezS3jG\nuL8Ga+0yLTazsjLNf6tsWi27qH0+Q0QlM0xi9MzHtdRfSpvq/v7e3b7WHKmXIUQUlxgV5FeX\ntsrNa0isXDz9/Dyd1euDWga89t36aQ1zA4FI0uOnFHWLjEurBmk9s6lZfrCwrD/9WHp+30XE\nN71xskyRtoxZEmX4qi62+QFOYm5lqTWRwqXPL+EK3W6IqFTi/1vY36/YjdEkLk0nbo4sdHil\nfLjr/dbO+ZvZm5nnRxKT2m/uKHhSXcbEqKCI2jopWOtbLxLlde7YaeWfn9RBwcRo2SJqsZE8\n+8L8xvn9wMTCSms6vth75LbHvMxM9PyVMjH69If2AADzbj891SouW7wShOjdH7Vy0tSQmJlp\nooDYre+vUdpBQHpn/VuN8rcpkpiZ5x+gWAR9HJqq3W8xkTDmu7Z5N8qKW3/1UP/LY6AjqrqY\nGCVtyqdn1s4c2TnQUeukWGTuUKtu22Gf7b5XdO4v/dqvE9t72eSvzCCyrtlm7I8XUwpXLHNi\nVFDc/Gmgl2bPj66rtbtMv7ZuYntvraeV2Pp0fu+nk3Hak1pV91d1sgUAz/GH0go9oyxsel0x\nALH/tJOaV1fEM2ZHHfhmUgdPS+3UscTWt+vkbw9EZhb5zhBREQyTGBViNr9VV3PE3HhRgZmN\npQoRQvGJUUFQxYf99O4rHvnbwZs4Bvb44I/bmYIgyG6sfbOpm7lIbO0xZotWyjP9yq8T2tay\n1oQLsaVr49cW748q1L/+aFNknCx1pC1zlkRIv7Vj2agWBRdRNXGs33/arydiZLp9EFFZ5DwJ\n+2Vq70ZeLpZaa3KILRxr1u3wxue77mYU0Uzx6MCCQQ1d84OPyNy1ydAFO26nF65Z5q+8IAjS\nu39/2NnHVhNQxPYBvaZvi5QJt7/t16BBg3YLThfqqLQRtfhILigTL/05Z3AjR+3lSURm7sFD\n5/15IZ4XYYiMopSJUeHa7ED1l9ax208RBR4pfbwSBEEQcmIPzBtQ3zn/sMPUrnan99ZeSNaT\nMZRGbP+kR4BD/tmp2LJm6zdXHCh8UFVsJHyyqqM66Jj2+OVJMa+QgY6oahIJJa1tRlWRMivh\n0aO4J/FZ5tV8/LxcLEu3Fq08+cG9B4+TpeYO7l6+tZ3M9VTJjDp3/kEWLD2btaitM3/iyfmt\ne0/evmXZZeH41gUa56Q8uHP3UZa5c7WanrWcLArNapUnP4iIepwmOHr6+dSwK7g5CoDMqLPn\nH2QDlp5NW9S2KfyoNPrCmcgMQOze4JU6zqV4RpUs5UlMdEy8zKqap1dNNxuTwj0SUakk3Dp2\n46kAuNbvUM8ltywn9vKpiFSYVG/UNkBn8aXkG7t3hV67LWv+0YddnbUfUKXH3gmPTpM4uHt4\nerpaFY5XJYQIAIi/cfRmAuBSt319t6KmzSvS46LuPUiEvYePX027At98lUolFusJk7KEyIgH\nTzNM3X0DvFyK2odeN9oUGydLFWmRcvfklUcK2Pm1bqKV0c19wocnt/975tZ9t0FLRjYs9Jgy\nO+lxTHRsosK+hpeXR4EkDhFVnJCTHvcg8mESnGvV9qxmZ1bMbToaqsy4e5HRCdkW1X38PIs4\nIivvVx6QJ0VFRMWlqmzca/v5OFvoPK7boOSIWmwk11Bkxj+Kjn6cKnas6eVZ3bGoEElEz4E8\n9tLpiDTAolZwSx/bYirG3zx6Mx4ATGsEtfF3KPxwaeKVFmX6o4jImGSFnae/bxHxJL9uWkzE\n/UdJOTY1/XxrOpjri57FRUJk3D974WE2YF27RTPPkjarZKAjqmqYGCUiIiIiIiIiIqIqh1ct\niIiIiIiIiIiIqMphYpSIiIiIiIiIiIiqHCZGiYiIiIiIiIiIqMphYpSIiIiIiIiIiIiqHCZG\niYiIiIiIiIiIqMphYpSIiIiIiIiIiIiqHCZGiYiIiIiIiIiIqMphYpSIiIiIiIiIiIiqHCZG\niYiIiIiIiIiIqMoxMfYAiIieC2Vm3L1bt8JjpY7egXUCaztbiErbMPZUyLHMBq92q2P1TAdI\nRERERERERM8TE6NEVAVkhW9dvGD9tbTc/0qqdZz62dSO1SUlt5TdWr9keUi031tdmBglIiIi\nIiIiepnwVnoieullHv1m7vpromZvLPxxw5+/rZzexyspdMW8jXeFElumnVu9fEe08jmMkYiI\niIiIiIieLyZGiehlF39495ls6w7vTP9fkIe9jYtP2wmzRvoj7uC+i4pi2wnxh1esPCypXZsz\nRYmIiIiIiIhePkyMEtFLLvnMqTsqi+Ztm5pritzatfVH2unTN4pppozevvzHK06DPnmzoXnR\ntTIyMtK0pKenG27gRERERERERPQMvTBrjK5bt+7Ro0fFVFAqlSqVCoBEIhGLy5zwrWBzhUIh\nCELFm5uYmIhEpd0Shs3ZvIo0HzlypKenZ1mfQiMxMRFw86hpplXmUrOmOcITE6WAhd5Gslvr\nP98Y5fPWihH+aWuL6Xz06NEPHz7U/NfS0rJTp07lHioRvbjEYvH8+fONPYrSCg8P37p1qwE7\nVKlUSqUS5T0QMixBEBQKBQCRSGRiYvxj3ZycHPU/TE1NjTsSVPjn27AqePhtWJrPsFgslkhK\nsQj5M6b+2DyLz7C3t/frr79u2D6N5eDBg+fOnTNgh5XqC8K4WgzG1aJUnbjasWPHNm3aGLZP\nqrKMH9RKKTQ09Pr168YeBREZQY8ePSqSGE1KSgL87WwLFNrZ2QFJSclAdT1N0s+tXr4judn7\n8/t5SJCmp0JRBEHYu3dvuYdKRC+uFysxGhcXt337dmOPgoiMoGXLli9NYvTatWsMZURVU/Xq\n1ZkYJUN5YRKjamfPni3qukdaWppcLgdgZ2dnZmamt04xjNs8NTVVfdXLuM3t7e3Lcc3NuM1T\nUlLUVy+N29zBwaEc106N2zw5OVl9Ba+CzR0dHctxAbA0zVUqVYsWLcrasy5BACCCSLdUPQSd\n+gmHV6w8LO4yd2onlxI7t7a2trOz0/zX3NxcKpXu3bvX6Fdoiej5UKlUvXv3NvYoyqmYw6qy\nysrKysrKAmBpaWltbW2QPsstJycnNTUVgKmpqb29vXEHAyAhIUH9DxeXkn9WnrUKHjwYVmZm\nZnZ2NgBra2tLS0vjDkYmk6nXwzE3N7e1tS2x/jOlUqmSkpIAiMViJycnQ/VpkMOqSsiAoayC\nZ1WGpYmrVlZWVlZGXvFeLpenpaWhcsRVQRASExMBiEQiZ2dn4w4GFT6rMqyMjAypVArAxsbG\nwkL/nXHPjVQqzcjIgEHj6kscysiIXrDEKBFRWTk5OQHpGelAfgIT6ekZQC19h1Jxu5b/eMlh\n4LIJzWxK0fmGDRu0/5uZmdmhQ4cKDpiIiIiIiIiIngMmRonoJefs7Azcf/xYCQ/N1NTkx4+l\nsHR21jM9JSbinkwp3/7xkAJ3ZiX/Nq7/bwh8a83yAW7PfshERERERERE9MwxMUpELzmnli39\n19w4F3ZF2Sw4NzOaEhZ2G7YdW9XXU71G69eGV9e+xT7+YsihOzZN+nerY+1SpzSzSImIiIiI\niIjoBcDEKBG97Ny69m3+54pDq78Pmv922xom6bd3frnxOqoN7ts8d+0oacThnRcTLPw69w92\nQY3Wrw1vrd38Vub+Q3ecm/QfzrmiRERERERERC8RJkaJ6KVn0+mDBU8XLfxj+cQj31mIpdIc\nk2qdPvxshH/efkzSOwc2brzp2Kdh/2Dj74tBRERERERERM8FE6NEVAVY1xm6aHW7u7fv3I2V\nOdYOrBvo46y1S6NFQNfhw4MsA1z1tnUNHjjc2po30RMRERERERG9XJgYJaKqQWLrUae5R53m\neh6y8O863L/Ihi7BA4YHP7txERGRXid/EZVc6Vlq+7Zg3AEQERER0bMmNvYAiIiIiIiIiIiI\niJ43JkaJiIiIiIiIiIioymFilIiIiIiIiIiIiKocrjFKRERERERELxiVSgUgOztbLDbMdB91\nhwDkcrlSqTRIn+WmUCg0/8jOzjbuYDTvhkqlMvpgBCF/AWijDwZaHxuZTJaTk2PcwWg+Njk5\nOdpvlFFo3g2lUmmov5Tm3SYyICZGiYiIiIiI6AWjztZlZmYaKjGqIZVKDdthRcjlcrlcbuxR\n5FIqlZmZmcYeRS5BECrPYFA5srQaMplMJpMZexS5FAqFJmNbQUyM0rNQ4cSo/PGF/45fuh3x\nRHD1DQxq16VFTYvSNRSeHl717X/2/1swOlhS0UEQERERvQyE7IR7d27deZBmUyugbh1fN6uS\nzvYFeUrM3Vs3I57Cybt+cFBN6+cyTCIi4zM1NQXg7OxsqMRoWlqaeo6bra2tmZmZQfost+zs\n7KysLABWVlaWlpbGHYxcLk9PTwdgampqZ2dn3MEIgpCUlARAJBI5OTkZdzAAUlJS1Dl6e3t7\nExMjzzzLzMxUp/Wtra0tLEqZmnlWpFKpOnNtbm5uY2NjkD6ZGKVnoWLf28zbmxYu3HQzw8LF\n01185Vzorh1HBs+ZN7qBbYktlZFbvvjhYLi8eWcVwMQoERERkTz63+XzfjyTkHvQL3JsOn7+\njD61zYtpsO+LeavPJuTeLCe2rz981ryhdY18JkRE9ByJRCKRSFT5+6wIow9GewBGH4y2yjaY\nyjOeyjCYZ/GxMfqLopdSRa6tCXc2f7XpJoInrPrzt+++W/Pn95Obm4ZvXfHXzRKXssi+vvaL\nP8Mry/0AREREREaXc+WXOd+fyQp4bc53v29av+rT4fVkF36a++PFIg+YUo4sn/HjOVXLtxZ8\nv2Hzpp8WDg6Q3fjj87VXeIRFRERERFQqFUiMyi/u++8J/AZO6lPLBAAkHj0mDaojij+y/0Lx\nB+SpYau+3Jndun1DAy8FQ0RERPSiyji550iypMmbs0e28HK0dvBsNnzW+OYmaSf2nkzX3yDm\nwPYz6R4DZ00f0LiWvaV19aCR7/zPyyT1xNGrRt4xhIiIiIjoBVGBW+kfXLuWDs++zd3zi1xa\ntPD57fa1q/fRLLCIVsLT/V9/c8Ks18Ipwf+GHSuq79u3b8fExGiXqBfrlclkRa0go1lsonz7\nr7G5unk51ux4oZtr3q6KNy/HzpWa5uXb+LLyNC/Huk6lac4VZIioKlFePH1WLmrYto29psi2\nVbvGknPnT19QdOqoe8R258CBB/Ae3T1AsySR2HvYd9uH6XatUqkyMjK0S9QbMgiCYKgta7X7\nMfo2uIZi8BdSqd4ZA/71K64yDKZSfYafxWCM/qKIiIgqp/InRtUrHge5uGoXuri4AFHJSQKg\nd+kH5f0tX6y55jbsi7GNrC79W3TnISEh27Zt0y7x9vYGkJ6eXmIKpoKbwVXl5urlvdm8HCq4\nH2JVfu3FNGdilIiqkuTERBXsa9bUXqjdqmZNJ5xPTEoGXAvXl0VHP4VVN/8a8uT7t2/eiUwQ\nu3oFNGjoba+7dHt8fHyfPn20S+rVqwcgMTHR4Fs5Z2dnV6pteSsiMTGxkndYEampqcYeQr6s\nrKwKHo0YUKXaylmlUhnqY8PDKiIiIr3KnxhNT0pWQmxrU2CDPDMbWzMok5LTAHvdJtnX137+\n58PAcSuG+Rl5jz8iIiKiyiQpKQmwtS24gaWdnR2QlJikJzGamJgI2Cnv/PTJ4j2R0txCK+/O\n46dP6ezBfS2JiIiIiEqh/IlRCwtzQJWjKLCrvJAjzwHMzEz1NEgNW/XlzsyWHy3t61HiRmJ1\n6tTp2rWrdklUVBQAc3PzoqY2aG6FNjU1Lcf0B+M2l8vl6ttbXsTBv9BvnaGam5mZlWODPM1r\nN0pz47720jTn1AYiqlIE6G62KggCoFTpW+4kJSUFSDm89Wa38Uunt/B1EqXcO/3XD6sPf/e5\nq+eKEX7aqVGRSGRnZ6fd2NzcHAbdslb7Lt2XZsdYg785leGdqVSDQd54KsNgKuc7A27lTERE\n9IyVPzFq5uRsDaSnpQMOmsLMtHQBFs5OVjrVFWfXfnM8s85rnZxjr1+PBYAHqQKQHnPj+nUT\n25r1vRy0f6sHDhw4cOBA7fZvvPEGAFtb26IyOGlpaXK5HIClpaWZWZlnpBq3eWpqak5OTkWa\nq1NIFWxuZWVlaqovqV2Jm6ekpKjXnzV6cxOTMn+bUlJSNK/9+TdPTk5WLy1qbW1dweYSSZnn\nJpWmOROjRFSVODk5AfHpBZYCRUZ6BuDs7KynvrW1NZDiN3z2u13dRABQrV73996LvjHtn0NH\nIkb4aS/27ubmdvjwYe3Gx44du3TpkrOzs6FupdfcCm1paWltbW2QPo1O/xtfdgkJCYbtsCI0\nhy729vbl+PU3rMzMTPXCC1ZWVpaWliXWf6ZkMll6ejoAMzOzwlO3nzuVSpWUlARALBY7OTkZ\nqk+D9ENERPSSqcDxkLt7NSAqMlKF4LxjaiEyMgpwd3fXra1ITcmE7PbfS2b9rV18e+uns7ai\n+fvb5nYuc1KKiIiI6CXh6Owswr3Hj7JQR3N9Wfr4cTJE3nrzIi7OLkCyd203rSvLYm/vWsCF\nJ3E5CORxFRERERFRSSqQGK3WoXO9Db+cOXJ+XHAL9SVe6eXQsGRx4MAOHrq1zZu9uWTJYO2S\nm3/N2Xg1YPCC0cEmtjWNfMGaiMgQuOUrEZWXJLhlc7PTl8LOZnXumJsZlV0Iu5xjGtyqqb4k\np3X9+l64cufWA1Vjr7wr1Iq7d6OAGl7ezIoSEREREZVCRRKSzp0Hddq26NA3i5zffq1tDcSG\n/b3mUIpj58ld3XIrPP3v2+9C4927TH23o4vI0auBY4HmmfYiwLZm/QYNePRORC8HLuBFROVm\n+0rf9mvPHlr79b/e73f1tsyOOvzdLydkdp37tc/d0FKIPrXldIyoVpshrWsC8Ow9sOm2lVuX\nfeUwedQrAU5C4q0j677fn2DVZHJ3T6O+ECIiIiKiF0WFZmpaN3/3ixlmy37Y9fXc7QBM7fx6\nfTJjfAubvMelcbevXInxaiir8DCJiIiIXm6mjScsmpQ6/6fv3xuxxsJULpXDqdmEhROC8xYP\nF6KOb9x4UvJKTXViFHad3p/39PPPN/046/iP6homLsFDZ7zfw52XaIiIiIiISqOCt7CL3VpP\n/Lr1uPS4h/GCq2d124LduXf7YEmQzMLdVW/besMWLellw5voiYiIiADA3KvX7O+b3rt9K/xh\nhrWnf706fm5W+ZsjibzaDR/uKfaqpSmwbzBs4Y8dI8LDIx8miZxq+tRpEOBmYZSRExERERG9\niAySljSxreajb+9Gczf/Bm56ytVsa9VvUKvIR4mIiIiqHLGVm3+wm3+wnodEnm2He7YtVCix\nqRYYXC1QX30iIiIiIiqeuOQqRERERERERERERC8XJkaJiIiIiIiIiIioymFilIiIiIiIiIiI\niKocbn1EREREREQvvD+XV6S1OWBe8TG8/nHF+3gxCLLk6Ng0mxqeThaiomtJ48IjEuR6HzJz\n9QtwtwCQGXvrfrKy0KNW1ev4OPNUlYiInj3+2hAREREREVHpSO/t+Gr5X+cfZSkBsYV78JCP\npw0JsNJbNWbfF7NCnup9yG3w12tG+wE5F9bO+PKsUOjRwLfXLu/nbOCBExER6WJilIiIiIiI\niEojdsens367YxH06vju9ezTbh3csmPDnDmi5V8N9tIzc9S11esT3LILFWbf2L3xpKxRg+oA\ngEexsQJqtBvdr76lVh2HetbP7BUQERFpYWKUiIiIiIiISqa8tjvkdrbnoMVzx/iZAWjVup7Z\ntPc3/7Pn6oB3gnRPLe3rdu5Tt2BRatjXf8d5DFo6IdgaAJSPYuNg2aXroD7BxdyST0RE9Kxw\n8yUiIiIiIiIqkXDp8JFk+HTq7meWW2JSu3tnf6QePXKx8N3weiUc+ea7U+6jpo+ok7ui65PY\nWCW8vLxV2YmxEREPEjILrzZKRET0THHGKBEREREREZUo7XFcFmz9A6prlbkGBDgi/HFcCuBY\nQvP00J9/vuA48JsBnpK8otiYWEhcw76Z8Pulp3IAkDg3fHX85Ndb1zAr1PjYsWO//PKLdom9\nvT2AlJQUsdgw032Uyty0bGZmZlZWlkH6LDeVSqX+h1Qqlcv172D13AhCbt5boVCkpKQYdzAa\ngiBUhsFoPjbp6ekikZE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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 780, "width": 900 } }, "output_type": "display_data" } ], "source": [ "sce@metadata$tensor_res <- liana:::format_c2c_factors(tensor$factors)\n", "\n", "h_ = 13\n", "w_ = 15\n", "options(repr.plot.height=h_, repr.plot.width=w_)\n", "\n", "plot_c2c_overview(sce = sce, group_col = condition_col, sample_col = sample_col)" ] }, { "cell_type": "markdown", "id": "73b725f2", "metadata": {}, "source": [ "The figure representing the loadings in each factor generated here can be interpreted by interconnecting all dimensions within a single factor. For example, if we take one of these factors, the cell-cell communication program occurs in each sample proportionally to their loadings. Then, this signature can be interpreted with the loadings of the ligand-receptor pairs, sender cells, and receiver cells. Ligands in high-loading ligand-receptor pairs are sent predominantly by high-loading sender cells, and interact with the cognate receptors on the high-loadings receiver cells." ] }, { "cell_type": "markdown", "id": "5c355c30", "metadata": {}, "source": [ "### Downstream Visualizations: Making sense of the factors\n", "\n", "After running the decomposition, the results are stored in the `factors` attribute of the tensor object. This attribute is a dictionary containing the loadings for each of the elements in every tensor dimension. Loadings delineate the importance of that particular element to that factor. Keys are the names of the different dimension." ] }, { "cell_type": "code", "execution_count": 33, "id": "f3f71114", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 'contexts'
  2. 'interactions'
  3. 'senders'
  4. 'receivers'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'contexts'\n", "\\item 'interactions'\n", "\\item 'senders'\n", "\\item 'receivers'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'contexts'\n", "2. 'interactions'\n", "3. 'senders'\n", "4. 'receivers'\n", "\n", "\n" ], "text/plain": [ "[1] \"contexts\" \"interactions\" \"senders\" \"receivers\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names(tensor$factors)" ] }, { "cell_type": "markdown", "id": "c7895be2", "metadata": {}, "source": [ "We can inspect the loadings of the samples, for example, located under the key `'Contexts'`:" ] }, { "cell_type": "code", "execution_count": 34, "id": "0b140002", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 16 × 7
Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7
<dbl><dbl><dbl><dbl><dbl><dbl><dbl>
ctrl&1010.27538790.25053140.26906100.21488393.192032e-100.21261950.2452438
ctrl&10150.30733170.29156130.29112250.22950182.224032e-020.23481300.2771375
ctrl&10160.25638270.21893450.23319330.24792142.264486e-020.24366040.2151357
ctrl&10390.29902600.29445410.28031460.22875311.721946e-020.22716190.2805458
ctrl&1070.29861280.20800370.26114330.27661605.844633e-050.22619540.2447433
ctrl&12440.29116490.26519030.27657180.25861656.973772e-040.23997310.2612663
ctrl&12560.29462870.26707340.28266190.19667894.576398e-020.20933610.2895984
ctrl&14880.27182690.22158980.25798910.23830752.803234e-070.19157620.2818053
stim&1010.23347430.21816550.24632300.21902903.502629e-010.26588290.2658360
stim&10150.19858350.31480010.22692000.23952674.272637e-010.26380370.2652130
stim&10160.21116270.26751030.23961570.23539783.159429e-010.25887870.2261185
stim&10390.22308130.22317510.21524000.25425732.852869e-010.27381540.1957329
stim&1070.20129760.18924160.23737820.32999713.240025e-010.29051010.2175857
stim&12440.19021670.23391650.22755280.30020133.769016e-010.27387580.2494139
stim&12560.20924260.27337100.22081630.21397653.677670e-010.26681020.2410888
stim&14880.17644070.22415100.21437010.27978263.575881e-010.29417260.2202224
\n" ], "text/latex": [ "A data.frame: 16 × 7\n", "\\begin{tabular}{r|lllllll}\n", " & Factor 1 & Factor 2 & Factor 3 & Factor 4 & Factor 5 & Factor 6 & Factor 7\\\\\n", " & & & & & & & \\\\\n", "\\hline\n", "\tctrl\\&101 & 0.2753879 & 0.2505314 & 0.2690610 & 0.2148839 & 3.192032e-10 & 0.2126195 & 0.2452438\\\\\n", "\tctrl\\&1015 & 0.3073317 & 0.2915613 & 0.2911225 & 0.2295018 & 2.224032e-02 & 0.2348130 & 0.2771375\\\\\n", "\tctrl\\&1016 & 0.2563827 & 0.2189345 & 0.2331933 & 0.2479214 & 2.264486e-02 & 0.2436604 & 0.2151357\\\\\n", "\tctrl\\&1039 & 0.2990260 & 0.2944541 & 0.2803146 & 0.2287531 & 1.721946e-02 & 0.2271619 & 0.2805458\\\\\n", "\tctrl\\&107 & 0.2986128 & 0.2080037 & 0.2611433 & 0.2766160 & 5.844633e-05 & 0.2261954 & 0.2447433\\\\\n", "\tctrl\\&1244 & 0.2911649 & 0.2651903 & 0.2765718 & 0.2586165 & 6.973772e-04 & 0.2399731 & 0.2612663\\\\\n", "\tctrl\\&1256 & 0.2946287 & 0.2670734 & 0.2826619 & 0.1966789 & 4.576398e-02 & 0.2093361 & 0.2895984\\\\\n", "\tctrl\\&1488 & 0.2718269 & 0.2215898 & 0.2579891 & 0.2383075 & 2.803234e-07 & 0.1915762 & 0.2818053\\\\\n", "\tstim\\&101 & 0.2334743 & 0.2181655 & 0.2463230 & 0.2190290 & 3.502629e-01 & 0.2658829 & 0.2658360\\\\\n", "\tstim\\&1015 & 0.1985835 & 0.3148001 & 0.2269200 & 0.2395267 & 4.272637e-01 & 0.2638037 & 0.2652130\\\\\n", "\tstim\\&1016 & 0.2111627 & 0.2675103 & 0.2396157 & 0.2353978 & 3.159429e-01 & 0.2588787 & 0.2261185\\\\\n", "\tstim\\&1039 & 0.2230813 & 0.2231751 & 0.2152400 & 0.2542573 & 2.852869e-01 & 0.2738154 & 0.1957329\\\\\n", "\tstim\\&107 & 0.2012976 & 0.1892416 & 0.2373782 & 0.3299971 & 3.240025e-01 & 0.2905101 & 0.2175857\\\\\n", "\tstim\\&1244 & 0.1902167 & 0.2339165 & 0.2275528 & 0.3002013 & 3.769016e-01 & 0.2738758 & 0.2494139\\\\\n", "\tstim\\&1256 & 0.2092426 & 0.2733710 & 0.2208163 & 0.2139765 & 3.677670e-01 & 0.2668102 & 0.2410888\\\\\n", "\tstim\\&1488 & 0.1764407 & 0.2241510 & 0.2143701 & 0.2797826 & 3.575881e-01 & 0.2941726 & 0.2202224\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 16 × 7\n", "\n", "| | Factor 1 <dbl> | Factor 2 <dbl> | Factor 3 <dbl> | Factor 4 <dbl> | Factor 5 <dbl> | Factor 6 <dbl> | Factor 7 <dbl> |\n", "|---|---|---|---|---|---|---|---|\n", "| ctrl&101 | 0.2753879 | 0.2505314 | 0.2690610 | 0.2148839 | 3.192032e-10 | 0.2126195 | 0.2452438 |\n", "| ctrl&1015 | 0.3073317 | 0.2915613 | 0.2911225 | 0.2295018 | 2.224032e-02 | 0.2348130 | 0.2771375 |\n", "| ctrl&1016 | 0.2563827 | 0.2189345 | 0.2331933 | 0.2479214 | 2.264486e-02 | 0.2436604 | 0.2151357 |\n", "| ctrl&1039 | 0.2990260 | 0.2944541 | 0.2803146 | 0.2287531 | 1.721946e-02 | 0.2271619 | 0.2805458 |\n", "| ctrl&107 | 0.2986128 | 0.2080037 | 0.2611433 | 0.2766160 | 5.844633e-05 | 0.2261954 | 0.2447433 |\n", "| ctrl&1244 | 0.2911649 | 0.2651903 | 0.2765718 | 0.2586165 | 6.973772e-04 | 0.2399731 | 0.2612663 |\n", "| ctrl&1256 | 0.2946287 | 0.2670734 | 0.2826619 | 0.1966789 | 4.576398e-02 | 0.2093361 | 0.2895984 |\n", "| ctrl&1488 | 0.2718269 | 0.2215898 | 0.2579891 | 0.2383075 | 2.803234e-07 | 0.1915762 | 0.2818053 |\n", "| stim&101 | 0.2334743 | 0.2181655 | 0.2463230 | 0.2190290 | 3.502629e-01 | 0.2658829 | 0.2658360 |\n", "| stim&1015 | 0.1985835 | 0.3148001 | 0.2269200 | 0.2395267 | 4.272637e-01 | 0.2638037 | 0.2652130 |\n", "| stim&1016 | 0.2111627 | 0.2675103 | 0.2396157 | 0.2353978 | 3.159429e-01 | 0.2588787 | 0.2261185 |\n", "| stim&1039 | 0.2230813 | 0.2231751 | 0.2152400 | 0.2542573 | 2.852869e-01 | 0.2738154 | 0.1957329 |\n", "| stim&107 | 0.2012976 | 0.1892416 | 0.2373782 | 0.3299971 | 3.240025e-01 | 0.2905101 | 0.2175857 |\n", "| stim&1244 | 0.1902167 | 0.2339165 | 0.2275528 | 0.3002013 | 3.769016e-01 | 0.2738758 | 0.2494139 |\n", "| stim&1256 | 0.2092426 | 0.2733710 | 0.2208163 | 0.2139765 | 3.677670e-01 | 0.2668102 | 0.2410888 |\n", "| stim&1488 | 0.1764407 | 0.2241510 | 0.2143701 | 0.2797826 | 3.575881e-01 | 0.2941726 | 0.2202224 |\n", "\n" ], "text/plain": [ " Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 \n", "ctrl&101 0.2753879 0.2505314 0.2690610 0.2148839 3.192032e-10 0.2126195\n", "ctrl&1015 0.3073317 0.2915613 0.2911225 0.2295018 2.224032e-02 0.2348130\n", "ctrl&1016 0.2563827 0.2189345 0.2331933 0.2479214 2.264486e-02 0.2436604\n", "ctrl&1039 0.2990260 0.2944541 0.2803146 0.2287531 1.721946e-02 0.2271619\n", "ctrl&107 0.2986128 0.2080037 0.2611433 0.2766160 5.844633e-05 0.2261954\n", "ctrl&1244 0.2911649 0.2651903 0.2765718 0.2586165 6.973772e-04 0.2399731\n", "ctrl&1256 0.2946287 0.2670734 0.2826619 0.1966789 4.576398e-02 0.2093361\n", "ctrl&1488 0.2718269 0.2215898 0.2579891 0.2383075 2.803234e-07 0.1915762\n", "stim&101 0.2334743 0.2181655 0.2463230 0.2190290 3.502629e-01 0.2658829\n", "stim&1015 0.1985835 0.3148001 0.2269200 0.2395267 4.272637e-01 0.2638037\n", "stim&1016 0.2111627 0.2675103 0.2396157 0.2353978 3.159429e-01 0.2588787\n", "stim&1039 0.2230813 0.2231751 0.2152400 0.2542573 2.852869e-01 0.2738154\n", "stim&107 0.2012976 0.1892416 0.2373782 0.3299971 3.240025e-01 0.2905101\n", "stim&1244 0.1902167 0.2339165 0.2275528 0.3002013 3.769016e-01 0.2738758\n", "stim&1256 0.2092426 0.2733710 0.2208163 0.2139765 3.677670e-01 0.2668102\n", "stim&1488 0.1764407 0.2241510 0.2143701 0.2797826 3.575881e-01 0.2941726\n", " Factor 7 \n", "ctrl&101 0.2452438\n", "ctrl&1015 0.2771375\n", "ctrl&1016 0.2151357\n", "ctrl&1039 0.2805458\n", "ctrl&107 0.2447433\n", "ctrl&1244 0.2612663\n", "ctrl&1256 0.2895984\n", "ctrl&1488 0.2818053\n", "stim&101 0.2658360\n", "stim&1015 0.2652130\n", "stim&1016 0.2261185\n", "stim&1039 0.1957329\n", "stim&107 0.2175857\n", "stim&1244 0.2494139\n", "stim&1256 0.2410888\n", "stim&1488 0.2202224" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tensor$factors[['contexts']]" ] }, { "cell_type": "markdown", "id": "be12039d", "metadata": {}, "source": [ "## Downstream Visualizations" ] }, { "cell_type": "markdown", "id": "8e1f9bd1", "metadata": {}, "source": [ "We can use these loadings to compare pairs of sample major groups with boxplots and statistical tests:" ] }, { "cell_type": "code", "execution_count": 35, "id": "8d39b16e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "sample_loadings <- liana::get_c2c_factors(sce,\n", " sample_col=sample_col,\n", " group_col=condition_col) %>%\n", " purrr::pluck(\"contexts\") %>% \n", " pivot_longer(-c('condition', 'context'),\n", " names_to = \"factor\",\n", " values_to = \"loadings\") %>% \n", " mutate(factor = factor(factor)) #%>%\n", " # mutate(factor = fct_relevel(factor, \"Factor.10\", after=9))\n", "\n", "h_ = 10\n", "w_ = 15\n", "options(repr.plot.height=h_, repr.plot.width=w_)\n", "\n", "# Do pairwise tests\n", "pwc <- sample_loadings %>%\n", " group_by(factor) %>%\n", " rstatix::pairwise_t_test(\n", " loadings ~ condition, \n", " p.adjust.method = \"fdr\"\n", " )" ] }, { "cell_type": "code", "execution_count": 36, "id": "dcf4793f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { 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YLbjY9PSfj51UO0UROBWXcCouQe0UAGzqzJkzR4868Hkho6KiROTa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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# plot\n", "pwc <- pwc %>% add_xy_position(x = condition_col)\n", "ggboxplot(sample_loadings, x = condition_col, y = \"loadings\", add = \"point\", fill=condition_col) +\n", " facet_wrap(~factor, ncol = 4) +\n", " stat_pvalue_manual(pwc) +\n", " theme_bw() + \n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))" ] }, { "cell_type": "markdown", "id": "eab261db", "metadata": {}, "source": [ "Using the loadings for any dimension, we can also generate heatmaps for the elements with loadings above a certain threshold. Additionally, we can cluster these elements by the similarity of their loadings across all factors:" ] }, { "cell_type": "code", "execution_count": 37, "id": "bdb255c8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: grid\n", "\n", "========================================\n", "ComplexHeatmap version 2.14.0\n", "Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/\n", "Github page: https://github.com/jokergoo/ComplexHeatmap\n", "Documentation: http://jokergoo.github.io/ComplexHeatmap-reference\n", "\n", "If you use it in published research, please cite either one:\n", "- Gu, Z. Complex Heatmap Visualization. iMeta 2022.\n", "- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional \n", " genomic data. Bioinformatics 2016.\n", "\n", "\n", "The new InteractiveComplexHeatmap package can directly export static \n", "complex heatmaps into an interactive Shiny app with zero effort. Have a try!\n", "\n", "This message can be suppressed by:\n", " suppressPackageStartupMessages(library(ComplexHeatmap))\n", "========================================\n", "\n", "\n" ] }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "library(ComplexHeatmap)\n", "pushViewport(viewport(gp = gpar(fontfamily = \"Arial\")))\n", "\n", "h_ = 10\n", "w_ = 15\n", "options(repr.plot.height=h_, repr.plot.width=w_)\n", "\n", "liana::plot_lr_heatmap(sce = sce, n = 5)" ] }, { "cell_type": "markdown", "id": "f0542e56", "metadata": {}, "source": [ "Here patients are grouped by the importance that each communication pattern (factor) has in relation to the other patients. This captures combinations of related communication patterns that explain similarities and differences at a sample-specific resolution." ] }, { "cell_type": "markdown", "id": "189d72cd", "metadata": {}, "source": [ "### Overall CCI potential: Heatmap and network visualizations of sender-receiver cell pairs" ] }, { "cell_type": "markdown", "id": "54758184", "metadata": {}, "source": [ "In addition, we can also evaluate the overall interactions between sender-receiver cell pairs that are determinant for a given factor or program. We can do it through a heatmap where the X-axis represent the receiver cells and the Y-axis shows the receiver cells. Here, the potential of interaction is calculated as the outer product between the loadings for the sender and receiver cells dimensions of a particular factor. To illustrate this, we chose Factor 5, but this can be repeated for every factor obtained in the decomposition." ] }, { "cell_type": "code", "execution_count": 38, "id": "b272a387", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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H3HD7q7Oi7vbHnbpzo197/fTF2DGvvffhlKJ6zdt12K19s+oV9/KC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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "selected_factor <- 'Factor.5'\n", "liana::plot_c2c_cells(sce = sce,\n", " factor_of_int = selected_factor,\n", " name = \"Loadings \\nProduct\")" ] }, { "cell_type": "markdown", "id": "dd9139a7", "metadata": {}, "source": [ "Similarly, an interaction network can be created for each factor by using the loading product between sender and receiver cells. First we need to choose a threshold to indicate what pair of cells are interacting. This can be done as shown in [the extended tutorial](./05-Downstream-Visualizations.ipynb)" ] }, { "cell_type": "code", "execution_count": 39, "id": "3cc2f9eb", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "threshold = 0.075" ] }, { "cell_type": "markdown", "id": "94dfb921", "metadata": {}, "source": [ "Then, we can plot all networks we are interested in:" ] }, { "cell_type": "code", "execution_count": 40, "id": "391b9080", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "[[1]]\n", "
\n", "\n", "[[2]]\n", "[[2]][[1]]\n", "\n", "\n", "[[2]][[2]]\n", "\n", "\n", "[[2]][[3]]\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# format for use with cell2cell\n", "format_py_df<-function(df){\n", " df<-df %>% \n", " as.data.frame() %>% \n", " textshape::column_to_rownames() %>%\n", " select(-contains(\"condition\"))\n", " \n", " return(df)\n", "}\n", "factors <- liana::get_c2c_factors(sce = sce,\n", " sample_col = sample_col,\n", " group_col = condition_col)\n", "py_factors<-unname(lapply(factors, function(df) format_py_df(df)))\n", "py_factors = reticulate::py_dict(keys = names(factors), values = py_factors)\n", "\n", "c2c$plotting$ccc_networks_plot(py_factors,\n", " included_factors=c('Factor.1', 'Factor.5', 'Factor.6'),\n", " sender_label='senders', \n", " receiver_label='receivers',\n", " ccc_threshold=threshold, # Only important communication\n", " nrows=as.integer(1), \n", " filename=file.path(output_folder, 'network_plot_qsp_r.png')\n", " )" ] }, { "cell_type": "markdown", "id": "17e0c5d3", "metadata": {}, "source": [ "![network_plot_qsp](../../data/quickstart_pbmc/outputs/network_plot_qsp_r.png \"Network Plot QSP\")" ] }, { "cell_type": "markdown", "id": "588f5692", "metadata": {}, "source": [ "## Pathway Enrichment Analysis: Interpreting the context-driven communication" ] }, { "cell_type": "markdown", "id": "ce5fd54d", "metadata": {}, "source": [ "### Classical Pathway Enrichment\n", "\n", "As the number of inferred interactions increases, the interpretation of the inferred cell-cell communication networks becomes more challenging. To this end, we can perform pathway enrichment analysis to identify the general biological processes that are enriched in the inferred interactions. Here, we will perform classical gene set enrichment analysis with `Reactome` gene sets." ] }, { "cell_type": "markdown", "id": "c1a1c9b1", "metadata": {}, "source": [ "For the pathway enrichment analysis with GSEA, we use ligand-receptor pairs instead of individual genes. `Reactome` was initially designed to work with sets of genes, so first we need to generate ligand-receptor sets for each of it’s pathways:" ] }, { "cell_type": "code", "execution_count": 41, "id": "e9f4038e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# Generate list with ligand-receptors pairs in DB\n", "lr_pairs <- liana::select_resource('Consensus')[[1]] %>%\n", " select(ligand = source_genesymbol, receptor = target_genesymbol)\n", "lr_list <- lr_pairs %>%\n", " unite('interaction', ligand, receptor, sep = '^') %>% \n", " pull(interaction)\n", "\n", "# Specify the organism and pathway database to use for building the LR set\n", "organism = \"human\"\n", "pathwaydb = \"Reactome\"\n", "\n", "# Generate ligand-receptor gene sets\n", "lr_set <- c2c$external$generate_lr_geneset(lr_list = lr_list, \n", " complex_sep='_', # Separation symbol of the genes in the protein complex\n", " lr_sep='^', # Separation symbol between a ligand and a receptor complex\n", " organism=organism,\n", " pathwaydb=pathwaydb,\n", " readable_name=TRUE\n", " )" ] }, { "cell_type": "markdown", "id": "c04c71bc", "metadata": {}, "source": [ "Next, we can perform enrichment analysis on each factor using the loadings of the ligand-receptor pairs to obtain the normalized-enrichment scores (NES) and corresponding P-values from GSEA:" ] }, { "cell_type": "code", "execution_count": 42, "id": "f7bd340d", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "lr_loadings = factors[['interactions']] %>% \n", " column_to_rownames(\"lr\")\n", "\n", "gsea_res <- c2c$external$run_gsea(loadings=lr_loadings, \n", " lr_set=lr_set,\n", " output_folder=output_folder,\n", " weight=as.integer(1),\n", " min_size=as.integer(15),\n", " permutations=as.integer(999),\n", " processes=as.integer(6),\n", " random_state=as.integer(6),\n", " significance_threshold=as.numeric(0.05)\n", " )\n", "names(gsea_res) <- c('pvals', 'scores', 'gsea_df')" ] }, { "cell_type": "markdown", "id": "009f99f6", "metadata": {}, "source": [ "The enriched pathways for each factor are:" ] }, { "cell_type": "code", "execution_count": 43, "id": "8fda4d1c", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 14 × 5
FactorTermNESP-valueAdj. P-value
<chr><chr><dbl><dbl><dbl>
0Factor.1IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL1.2081940.0010000000.01611612
46Factor.3NEUTROPHIL DEGRANULATION 1.1560930.0010000000.01611612
92Factor.5SIGNALING BY INTERLEUKINS 1.1379170.0030030030.03453453
2Factor.1ADAPTIVE IMMUNE SYSTEM 1.1364810.0010000000.01611612
93Factor.5IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL1.1237320.0020020020.02479402
47Factor.3IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL1.1221840.0020020020.02479402
23Factor.2INFECTIOUS DISEASE 1.1210330.0010000000.01611612
48Factor.3ADAPTIVE IMMUNE SYSTEM 1.1169040.0010000000.01611612
24Factor.2IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL1.1164820.0010000000.01611612
25Factor.2ADAPTIVE IMMUNE SYSTEM 1.1017460.0010000000.01611612
49Factor.3INNATE IMMUNE SYSTEM 1.0994810.0020020020.02479402
69Factor.4IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL1.0952600.0010010010.01611612
70Factor.4ADAPTIVE IMMUNE SYSTEM 1.0680200.0010010010.01611612
117Factor.6CYTOKINE SIGNALING IN IMMUNE SYSTEM 1.0667450.0010010010.01611612
\n" ], "text/latex": [ "A data.frame: 14 × 5\n", "\\begin{tabular}{r|lllll}\n", " & Factor & Term & NES & P-value & Adj. P-value\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t0 & Factor.1 & IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL & 1.208194 & 0.001000000 & 0.01611612\\\\\n", "\t46 & Factor.3 & NEUTROPHIL DEGRANULATION & 1.156093 & 0.001000000 & 0.01611612\\\\\n", "\t92 & Factor.5 & SIGNALING BY INTERLEUKINS & 1.137917 & 0.003003003 & 0.03453453\\\\\n", "\t2 & Factor.1 & ADAPTIVE IMMUNE SYSTEM & 1.136481 & 0.001000000 & 0.01611612\\\\\n", "\t93 & Factor.5 & IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL & 1.123732 & 0.002002002 & 0.02479402\\\\\n", "\t47 & Factor.3 & IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL & 1.122184 & 0.002002002 & 0.02479402\\\\\n", "\t23 & Factor.2 & INFECTIOUS DISEASE & 1.121033 & 0.001000000 & 0.01611612\\\\\n", "\t48 & Factor.3 & ADAPTIVE IMMUNE SYSTEM & 1.116904 & 0.001000000 & 0.01611612\\\\\n", "\t24 & Factor.2 & IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL & 1.116482 & 0.001000000 & 0.01611612\\\\\n", "\t25 & Factor.2 & ADAPTIVE IMMUNE SYSTEM & 1.101746 & 0.001000000 & 0.01611612\\\\\n", "\t49 & Factor.3 & INNATE IMMUNE SYSTEM & 1.099481 & 0.002002002 & 0.02479402\\\\\n", "\t69 & Factor.4 & IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL & 1.095260 & 0.001001001 & 0.01611612\\\\\n", "\t70 & Factor.4 & ADAPTIVE IMMUNE SYSTEM & 1.068020 & 0.001001001 & 0.01611612\\\\\n", "\t117 & Factor.6 & CYTOKINE SIGNALING IN IMMUNE SYSTEM & 1.066745 & 0.001001001 & 0.01611612\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 14 × 5\n", "\n", "| | Factor <chr> | Term <chr> | NES <dbl> | P-value <dbl> | Adj. P-value <dbl> |\n", "|---|---|---|---|---|---|\n", "| 0 | Factor.1 | IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL | 1.208194 | 0.001000000 | 0.01611612 |\n", "| 46 | Factor.3 | NEUTROPHIL DEGRANULATION | 1.156093 | 0.001000000 | 0.01611612 |\n", "| 92 | Factor.5 | SIGNALING BY INTERLEUKINS | 1.137917 | 0.003003003 | 0.03453453 |\n", "| 2 | Factor.1 | ADAPTIVE IMMUNE SYSTEM | 1.136481 | 0.001000000 | 0.01611612 |\n", "| 93 | Factor.5 | IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL | 1.123732 | 0.002002002 | 0.02479402 |\n", "| 47 | Factor.3 | IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL | 1.122184 | 0.002002002 | 0.02479402 |\n", "| 23 | Factor.2 | INFECTIOUS DISEASE | 1.121033 | 0.001000000 | 0.01611612 |\n", "| 48 | Factor.3 | ADAPTIVE IMMUNE SYSTEM | 1.116904 | 0.001000000 | 0.01611612 |\n", "| 24 | Factor.2 | IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL | 1.116482 | 0.001000000 | 0.01611612 |\n", "| 25 | Factor.2 | ADAPTIVE IMMUNE SYSTEM | 1.101746 | 0.001000000 | 0.01611612 |\n", "| 49 | Factor.3 | INNATE IMMUNE SYSTEM | 1.099481 | 0.002002002 | 0.02479402 |\n", "| 69 | Factor.4 | IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL | 1.095260 | 0.001001001 | 0.01611612 |\n", "| 70 | Factor.4 | ADAPTIVE IMMUNE SYSTEM | 1.068020 | 0.001001001 | 0.01611612 |\n", "| 117 | Factor.6 | CYTOKINE SIGNALING IN IMMUNE SYSTEM | 1.066745 | 0.001001001 | 0.01611612 |\n", "\n" ], "text/plain": [ " Factor \n", "0 Factor.1\n", "46 Factor.3\n", "92 Factor.5\n", "2 Factor.1\n", "93 Factor.5\n", "47 Factor.3\n", "23 Factor.2\n", "48 Factor.3\n", "24 Factor.2\n", "25 Factor.2\n", "49 Factor.3\n", "69 Factor.4\n", "70 Factor.4\n", "117 Factor.6\n", " Term \n", "0 IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL\n", "46 NEUTROPHIL DEGRANULATION \n", "92 SIGNALING BY INTERLEUKINS \n", "2 ADAPTIVE IMMUNE SYSTEM \n", "93 IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL\n", "47 IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL\n", "23 INFECTIOUS DISEASE \n", "48 ADAPTIVE IMMUNE SYSTEM \n", "24 IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL\n", "25 ADAPTIVE IMMUNE SYSTEM \n", "49 INNATE IMMUNE SYSTEM \n", "69 IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL\n", "70 ADAPTIVE IMMUNE SYSTEM \n", "117 CYTOKINE SIGNALING IN IMMUNE SYSTEM \n", " NES P-value Adj. P-value\n", "0 1.208194 0.001000000 0.01611612 \n", "46 1.156093 0.001000000 0.01611612 \n", "92 1.137917 0.003003003 0.03453453 \n", "2 1.136481 0.001000000 0.01611612 \n", "93 1.123732 0.002002002 0.02479402 \n", "47 1.122184 0.002002002 0.02479402 \n", "23 1.121033 0.001000000 0.01611612 \n", "48 1.116904 0.001000000 0.01611612 \n", "24 1.116482 0.001000000 0.01611612 \n", "25 1.101746 0.001000000 0.01611612 \n", "49 1.099481 0.002002002 0.02479402 \n", "69 1.095260 0.001001001 0.01611612 \n", "70 1.068020 0.001001001 0.01611612 \n", "117 1.066745 0.001001001 0.01611612 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gsea_df <- gsea_res$gsea_df\n", "gsea_df %>% \n", " filter(`Adj. P-value` < 0.05 & NES > 0) %>%\n", " arrange(desc(abs(NES)))" ] }, { "cell_type": "markdown", "id": "f51eb435", "metadata": {}, "source": [ "The depleted pathways are:" ] }, { "cell_type": "code", "execution_count": 44, "id": "3a87a6f2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\n", "
A data.frame: 0 × 5
FactorTermNESP-valueAdj. P-value
<chr><chr><dbl><dbl><dbl>
\n" ], "text/latex": [ "A data.frame: 0 × 5\n", "\\begin{tabular}{lllll}\n", " Factor & Term & NES & P-value & Adj. P-value\\\\\n", " & & & & \\\\\n", "\\hline\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 0 × 5\n", "\n", "| Factor <chr> | Term <chr> | NES <dbl> | P-value <dbl> | Adj. P-value <dbl> |\n", "|---|---|---|---|---|\n", "\n" ], "text/plain": [ " Factor Term NES P-value Adj. P-value" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gsea_df %>% \n", " filter(`Adj. P-value` < 0.05 & NES < 0) %>%\n", " arrange(desc(abs(NES)))" ] }, { "cell_type": "markdown", "id": "cd9ebbd1", "metadata": {}, "source": [ "Finally, we can visualize the enrichment results using a dotplot:" ] }, { "cell_type": "code", "execution_count": 45, "id": "39188e96", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "gsea.dotplot<-function(pval_df, score_df, significance = 0.05, font_size = 15){\n", " pval_df <- pval_df[apply(pval_df% \n", " mutate_all(function(x) -1*log10(x + 1e-9))\n", " score_df <- score_df[rownames(pval_df), colnames(pval_df)]\n", " pval_df <- pval_df %>%\n", " tibble::rownames_to_column('Annotation')\n", " score_df <- score_df %>%\n", " tibble::rownames_to_column('Annotation')\n", "\n", " viz_df <- cbind(melt(pval_df), melt(score_df)[[3]])\n", " names(viz_df) <- c('Annotation', 'Factor', 'Significance', 'NES')\n", " \n", " \n", " dotplot <- ggplot(viz_df, aes(x = Factor, y = Annotation)) + geom_point(aes(size = Significance, color = NES)) + \n", " scale_color_gradient2() + theme_bw(base_size = font_size) + \n", " theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=0.5))+\n", " scale_size(range = c(0, 8)) + \n", " guides(size=guide_legend(title=\"-log10(p-value)\"))\n", " return(dotplot)\n", "}\n" ] }, { "cell_type": "code", "execution_count": 46, "id": "2639e425", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Annotation as id variables\n", "\n", "Using Annotation as id variables\n", "\n" ] }, { "data": { "image/png": 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M2IiGp9VSuMirpDpny+1HUvnzypoU7tuJ1d\ngdJh+owe7fg0whWLTTx9ppjUB74+WlzrEnXf+Z9/+GGIv0GdmD++eoRC9s6dB930ACwUstnZ\n4hu/VFfVd1P7FCJiGObePYm9Z2dmyKFZeFtCIZuRVirpr7l3H3VbuEFElCt5ebMyy7ojwOIw\nTGZmt6xJxeXl5aWmJuWbifYwDOPv79/135GOmZkZUW5GRqvDsSwz8+HTP4G8vcJNVIhU3AMG\nqF+OioytCRrc/CSSERVVxjhM9utJ8fKZRDZ6QcvmXb6xKXnnlivenwSJe+4rSE19SGTq5ia5\nDpW6m5sNRSTdTE2jII/25ibIO385gxhjvr9Iu/raB3m5uWLqyXG1jE31eET1ubn3iYydnCT2\nAKis08tCYsVQOamODkuopx7DA104RLp+/k6/XbsRE560zKvDnuRaeZyamkuk5eQmuYt95X7u\njionrmSnptZODGrviYUtu3QxRUDagXyXllcibp8pIRMiVh+O3bkrof9bBie2nrlPBiOXTrNt\n2028NJRsp4WMifzwxN8bDwzcMNO6y0dZR5u7XRIWualzFLWAwAFqROTl760SEZEUHl05eFjz\nA2JBfj5LjK29rfj1rtHb06+LDZy6cLB0fCSSqYtLj7/y8g5/9aVw1pRhvg7Gas0rQEXHyLjD\n3b/2QX5ublMlD7ax9vGDvNTzhw5nMUZDZw83Ff1mFzZQC6rOLjaUkJ6Wfof6WxNRXXJyGstx\ndnfh6VR79KWU1KSU+mEBPCK6m5ZWS+To3PqRWVB5PzdXXMcHaj3NDVqNxlRdmpsrrioCT8/U\nWGRUInWPeUsC49aG/bnppP83Y2Wp0CTFPtZpOv6LFnglb0g4uvn44O+G5+/ce62O57hkyWAx\nFXm1fQJcN6XERl5f7uH+bNEakqPianhe/t6q949KMzu2MunI2TtEPGublvWxW+wiLRU9bjls\nQGZqaj2Ro5ub5KXWc3ezoGvZqak55CFrV2OMmbkp0cPi+2VEhhK/JdO2kOLgetFFaqPD3VJw\nMzWdJa69m5PE1vqMubur/r6c4tTUcrJu0cZO3ifwlrM0HLFixuWVO9P3bb3IX+Me+/uhOwJN\n/4XzxbWGYewC+PrHj0ZGloxrHliQiqMib5P5VL4Fc1Ca+QnLLx298pBIx9qm5c2AoPK+uBNA\nXXmLm+8urEDp6JiZa9HN4uJiInFvUcvz82uJHOzsxV/RVUyc/dpMVl9RKPa8xmgYmPeUwxOR\niJycnO+//77VjBiGiCorK0UewB48eFJf342VTYqKHldUiKkxde+unAdPaYll2YL8R2LnS0S5\nuZ3LRztWXl7z4MFDLldM7JZ7T2L20XUcDpOf91DS8hbkP2S6OhiuGEJi8/MkrmTpNTQI9u+6\nlZ3x0NndYMoMx05HMS+u0wrJ1NTUhg0bduLEia6MFMvhcAYMGPDimqiQw8Rgu6+++efo2dpJ\nE56d0x78fTyS5foFj5Nbmxt47pUOOEjZje+jcSU88mr14EFNNeTZzMioUsYx2E9PQljRiUlk\npD9s+ewrIb9f3f57jOd7bd4WEpWVlxFxzMzbq/ekamamT0llZWXVRM+q/j9I+Tu0/nk7AMGT\nipI7STHXyvX6L/1klmhT5qyDH4aIu70xnPjDtvnWRPn5eWzbVLBpCLdWH5n5TwvonrfRRBWR\nV1IayXBgUD+GiEjH199xy40bsnc0WF5eRkRm5u1FnIyZmSlRTlnZQ6Jnr3uq086Fhj4f41pY\nV1mWey0msUDFeeZHi/uLPHYq205fPirqk1OXtv7KNYi7LdQJXDLHtfPdIfL6zV4xIvazM4c2\nHuKvn9a7i83rOtrczaRf5Pr4sJhq0hwc6MUjIlLx4nupRkSlhEVXDBv+9IGmsaCwhEjfyLj7\nzhKdPViIqOMjkZSdZ7wz+fbaQ+lJh35KOrRRy8ze1c3Vzc3N1cnGSF2Kmjk5hz8OOSzymY5X\nyP+t8BadmdQbqCM9XZxNKb0gLe0BWfcgQWrK9QaycXPVINJw9zCl7JTEVEGAB5cepqffJzJ3\ndmndI2h1+IaQcHG/673qwGeDWz4npGwPCRH3ResF236YIPpMqum7cLFv0rqYPzad8/lquPhq\nVGJIsY91hd6QZXMvrfj1xp+bN94ujXjMtZ6zbJSR2HOZpg/f/dekmIiU5e6eTTtzY1Lk1Scq\nXnwvVTolborSxKOhVU/XF9tQVZ53MzYup5JVtp0+06/lWSHn4MdiN3xLgrLyR0Tq5ubtNaI2\nNDNTpuyysrJO9KWuq6+vRFRcVNxOmiDrtujw4HrxRWqjg93yUXl5I5GpuVl75y8zM1OisrLS\nMqJWz+fyPYG3wpiOWzE5bFVowu7Nv8Teular5r5iYYD4BWZs+XzDo8cjI0snBD9dvuKoqBwy\nn843J7EPcvnRf4bmPz0IhPWVpXdSYpPya0jdY/7rzi2/Vx25MSSyg4J2aQVKxUDfgCinuLiC\nSMwKKCgsIFIxNm63J2gRRae/CTkt5nPVIWsOvNWpwSDbVVlZGRcX1/KTpoCjoaGhoaGh9Te7\nt8FqQ4OwpqZOWVn0ulZV1b3zffKkUWRJm1VXddwIu3NYlq2srNXQEPP+qbq6u2ZKRCxLT2pE\nt2zLWbPyzzeIWKqpljhT6d1IKb2RUkpEcdFFbp5Gln07eQ3ueknkYuHChcePH+/KLwiFwoUL\nF8qrPOLkR+y9mCPQdZ843kWbiBiPxUsHfPv2n+8smTJg5/heHGq4vX3+J+cb1SeumG/e4Y+B\n7F7tgIOUPPi+GpcuR16tGjRYk551puE02U/yJa8Tk8iIMRoTMjNs5Y7wLdsHubYdW1QgEBBx\neMrt3hI1pfv1DfUtAo5rf4sZ5EzLdch4H4s274ANvYKH2Ih5/Nbs13STIWgUEJHozf7TUQBb\n8jGb2l0Bx4PI8BsCMhkYaPP093X8+E6/3bguc0eDAoGAOqxh22KNPlOdfi40vc0XVa2Hjg+0\nFvO6jOc8Z9nQq59fCD9fTOoDli/o37U+9FVd5y4bcvXLiwc3HuevCzbr0jruaHM3k3qRa6+G\nX60lreGBTx/4iOfJ91KNikwNj3ww/Gmv/fV1tUIiHq+zw2dIobMHS5MOjkQi0nKZ8+3vQUlh\nV6Lik6/dzEmNzE+NPLmPlHv044+btWCis067m8XAY+JQu+YdVVhbfiflakLCr6s/f/jZx1Nt\nW85O6g3UIUsXF62DBVlpaY1j/ZUyk5NryNzdXZ+IqLeHR88DJ5KTMlmPfsL0tCwiLRdnkd7d\nVGyHBHuKe6I0tRS5FzTnT+eLu6b2sBfbgZoOf8kbl6/9FL9z84X+a4ZK192ZNPtY1xgMXzHz\n8srtN85HEGM2afkEiWcyLV++268JsZEpKzy9uPS0/oaqF99LlcQPmFuadDQ0qdUnSlpWQdOW\nLxrTuqmsgddECcPEnkt/1k5SKBSwRDzldmuDcdTUVIiq6xvYtuftDjU9VLX3Sk72bdHxwfXC\ni9RWu7ulVNcNnpo6R+S68ZQ8T+AiuL2nhARHvHsw9nwcKdstXDpM8lnChs83PnYkMqokuCl2\nLIqMyiGzGfzeJCHgiPozNKrlBwxPz37E3BXzRLrKVrEeHOzd9qVh4+0rB+OejcDYxRUoDabF\nf0WxdbX1RKqytazRcR41yknMkwgJFewAACAASURBVJuy1YtqaS9B93S+0QpHXGjfTb1+PJ+p\n5N/nqXRjE3hJs2W6tdk9QwxH4vKqqCgxxHRHxsHhymEjNg1201TH5F/QpeWYMWP4fH50dHTn\nKnFwOBw7O7u5c+fKvWAtXNu6aN7eWqevfJoCDqLeIfv3JQ+fu2dC75O2zha1WTdyK9U83z68\nebrcHk+hpVc84CAld76P5oWwiNjqwUM1iDKiossYhym+ekQSY+lOTCIrxnTcm6+Hrdp3afPu\nQRuXte7SjnR1dYmK8/PvP69K0IYgL6+QiKOr26I2eethYoltqCq7HXNw78nQNWto/c/TrVqd\n4Yw8J00f3c5rNXMLc4aKCwoKiayef2ox9vMNAc/qTgsSt7+757oUS9tJZeFhqSzx1B/EhYY+\ne8XxmNUmqogPi6sJGCj9HbOuri5RSX5+PpGdxC/l5uU+++ozrcdMJbax5sG9pON7D/399ceC\nr7YscW5z46TmPn+OT8QPsXU2ry8O6nqf1epeC5YGJHwTEbrptN83o7tSD66jzd1M2kWuuRoW\nX0uMftWtA82BSF2tDtH91LCIstHj9YmI1E1MdCjlfnGRkHqLvWsQ1lVV17NcFU11Xievv509\nWJ5p90hswtG08Bo9x2v0HGqszE+/fj0991FVaVpk+K41N4s++3lFe+MkGHhOnD621ZWHnXt9\n28efndj39U7b31e4P9+BpN5AHWL6uTgpn4lJu3Wb/FWTkx+SHt/taTsgOw939RMXkpLuUj9B\nenodKXs62YmsdxWbIdOnO0kzHwv+9Ol+shSsx+Blcy/f2Jywc2uE9wcSXji3ItU+1kWMydil\n4/5561CB9pAF09prSaDhE+CunBgbkbzCy0vpWfsUvpfEKloth4klYpQ0DU0M1MXcExp4Tpw+\nuu3NSWbt+ecBh7KurjrRo4L8KnIQ3/0+ET3My6si0tHV7cSR9Ki0tIHI2FhyJddObQspDq4X\nXaS22tktdXV1GaKCgnyWJMcTBXm5QpHrRjP5ncDbULKeuvC1C1+ce2gydtGYdlt92fL5hkcO\nRUQWTwg2JrofFZVN5tP8Jb7vazFMLBExPB3jXj1UxZy9VWwGTZ/u2ubj2vMZzwOOLq/ADpWW\nlhKpGhuJPZ0wvUyMifKKi6rJWezLBra+pqpOwOFpaDx/ktZ1GjV9+osbRcXQ0FDkGYlhmF27\ndqmqq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DYWP6ng9sH1W2NL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\nv2tZgcuYG6+JNP3dU+cUkjB5b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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 720 } }, "output_type": "display_data" } ], "source": [ "h_ = 10\n", "w_ = 12\n", "options(repr.plot.height=h_, repr.plot.width=w_)\n", "\n", "dotplot <- gsea.dotplot(pval_df = gsea_res$pvals, \n", " score_df = gsea_res$scores, \n", " significance = 0.05)\n", "dotplot" ] }, { "cell_type": "markdown", "id": "df07aabb", "metadata": {}, "source": [ "### Footprint Enrichment\n", "\n", "Footprint enrichment analysis build upon classic geneset enrichment analysis, as instead of considering the genes involved in a biological activity, they consider the genes affected by the activity, or in other words the genes that change downstream of said activity [(Dugourd and Saez-Rodriguez, 2019)](https://www.sciencedirect.com/science/article/pii/S2452310019300149). \n", "\n", "In this case, we will use the PROGENy pathway resource to perform footprint enrichment analysis. PROGENy was built in a data-driven manner using perturbation and cancer lineage data [(Schubert et al, 2019)](https://www.nature.com/articles/s41467-017-02391-6#Sec8), as a consequence it also assigns different importances or weights to each gene in its pathway genesets. To this end, we need an enrichment method that can take weights into account, and here we will use multi-variate linear models from the `decoupler-py` package to perform this analysis [(Badia-i-Mompel et al., 2022)](https://academic.oup.com/bioinformaticsadvances/article/2/1/vbac016/6544613)." ] }, { "cell_type": "markdown", "id": "ecdbdba0", "metadata": {}, "source": [ "Let’s load the PROGENy genesets and then convert them to sets of weighted ligand-receptor pairs:" ] }, { "cell_type": "code", "execution_count": 47, "id": "53f6e6a3", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# obtain progeny gene sets\n", "net <- decoupleR::get_progeny(organism = 'human', top=5000) %>%\n", " dplyr::select(-p_value)\n", "\n", "# convert to LR sets\n", "progeny_lr <- liana::generate_lr_geneset(sce = sce,\n", " resource = net)" ] }, { "cell_type": "markdown", "id": "81d63114", "metadata": {}, "source": [ "Next, we can run the footprint enrichment analysis:" ] }, { "cell_type": "code", "execution_count": 48, "id": "158158f8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# interaction loadings to matrix\n", "mat <- factors$interactions %>%\n", " textshape::column_to_rownames(\"lr\") %>%\n", " as.matrix()\n", "\n", "# run enrichment analysis with decoupler\n", "# (we fit a univariate linear model for each gene set)\n", "# We don't consider genesets with minsize < 10\n", "res <- decoupleR::run_ulm(mat = mat,\n", " network = progeny_lr,\n", " .source = \"set\",\n", " .target = \"lr\",\n", " minsize=10) %>%\n", " mutate(p_adj = p.adjust(p_value, method = \"fdr\"))" ] }, { "cell_type": "markdown", "id": "a7926cce", "metadata": {}, "source": [ "Finally, we can visualize the results:" ] }, { "cell_type": "code", "execution_count": 49, "id": "10bbc976", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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S7jv+4gO1Xu/iX37fa89Xbv99N936uM57zXuevkr1H3z2o1/focm3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P//Ppw4+9f8a1E7vRLm6eDQDtbWbVPj/d\nTsbxK5AEr7PiT0u7Bglw6O+a+HdCkgTS7ttgwxJo/0+jbhWRGvvDdq7vWINtSIV0A4DdoX7D\nAkib63kc+iyiQ794ARsQabU3bCHa+WOP77p02gnxw10HdgWQYgfMRNl2ooMDaom0O4UC7T9g\npnGzqg02rE4IOCQBJDRuWBx3/HFY8tHrr5f+eljdjdno2rXvAv3HjMlN0ipEXacbXX27C4cX\nFbUWJRgHNqxc8drHW3YV7ztYGYrZssdzePMP7t9vwl1Q0HBQiOz2+ZofGBs5cLAaOKqw0UBg\nubAgD9h/YD9wKODIzclpdbsHTv3uwqkNF0kDZ86c8Phf39u6pRgTh9R7YMaMGaNGjar/1D17\n9mzevDkzM9Pnc6ygul5lK7LmabIBwWDQsiwAbre70QgaKyaMGrg9muRKq5oLAGzbcrtccr0B\n1YZhxDsYS5KUkXF4MlQJACxV03xaGg0drmNGPW6XIhocIgKBQPzmhsfjUZTDD0m2BcvQXG7F\nnSY9ruNCYSiyy+Vr/CWPRqPxvleyLHu9jQsrxMJyNAJvhltSU2CAvZTdDaqhZmU5eABsFylc\nI8WMpltb95VQVdXtbnzWUQMVtpB9GanxGRNh61WaJJruB9u2A4HakRqZmZlNr/HU6pAtSany\n506EbERghLMyvHbDCkSRSMQwDACaprlcDc4RkhVDxK+5PUq61OCI2nIoBK/bp7iadFcxzdCh\nbmLNfiX0gBwEMnxu2ZUmewMAYlCgNvqeh0Ih0zTR9CthC8SEqipqx6qYdGOKZUimkeXz1f9a\nxGKx+AgdIURmZoNWu2zFYOpur9flXA2OkEO9GjulB0fCe3HYZVecceMVbzz0j88vu3GUBgDW\nniceWOUXx1//w0lJW4Woy3SjgKN1wU2P/u7mZ3ZKgyZMmfyt6UMG9O83eKjv3UU/eHh//HGX\nywXEdCPRuFjL8MpAOBwGGlyehCNhIM9dr4KcJHfgMKv27p0LVFU1Gnc4fPjw4cOH11+ydu1a\nNNcA6kqWG0ZYaJqrUcOj7iivKEqjzYvqAODKUKT2Fpnr/mK6sEyXdrj6eV1/RSFEg/0Q02FB\ndbU130qKipqSZbrcDebKrbt0UVVV0+o1RvUwLKiuVifES0G6G6Yuu5qkeKZpxq9mJUlq+ss1\naiAEPBlqanwvBjhf6lz07+/gAbB9bC/8lS6pcdm8WCwW/49mvhKmAcsUGRkp8xkT4faKcNCl\nNZ74wap3F1LTNKlRlWLbhhGFJzOtdoUiwwhrwkbDDxU/RACQZbnx5w1FAaiejLQpUC37ECoD\nDM2V1fihWCxW15ZwuVxNA45IOYQET6aaTmUlTYGYDk1xiXpnj0gkEg84GrWpTB0xQHMrIv0m\nZTNlhA2t4QFTCFFXgqTxTyOsQ0iauz1VnJOtQdumq9idU4OjHX1hen/nhqvvmPH3RTNmBn79\ng6m9K//3xO23rAkOuuz3Vxy6aAk9ddnoX6/DlJs/evw7uYmtQuSYVDmYRjesWrkjMvL7D/75\nW4V158Dd9Qpf5vXr78aWr3cVY+LAw6vtX/fQkv8pkxYsnNwLDUl9+/YBinfs1JFf71hWsmNH\nEKKof8LlcczPn//7C1vzT7n8eyfXL5BqlJRUAsMHtTq4pbtQ3TBC0INwNS6K2jzbhh6CrKbZ\njBmH1M7/2lZ9e9s6NGlISlzFtp+iIRqCobdd/9yyaieuT690A4DmRTAMPQQt4ekvLBNGGKo3\ndb4Xxxzj9BZ0j21IkOYGBMKBhOcFAEIBAHCl0RQqANw+hPwI18Dbnh5AoRrYFjyJnWlShaxA\nVhANwuVN6BgYn1ZW1dIm3QCguCGrCNcgI7/tJ9dnmYj4oWWk26QZkgroiOlQ27pUt23EDAg5\n3fZALVmBJMGIJHTANGMwY+2bkCiNOBxwIOP0299e4/vOwjv+/uM1twFS5oDJ16546q/n1XV0\ntwMHd+3ahWF+K+FViByTKgfUsn37dGj9BhQc3uDQ5jVvF9fdXhejJ070YfeLy9b7637P+raX\nlr7wxnuV6uGxYLZ16NHBJ5/cTwTXLVtZXDcDhu3/4MkXvoJ7/KTjEx6VLg/0hd97Z/kjT31a\nr09bbOdzz31gykMnnNB4VphuSXFB1qAHYMUSen7UD9uCK926Uh4iJEjxjKPl3WHbiOntmAUu\nFcVb7Ua0tf2AQ3GXEO243ksdWgYkBeGqdrQSQpWwbXi6wbCPRI0d6/QWAMcf7/QWJEyS4clA\nNAw90vaTARg6IkG4vO2eUbWb82RC0VBd3o5WuW2hpgyKlm4BBwCPD5aFsL/tZyKe8nR0StVu\nLCMPsQgiNW0/s75gOWwLGSnRVGoPISBrsMzaaVJbEYsAdtrMF9wczQPLQrStCWJsC5EghAS1\nRwYcNmwr+f/aWdcl79TFL++orN6/+X8b91RV7Xnr9vP61btK9Jx32xtvvPHG7TOzE16FyDGp\n0uTqW1SUh6/fevgvvfZPHtnPHSje9OaqV7ZFc2RUfbTy6bcyzpk69OQFlxz3wf3rbvu5/7xz\nTx6WL5VvfnX5mr3ayIWzx8kAIEsysP/Tte+O9vc7+vgh2cNmLzz9tVtef/S663fPPP34Plr1\n9vUvrf6oTBu+YOEZrVXHKXnpT799dpd00pUPXjEegO+U78x5cdPSl/50bdn0sycMybKqdn3y\n2pr392Lw3KsvHNw1e+fIubMQKkeoEhn5bdxG0IPQg1A9aTVlfWOKipgF04BtNRNhWDHEYoAN\n1ZV+fRYa0DyIBBENQfNAaS7KsSzoIVgWXN60vP0kBLy5CJQiWIbMBO5JRPzQg3D7Uir4OuUU\nSFKn1DdL3BlnOPnu7ZWRBT2CmgrkFDT/u6hjxlBTDklGZgolXokRAtkFKN+LigPI75fQKhUH\nYMbQq3/q9G5KmOqC5kE0BEmGO6O1Z4b80CNwZ7TxzUlBnlwEK1BzAKon0QOgHkKwAq7MdnSR\nSyGKBttETIdtN99esm3EIrBMKFqa9oeNUzSoMRhRAHC18OuwTESCsC14fGnerGqZ0z04DpG8\nfUaNa64bu9RnzKnNd29vcRUix6RKwCGNWfDbhWV3LHl32YPvAlB8g06aveiec5VXF9+y9P3H\nnzxq/NShvoIZN92R+e87H3zlhUc+sgEIz8CpC6//wQWD4ieOfuO/MXzl0q0r/rx4zbTFy346\nDt6J19z5+9533/v8m0seegOAULKGT7/mmu+fObDVo6sVqigpKZGro7X/rw2bf+NN2r/+9dxb\nKx/5EACEmnPMjGt+/N0zh6TK3gVkFZ4chCoRLIM7u4WTsYWoH3oIspZSN6g7RnHBNGDGYJma\nQKbbZcOWJQl6BLAhJChaWl7SNyAE3F5Ew9DDiOlQNFkSpmkLIaR4L+uYAYg0vDtdj+aFOwuR\nGvhLkNmrtb95uArhaigueFOrfHheHk48Ee+/79gG5OdjwgTH3r0DhISsfFSVoqoEmblwt3Bx\nFg3DXwnYyC5Iz8sXTyZ8efBXoGI/cvu0dlli26g8gLAfvny40677Rpw3C7aJsB+mAY+vmb+4\nGUO4BoYOzQ1PunXfACAEcgagYhcq9yB3YNsZhxFGVTFkBdl9u2T7nKB6YERgGrBikFUIyEKY\nAATkWBSmAcS70KZb2NVEfIBevEOo6m7QrcAyYegwohCAOzON2xKts21YZie8rKN3Logc1C2m\n3TYrd32xN+AuHD6ssPVeAXak+uD+kpCnz8A+vkMnBFuPxlSXWq9pZYZKi/fVKPn9++S6G7Ux\nrNCBncUhd+8B/bMP9we09eoDxaV6Vr8BvbwNnx/a/+WOcrXPMUf3qnf60cu2bz0QFtmDRg9s\nUE0rFqosPVgaVPL798v3JNyaXbt27XXXXbdq1ao+fZxPP00doUrYFhQNigehSI1pxQDhcWVI\ntmaEYVtQPfBkp+EduObZNqyYbcbEof8TsgxJTs/LlVYYOmLRZu4FKCpUV/oHPYfCC0mGOxuu\nDIQjoXjZPFVVs7OyjQhCVTB1qJ42QpBu6u67cc01jr37VVfhnnsce/cOM2OoLq8t1uP2hk07\nFIkCcGtqhqYgEoShQ1aQlZ9+9+obqCpBoBKqC9kFcGdYllVRURF/JC8vT5IkRIKoLoURRWYu\ncgqd3dhOF/YjEgQEVC1q2nrMBOBSFU3YiOkQAu6MtI14AAARP6r3QsjI7lM7jjUWi9XVW8/P\nzxdCwEaoCv6DEDJyB6b/iAQrhpjezKWmpPSIGyWHxXTo4XhvQcuGDUjiUFtSUaF5u0lVmgMH\nDpx//vkAfvvb31544YVd86bx5nfSad70HU5O1KpukZXKuUOKErrnKdzZfY5q1HdAaK5GrUfZ\nWzB4WPPdySVvn6NHNHlVLbvv0GZ7JHj7jixqcm9B63V0UeOSpQCgeHP7HpVat24bkzVkFkIP\nQA8hVg0JWfGzTcyofdTlS+uRok0JAVmNGmYgEBBCSJKUm5vaf+IOUjWoGsxYMOCXhLAsy+Xx\nKFq6zZnSCk8OFBdClQhVIFQJSXELWxaAFZUra2BbEDK8eSk7rP4738Gvfw2H5sbDwoXOvO8R\nkhXkFiISRMiPQJUHqC0mGI0gCkgSMrJ6RHfrnEJoblSXoqwYiibcXrdp2EIStiWqSxENIaZD\nVpDXF94mE2ykH48PmgfRIPSoy7Zc8TOoaUKS4PLCnZH24bjbB3kwqvaishiaF55sSPXyC1NH\nNIhwJWI6VDdyBvSAzguApEBTYFsIBsO2aduwVVVxexvPW5f+FA2KBtMwo1EzpguImGW7PF4o\n6Vqyvn26yxAVorTQLQIO6j6EgMsHlw+mDn9NyDYBYbvcWppN4dYB3aGvk8NkJRKz4vtBFXL6\nX7k1pHqQ7YERhh6CHoEwVUBAslU3VC80TyrfiMvPx0X4KAAAACAASURBVA9+gLvucuCtp0/H\n+PEOvG9SCAFPJjyZMHQ9FDANHTYkVXVlZKZlzd0WebPgyUSgCmG/CFQd7p9gAJoH2QXIzEnl\nn0c7yQq82fAi6K+J6VEAqsvtzUzR7LMjVA96DUWwAqEKVO8HoAD5kGzY0v+zd5/xUZRrG8Cv\nmdm+m930AoTeFESqhaIgHkHsgr3BAQSVYwVFLKBHRGygHsUKqHjAgkrxADZQUZCX3qQGQgvp\nyfY2M++HDRjSSEKSZTfX/8cHMvvs7L2zszsz9zzP/eTlAoCkhS0Nxtgwx9nABBGy4g8EAwA0\nOlMjO36WImlljWp3ewEIgqBvrHOmlKHW03CSRn/eSo0WExxUMUkHVfLJkAGIOkkQG8F9FqLT\n0RqhNUJwe08OUbHYoqIgzeOPY/ZsOJ0N+qKCgMmTG/QV64lWF9DoPQEFAvSSTt+oshshgoiY\neMTEK8FgcUGeCFWBYItPFDWN9xxDgRBQBQCaxpPcOUEQYUmEJRF+N7xOxevyq6ogiKrJotdb\nhKgfk0JUY2q9VPpWmOCgxqrRHXeJiKispk3xzDMN/aLDh+Piixv6RaleiaIsagKiVhY1Z8mI\negojnQmmeEWMcUpWh2hxmhOiv+IGUe3UyzSxLDJKjRXPP4iICHjkEfTq1XAv17QpXn654V6O\niIjorKSq9ZLg4NBqarSY4CAiIkCrxeefI7ZBRsZrNJg/H4kVlWsmIiJqZOqlBwcTHNRYMcFB\nREQAgFat8M03MNRzJ3JBwKxZ6Nevfl+FiIgoIrAHB1GdYoKDiIhO6N8f8+ZBW59FhV98EaNG\n1eP6iYiIIocKKErd/2OCgxotJjiIiKiUoUOxbBli6mFiS0nCrFmYOLHu10xERBSh2IODqE4x\nwUFERKcaOBB//onOnetynUlJ+O47jB1bl+skIiKKfKzBQVSHmOAgIqJyzjkHa9bgwQchSXWw\ntptvxtatGDSoDlZFREQUTVQoct3/UzlNLDVWTHAQEVFFLBa88QbWr8dVV0EQarmSiy7CDz/g\n88+RmlqnwREREUUDtX56cHCICjVaTHAQEVHlunbF0qXYsAFjxiAurrrPMhpx22348UesWYPL\nL6/P+IiIiCJZ/dTg4BAVarQ04Q6AiIjOet264d138cYbWL0aK1di3Trs3o3Dh0+5Q5SSgo4d\n0a0bLrsM/fvXS5lSIiKiqKPUw3AShQkOaqyY4CAiourR6zFwIAYODP2lBoMFBw+KDodisdia\nNdMYjeGNjoiIKOKoav3Uy2CCgxorJjiIiKhWJEmNjZVjYwFAqw13NERERBGpPhIcLDJKjRYT\nHEREREREROHAHhxEdYoJDiIiIiIiojBQ66cGB2dRoUaLCQ4iIiIiIqJwqJ8eHExwUKPFBAcR\nEREREVEY6ExI6VAvqyVqnJjgICIiIiIiCgNJC2tKuIMgiiJiuAMgIiIiIiIiIjpTTHAQERER\nERERUcRjgoOIiIiIiIiIIh4THEREREREREQU8ZjgICIiIiIiIqKIxwQHEREREREREUU8JjiI\niIiIiIiIKOIxwUFEREREREREEY8JDiIiIiIiIiKKeExwEBEREREREVHEY4KDiIiIiIiIiCIe\nExxEREREREREFPGY4CAiIiIiIiKiiMcEBxERERERERFFPCY4iIiIiIiIiCjiMcFBRERERERE\nRBGPCQ4iIiIiIiIiinhMcBARERERERFRxGOCg4iIiIiIiIgiHhMcRERERERERBTxmOAgIiIi\nIiIioojHBAcRERERERERRTwmOIiIiIiIiIgo4jHBQUREREREREQRjwkOIiIiIiIiIop4THAQ\nERERERERUcRjgoOIiIiIiIiIIh4THEREREREREQU8ZjgICIiIiIiIqKIxwQHEREREREREUU8\nJjiIiIiIiIiIKOIxwUFEREREREREEY8JDiIiIiIiIiKKeExwEBEREREREVHEY4KDiIiIiIiI\niCIeExxEREREREREFPGY4CAiIiIiIiKiiMcEBxERERERERFFPCY4iIiIiIiIiCjiMcFBRERE\nRERERBGPCQ4iIiIiIiIiinhMcBARERERERFRxGOCg4iIiIiIiIgiHhMcRERERERERBTxmOAg\nIiIiIiIioojHBAcRERERERERRTwmOIiIiIiIiIgo4jHBQUREREREREQRjwkOIiIiIiIiIop4\nmnAHQERERERERGcxJW/Td9+t3pWttvjH8Ju7WcMdDlFlmOAgIiIiIiI6y+1e+O8F2xXg3GGT\nb+rUoK8S2PbKZZc+sbpQBdDZM+jmbtbDK16bvdYJtBwy/p5e5noLpppyV779zq95uu53PnlN\nm3DHQuHGBAcREREREdFZbtdXz09ZEASGdazPBEcFr+L5+vmnVheq+vbXPTR6yEUDUwEcWv7q\nlJnHgf6JY8+CBEfOz/+Z8sIuy6iLmOAgJjiIiIiIiIioEhm7dgUA/ZDJ86ffbgx3MERVY4KD\niIiIiIjoLDfwte27pgCIadLArxIMBgEYExL+zm50f/K3XWNlwJSSWJ/BENUYExxERERERERh\npjoO/PnHxt37DmQHYtNbt+na55JzEqRSj+uz1y9YvA9tr53Y4dQUh1y0Z9XylVszi4TYlt0u\nH3JJmxghc9krc9e521//9G3nSwCw95up/92qdL71maEdANeBX7/7cfPBQjWxVYfulwzsmqKt\n9FX2Lpr6381Hfs0D4N0477nn1iK5/7j7Lk0wFm/7asHWYOrlD3ZIP/VdyI4jOzauX79pn9PW\nskPH7r0vbh1TwXsNZG1etXZH5uGjeYGY1GZNW3fv36+dVSjdovD3D9788VjqZePG9EtAMG/T\niv+t3ZXlMjVt1/migf3aWk40+2vhv7/YfvzXPAD+UIBNLn9wdJ+42nwAFB1UCquff/65R48e\nWVlZ4Q6kAgUFBbm5ubm5uR6PJ9yxhJPH4wlth4KCgnDHEmZ5eXmhTeHz+cIdSzi5XK7Qdigq\nKgp3LOGkKEruCYFAINzhhJPT6QxtB7vdHu5YwkmW5ZO7hCzL4Q4nnOx2e2g7OJ3OcMcSToFA\n4OQuoShKuMMJp6KiotB2cLlc4Y4lnHw+X2g75OXlhTuWCmRlZfXo0aNHjx4LFy5s8BcvXvPG\nnV0TxNKXaUJMx6Gv/VHquOL8cDAADP6w9OlHMHPxw91PmdQkrvfEZcd+HJcI4Oq53pJmS+80\nAJpbvw0eWfLIhbGlMwnmdkPf3uSt7FWWDjeUuXrsPGXXyRWi60v7Sr8L+8ZZt7TRl24tJV80\nZs4WR+lGwb3z7+vXVHfqWoWYdte8+GtxqWb7XuoJoOsLu+yb3r6hVenmUsrFj3x34uLpq1vL\n3rDveWpM1NiIICIiIiIiovBQD8694+qH5m3O1zXvd9u/Jk5+9okHhvVMEh27Fj429P5FeVU8\nc9+H1/S8buZGuybl/CF3P/zk+FHXX5Tu/eOlq3qMXuKsoLn7j6euvOmLhAnf/N+BIrf9yPr/\nPnShzbV34b+GPbs2UPEL9J700+rVc+5uDsBy9aurV69e/ck/m1fc1PHDQ7163/f5fp+13YCb\n7nviqfH3Xt8lTslZ+94/r37gfwUnWrn+ePy6u2f9djQQ037wiIefnDJl0iMjr+ocLzr2Lpl0\n/agvcsusVDn86e2XP3Fw0Durdhx3uvJ2/zDj+lbIXjPj9pFzsgAA/aesWr36k7ubAzCGAvzw\njqZVbDGKehyiQkREREREFC675766NB/i+ZNWr5na40Sdi6c/vvbc4Uuy5s9e9v51d1Vc2tOx\n+NmJy3JVU/dHvv7fa4NSQv0yite9eP2Qp1ZlVtA+uOSVtwe+u3PJvekiANh63Dbz6/zNLf/1\ny/5vl2yfflG3Cp4S1653n3bm5SYAmtTOffr0qew9KNtef+Q/u71Si1tmr/j47g6hbhzTX/hu\nZK9r5mZ+Mu6l+zNevhBA8Kd3Z+0MIPaK9zf+b1SrEwNw1H/Pu7HLXd/mLV20OnjzDaUvULe9\nP809buWWNy8xAQDaX/7w/Nn72g54++iKb1d5RtxmREKHPn06xC43AZCqDJAaC/bgICIiIiIi\nCpPAli1/AWh6xXU9SiUyUm96fMrYsWNHd7MVVvK8XW9N+TwfaPPA+6+eyG4AsF0w6aMne1Z8\nkae2vPf5kemlH2vSv397ABkZGWf2HooWTH5thwLrDdPfP5HdACAmXfXaM4Mk4MDKkozLwaOu\nDl27XjhmwvBWpcqLCGlDb+wNwJORkV0mYv3lk54uyW6EGPr0v0gDyBkZh84sZIpS7MFBRERE\nREQUJtq0tAQg98jnr35w9wejOttKchWmvg/O6lvF05y//7ZJAXqMurdHmXRG61GjB05Y/4Na\n7in6vpdeKJ26KCamohqgNbZ19WoHEHfL6KHWUx+IH/ryIstwp9QyVEWj7X0LN91X/unBvXsP\nVrzicy69NPnUJdqYGANQ0QgcIjDBQUREREREFD59H5zY9+PHVh/68t4u/3u+12WDBva/pF+/\n/pf0bG4WqnjW/n37AGjat29V7iFb+/bJ+CG73PIWrVrVT/99+759uQBat29f7uoy9ryrbjmv\n3BNk+6EdW3fs3pdxYP++3TvWr/x+9YEKVyy1alVJyQ+iijHBQUREREREFC5ix0eXb0h/Yfzk\n91f8dWTdko/WLfloGmBI6zHknvEvPn1rB3OFz8rMPAQgMSVFKv9YWloaUD7BYTKZyretCwcP\nHjz5sqdh3/LJlCemffzTroLgiUWSrV2/ns1Xra9gzInWZOL1KtUIa3AQERERERGFkbnjTdOW\n7szO2bXysxmTRl/fp22cxpu14euXbuva/4XtwQqfkpKSDCA/J0cp/1hubtnpSOpXcnIyAOTn\n51fdTt768qC+98xYscvd5JI7J0x77/Plqzfvz3EW7Plx/AUNESc1AsyIERERERERhZ0mrkP/\n2zv0v/1hIFiw6bOHbx756b71015bMWnOVeVvS7dv3x44Eti3LxMoM0rFk5GR1UAhh6S2axeD\n3x0Z+/cDTU59KLBryfs/HUJqvxFDu4hLp72w1glz/9f/7/tHztGWauXyehs0YIpe7MFBRERE\nREQUHsqKcelJSUnpo77zl1qqie92z9N3nQvAfexYUUXPi+vZszWAdR99sKVMH45jn8xZUUG3\njvp0XvduGiB7/kdL3ac+IP/6xohx48ZN+bHQCBzeudMBoMfNd56S3QDk33//s+GCpajGBAcR\nEREREVF4iOe2aZKXl3fky7fmZJROS3i2L/zfbgAtu3ePr/CJvR59drAZ2P3WmEk/552cMsW9\n/Z0xz6/yV/iM+tNk1OTRTYHczx4b91VG4ORi75apT36cD8QOHnyhADRJTxcB7Nu61VPquYFD\nX40aOzsXABwOR+1j8DmdgdO3omjHISpERERERERhkn7bQzdMu+Ob3BUPXNBt2U1DuqVbBWfW\nrtXffvvbYT8SBk0c1a2SJ6bcNX3ye6sfX/Pn9MHn/zTkyv7np3j3rVm+ZOV+07V3D/r1kxVF\nOp22kqfWOeNlU169+ds7vtgz5+aef14xZGCv9rHuA6sXLliZ6UXSDe++cp0BgHng0MGx3/3v\n2Hu39HWNuaNP6xhf9v5NP8yfvyor5YKuKes2Z+94Z/hw5/0PvnB3d0ONXtxoBBD4ceYjM/LP\nT+9x07AesfXzLikCMMFBREREREQULqm3f7TwkDB22jc7ty56d+uiE4u1qRcPf/rNN8a0qbTP\nvdhlwk9/Jt17/X3z9q5f9NH6RQBgOW/Up1++af537Ccwms0N2F0/+db5m1O73HPHlOU7V3y2\nc0VooZR88X3TZ710S2roz7QRs+fvGDrijd83fvrixk9Dy0wd73hnzdv3tfxpRM/b5x788+PX\n4vo9U8MER8shN3R9btPmvNVvP7oaPV+6jAmOxowJDiIiIiIiovCJ6zdx4faxe35bvTnj0OHj\nTm1iesuW5/Tq1zVNX6qRrvudU6ZchLanXPwbzx3+6Y7rn9u8ds2f244h5bzLrr3i3Fjx6BuZ\nfqB9evqJZu1vfHpK22Bq33KDXWL7jnvzwwdb9uhXxaukXjZuiibP0L1tqedVtEIxuf9TyzLG\n7tu2cdPGbZm+hNbnnNP9wl4tY4RSbVIGv7p63wO//7Rmx75DhVJCqy6XXtGvfawGwI1z9mdP\nWrs+y9ihuxVAfN97p0y5WtOlY7mN1e6mabMGpJ7T9+Sbg9j1mdXbL/pq2aZjPlPSuZelVLWp\nKdoJqqqevhXVm5UrV06YMGHp0qWpqanhjqWswsJCWZYBWCwWg6FGadSo4vV6nU4nAEmS4uLi\nwh1OOOXn54d+MaxWq06nC3c4YeN2u91uNwCtVmuz2cIdTtioqnpyOrjY2FiNpvFmzF0ul8fj\nAaDX62NiYsIdTtgoilJQUBD6f3x8vCg23jpfDofD5/MBMBqNZrM53OGETTAYLCoqKY6YkJAg\nCELV7aNYcXFxIBAAYDKZTCZTuMMJG7/fb7fbAQiCkJCQEO5wyjp+/PjVV18NYNKkSTfeeGO4\nwzm9/HULvtpULLa5YtTlrU79dhXMu77lXYt8Q+cXfnVr493fqHFqvOejREREREREEcqS9d1j\nY+e5Um5N2jH/+r/zRWreD09PWeSA/uobrmR2gxodJjiIiIiIiIgijH7QQ492X/jvjQtu65U/\neswtfTokCoWZf/3x5TuzV+dA0+Xx525pvN1MqfFigoOIiIiIiCjSGHo+/90iYcyDM5b88NbE\nH946uVyXdunDH8yf0p1XetQIcbcnIiIiIiKKQKn/eG7Rjod3r169ed/BQ7nB2OZt27Zt37lL\nhyT96Z9LFI2Y4CAiIiIiIopQYlyHS67pcEm4wyA6KzTeCudEREREREREFDWY4CAiIiIiIiKi\niMcEBxERERERERFFvFrU4Chev2Den/lIvPD2W3rG1X1EREREREREREQ1VIsEx9HFz4/791+I\nv7f9LT3/UfcRERERERERERHVUC2GqLTp3TsZQMHGjZl1Hg4RERERERERUc3VIsGhHzx19sj2\nOqx/adzcw3UfERERERERERFRDdViiAqQctUHv688574RT488v+uyB8bd2rd907SUeLNGKNtQ\nF5+eHqc78yiJiIiIiIiIiKpQmwTHzqk9erywE6rsVwJbvnhh9BeVtuw8Zfu2yZ1qHx0RERER\nERERUTXUqgdHiCDpDFLVTXTlO3UQEREREREREdW12iQ4zn1qg+epOo+EiIiIiIiIiKiWalFk\nlIiIiIiIiIjo7MIEBxERERERERFFvDOowQHI+RvmzZy1dMPejIz9+w8V6VJbt2vXrn23QWMf\nHXlh8mnKcxARERERERER1ZVaJzgca2eOGjn5y5129e9lB7blHtj2x/dfz31n5i3/nvPBQxfG\n1EWIRERERERERERVq2WCI2/R/Tc+8kUWYEi/9K5/3nhB+xYtmsT4cjIP7F636KO5P2X+9fnD\nNxpab517TULdhktERERERERnj/2z7xn1qXzXh/P+2SbcoVCjV6sEh/t/j46clwWpw5ivV751\nbZq29GMj/jXx2SUPDrjx3d0fj3zs1sy5g411EygRERERERGdbZwH1q1aFezrDHccRLUsMrp9\n1ap84NzHv/hPmewGAECbds1bXz7eCchduXL7GQdIRERERETUWBUVFT399NMDBw7s1avXyJEj\nt23bFu6IiM5etUlwFG7ZchhIGXBFl8r6f2jOG3RZGnBo8+bCMwmOiIiIiIio0dqwYUP79u1f\nfPHF7OxsSZIWLFjQvXv3mTNn1tX6t7w5dMCAKb8C+Rs/mzLm5quvvOa20RPf/eVo4NRmSu66\nea88Pe7O6665afSEF95etsdz4pG1Lw4aMOrTQ8DRT0cNGDBk+v/VVWREtVKbBIfP5wOg0VQ1\nvEWn0wEIBAJVtCEiIiIiIqIKybJ8xx13APjtt9+2b9++du3affv2XXLJJePHj9+6dWudvETR\n3j9Wrdq+9ZsRF1438y9tu55dU46vmHFf/y5DPz58sk3+r/++vEvvux6f+t7i9Vt//ey1Z8YN\n6db97k93BQFAb0tJTTBLgGROSE1NtunqJCyi2qpNgiO1a9cU4Oivq/aplTU5uOqXTCCta9fk\nM4iNiIiIiIiokfrzzz937949ZcqUPn36hJakpaV9/PHHqqrOmzev7l7n+0lPeV5au/bz/0yd\nMu3DleveG2QpWPLKR7tCD3p/febOZ1cWtx/52c6ioqOZ2fbs1a9cYd396b1jPjgMoNsDn8yf\nfmNTIPXG6fPnzx17ft2FRVQLtarBcX6fPmZg09Rbn/7DUcHDrv+bcuvz6wFL795dzjA8IiIi\nIiKixigjIwNAjx49Si9s1qxZamrq/v376+51nF3HvjqsqVTyV+rgwecDJ14g++N/f3AYHR/+\n9P3bzzGLADRJfcbPf+16i/fXF179re5iIKobtUpwxNw04z9XxcG74cV+rbrdNuWDL5f9sn7H\n7p0bfl2+8KPn7+rV6uLn/vQg9qq3Zgyz1HW8REREREREjYDNZgOQk5NTeqHP5yssLIyNja27\n12l64YXNSv1pNJaaB3PHli1BtL/5th6lrxvjb7r1HxKObd2aX3dBENWJWk0TCzQfPnf+7mEj\nXvsla/OC5+5dUG6tqf0enT1nePqZRkdERERERNQo9e3b12g0zpgxY/DgwVptyeyVb775psfj\nueKKK+rudVJSUip7yJmRkQt0bdHi1MXaFi3SSnp5JNRdHERnrpYJDiBx0LRV+0Z9N3Pq299t\n2LM/IzPbGdSYk5u3adO++5D7Jz1yTTtzXYZJRERERETUmMTFxT333HOPP/54165dhw8fbrPZ\nli9f/s0331xyySXDhg2ru9cRBKGyh4w2mxZwOByAtfRyp9MJpFnYX5/ONrVOcACAqc1Vk2Zf\nNQkAgq5in95mPqPVERERERER0QkTJkxo0qTJxIkTH3/8cQB6vf7xxx9/5plnJEk67XPrgtS2\nbStg1+bNXjQ1/L344KbNRRAvbd+mQYIgqr7a1ODI+/2j6dP/89OR0ss05lOzG84Nn02fPv2b\nv5QzC4+IiIiIiKjxuuOOOw4fPnzgwIHt27c7HI7p06dbGrDnxHk33NhOKPrv1Bm7fCcWqQWL\nJ7+xHuYrr79c/3dDReGVH50FapPgyP5x5sSJU5dmVtXGt37uxIkT3/w5t5ZxEREREREREQCg\nZcuWnTp1OlmJo8Foej75yl1pgTWTLu5z15Q35376wcuPDOk57JMjhl5Pv3J3akkbjQY49NOn\nC1f8tDWn6tUR1bPqjilx7v/9933O0P8P7nMAvoNrV6xwVtxYdh79/tN1ANxud10ESRRmoqoI\ngFC7WYeIopLfC5fd7LELiqwKoigEYY6BoZFWXxIURZKDggBBaaQjNVUVfie8TiHgtEAVISiO\ngGCwQGdB5eO6qRFQAUUM7RLhDoWIas163ewNK1qPGvPK/OcemgdA0CX2Gj179usjzjlxatzu\nypt7zXz+zxnDBn8wfLljzqBwRkuNXXVPxTLnjR08ZXvpJd+OH/ztaZ6U1K1bs9M0obNV0AcE\ndIIMQYDskxQtxIYZ6Hf2kINwFsHjMPg8f484dOXBaIHZ1mgv5EhVoQRFUdGrKiCKitzYvhoq\n7AUoOI6ATwCMgCoIgqrC50ABoNHCloS4ZAiNIxvo88BZCLfDJAdNoSVuwJ4NYwwssTCYwhtd\nw1BlOHLhyoMiAxAg6AVJVWXB6RacORAlmBMR02j2CApRFXiK4XPA79EA8aGFuUXQmaG3wGgF\nmPYiOmt0fejrVcP0bVJLLzNf8/IPrXKTup5cIKX9Y/J3Gc94cjN2ZXoT23dMt556Eam74Jl1\neQ/u37LbYWnZsWHiJqpEdRMcusRWHTsGQ//35WUcyJPjmrdLqfzkTTQmdblh8vOXN3QfKjpD\nqgKvHT4XVAWAOXRG6g/Ab4dGD4MVWkPVK4gKqoriPNjzoSrQ6GST1RdUVEGQVMUgCXAVw1kE\ngxnxqdDqT7+2qKCqkP3QwiSIoqIqakBSpMZ2YY+gHz4Hgj6oqkGEAYAahN0DSQe9GbrGcDEb\n8CErAz4PdAYkNFFN1gKnS4UAVY2LMUteFxwFyD+G4lyktoIxqgury0EUZMFlhyBAbwoYLL6g\nAkArCnrIcBXBWQijBQlp0OjCHWs98jlQcAhKEPoYmOKgMytF9oLQQ7HWeL9TdBfCkQ1XPuJb\nQB/VewSd5C4qSXhJWuitij/oEQQVqqjTmPwu+JxwFSAmqRHtD0oQQT/EoEkPAVDhFwMiNDpm\n/ehsYWt78aVtyyzTNOl2eZPyTUVjUttuSZWtSLK16X5BHQdHVHPVTXC0e2DxXw+U/H/Hc+d1\nnpJ3z3//mtGnvsKisAh44CqAqkBrgNYIt9cuq0FAMOrNoqLzueHMhdYIc3xUH5UVGbmH4XXD\nYEZsMvTGgNfrdjoBSJJkiIuDIsNeAHs+jh9AYlMYY8Idcf1SFQQ8CPoAQCMYAVUSBNkH2QdR\nA60RUiNIY6oqPEXwuyGI0BqhCH5fwKOqqlaj00mmgAfuQvicMMdDjOIBCh4nju0HgOTmsCZA\nEKCqqtMNAIKganSINSE2Ca5i5BzC0b1IbgFrfHhDri8+D3IOQwnCGg9bEiSN3+XyejwAVL1e\nHxMDRUZxHuwFOJaB5PRo7fDlKkDREUhaJLYpuVgtXV5O1MAUD1M8fA4UHkFeBuKawRSle0Rp\nqgpVhk4yao1GVVVFQVCCEKVG0WdBVWE/Dq8dWgNsTaAzIRhUioo8oUetCUZBELx2OPNQdBTm\nBFgSwxtvvVNk+F2Q/QAgQFKhAIIaFP1O+AGtEVpTYxrDpaqQg1pVjjcbBAiKqiLggyhBiuID\nJxGFQW1+U2ydB996q6Nb3R2W3OtmT120v7JHDT2HP3NDuzp7MaqEzwl3ISQNTInQ6AHAHZAh\nKwAknWIwwGCDtxheBxw50dvfWFWRfQh+D+KSmACgogAAIABJREFUYa1kFxclxCbBYkPOYeQc\nRnLzKL5TLQfgd0JVodFD0qHYXqCoCoAYi01UtUEvfA5oDdBGdecFVYEzD3IAejMMVggi3O6g\nGgxAACTBaIPRCp8b3mI4cmFJgBSVN+z9XhzbD1FCkzbQG6tqabYhvSOy9iMnExoNTNaGCrGh\nBHzIzgSAlBaVZi5ECXEpMNuQcwjZmUhtdZqNFoG8DhQdgc6EhJanyevpY5DcDvkHUHgEohaG\nqM4JywEoQQAQQgN1BEEQJDkAOQBJG9UJUACAPQteB4yxsCZXmtAxWKG3oDgLrnwA0ZzjCPrg\ncwKA1giNAQ6nIxAIADCZTHqtKehFwAPZD721cXSHDAYQDAAqgKCsKKoqiaIky5CDCAag1TWO\nrUBEDaE2B9tmQ1+ZP7Tk/0F75vFgi2Yn7sl49v7+p7/tReemGGqSkJYLDmzbtk3QGU3aCi6a\nzS3qplLp8R9nvrVSGfCvRy9PPX3jM+Df8/WMjzc0v33qbZ3q9XXqVMALdyE0elgSK81cCAKM\nsZB0cOXDmYeYpGi8H5V/DH4PEpvCbDtNS40OqS2RdQB5R5HWKip7ocsB+BwQRBhOnH6pUEMP\nCaKq1UFjgN+FgBeqCl103qIGVLgKIAdgiqt8EIoAvRkaHVz5cBXAkhR152mqiqwMAGjaBrpq\nXKhrtGjSFod34fhBtDg3qu7OqQpyDgEq0lqffoSazoDUVsjKQM5hNG0TTbuFEkRhJjQ6JLSq\n1tsSNUhohdy9KMxESscovc5XEfRDVSBIkDRwupw+nw+A0Wg0Gc2hHIeiROWxooQrH15Htfpl\nCCJim6LoKFz50Bqic6yK7IfPAVEDQwyEct8RSQtJC40ePge8xTDGRulNo5MCPshBiBI0ukAw\naPd6AAiCkJCQADmIoB9+L7T6qDpYEFH41PqnRM3/Y8a/Hpq5ZOPhwf8NfnlLyY+3/funB4xb\npU+/YtKcOU8NbFKjs7nmQ19567bmtY3n9DzZe7Ztk8/11N8rAIBz4wcvf/x7jori+n2duqSq\ncBdA1FSV3ThJZ4KqwF0IrzPqbsT53HAVIyb+9NmNEFFCcjqyMlCYg6Roq6erKvA7S7IbVeS8\n9Bb4XQj6IGpKOv5EGZ8LQR+MttOX2JC0MMXDmQdPEcwJDRJcg7Hnw+9FSotqZTdCJA1SW+Hw\nbhRkISm9PoNrWMX5CPiRnF7d+jsaLZKa4fhBFOUivn6T6w3Jng1FRkLrGiRtRA3iWiB3LxzZ\nsDWtz+DCJJTdqLCbhiBCo4cShByA7I/OTl5yEK586Mw16JFhS0P+QThyoDNH2zANVYHPAVGC\nwVbVW5N0MNjgKYbXDmNsA8bXwIJ+yEFotBWn9yQNRAl+LwI+CCLE6M70EFFDqOXvSPZXd51/\n6WPz1x92KprSkzHrkpql6OE7/P3kf3S/5+v8uonxLKHKsnq6JnmrZry+Iud0zc42PgcUGaZq\n30DQW6DRwWsPFSKNIkW5JcNPqk+rhyUObjsCvnoLKzwCHqgq9JZq5LzMECUEPECk7fmnpSrw\nOqDRVfcGY6hlwFtSsiR6FByHzljjghoGMyyxKM6DHKyfsBqcqsCeB4OpZuNuDGaYYuAoiJrt\noAThzocxtsaFdXUmGG1w5YfmW4kqSrDS7MZJogaiBooMNerePgB3PlQVMTU5fgoiLEmQA/BG\n0B2h6vG7oarQx5w+cSNqoDNBCUbdUeMkRUEwAElTVeclQYDOAEGIvlMpIgqLWvXg8Kx89pHP\njga1La+d+v6MMZe1/vsOTtzNn2Zd9cyXE28f9Z8Nnz389KgrZ/Wvy3HH/uwtP33/x+5juUVe\nrTUhpXXPgZdf2MJS+vAhF+xcuWr97r2ZBWJsWquuVwzp29wkALu/nPzJmqO5gLryrad22roP\nnzw0VNVDLd7zy0/rdh88mBOIbdayVZe+/+jR9O+f4ANLpn24rvWdzw9yf/rqO4u3qYNfmj3q\nnEqjkw9/8/I76/WdOyVv35FTh++63vndkLTQ1uSTMljhzEPAG0UzR8hBeF2Iia9xN3JrPBwF\ncNlrlhk5u6kqgj5Iuur2JNca4XMi6I+2ThwBL1QF+pr0VDJY4HPC746iTeF1I+hHUrPajEmz\nJcJZBLcdMVFRW9LjhKLAWvP+OdYEuB3wOGCJq4ewGprXAVWFuVYfqSmh5H61KRq2xN+UIATh\n9D+YkhaqDDkITfQMVyrhdUJnrvHvniEGDk1J2Y6oETqAavTVPoAaEPAg6I2io0ZpcgDA6Ydm\nCQI0OgR8kGVIUff1IKKGVasEx2/z5x+B0PHRhV9N6F5uAgXB3P7mt77M/L/2j//51cJ1s/pf\neuZBhri3vj9h8tLDsmSMS4rT+f7avGbV94sW95/42qMXl5woFW2Z99IrX+60Q2dLsimb1//2\nw9LFvzz42lOXJWlNsbHWPBFQ9dbYWJsp9LYdOz5/6eX52woVyRgfr3Ou//37b776bsD9kx4c\n0Cz04+o6tmvbdmxcMGXhVxmqMa5jfBUHH9/OT6Z/ujdp6Ev3GmY9GEEJjlCnWWP1xmScpA2l\n2j1RlODwugDAVPNRNxodtHp4nUD0JDiU0AlJtU+2JB0EAXIg2s7Pgl4IQs3mAhZEaPUIeOst\npobncQCo7ritMowxEMUoSnC4IAgw1LxggN4EUYLHFR0JDp8TgghdreomhDqF+ZxRleBQFahq\ndeeTEiQoQahqVA3KCPqgBKGv1bdcb4bHHlUbRPYDak2OhgI0+pJek1GzEf4mByFpqvXGJA0C\nPihBJjiI6AzVJsFxfM8eB9DiquvLZzdOaHX5wNb4c89ff+Xh0uoOx7Tv/eW778oeHjXNeg06\nPxkADi95b+lhTfthLzxzRyebBMjF2z+bMvmrVQtX3n3xjYkAPOtnv/rFTnS+68Unbuxsk1T3\nweUzn5219t1ZP/d6duBVj0w497P7N34u975nwh2tAAD+HfNe+2ybK/0fj00cfUm6QZCL/1r8\n+ktzV779dsfzpl15Mmp53VdLW1/95Ht3XZxWRf7Zvm7WK99mdxjx+h0dtAsrb/bmm29+8skn\npZd06tQJQEFBgUYTptJKslaCzeWxO/3+ypo4nU6n01lmoSTE+r3w5BXVc3wNxOh3mYECh0tx\nVlqmRZblvLy88stjFGiDvoKKHopQWsGkFUyFxfmVDTux2+1llhhEm+IXHXmF9R9dw5ECsYKA\nvPxKd/JAIFB+l5Bks6AY8/Iq3XqRxeKxGwQhr9gBOKpoVlRU8VaKEzSK210cFd8Oq8cpQSws\nKKiijc/nC5WWLCNWEOF1F0XFdgi4bIIk5uef5steUNmGkuK8biUvL3qGJeg0BqPeUmwvkpUK\nRiF5PB6P5+/DikbSmg224iJ7UK70mBtxVL8OsLq8xe68QBXN8vMrGLmsBo1Qzfm5hRCjZOiO\nFiYNTEX2AhUVj+N1u91u9ynl8yXodYgpzC9SECUD2UIkUYgzGZxujzdQweFDVdUyB1CbUS/I\n/qLiqo419a3SHy4iihy1uagOVd0ITXZVmdBvt6rW4Py+cP2X760vu9AwMCWU4JBdhlb9+g0Y\nfEsnWyizK9k6X9E7/av9x7OPA4nA0e/++0uxpusDE27qbAMAwdTyyvtv/vXP97eu3x4YeHG5\nXEzRzwu+z0OzYY+MuzRdCK3wnBvG/3PXyNfWfP7NjitHn5wCRWl+7UMjL06rInI158fXZ/4c\n6PXwhOvSJRyv/ls+GwgQAahCjctpqIICJXqy7CIUAEqtJoZRRVGUo6oeiVBSnacG319VVUQh\n2uqfC6qoCjU+3VShCCXPjYbzdUGWa/e9CFFEUYyWigsiVLW2BfAUQdRUdPUbkRQRYu1/8QRR\nUYNRdZ9aFEUAavWqUoXOi8QomzNDEQEIYq1SuoICQJWFKNokAoDKshsVCjUWalsX76wlCgIA\nRanujqGoqlaKto1ARA2vNhckiT17tsSPB1f+sFPud26FV7jKnp9WHQGade9eg077KQMfemhg\nSpmFYlyL0H+kjteN73hysep35BzauujXg0BJL1l5/94DKjr16VO622vslZPnXRoQ9RW9y0MH\nDspo2vfStqXPs2L69us2Y82agwcd6HRirEJsl/OrnNslePCL6e9uMg589qHLEiLynE0FIKDm\nJyaqACEablCHqBABiEJNTklOEFRVjaJTMwAnUhtCTXIcghJtVWdDJ521+FYLAKLm26GKolCT\nVHUZgqKcSX7krKJCEGu7kwtqFG0HQYFS+/eiKrU54JzNFEUBAEGozu+lIISufqNqC4Q+UFWt\nzQALVRVOriE6qDU/gJbcaoqyvQIIHTpEUUD1styicCZHGyKiErW643reFYOavPTetmlDx/X8\n6c1rm5TpHCHn/jDhpqmbgcTLLutSg7Uaktt17lxVKiFwfP3iRT9t2n3wyLHsQndQlYzGv8PP\nzsqSYUhKOnVUsGSIiTFUuDLv8exioFVymQyMlJwUD2QdzwJOJDjiYquqfeXdNnv6/IyUoS+N\n7VGN6g3XX3/9RRddVHrJ3r17d+zYYbVabbZaDXE/Y0pQcOfBqDdrTWWrjDocjtB5m9Fo1OnK\nDtBx52oFjWIMU9h1TnQDRS6ryaBqy+4xfr8/1MFYFMWYmAo+Zk2eHdCE6xOsD0pAlL2wWmyC\ndMq5ht1uD91+NJlMWu0p3/yAUyNKsNW0msvZzVcsyQFYyn2yXq83NAxBo9GYzeayz7JLsgyr\nLUpmUZZUn+Bz2iym8jUGVFU9OVjJYrFIFQ2c1tiPKXpTdHw7JMUjep02q7X8ePKTu4RWqzWZ\nKihNpPHkQ6ePju1Q7JACbrHC91J6l7BarUJFF7x5xzVasxIdm6KEKkBGjMWKUn0h3W53qKOr\nXq83GEodVhQRCkwmI4SKz04iUdAr2B0w6S06S9kMoCzLJ4e4VrhLuGXJC9hiLVFzm0Dxi0EP\nYsw2UXPKAdTlcgWDQZTfJQDZK8o+xFjNUZMZL6GqUAImg0Fv+vvoEAgEQr28BUGwWk+ZkUqr\nBFQI4f1x8HqjqYYWUSNVqwSHbsArCx5dddnru969rvXi3rfcc/2F7Zunp9mU/CMH929aNnfu\nigw30HT4nNevrLspVFzbPnlq6lcHxOY9+/S+blDLZk2btGgd88fkUbOzQo/r9Xog6A+o1bzh\nqjObJMDj8QCnnIt6vB4g3lAqcLHK7nK7f1l5VDE23fHJ80+VLPHlHAOw/b9PPfUd0gY9Nu6S\nUnVFmjdv3rz5KUkcl8sFQKPRlLlcbDhaeCUofklrK3txcvJERJKkMuEF/VBkmGJErTZaTknM\nVhTlaALe8nVGZbnk1oMgCBV8TIoMvw8xcWH7BOuBKsHjhaBoymV7SpTZY5UgAiokHaJpIwBQ\nDAj6IEJb5tL+5AC9CncJtx8afRRtCnMMirK1AS8MZa/bSw9C1Gg0FRQS8rohB0WzVYyOrWG0\nwG3XKgEYyma1/CdqGImiWMFH7/dCDsKaEB17hcECnx0IasvPvVXSlwEAoNFoxHIjevxuKDIM\n0XTsAAAEPBAgabR/H0ZPvvcyu0TQBwjQ6KJqQJ9GA4eIoFcyx1V6IgFAq9WWT3AE3dDoodNH\nw1cjRJUQ9EBQNGW+7lWdU7kgStBG115Rwq+IqiKWOpkofeA4ZTsoMmRV0Oq0Uji3Q9gq4hFR\n3anl1zim3/TFnyljHntn1ZE/Ppn2xyenPirEdrlj6kf/ubrmc+lVyrd+6eIMb8eR7790XfLJ\ns6JDLtfJBvFNmhqwO/PgEVyY/vfTsn75YP4GzUV3j+hdttSpmJaWChzJOOBHQqm+CTkZGS4I\nnZumVjcwXUxScrI7kJ+dfWKJ7AoC8BZnZ3uhd0XCmOvQHJ9yoLpF4AH4HACgjZopVHBiMhRX\nMWyJNRuV4CwC1NpMv3IWE0RIWgR90BhQnbtqAU/olL3+I2tYWiM8xTWb8cHvgqrUbNLls50x\nBqIER0FtZkJxFgC1nYHlLGSKQb4AZ1H5BMdpOItKnh4VDDYUH4O7CLaa7+eeIgAwWk/XLtKI\nGihBqDKEKitTKTJUpQaH2kghCNCZ4XNCkWs203rAi6AfluoWo48MJQdQL7TGah1AZT+UIHQ1\n/FGJGKG5UYIBaE633wf9EAROodI4ebfPGXPvc4s357Z6cs3GZ2rS/5+oIrW+haJpf/OMlXt3\nL5s5fsRNV/br1i41Lr5phx79r751zLNzN+zf9On9Pev0tzrv2DE/dE2aJf0dsHvHitVHTqaC\nhU4XXhiDQ//7Yo3jZGrYv3fZgiWr1hZq/744UU+WOmpx8cVNBNcvXyw+crJYqupY998l+2Do\ncdH51T79OOeeNz881bQbmgPoed+HH3744bNXJtf+PTcYgxWCAHe1p0MJ+uB3Q2+p2XlMBLAm\nIOCHoybzwigyivOgM9T4guespzVCVeF3nb5l0Ac5AK2+WmdykUWUoDPD70awgmkxKqAq8Dog\naaJo+mQAggBbIlz2kvliqy8YQFEezLaaTbR7NpM0sNjgLIa/Jn2YgwE4CmCKiZrtoNHBEANX\nHuSqSo1XQA7AlQ+DFVLUJUND82AGA6iiSIuqQPZDECFG4x1icxxUBa4KpkmpijMXgghjVQOB\nI5LODFWFr+zscxVQlZJ5lyvrLxnxJA1EqaTfbxUCPigKNLpa1b2i+rXhyXaCIMSNXlFna1wx\nOkYQhHZPbin5+/CsMaM/WePtePOYuy6KrnwnhcmZHWYNLQc/9Mrgh+oolqqkde4cj8zfZk9P\nzOrdsYnBeWTbr0u/3+uLlVC0cfGXv5kH92t98d13dlk365eXH3FcNeTitgli/o4fvl1xVNdx\nxLDuEgBIogRkbVn5RydHkzbnt7S1HTZiwE8v/vzJhCcOXTvg/FRd8f41y77bmKdrd/eIy6q6\nXZuz7IVJCw+KF9z3/r09GuCdNwBRgsEKTzHcRTCd7jxDCcKZD1GKwltwsMTCUYDCbOiN0FXv\nXCPvKBQZcc3qObIwEDXQmhBww+eE3lzpKUfQB7+rpHFUMloR9MJVAEsSqu42q6pw5UNRYKnD\nvmtnibgUFOch5zDSO1Q3r6mqyM6EqiKhST0H17Bik+GyI/cI0lpVa1OoKnIPA0Bc2RLaEc2a\nhpw9KDqChJbVvh5RUXQYqgprVZOSRSwBkg5BP4I+SNoKUhhyAEoQghCFyZ0QrRGGGLgLoTNB\nbzl9ewCufPjdsCRG3c2SMgfQyreGqsBrh6rAYIvq63qtHn4v/F5odBX041AVBPxQZEja0xxl\nKWrt2r5dRo+H58+d2DLcoVB0qK+fksMLn5j0rebmV6deUzcndeJ5d08akTdj/h9fvP8HAE1M\n8wuGTf7PEM0PU15c8Oe8/7bq0a91TNKVz86wzJn5/vdL5m5UAQjG9H4jnhh1ffPQobNJj77t\nFi/Ys+ilKSsGTvnioe4wXfjgzOdS3nr7m1/nf7AKgKCxthv04IMjL0+v8jCjuAtycnKk4urd\n0o0QBivkAHwOqDJMcZXeig944coHVFiST9MRN1IlNUPWAeQcQlI69FV2v1ZV5B+Dx4nY5Ojr\nvhGiNQAqAh54gtAay45AUYIIeCAHIGqgj5Ku9xUQRJgT4MyFMxem2ErHnsgBuAshB2CKhSZK\n7tOXImmQ2hLH9iMrA2ltcPqpUlXkHobbjsSmp/keRRyNFklNkXMYOYeQ1Pw0vakVBbmH4fMg\nsUnUdN8I0RphTYX9OIqOIbZptZ5SdBReB6xpUXunWhCh0UP2Qw5ADkInGQWdBoBW1AU8JxpE\n9f1payqCPhRnwZZ2+hyHuxDOPOjNMEdfRhgAoDMBCgLekuEn5cclBb3wu6Gq0MdE4ailUwgC\ndAYEfAj6IQc0EAxaSVUhiSL83pKeHRXmPuhUwWBw3759DofjnHPOsViql0esC53H/7RvVECs\nw+R0vxe37JuoamNPFBVQFAWosIw/Ua0I6hnNyKQEfYHyfc4Ux56ZN/WatMo49sfCWQNPvxa5\n8OBfR52G5HZtk6s+BVS9xdlZOW5janpqzInfQdXvC2r12lKnDLI798gxuyahaWqcoczJp+I+\nfuCI25DSrKnt74s11V98/Eiu39qkWaLp1PburF0Z+drUDm0SS/3q+vP27znuEWzNO6VX2IfB\nn7tvT7bX1rxzxQ+XsXLlygkTJixdujQ1tdp1P+qNpwheBwQRhhjoTCh2FIbqa5rNFg0MPhcC\nHogSLElRfTD2eZB7GIoMWxKs8RBEr9cbKgIvSVJcXBwAeN0oPA6/F9Z4xIX/g6tXsh9+N1QF\nEKCoQRWKAEEUNFAFCNAaoqveRCXkAFwFUILQ6KEzIah63B4XAK1WZ9JbAx743RAEmOKiemsU\nZiPvKPRGpLYKdXFSVTU/v6RLemxsbElttmAA2Zlw22FLRHKVc2xHLkcBCo5D0iA+LVRZw+Vy\nheZa0uv1JSdpHicKjiPgQ1wybDWYMT2CFB6CuxAGG+LSS27CK4pSUFAQejQ+Pj5UaFMJovAw\nvHaY4hGXXsX6ooQiQ5Uhy6qAkhlhJUkQpSi9K3AqOYDCI5D9MMXDHA9RQjAYLCoqGfiZkJAg\nCIIcgDMXXgd0JsQ2jcKxjaWVZDEUCCJk+GUlKEDQanSQJVWFqIHeEp1Dliomy5ADp4xVEQSI\nEjTas2c/OH78+NVXXw1g0qRJN954Y7jD+dsvv/xy77337tmzB4DFYpkyZcpjjz1Wd6t37vpm\n5gszP//9rwPZPlt6h57XPTBl0j3dSnp1L7xNGLbg6rnOJfeU3M+zb/xg0nOf/vzn1pyYjhf3\nHzZ+6nWrB7V/2jLz+G8PpZS0/3HcquxJgZcffeb977dmBeNadPvH6OenPXJJqggA+e8NSBy7\nfvhSx5yrij+6MnbU8pNxtJ24fu+0KOkgT2FU259V356PHx77yqJ1f2W5Kh1wqr30gq7VWpkU\n17JztWr4CQZbaqsy5eqEcrW3JVNSi7YVn06KptQ27cutVWdLa11hETxTWsfO5fKVusQ2nasa\nIKZLats5Qk9mjbHQGuAuhqcYnmJAiJUEBarg9YpQIQgwxMBgPXsOQ/UjdP2WdxRFOaGR85Kk\n08gBVRC0kGHPh9sOnweihIQmsETd0OFyJB2MupLbkn4fRGgAVRBVrV6QdNG+M5wgaRGTDJ8T\nPifchQCMGhgAqD7B5QQE6EwwWKOwo/Up4lKg0SE7E4f+gjUB1gToTx2Y5PfCUYCiHCgKEptE\nc+4vJh4aHfKOIecQdAaYbZIqSqqsqpCCPhT74HbA54akQXI6TNE3nK9EXHNIOjiyke2EJRmm\nuLLX8KGeTc4cKDKsqYiJqmE6lRIlQILb4QzNrWMwGHTG6OzlV56kRUILFB+HuwCeIhhiIBkE\nNagRRFWVBa8dPhf8TqiAKQ4xSdHcnyVEY4CkR9AL2Q8loNVCB0ANqho9JF00dvermiRBkvx+\nv8vpECCoQFx8zWtXN0qHDh266qqrXCfmVnA6nePHj4+Li/vnP/9ZF6uX97x9w0XjfnTEpHfv\ne81Fwt7ffln6yvCVG12bfri/TfnvaO7/Hrhk2Du70KzXgCE9Tcf/7+uJly9f0CkAtDulWcGy\n+/t/ceTmp2aMbq3NWPbypFfGD/5L2bx2QpnLMGOff737bruPH3prTYtbXn10QJOuUXpfhBpW\n7RIcxV8N7z18QZW1pKxdRrz+yi1R2vMwimkMsBogBxDwwOMKqKoAQdXqJL1J0lZvQo1ooNEi\ntSXcDjgK4CjSQv07jeECNFrYEmFNiPbL2VNIOkg6OLzFoT5fVotVo4vSoeSVKEnwWRD0w+MM\nBAKyAEGUYLToo7LAasVi4mAwI/8YivNQnCdIGhtEVZQEVdE4jiPoBwCTFYlNyuY+oo/RgmZt\nYS+AsxCF2QbglIEXGi1ik2BNrMZwnshmTYUhBsVZsGfBngWtQZRhFURFVcS8fDHgBQCdGba0\n6J0konJn1kM2UgkiYpvA74E7H1471GIJKDmE2u0QROhjYE6Iwlm3KiMI0BqhNaK42B4IBAXA\naDLqTdH+C1klWVEBtfycwVSZ+fPnu1xlq75/8MEHdZTgWD/rpR+LU25f/Ndn18QBgHr0vYFt\nxv707md773+27G1h/5oXxr2zy3DZ6ysXPXK+BYB8dOmo/tfOPVImwZE//+0DH+xePrKJAOCy\nAX2Ne9Pu/HbBogMTJrQ6ZX26jkPGdJTWjn9rTZN+w8eM4YUj1YlaJTgOfvTyF/kQmt7w+sdT\nh3bU7p11+5CpGy98ZfsXd8V6c3eu/nzaY1NXxg4cfVuXRv37HckkLSQtPLJLkWUAWotFV3a0\nTyNgioEpBorsd9p9bpcAFZLGEhsfZWPpqWYEaPSQ5IBPdQMQtVqdsZHtD1odUlsisQmcxfA4\nBK9bkgOKIKh6o2BLhCW2ugV6o4AgwpYIWyICPp+jOOjzApB0ekOMrRFtBEBnRlJb+N3wFsPn\ngurVKIogiKpgQEwyDLbomlSIqkdnhK4ZVAVep+x0eKAIEBVrnEVnbORXtY0y6UVn7OjRo+UX\nHj58uG7Wrhw9ehxINptPjLEVmt455/fORwLJ5aeC9C6a+e4BdJ78xsPnlxQBkZpe/eqz13x+\n92LPKQ3VxNvHD29y8ttu7dq1Nb7NyckBTk1wENWH2iQ4AuvWblCgu2LyBw8PTACQ/uQD/3hp\n+Pdrt8eNH6ZLSW3RuV93c+8uk+6acNXmtwc2XA0covogSorO6PPLACRJYnaDCAA0OsQmqbbE\novI1OBohrT6oN3sUEYBepzc0puzGSToTdKaKa3BQoyWI0JpU4cS0yjqjpXFnN4hqKT29gvJF\nLVq0qJu1i32uvjLuyyUf3nRRwYg7rx14ad+Lu7Zu0aNPhWvfv3OnH4n9B3Qu/VVOGDCgCxb/\neWrLjp06lb412njPECgcanPykXXkiAK06NHjRD8i83nntYZ/x469JX9rz3lw4rCY/e88+X5m\nHYVJRERERETUyNx+++3l5xgZO3ZsHa3xCM5XAAAgAElEQVQ+5e7Pfps34ZpmWUtfm3DPkAva\nJCS0vuT2yV/vcZdvmpmZCZSbF6GiiRJstgrLGxI1hNrfXZFKTY/XqlUrIGPPnuCJBaZevToB\n63/62X5m4RERERERETVSTZs2Xb58eadOnUJ/2my2N99886677qqzF4jpdMfLi7Yczz+0ftkn\nMyYN763bsuD5oRffviC7bMO0tDQgO7vM8tzc3DoLhagO1CbB0SQ9XQQyt293nlgS17ZtPPwb\nNmw/2cZsNgNqdnZOXQRJRERERETUGPXu3Xvbtm379+/ftGlTdnb2v/71rzpb9cFlr06d+sb3\nhyCY03sMvuvhqR8t3774gWYoWPrtb8Eybdufc46I3F9//av0Qvvq1dvqLBqiOlCbBIfmgt4X\nSPAs+fej32YGQovO79ZNxO4vF2wumTPWv379VkBo1qxJnUVKRERERETU+AiC0Lp1665du+r1\ndVoPTvzri6effviRtzb8XSbUWVysAKnp6WULZ5hvuP+eVGyZ+cg7O0sGsKi5Pz/53EJHXQaE\noD0rMzMzM6+CMTJE1VGrISotRj5xWyLk3R/c0Drh8ncOA4gfcl1fLfa8Puym5z/+8osPnxk6\nem4OxC4XXcDi6URERERERGef5rc9fEMydr7ar/V5l95wzz9vH9yzZcuhnx5LHPT0mJ7lGpuv\n+Pcbtze3r3jgwk59b7hn5B1Xde086LO4e25sD9Rd3iVz1rUtW7Zs+69ldbQ+anRqV4PDcv37\nq+fcfa5ZUBz5hX4AaDJ62mMddYH9X08efvMto19Yejgopo+cNq7s7MlERERERER0Nki7/ZNf\nvnr+jp7xzl0/fT5/2TZ7/IX/fPW7tV+PbStV0LrpzfM2/Pz6iD6Jx35dtHyHv/Poeeu+H9nU\nAdFq5dSZdJao7Zw9xg7DP95xxxsHtuyVQ4VzDb1fXPVjylPPz/l5Z4EuvVOfoROnPdafOzoR\nEREREdFZytJx6DPzhj5TyaND56vq/JL/e/IP5Th1XR6cvfyRvxsUfrjzOFq1b68p3/6EDk9t\n8I/3QwsASBizUh1T6sFBcxzqnFJ/t3ni/9Qnav9uiM5oUmJtbKuevU7+JaT0e/jDHx4+45CI\niIiIiIjobJI95//Zu8/4KMq1DeDXM9t3s6mEJCT0IkiXriKCYDlSlI6IBUU9x8Y5iqLYENTX\n3gvqsTdABA5FFAVEkaJSBELvIYTUzfY2M++HDZheMCHZ5fp/8Iczzz5z72R2yj1PGdly2t5h\nHx38342NihY5fnrihR/V9o9P7FHpJyWdwVj38REB1UxwuI/8/tthl65J1wvbxtZ1QERERERE\nRNSwtLjx0RvfGvnxbd36LLry0p5towr3rJr72ZqMxmO/uKuzqO/giIpUawyOQx/dfOmll454\n6Y9Sy1XZ7/P5fEGlDgIjIiIiIiKiBiJxxHu//Pj6bV2CO5bPmf3kK3M3uttNenHNji/HNar6\ns0Rnyd/qopI+u0enJ3Y0uuOHnLcvq62AiIiIiIiIqMHRNbn0rtcvvau+wyCq2JnNokJERERE\nRERE1IAwwUFEREREREREYY8JDiIiIiIiIiIKe0xwEBEREREREVHYY4KDiIiIiIiIiMIeExxE\nREREREREFPaY4CAiIiIiIiKisKetftFgwdE9e/YUX3Iw11fu8tP0jVq2TND/nfiIiIiIiIiI\niKpUgwSHbe7k9nNrsBxApyd2bH+84xkFRkRERERERERUXeyiQkRERERERERhr1otOFrd+vmG\nKz1nULsprdUZfIqIiIiIiIiIqEaqleAwpXbpk1rXkRARERERERERnSF2USEiIiIiIiKisMcE\nBxERERERERGFPSY4iIiIiIiIiCjsMcFBRERERERERGGPCQ4iIiIiIiIiCntMcBARERERERFR\n2GOCg4iIiIiIiIjCHhMcRERERERERBT2mOAgIiIiIiIiorDHBAcRERERERERhT0mOIiIiIiI\niIgo7DHBQURERERERERhjwkOIiIiIiIiIgp7THAQERERERERUdhjgoOIiIiIiIiIwh4THERE\nREREREQU9pjgICIiIiIiIqKwxwQHEREREREREYU9JjiIiIiIiIiIKOwxwUFEREREREREYY8J\nDiIiIiIiIiIKe0xwEBEREREREVHYY4KDiIiIiIiIiMIeExxEREREREREFPaY4CAiIiIiIiKi\nsMcEBxERERERERGFPSY4iIiIiIiIiCjsMcFBRERERERERGGPCQ4iIiIiIiIiCntMcBARERER\nERFR2GOCg4iIiIiIiIjCHhMcRERERERERBT2mOAgIiIiIiIiorDHBAcRERERERERhT0mOIiI\niIiIiIgo7DHBQURERERERERhjwkOIiIiIiIiIgp7THAQERERERERUdhjgoOIiIiIiIiIwh4T\nHEREREREREQU9pjgICIiIiIiIqKwxwQHERERERFRw+P7fJgQQvR/82R9R/J3KLuf6W1InLTY\nUd+B1EBk7Pmw5V55e4quy8ytgTP4LBMcREREREREFKL+fG/Typ/tPXsXz5o8uFPTRmajuVHT\njpfd9MTXu9wV1bb/9due3NLt4SdGWOsmXKqSe+FEqxBj5sp1uA3/kaVP3TJsYM92KVZzXNPz\n+1w26t63NmRXsEU56+c5D4zud15qnNnSqPUFA8Y+PDfdVWy9ecijMy7a+/SUF3YrNQ6ECQ4i\nIiIiIiICAHhWfjg3o5L1Baum9bngmsc+/HFnRp7H58nLSF/18cwxPXre+11eOaUzPvjnIz8n\n3PLUP1vXVbxUlYKvP1zsrNMtZHwzuVvHYY98sHTNH/uyXN7CjF2bVn3z2p0Xtu0546cyW3b/\n/uzgrpfe8fyCDXszbR533sEta+c/M75bpxu/yfmrUNptT/0z5fcnb3/zYE1jYYKDiIiIiIiI\noBTu+uDmKR9V0i8jf8FtY17Y7oKp+23vf7/tRH7mtu//e1s3Ezy7Xht3y1e5pevb8MrTPzjP\nu/XOwcY6jZsqEszd9OJ1/17iqrrkmbMtmHbHh7tcUtKAh+b9fjjP43ee3L3y1XHnmVT71mdu\nvO/HEn2T8hZOGTF9TbZi6fHPd5b+ftRmO751weODk0Xg8Ce3TP74r8ya/qI7p3Tzrn32xZ+D\nNYuGCQ4iIiIiIqJzWMGG9x779+SR/dukdbxl7lG14oJ/vvrY1/lA8uhPlr1zy5AuyXEpXYZM\nfmf5p2NSgMLFM1/ZWqK0a+lr/z2IbpOu71zH8VNpmT+8OuPuG4f3btG0z/0r8ut0U3vemvlV\nDpB0yyfLnx7To3m8QWtufN7ge77435O9dFCPfPj+938NpRHc9Nz9X2RC2+7OhSveuv3qHk1j\nYpp0HfnE4q/ubA3Ylr7x+eG/6m1z/aR+4vjHry0orFE4THAQERERERGFD/eeuU/cNuby3m0T\nrbFpnS6+euLdz6/IKO9Ft3zix2cnD+6UFm8yWpPaXjhuxrw9Xuyc2VkI0e/FI3+Vy/7xtVmv\nfLjwl0POSpIbAHbMm5sOoMX1949OEaeXipRR0ya1ArD76693FCt94tPXvrah7/UT2xRb+N2U\nOCHEhAWAc+eXj0y4qF1KtCW2RffLxtz2xNd7vGewM5D19gCNECLm5mX+MutcX4w0CyGMQz+y\nnVok522bN+vGwT3Pb5FkNZrjU9t26Tf8rtdW7Ku8E4c8d4wQQvR/tUzrlj0zOwshrFO+K7Vc\nzV77+r+G92qVFGM0xTVt3+vqO15avvesDbR6aPH/Pf3GJ0t+O16tXRo4+u3Tk6/o1jwxymhp\n1KLjhddOnbP6SDX/GIE/t+0C0PS6Wy83F18utRt1bWcAge3b95xaFvz+nfcOAoYrH35qSKNi\nZc0D7v73sG7duskHNxfLZjS/blJ/jWvhax9U1mOqDCY4iIiIiIiIwoN750eTel4wfuZ7X6/8\nbX+uy35857rlX7zxwFVdL7p30eESs04UbHjh6p5XTP/wx53HC7x+V/b+9fOeHtf7ortLFQOA\nFrd+vfWUhXdWOFxG9tq1ewAkDR/eu9SaniNGNAGwZ82arNPLCpctWhNAu8svb1G2JjX3uzt7\n97ru/5Znxna/4vLu1qwNC96bOab3hfcuOlLDHglA8sixl2gA+5KFq0qPaelcOn+FBzAPv2FU\nbGjJyYXXd+0+7rFPfvxj15Fsd8BXkLl/+4Ylb957Vfcrntnqq+mmK6Jmr559ZbdB97y95PdD\n2Xa/z5ax5/flc+67ukvPyfMP1+VQn6d1m/7D6b/oh+MSKikZPPK/af27XT3jw++3Hc11+z15\nR9LXL3r1jkGdLr7/+5zK810AgAN79gQBtG5d5rCJjY0FALvdfmrJr0uXFQDGq6+7NqZU2XZ3\n/m/Lli2b54wsviL58ss7Q16/eGl21WGcxgQHERERERFROJD/fHrsLZ/tckvJQ2Z+s3F/nsd1\ncvcvn93XJxb5m16bcNOcY6dLOpbePWzad5lyTK97Plu3J9ftytq55t1bOvo3v/HIR3tKV2tI\nOq/rKR1STBVtfffu3QDQsVMnUWqN6NjxfADYs+d03f41P/4sw9q3b8dyavpu+vi3sns9ve7o\noU3L5y9cvf34/mX/6WG1b3lt/A3v1+h9PQAkjRo7QALy/rfo55KTbhTlN2KuvWF4aAoX9/x7\nbvzquCo1H/78tzuzXb6g352z+8fXrztPB9evjzz0VS01sMj66OZhj35/Qm186YNfbNif63Hn\n7f31i2n9G0u+vR9OGDprW41yOD/9M1GIXs8eqbpkcZbUjqf/om0TdRWWU/e9PHrUCxsLtC2G\nzl68+XCB15WVvubdOy6IhvOPF0eMefNQlVtq/8hWVVXV1XeWzqLYf/nlTwCGbt3aFy059scf\n2QA6X3RRdPW+Reu+fRtBXf/Dqorm6CkHExxERERERERh4MTH019OV2Aa+PqGbx+7tnfreIOp\n8XkXTXxhzZrHukjw/jRr5orQcJLqzlcf/TIXaDf1uzWvTrywXYLRlHT+gCnvr5s7KeWMty5n\nZeUAkOLjS79/B2ISErQAck+cOPX0vuHHVW6gV98+5T1x2mzy2Lf/91CfUxVJKVe9uOyFIUb4\n1j79zOqyXU0qlzxyTH8JyFm8cF3xDIdryfxvvUDimElXFo1x+vvq1Q4g/uZ35t1/5fmJZq3Q\nmBqdN+iuT16dGAMomzdvLbf6GvKueeKx5S4YL37h5x//b0Kf1gkGY3zbfhOeW7PuuYtMkHc+\nN/3jnKprOTsK5j70zO9BxI/474YlM4Z3bx6jNyV1GDDl7fUrp3eU4P3p8ceWeWpUoRpw27Iz\n9m1a/Ppd/7j1i1wYO/17xtj4opVF6a/k5CT/vgWPjr+4fUqsOapxu15DRt316k8ny2vZ0qtv\nXwn+1T/+XP3pYpngICIiIiIiavjyFn72rRtoesvjU5priq8wdv3P9GuigezPP/1eBYBN78/Z\nqsBw5QPT+5QYGCFm+MN3dSnd/KK6XE4nAMQlJJTzEJmQEA8ALlfRhB256enZQFzr1vFlywJo\nNvnfI0ulSZJuvH9SInDsow9/qEbPiJIfHTl2gAbIWrRwQ7Fwl83/1g2kTLhhsDa0JNj8+vcW\nLVq0eMZAQ4mPaxIT4wG43TVoKFAh3//e+m8G0Oz25+9pV2I/SW2mPjOlCeBZtXzNGQ02UvtO\nfvnWggKg27QXJiWVWKHv/dis0RYg/9vlG2ryxzjy0oWWuKSm7fpcc8+b6+wdxr+yatXTfU7N\noCPn5RUCiNHu/r8hfUfPnrtuT1ahx5Wz7/cfvnlz6sD2ve9ZXmaAk+jWrROB/PT0Sib2KYUJ\nDiIiIiIioobvwP79APQDBvcv0+cgZvDgngC8Bw9mAnDv3XscQMdBg5JKF2x/2aDUuohNp9MB\nQDBY1IIjKysLQEJC+cM/SD1699CUXmjo1aszAPeBA1nlfKRSjUeNvVQCji5atPnUoqL+KS0m\n3nDxqS1pm184YsSIERe3PJ3fUNzZ+zZ+89RjxWfv+JsO7tkTBCz9L+1V5gtqevbpIQH+zZt3\n1trm/pa9e/YASB1waZsyq0x9+nQBkLd589EaVKiJapyW1qJd1779eza3+Pat+uzdBZtPj97q\nDCXIChfOeuKPxqNnL9i454TdlX/kz+/euKVbjGrb/PoNt39WOpEROoBOnDhR7RCY4CAiIiIi\nIqo3A0VJugmLyy3n278/A0CzFi3KeYhLbN7cDODAgQMADuzfrwJSs2ZpZQs2a9bsDOO0WCwA\nYCsoKOelflHzB6s1NNpFFQmOlGbNyhkXIq5Fi2gABw8erHFsSSPHXKIBDi1cuK0onqXzl3uA\nDjdM6lGyxUrh7hXvzbp7wtX9u7dtEmOMSmrXd9QjyzJr2makQuq+ffsBuD6/VivKME9cogAo\nLKzZzKd1xb5vXzaA46/2KxuqSL1vPXA61rw51TpK025fduzYoT1b16/97VDWxulNd3z0z0Ej\n3j4QWmkym0N/iiYTP/tl/oyRvdslW81xzTpffuf7qz69IQnIW/zwi5tKVhg6gEIHU/Voa74f\niIiIiIiIqHY0a9++ffH/1zSJKrecqqoAIES5fUy0Wg0An88HwOv1hkqWW+5MHwE1SUkJQJ6c\nn28HSvUv8Z88aQMQ07ixvjpVBQJlZnIBoPp8fhRrBVITjUeOHXDnmlX7Fy7cMatrJ7iXf/2t\nG+hyw/VdihWy//Lk8JFP/pQjA9DFtezYbUj/tu079x7YasvUie8cqPlGUTbaoMPhBWBp2adf\na2sFH9E0N1awBgCOv9gv7f4NJZflTm8hpp/+P+34rwNfjjqzcItzOBwAENvukp7NKvqzJaZo\nAEATW92j9BQR1evxGSOfH/nFqtmvrf/nq/0AfVpaIpCNNpOnXVMq7xU37F8T0j55JePYH39k\no3fjYrUIAUBVqj8GBxMcRERERERE9ebjXbuqVc7Ypk0qkHH0yBEVZQbSyDt0yAGgbdu2p/57\nVDl69DjQolTBo0dr0ueghPbt2wPrsGf3bqBPyVU7tm9XAXTo0KFoQXJyMpCXl5dXNgIA2UeO\neIDS07WcOHjQC6BNm7I9JqqWOHLswLtX/bBz0aL9T3RKWTZ/uRtSvxsntv2rhOe7+0Y+/lOO\nSL780defvWtYt8ane6rsfHTaGWwx5MiREvtT16ZNM+CwdtATK9+/8owqNDTrNXjwX8mDgl0/\n/XHc2OaiPi1O7y6pU+NyP1lTTdq0MeEXT8K1r678v26VF40d9/GuceWt2Pvq1aPeP4oBsze+\nMcJcap22WbMU4HDm/v0e9DMBqampQDaaN29etp4WLVoAGcjIOA4U+3Z5eXkAklOqPzQuu6gQ\nERERERE1fKEnf9/aVb+WaeLgWL36DwD6tm2bAYht1y4RQPpPP+WWLnhs3bpjpZdVV/KAAe0A\nHFu6dFupNTuXLDkEoPWAAacG+EhOTsap59NyrP9+pbP0soxFi/4AYGrdusmZRFc00ui2hQsP\nepbNX+aBZuAN1zUtVmDdwoU5gHHky4ueHF0suwEEjxw5Xr1thBrIFLd785aSQ5OGUkyFmzeX\n08+mMP27BQsWLNyUWdk2Go15bWUxLw6LAc679fNii76b0b964VZBtG3bBsChzZttZVfmbF6y\nYMGCZdsq+AOe0iLZvHfHjh3Lftledp1v375jAOLT0kK5mbROnWIApKenly27b98+AJrOnUs0\nEzmV4EhOrsbXCWGCg4iIiIiIqOFLuGbCEBNw+N2ZHx0v0Wbfv/2l2fMLgIQx4wZLAND3uuta\nAp5lzz2/ucQTuWfNsy+vL28+zurpPGZMewC7v5iz1lFssfPXOV/sAHDeuHGnGwIkdOiQCBQc\nOJBfbk358556ZWeJ6WDzv3v02dV+oMmkGwef2TwvideOuVQLbF74xdvzl7ugHzJpbInn4lB2\nwhQfX7LliHr083eXO3C6C1C5JIvFCGDP5s0l0hnO/z392o6SJeOvHnuZGdjy0owFpXID/k1P\nj75q9Ojr5vxZchKX+tNm5NjuWigrZz+6ptRssI5vp189fPToW788VrpZRin63r27ATj88cuL\nCkquUfa+9uoyGdD16tW1qOyV/5rSCjjxwcx3SyXZjn/83OcnAU2Pi/qW+OM4Dx7MBmI7dGAL\nDiIiIiIiooiSesuz954n4F55R9/hzy7beswe9Ocf3DT3wUsvfWKbAuMljz99TWhsDP3Fj8we\nZoWa/tzggdPm/XbI5g8UHNr0xb8HDH/zeHy8FRWO5FGFbv+eeW0ccPDt0eNe+OWIQ4FsP7z2\n+THXvL4XiBs56z/dT5cUfQcNNAG/bdxU/vAJ8qZHB1z+8LyN+/LchUc3f/vahL4jPjoKGAY8\n+uiQykaoqEyjkWMHaoANzzy61AXz0BtGlZyittsFFwigYP6zz64/GWoCE8jbvmD2yEG3Lc4D\nANdvP/6c6y8/yyG6desKwD7vvpvf22pTAPjzd86999LrPw00Tiw5Xmrq5BendZGQ99UNF014\naUV6piMI2Z35x7yHrxr6/C5Vaj7l/vHlj7x69knt73vp9qbAoTevufi2OT/tzXYrCDiOrPvo\n7kHjP8iCvsu9U68q3Y+otJa3P3lzKpAz96ZBk99aueNYgdfvzNq3/quHrhw8Y4MX+gueeP7G\nU38Hqe/dUy/UovD7uy6+6rH5mw7kebwFhzcvenrkgDuW2KDt8tCbd7YoUflvGzYo0A0cfEkN\n0hYq1atVq1b16NHjxIkT9R1IOfLz83NycnJycjweT33HUp88Hk9oP+Tn59d3LPUsNzc3tCt8\nPl99x1KfXC5XaD/YbLb6jqU+KYqSc0ogEKjvcOqT0+kM7Qe73V7fsdQnWZZPHxKyLNd3OPXJ\nbreH9oPT6azvWOpTIBA4fUgoilLf4dQnm80W2g8ul6u+Y6lPPp8vtB9yc3PrO5ZynDhxokeP\nHj169FiwYEF9x3KK97OhAHDxG1l/LXNue++69qfeqgvp9INfXO+7vznoL/Zh5fi303rH/lVQ\nAIC+zQ1zlzzUHsDgdyr4K+ye3anMRovLW/nvjkUPvbqYeGvRXKjmzvf/WOpWueDdyyXgvMe3\nl1i64tZYAG0mThvdtMw0qtHd71546G/dUuS8d1lRrdbrF7lLr3VvmNYxlIzQmBNS0xpHaQCI\n+H5Tv1r0aM+iLEXa1A1quXvesfqedkV7WzIlJsfpAcDc9b4fP7spCoi6dUXxDaV/eFNny6mv\npdGe+qaapKFv76rhF1xzRyOg5/8dPsM9oqrqL3clA8Dor4LlrbVtfGVkq1NNSoRWe+qI0re6\nfm5G9c7c+d/d1yWmvHxZVIfrP9pdcqvBfR+OSi3zlweiOk78b7q/VMUHn74AwMVv1ORZmS04\niIiIiIiIwoOly62f//HHl4/dOmpwzzaNzJaU9v2uHHfnc9/+ue61a1sWb0ogmlz53Nrfv3l8\n8oiLOiSZDdFp3Yf+54P1f3w8VpzIAKTY2Irm+KhC/OCXNm1Z8MQNAzs0ifI7g9Gp5w+6cebC\nLRufHxRXsmDs1SMGaLHn+++PlK1E23LivC2/vvvAjUMv6pBkjWnaZcDIWx77+rdfX7umxd+a\nBaPRtWMHaQEgYfSkf5Rpe2Dq8+zPv336wMh+HdNM7lyb0qjb1be/9MP2n18eN+LRud/837+G\nD7j4in/0rGAAz6hLX1q3/r17r+55XhOr6nDrm180/pG5v61/YVCniyfedNPEC1OLb6jDTR/+\ntvWb2beNubxXuySjPqZ5x75X3DRr0c59S+5oX8MvOODtHFX97cFyxuWsHTG9712wddOnD998\n7cALWsXrDY1adblo6O0vf79356djU6vXyifu8hc27/3ljbuuHdT7/GbxZnN8884XXjH27ld+\n2vvnpzeeVzKboWlz0/wdv3368PUDO6YlWAyWpHa9hoy+47kVO7d8NrlDqamDs1eu3Aapz4ih\n1R+BAxBqJT2NqO6tXr162rRpS5curcnIKWdJQUGBLMsAoqKijMYzbSkW/rxer9PpBKDRaOLi\n4qosH8Hy8vJCZ4zo6Gi9vlpzgEUkt9sdmupdp9PFxMRUWT5Sqap6euSw2NjYM590Lvy5XC6P\nxwPAYDBYrWd4vxgBFEXJzy/qah0fHy9J5+5LFIfDUdTT22SyWCxVlo9UwWDQZisauS4hIeHM\n2sNHhsLCwtCkmGaz2Wyuokd7BPP7/Xa7HYAQIiGhoTTSPy0rK2vo0KEAHn744ZEjR9Z3OHXn\n5Kv9k6f+0nrGlv2zq5g5429zLhyXNnJey9n7tsw4PS/Kd1Pirnzf1v6Rrbtmda3jzVN4O/Zq\n/xZTN4/4POOb62rwCHbu3nwQERERERFFIvf8G5ulpKRc+uKeUivkza+9tQ6I69+/Y91HETX8\n3sktsPXTz3ZUXZaopEOff7pOSZl0z5iavWBmgoOIiIiIiCiSmC/pf35BVtZPs295/PujpybI\nUHLWvz5h/Et7VbS66dbLdJVWUDs0F059aKBlz/tv/OA9C1ujCBLY8Oa7f+gveuD+S2t4oDLB\nQUREREREFFGSbvnvx+ObSbZ1T17RsnFKuy7dO7dKjEq+8J75+7xRF0z/+ImLzlLP0mZT5sy6\nOO+DGW8fqMmnFk/SiWrTTVhcV9FTfTn+3sNvHe/x6Lt3t6m6bEnnbpdpIiIiIiKiyCRSx32x\nvfO4t154+5uNew8f3O2JSm3Xr0ebLoNu+M+dw9ucvdGBRNt73n3sy+5PP77o1s+uqe44Vf2m\nLVtxfXWHihTJ3asuROHE88Os2WtbP/TbA+eXM99KFZjgICIiIiIiijgi+vxrpn9wzfR6DkPT\n4aFN3odO/d8V7xWo71X1kcZdLr+iS91GRQ2YafA7mcEz/Cy7qBARERERERFR2GOCg4iIiIiI\niIjCHhMcRERERERERBT2mOAgIiIiIiIiorDHBAcRERERERERhT0mOIiIiIiIiIgo7DHBQURE\nRERERERhjwkOIiIiIiIiIgp7TI4IiIMAACAASURBVHAQERERERERUdhjgoOIiIiIiIiIwp62\nvgMgIiIiIiKiM2ez2VasWLF79+4TJ06cOHEiKioqOTk5LS2tf//+PXv2FELUd4BEZwkTHERE\nREREROFHVdWFCxe+/fbba9asCQaDAIQQkiQBkGU5VCYlJWX06NEPPPBAWlpafcZKdFawiwoR\nEREREVGYWbduXb9+/UaNGrVq1apQdgOAqqqyLJ/ObgDIysp644032rRpM336dLvdXk/BEp0l\nTHAQERERERGFk1dfffWSSy757bffACiKUklJVVVVVfX7/c8+++wFF1ywe/fusxUjUT1ggoOI\niIiIiCg8yLI8efLkqVOnqqpaeWqjOFVVARw6dKhPnz5r1qypw/iI6hUTHEREREREROHhwQcf\n/PDDD3EqZ1EjiqI4nc7hw4ezHQdFKiY4iIiIiIiIwsBHH3304osv/p0aFEVxuVxDhw4tKCio\nraiIGg4mOIiIiIiIiBq67Ozsu+66KzRJyt+hKMqBAwceffTRWomKqEFhgoOIiIiIiKihmzlz\npsvlqv64G5WbM2fOvn37aqWqcgXsmbv/2LTtYLZLrrowUW1hgoOIiIiIiKhBy8jIePfdd2ux\nQlmWZ82aVYsVnubaPe/+gWmWmNQOPft0a51kjTt/3NPfZzDNQWeFtr4DICIiIiIiososWrQo\nGAzWYoWqqi5cuNDv9+v1+lqsFvYVd1427uOTSZfcPnts32aG/O1L57w1b8bQvb71G2f2qNUt\nEZWDCQ4iIiIiIqIGbeHChZIk1Vb/lBCn07l69eorrriiFuvc++aMjzO1vZ/9ac0D5wkAwK03\n9RnTbvTXz7/03WOfD9PU4qaIysEuKkRERERERA2XLMtr166t3exGyI8//lir9QW3btkOdBs7\nrii7AQDx//hHH8CTnn64VjdFVB624CAiIiIiImq4srKyard/SohGo8nIyKjVKuXe9y1ZcUti\n56bFlimHDh0B9KmpibW6KaLyMMFBRERERETUcGVlZdVFtaqqZmZm1mqVhhZ9rmgR+mfh3jW/\npJ/M2vvL3Hfm7Em7+qVZY6JrdVNE5WGCg4iIiIiIqOEqLCysi2oVRbHZbHVRMwDseHvC0Fey\nAEDb/o6v59ze3VBXWyL6C8fgICIiIiIiariSkpLqolqNRpOcnFwXNQNAx9u/+O7HdVvTt/7w\ncqcVIzsNemaLt642RXQaW3AQERERERE1XCkpKXVRraqqaWlpdVEzAMS2H3h5ewBAh/8+viL2\n5idmfDFl+eRGdbU5IgBswUFERERERNSQxcXFxcXF1Xq1iqK0bt26Nmv0fXN9Ylzc4NeOl1wc\nnZRkhv/QoePlf4qo9jDBQURENaTIyDuBI7uisvZbM9KjTuyVju1G9jEEA/UdGRERUQQSQgwb\nNkwIUXXRGho2bFhtVmfo0j7VZlu/ck1B8aXKtl83uiDatq3VZApReZjgICKianPbsWsjfl2C\nnb+Ko7uMBZk6t81gy5KO7cHuTVi/FNt/QWFOfUdJREQUaUaMGKGqai1WKIRo0aJFp06darFO\noPWY6/sa3Eun3/jKxlwFAFTX7q/vuenFnUgef+vVUbW6LaJycAwOIiKqBjmAA3/i5BEICY2a\nIKGJGp2Q53CFVsZazFqPHXmZyDmObWsRn4y2F8Bgqt+Qzx5V0ShBAELV13co9cTvhbNAuB2x\nXo9QFVVIwpsPsxVRcdAb6zs4IqJIcNVVVyUnJ2dnZyuKUisVqqo6ZcqUWqmqGHHev//7/MrB\n/1ny775JDyU0aawtOH7SJWsa93vkizeHJ9T21ojKYIKDqGIBP7xOvdcdG/ALQJUk2AIwWmAw\now6aCDZkioyAB8JvFqqkQgm4NBKgPScf5eQgFJ9WCpoBACLoPzf2g9eFHb/CbUdSc7ToWJS5\nUFWgKMEBnR6mZMQno2UnHNuD4/uxZRXO74voiL6X8XvhtMHjsAQDltASTz7sWpitsMSeK/kd\nnxs5GXAVAhBanSokRaMXiix8brgKkZMBSwwS02Aw13egdNapkANCkg2AgFDlwLlxtiSqMyaT\n6cknn7zttttqpTZJkho3bjx16tRaqa0Ezfl3f7dn4NdzPl29ff/hXNF4RPvO/cfdMrpzzLl1\n80z1hQkOKocSRNAPrRqlFQJQFZ8UFNDoz6WH+oAP9jx4XQCEpFEBVUiSqsBth6sQGi2s8bDE\n1HeUZ0PQD48NQS8ACJggVKEKvwN+ByQtjNEwnDONDf0u+ByQgwD0EvQA1CCcXggJhigYrJH7\nA/G6sGU1FBnn90Wj1CoKa/Vo2RkJqUhfjz/XovPFiEk8K1GeXYqM/KzQUz0M5oDe7JcVVVX1\nGkmvynDY4CiAORrxydBE9HU27wRyMyBJiE9BdLyiMxbm54fWxMfHSwEv7HmwZePwTiSmIb5O\npgBomASglaTQP85BShA+BwJeqIpGA2tooSsHQgO9CQYrBHtIn3tUFbIfsk9rQIwQkgrF74JG\nD42uviMLKzfffPPLL7+8Z8+ev9+IQ1GUp556ymyuo+yztdPo+58dXTd1E1Uqom+8qOaUIHwu\nKEEAENCoqgwIyBqfExDQGaE3nQP3a247bNkAEBUHS7QvqDidTgAajSYuNgYeFxz5sGXD60Rc\nCqRIvk3z2OC1Q0gwRkNnQqEzX1UVAFZLDII6nxPufPhdsDSCpKnvWOuSIsOVB9lflNOR4fX4\n3EJAq9Eb9VEBN7x2+FywxENrqO9Ya50cwI5focjocgms1R6/PToe3Qdh62qkb0T3gTBa6jLE\nsy7gQ/YxBP2IikVMIrQ6v8vl8XgAqAaD3mqFHERhLpwFOOFGYtOIbcqRdQiFubDEILkltDoA\nKHXDbTAj0Yy4JGQdQk4G/D4kt6iPQM8iVUXQDzkYpcGp5zYFXhc0Ouh058DlEwB8DngdAKAz\nQuhkj9epQgGkKFN00Ct8TvjdMMVCF6E/i3KoUBQYdGaDFgAkIVQZIqIvmqWpCHgR8EBVIYQE\noahQBKSAFwEvJA30FqY5qkur1S5YsKBPnz4ul+tv5jgmTpw4efLk2gqMqOFoEAkOx4pHJ761\nraK1lsuf+PKuC85mPOesoBc+F4SA3gytAbbCQlmWAUSZo3QaY8CDgAdyAMbIfvfitKEwB3oj\n4lOKXr2GWi+ECAlmK8xWOPJhz0NuBhLTInV3uHLhd0Nvhjnu1K2Ys2hoK6FRQ2/hfA64bXBk\nwZoEqUGcTmqfEoQzB4oCU2xRcxW3W4FfUQFIssECgwVBL9wFcObCEh9xd+0H/oTbjvP71iC7\nEWIwoWM/bP0Ju39Dt0vrJLZ6EQzg5BGoCho3hclafhmNFvHJsMQg5xiyjyC5JXQRl/rKy0Rh\nLmITkdS8iud2rR6p7XDyCApzoDMgIXLbcchB+L1QVUgavwp/IAjAoNfpAAR8CPphMEV4Mhjw\nFMDvhtYAUywkLYJB1eULTa4k6y0wREH2w2ODOx/GmHOiAaASLHpppJG0iiwLISShkQNAEBrt\nOZHmUJWixo8aHXQmyKrfbbcDEELExycEfQh44LVDZ4Ke/diqp0OHDvPnz//HP/4hSdKZ5TiE\nEN26dXv33XdrPTaihqABPZGY0rp0TSvn3GZsGVsr9R/6/F/3zpXHvTpnYstaqa8479G1X32+\nbMvBjBOFiE5KbXXBlePHDGwVFVavaoI++FyQtOXnLzQ6aHQI+uBzwmuHMSZCW+P73CjMgcGE\nhNQqvqE1HhotCk6i4GREtrv22OB3wxgNU6W/P4MVGh2cOXDmwpoUgUeFqsCZC1VBVGJlvce1\nRkQ1hisX7nxEJUITMf3M3Q6cPIKk5lX3TClXVByad8ChHcjNRKMmtR1cfVBV5ByDIiOpRdXt\nMgwmJDVH1iFkH0OTVhGVCfW6kHsclpiqsxshQiC5OYJ+5GbAEh1pLXpCggH4vZAk6E3QaHwO\nhy8oAxA6vc5oKcp9eN0wmCK415LXDr8bektlFw6NHpZEuPPgLYSkibiMcHEq5ABUBUKCpIXd\nYQ8EAgDMZrPJYFaCkAOQFEiR3XJBhdcBJQi9BTojAMj+v1YKAZ0RWgN8TgQ8ECKij4dadcUV\nVyxcuPC6667zer2hl5E1ctlll82bN6/OOqcQ1bMGdJVt3P+2hyc0q+8ozkBg90f3PfTNMcS2\n6t7tos7Ctm/bpoWvbNywb9art3cJl7HjFbkou2GKruxmVWsABHwO+F2R+OJFVWHLhkaL+JRq\nPamboxHww1kAryvC7tflQNHrlMqzGyFaI8zxcOXBZ4cx4oYl8dqhBGFpVPXYeJIGlgQ4suEu\ngDXprAR3FhzZBSGhxflnXkNqG2QewJH0CElwOG3we5GQUt1eJzoDEpogJwP2vIgaiyQnA5IG\nKS1r0udCIKUVDv6J7GNo1r4OY6sXigy/F5IGBlP5lw+NFkYLfG74vDCaI7JvoxyAzwGdseoL\nhxAwJ8CVA48NWkNEpf6KC2U3JF05rXaEBhoN5AAUGRAR2/4RgM8NJQhDVGX9N4WA0VqUHZO0\n7KtSXcOHD9+wYcM111xz4MABIUR1po+VJElV1Xvvvff555/XaiP3sKNzXoReVWqd6s7a/efO\nTHc5qzKXzFl8TGk67Ol3Xnls2r333P/4a+88eVUKTix7d3HGWY/zTPndAGC0Vn2zqtVDa0TQ\nV9TkMqJ4HAgGEF2T8SSi46HRwp5Xl2HVA08hhIA5vrrl9RZojfDaodbOtGUNRWhIGp2p6L1T\nlYpG6AgU/aDCnqIg/wQSmvyt+S8kDZJbwlUIj7P2Iqs/oU4WUTXprWOOhtEMe17k/Dy8Lrjt\niE+u8YOIRou4JHgc8EbGL6QYvxdCVJjdCAkVABDwnbW4ziavHUJUKy2O0DNtLFQFvog4MZSl\nyFAVSNrKbig0OggJShDVeDINS4qMoBdaQ7VGpwoN1B0hV8+zpVOnTjt37nzxxRdjYmIASBVk\nToUQQggAAwYM2LRp08svv8zsBkW2sDq+XYfWLPp6+brdmbk2ry46IalVz8Gjxlx1fuxftxNK\n3uYFny7ZtGf/kTwR26RV9yvGT7iifayE318a/eQaPwDMvXf4XONlT8y79wIAUG07Fn/6zfo9\nh45kB2LSWrbqctl1Ey5tevo8vGPOjQ+v6PTw50MPzJw9b5cjYfizH9zaoVRQnl07DsrodPXY\nDqceAYSly7BBLb79/PD+/V6khUEbDlWB7IfOWN23KHoTgl4EfDCE1eFTNbcDGg3MFXSqL5eQ\nYImBPQ8RNFmoqiDggcFSs67ixmg4sxHwQB9BbVkCHkCFMboGHzFY4LUj4I6IvsS2bMjBWhgx\noVETHElHXibS2tVGWPXH54YcPJOGGNZ45GTA46rZ6aXBctoAnOEEwDGNkJcJZwGMEfALOUUO\nQlGgN1Td9E9I0OkQ8ENRIqwRh6og6IXeUoNBJbR6aA0IuGt2jg0XShCiGk0zJB1kH9QgRCQ2\nWwj6AFT3ahjqn+J3QwlGcpOWWmcwGP7zn/9Mnjx5/vz5ixcvXrlypd/vL1WmadOm11577ahR\no/r3718vQRKdZeFzCpGPL5r90Ac7vdEtu3W7+AK9L2f/ts1L3tm60/H8y+NaCQCQ9y985ImP\ndrqim3fqfOF5cuafv3/79tatGbNfmdKp+eDb/tV4/Rfz/lB7jJ3YJym1GQCoR5c9PuPdrYWG\nxud16nWhwXZgx8ZvXvpt467pz/2z5183oart59ee/vmwuUOv7h06lvNG29eo66jRrVp3KdE0\nX5aDgNBoSl/nfT6fz+crtQSAqqrVaVpWR4J+AELSVRhC6fAENDoh+6GaI+ilg6oKvwfm6LJ7\nofiScvaRwSyQp3pciIqQ25OAB1CF1ljZIVn2iNXqISQR8EAXQUdFwCskLSRt6V1R+SGhMwm/\nG6qihv2ECY4CAajRCRW9Xiy1Hyo8YszRQquDs7Aez3K1QnhcAFRTVOXvW8v5mkaLEAJep2qK\nhK59wm2H3qRq9WX3Q9WHhFYv9Ea4HeF+MBQn5CAAVdJW55cCSSvgV4MB6CIkJx4S8AAo58JR\n+dlSY0DQJ4J+NcJ6JagKoAqUuXYUrS350xCSUBSICPpFnCb7haQFSnaeqOSQkHQARNCv6upv\n7NUwPTXFxsZOmTJlypQpbrf70KFDmZmZmZmZUVFRKSkpaWlpzZqF4wgARGeuASU4cn55/9mj\npV/+6jqN+s/VbQBg/3eLd7rjLnnojfv7FWUf7L88c8dz69euzxjXqimAk8vf/XSnu8WwWbNu\n7RQtAPgPzp3+wOdL/rts2Muju1x+pTXnf/P+kNtcdOWVoUFGC37878dbC+P7TZ1936BUPQA4\ndn8y89Gvv53z9aBuN593as8o2385NGjGG3f1SSx/X8V2HXFD11NlA16XM+/onys/XXJMnzr0\nql6lr9hz5sz55JNPii/p2LEjgIKCAp2u3i7vOmHRwlRoz1dR/mnd5XK5XK7iS7Qw64Q5Ly8f\nFXwk7GigxKmqy+f35FXY30SW5bwyawWQAHhdTpevxoM8NUwiYJJgcbhsqrvCb+RwOMou1CDW\n74Unz1aX0Z1VWn+8KgXz8uwVFQgEAmUPCUk2SaolP8+mivA+JKIchUYh8hwuOKtuNFxYWFjJ\n2jiNXnHZCyv+cYWFKJ/DAJFnq+ybls1ih8RByG6XXQnvPRAS5/PKGr29qr9mQUFBucujVUnj\n8xSE+cFQXIxRJwlRkJ9f7lqPxxOaQvi0BLPB7/M67OWcRcOXRjZLMNudBaqzwq5Y+WV2kVB0\nWsQUFjhUTel3zmFNrzOaDVF2e6FcXm/eUoeEQWcyGSwFBTZFCe9LRlkm0UiG15VXfjckVVXL\nXkBNIsHr8dvd9fbrqOjEFS7MZnPHjh1DDxdE56wGlOBwH9u67ljphUbjZaF/yFFdx/2rSVLX\nvn+1rYhu3jwO6wvthUBTYM+yxbuD1sGTbghlNwDoW10zbvCvn6XnHPUhuUz3v8wfl23xartO\nvr0ouwHA2n7i5MvXTF+yfNkfN53X59TLV0330TdXlN0oaeOLY5/6WQYAQ7txz8ye2DYMuqcA\nAFShCrWi7Eb5hApAQFIRIddjoSgAlJq/c1cBVQgJkdK7HhCQAKiixt9IFQqUyJryThVqzf+y\nRR9RRbi34BBBvyJpa2VqHEWrk+TA36+nfkmqopzp3lAljRQp6WBJlQN/o3uFIkm6YIRcOEIk\nIRSlBn9cRVWlMD85lEOVcPrsV/0PQUHRvURECZ391eoNuxMqFvYXjHIIAEoNxx5SoQiOD0hE\nf08DSnA0n/DG6xXPoqJJ7XFlKgDInoKTJ7Kyso4f2f7D8mNAUddN77Fj2UDPLl2KZzIMfe54\ntU8FFWYePw407dq1RK8TTYeuHXVLfsrMzAMaFS1LaNWymjNDNOkzcnSaOT5Wk7tx2cLZT+ln\nPDSmXXj0MlYFhICoQY5DFRA1vpVpyFQh4QwH3VWFqp5BZqTBUqEIhP7ENbvnFJDUGn6koRPq\nGdxpFX0k/HeFqtFJigyoNZkpo3wiGFDCf2pMVZz5Y71QFLlGo9o0YKrQSH9jwFRJVdTqj9MQ\nDhRFrVHiSwA1SoiEhWKJ3Rp8tYg5W5ZSdDclRHXuqkLnlZq9ZAoPoTdhNbx8nNF7BSKi4sLo\njjOYte7Td75atf1IYQCAxty4VZtGMbAXXRFyTmYDlvi46vZpdefmeoG4+FJj4Yv4uDggJyf3\nrwSHNaq6o8I1vWTSDaF/XX6+esf9n744r+s7N7UrfmofP378kCFDin9mx44dO3fujI6Ojo2t\n3sjjdUD2S0EPoq2xQlPi+mq32xVFAWA2m/X6Ens24NaoQcTGRNCkoKqC7EKTTmuILv2H8Pv9\nbrcbgCRJ0dGlB0MTcgA+m8Fs0ZkjZG8EPMJbAKs5RmMofb9VWFg0jILFYinbqcqZpdXqVVP9\nHcm1zpMnVFVnLfONvF6v1+sFoNVqo6JKj6rgs0uBAKJjrbXR9KE+Sc5oFGTGWszQlT8Cvqqq\np3umWK3WsqMOnaZVgqo1th7PcrVCsgeFwxsbbS07AK/H4wn1TNHr9WZzmcS2qmrcOcJgCfc9\nECKcJ3WqWu53URTFbi/q0hUdHV3ukP4aRxb0hsjYFSFS0C8pcmxsTPFUoNvtDg31ZzAYTKZi\nkwqrihTw6QyGWHMkDMhyWtAj+eyIjoqVdCUuHLIsn+7SGBMTI0qeFoMeyRdAlNUs6ao373K4\nUAVkRFtjUKwtpNPpDAaDAIxGo9FYrImvooGC6OiIGIG4JL8DeslosZS4WwgEAqFez0KImJK3\nkaoCv0PSG3RmY72dH0IXdyIKa2GT4Mj+btbUN7fo2l816f5+ndo0T0uKM2ryFk27+eCJ0PrY\nuFggw24PVvM7mRMSjICtwAYkF1us2mw2oFl8sXYdotKmpPb1H72x+kS7ax4YfX6xO15Ny5bN\ngN927bahXfEUSuPGjRs3blz881lZWQC0Wm09ztgkCQQ9UGVNqaeY0zcikiQVD09V4QtCq0ek\nzTKlN0l+t6RNKrU4dEcCQAhRzlf2OgBIpigpUvaGZIa3ALJfY6h4PhSNRlNqVwR9UBXozeXt\norClM8FrhwRtqRHdTz+2lXtIuH3QGqDThf9+sMQA0LrtFU2kUnwwtrKHxF88TgR8whIT9seG\nKQqOfG3AG9ozxVV+SMDjhKpKZmuEnCXMVhSc1EKFtnSWM5QTD9FqteUkOIIB+D2ITw77g6E4\nAfg8WgDFvlRFF1AE/AAknV6KlBY9IZIZPjuUgEZfcaZCq9WWSnD4/BAS9KaI2hUhQQVClTS6\nv34C5R8SKoJBCA00kfSLOEXWI+gVGklbfJK+UmeJ4uUDXgDQGSSNtt56qUTUqYnoXBUuP+PC\n337a6tZf+ODsf170V0uC3Jzc0/+2pqZFY/ve9F3B/p1Pfyk1/ZOpL6yJHvn0rKHJKKVJahPg\n2PY/baOS/8oTy3v/TPdD3yS12tMAWvX2PzesP9LsyOjzWxVbnHniBKCNjw+Luc8kDTQ6BL3Q\nmarV3T40d2Z1ZjUPM6YoFObA64Kx2jOdqipcdmh1Fb3iDkeSBloj/C6YYqo7czAAnwMQiLCX\ncKEEh88JU7VfJgU8UGQYIuNVXHwyJAl5mX93pti8TAC1MN1svTNaIGngLCib4KiCswBCICKm\nUAGAqDgUnIQjH3Gl08FVc+QDQFTkNN8AAI0WQkLAD622iv5cqoqgH5KmZrNwhwNJA40efhcM\nUdW9cMiBopllI5KkgRKEKlcxb64cLCockXQGBL3Vnj9eRcBTdEdKZ+b48eMbN27cs2fPyZMn\nQwPZJiQkNG/evGPHjr169TIYIudOlahy4ZLg8Hm9KhS/PwgUJTh8Bxd9tjoPMPv9KiDQ+Yp/\nNF3x1cqPvrh05g3nRQGAmrv66+8O5RqGt/sruxEInBrnLnXQP7rOe2Pz3Hd/7vKf/slaAHDu\n/fL9b7PReNhVPSq5Q5HdBXnOAIwxjaMNgDivxwXmH35Z9snSQQ8PbRKKzZux5MOlGTD16tUx\nXK5ZejM8hfA5qp6OXg4i4IFGF4lXIEs0nPmw58Jgru7Aii4bgv4zuctv2EwxcJyExwZzOTMj\nlyPohd8NQzkt98ObRge9GT4n9GZoqtH9TVXgKYSkRSWNX8KJRovYxsg9jpadz3xKS1VF1mEY\nLZHwTCsEouNhy4HbAXO1k1g+N9wOWOMj5+dhtsJgRv4JxCSiRsOSKDLyT8BghikyUoDF6A3w\neeDzwlBJlleFzwNVRYQ+Zhij4cqF1169jLAKrw1CREo6uAxJC1WGHIBGVJjxCWVAJE0N3iWE\nF0kLrR4BLzT6qm8afS6oCvQRejzUqUOHDn388cdffPHFvn37Ti+UJKl4Yxmj0ThkyJBJkyZd\nc8019ThvI9HZES4JjsZ9L2n/5f7f377nwQ3d26eaXMe2b9yc3+yC9taNu9e9N9M86V9TLmw1\n8o6RG2ct+PqhOzd369ImQeSmb9pyzJN4xX3j2wEAjAYjkLH2kzlSx46DJlycljD41hvWznj/\nl+en7v+26/nJ+sKDf247WCClXnXH+I6V3crnfDvrto/3a/pPXzjtQgBRF02efMGWNza/e88d\nq7q0b2pVC4+k/3mwQI7rN/Vfg+IqqadBkbTQm+F3w+eAIarCV1ByAD4HhARDpLyJLEFIiE5E\nQRYKTiK+TJufsnxu2POgN8EcFi11akBrgN4CnxMaXdV3n3IAzlxIWpgiZBCSEowxCPjgykNU\nIqRKz5eqClcelCAsjf7+oJwNRrMO2Loax3ajVZczrCHrMNwOtOtRq2HVn+gEOG3Iy4S+JbTV\nSPrIQeQch0aD2Gq3CwwLiWnI2Ivso0huUYNPZR9FMIDklnUVVT3SaKHTI+CH1w2DsZwHVkWB\n3wNFgd4YOamukrQG6M3wuyBpq75J8NgQ9MMYE6k7AwAkPRQ/5FCTnZKXD1WBEoSqQEiQIvph\nUx8F2VZ0b1nJewK/C0EftMZqnVbptEOHDj322GNffvmlLMulugQWz24A8Hq9y5YtW7JkSdOm\nTWfMmHHrrbdWMmwWUbgLm6Rx6ogZM28d0taQ8+ea79ZsOS7aj3/mjdkzpt83vn8bi9vpVQAY\nO9/4wisPjunbVBzbvHbttpOGDlfd+fzL/7qg6DKbMnD8yN5tsG/l8tV7CgFAaj5s1uuzbxrc\nIbZw74ZfNh8XaX1G3vfyK//sWcP0caPLH31z9uTBHWI9hzavW78jE016j5z68tvTBybU8j6o\nWzoTdCYE/fAUIuhHqfG8VRk+F7x2QMAYHbFvG2C2IioWHgfyT6DyaQI8DuRlQtIgPvwb3pfH\nHA+tAe4CeGylD4biAh44TgJAVKPIPCokDSwJUBU4sou6B5dLCcKZjaAPphjowmV+6OqIjkdC\nExw/gMLcqguX5XXj8E6Yo5HUvLYjqydCQmJTqCpOHoG/qrHogn6cPAIliEZNI+0xzhKD2EQU\n5iD/RHU/kn8ChbmIbVzjCUMPCAAAIABJREFUDj7hQmeA3gBFhscFn0cnoNNIOknShhpueF1Q\nVOiNZQcuiSSmWGgN8BbCY6vwEqrIcOXC74beEqEvS04RAho9hAaKjKAPJn2UxRRtMUbrJZPs\nh6pA0larbWBYE6duGr0O+JxQy8wQLQfgKUTAC60hUho/nhWKojz77LMdOnT4/PPPZVlGmYxG\nuR8BcPz48TvuuKNnz57bt28/G4ES1QdRfJQ4OvtWr149bdq0pUuXJidXo8lA3Qv64XdBVSAE\nZDWgQgGEVtKpisCp9zMR+Rxbgj0PjnxotLDGw2z1+vxOpxOARqOJi4uD3wtHPrwu6AxISInE\nvjpFVBXuPPjdkLQwRkNnQoEtL3TGsFqjJUXvcyLggaRFVGIE7wYAkANw5UKRoTXCYEZA9bg9\nLgBarS7KFON3w++CEDDFQR8eM0PXhN+LLaugKOg+sNTwNKqq5uXlhf4dGxtbemw2OYita+Bx\notulkdA/pTivCzkZUFXENEJ0PITkcrlCHZ4NBoPVaoWqwlkAWw5UFY1Sa9CfJYyoKjL2wm1H\nbGM0bhq6MCiKkp+fH1ofHx9f9EZRVZB9FLYcWKKR2q66HQDDlKog4EcwWCIxLAQ0WugMEf7d\nQ1R4bPC7ISTozZD0ssNVqEIRqhQdFRf0Cb8bUGGMjtjOKWWpClQFwYAMCKgqBLQ6jaSJoLZ+\nVVHVojYaAISkBmW/ClUISSvpQzecOnNDeTeQlZU1dOhQAA8//PDIkSPrO5zy2Wy2sWPHrly5\nUogzfI6TJEmn07333nuTJk2q9fCI6l24dFGhs0Srh1aHoB+yH4pfkqBVoapQdSahNUTaO8gK\nRSdAb0RhLmzZKMzRa/VWBSqgCarIskEOQghY42CNj+xkjxCwNILODY8N7nwAkEScKhSowuPR\nqGrRm5lIbtFzikYHazJ8DvgccHkBmLQwQgBe4XQCgN4MY3QVfVjCld6I8/th20/YugYd+8Fa\nvXFZvC7sXA9XITr0ibTsBgCjBSktkZsJWzbseTBFaYVWL8sAdL4g/A54nJCD0BuR0AT6hnHb\nXuuEQFo7nDwMWzacNjRqAmt86Sc2RYYjH7mZCPoRk4ik5pH/hC8k6I3Qw+10yMGgqqo6vd4U\nWTPCVkHAFAedGT47fE4AGi2KThrufEBAZ4zcs2UFhAQhweNyhsaBM5vNem3k5cIrIwQMUdCZ\nEPQh6IckdAKSCkXSQquD5txI/dWWrKyswYMHp6eno+RcZjWiKEogELjxxhszMzMffPDBWg2Q\nqP6dS1cYqiYBrSHUPcEeavYWZYrSGyP0Hr0iRguMFnhd8DiFz6OXgwJQhIDeiKg4mKKgOVd+\nO3oz9GYEfQh44HEFBSRVyFqD0JslXXk9zSNVKJtjsCLog9cVCPoVAEIDc5Qh8veDNQ5d+iN9\nPbatRbP2SG1T2fEfGlX08E7IQXTog8S0sxjoWaTVI7kFPA44bHDbDar617iRQsBogSUWlkgb\nnac0IZDcElGxyMlA1mGcPCIZo6wqFCFJqiJ5ckPz40JvQmrbCMxzVUpW4QvKADTnzmv6YrQG\naBOhyPC5Za/bB1VAqBarSWcUEX62pIpJGujNgDZgt9sBCCGirOHVmbv+2Wy2IUOGpKen//0G\n+IqiCCGmT59uNpvvvvvuWgmPqIE4Vx7SiM6E0QKjxef1luiick4qynnJDkVVAeijo/X6SO86\nXB4hoDMioAR8qhuATqfTmyNzQoTSohPQfRB2/47DO5F5AMkt0SgFlpKPrB4n8jKLRhU1R6Pz\nxZH/TGuywmSFqnjshQGvB1C1BpM5OrZmc4uEu6g4WGLhLoTDBo9DH/AJVVVDP5WYRoiKgzma\n72fPTZIGOpPq8rlD/6szmXggEJ0xRVEmTJiwc+fO2hpeQFVVIcS9997boUOHwYMH10qdRA0B\nExxERFQNBjO6XoL8EzicjqO7cHSX0GjjtHpFo5OUoEYOIOAHAKMF7XqcE50RThOSotX7NTIA\nodGdW9mNECFgiYUlNjQGhwDU4mNwEBHR3/bKK6+sWLGidutUVVWSpOuuuy49Pb1Ro0a1WzlR\nfWGCg4iIqi0+BfEp8LqQlwlnoeKyi2BAkXSSNV5YopGQErFzZFC1cehyIqLadezYsf9n777j\n5CoL/Y9/n3Om785sSyMJoYgJoQQTuFSRplQVpEgQ0IuA5WLjiogoCoh6pShexQKRHxcwgKCi\nhHYRQhEpSidciNQ0kt3NluntnOf3xywhZdPI7k7Zz/sPXuHMzPM8M3vmzDnf85TvfOc773lW\n0Q3wfb+rq+u8886bM2fO0JYMVAsBBwBgM0WaNOn91tr+DayiAgAAhsJFF12Uz+eHb+3La6+9\n9utf//r06dOHqXxgJNF9FAAAAABq0dKlS6+//vrhSzcqLr300mEtHxgxBBwAAAAAUItuvPHG\nyhrDw8dae8stt6RSqaEstLzs8Vt++f2vffqYj5/4uW9cMe/V7FAWDqwfAQcAAAAA1KKbb755\nBOZszuVyd95555AVl372yo/tse/ss7571V8ef+Ku/3f5OR/bedcz/9I5ZOUD60fAAQAAAAA1\np6en57nnnvN9f7grMsbMnz9/iApL3nr6QWffk/vghfct6e9ZvqJv6cPn7a7X55x61h+GtI8I\nMCgCDgAAAACoOU899dRwz76xyhNPPDE0BS36fz/5Q1/8uF/N+96HJ8UcyR23/8W//sExH5zR\n8+LTwzvWBhCrqAAAAABADXrllVdGpiJr7SuvvGKtNcZsYVEL/t+cx7320884NvHutuAe5/zp\nkXO2sGBgkxBwAAAAAEDN6ewcuXkr8vl8Op2Ox+NbVoxduPBf0qwZM0oL/vybW+9/4pk3vYk7\nzTz4U58/YUbL0DQU2CACDgAAAACoOZlMZiSrS6VSWxxwdC9ZUpCxT33/g+f/+tlC87gxwf4V\nd9z86yt+dtjl8+746szg0LQUWC/m4AAAAACAmhMKhUayunA4vMVlrFixQrKPX/+njvPufTOV\nWvF2T2b53688avyye8/59A+eZQ4ODDsCDgAAAACoOS0tIzesw3GcLe6+oXea3Hzclbd+59Bt\nIpLkjtvnqzf819GR8os33vzcFpcPbAQBBwAAAADUnO23337E6po0adJQdBjZatIkR9r5gx9s\nW31r2157TZVef+ml/BZXAGwYAQcAAAAA1JxddtllZCpyHGfGjBlDUVJg6tTtpCVLlqy5ubOz\nUxo3ZUpkKOoANoCAAwAAAABqzvTp08eMGTMCFfm+f+CBBw5JUXuc8bmZztJrf3DtEv+dTXb5\njZffsFyJgw6aNSRVABtAwAEAAAAANccY89GPftQYMwJ1HXXUUUNT0A5nXviZKcl5Z8zc99+/\n+7Nr5vz3xWd+aOa/376y7cgrrzguMTRVAOvHMrEAAAAAUItOPfXU6667blircBxn5syZ06dP\nH6Ly2j4+58m7xp/yuat+9/2v/Y8kE53ykfP++KvvfWLiEFUAbAABBwAAAADUooMOOmjGjBkL\nFizwPG+YqvB9/+yzzx7KEp3xh/7ovjd/mO9c+NISu9W092/V5A5l8cAGMEQFAAAAAGqRMeai\niy4avnTDcZxp06bNnj176Is2kXHTZs3akXQDI4qAAwAAAABq1DHHHHPooYcO00wcvu9fddVV\nrksIgQZBwAEAAAAAtWvOnDmtra2OM/TXbl/60pcOOeSQIS8WqBYCDgAAAACoXVtvvfVNN93k\nOM4QZhzGmH322efyyy8fqgKBWkDAAQAAAAA17bDDDrv22mslDUnGYYyZPn36vHnzwuHwlpcG\n1A4CDgAAAACodaeeeuott9wSCAS2POPYd999H3nkkfb29iFpGFA7CDgAAAAAoA4cf/zxjz/+\n+HbbbSfpPUw76jiOMearX/3qAw88QLqBhkTAAQAAAAD1YebMmc8///y3v/3tYDCoTY45Kp0+\ndtpppwcffPDKK68MhULD20qgSgg4AAAAAKBuxGKxSy655M033zznnHM6OjoqGwcdt1LZWJlP\n9NZbb33uuec+9KEPjWhbgZEVqHYDAAAAAACbZ6uttrrssst+9KMfPfDAA/fff/+TTz750ksv\ndXZ2Vh4Nh8Pbb7/9rrvuesABBxxxxBGVUS1AwyPgAAAAAIC6FAgEDj300EMPPXTVllQqFYlE\nKgNYgNGGgAMAAAAAGkQ8Hq92E4CqYQ4OAAAAAABQ9wg4AAAAAABA3SPgAAAAAAAAdY+AAwAA\nAAAA1D0CDgAAAAAAUPcIOAAAAACgQSSTyWKxWO1WANXBMrEAAAAAUJeWLl16//33P/jggy++\n+OLChQv7+/sr2wOBwOTJk3fcccc999zzoIMO2m+//YLBYHWbCowAAg4AAAAAqCee5916661X\nX331gw8+aK11HMdaa61d9YRyufzmm28uWrTonnvuufjiizs6Ok4++eQvf/nLO+ywQxWbDQw3\nhqgAAAAAQN24+eabp06detJJJz300EOVUMP3/dXTjVV836/8o6en5+c///m0adNOOeWUxYsX\nj2hzgRFEwAEAAAAAdWDRokWHHHLISSed9Oabb2q1/GKjKv07fN+fO3futGnTfvaznw0aiAD1\njoADAAAAAGrdvHnzdtttt/nz52tzoo21WGvz+fzXvva1o48+uq+vb0gbCFQfAQcAAAAA1LTf\n/va3Rx99dDKZ3PKeF5US5s2bt//++y9btmwoWgfUCgIOAAAAAKhdc+bMOfPMM7UFHTfWZa1d\nsGDBAQcc0NXVNVRlAlVHwAEAAAAANWrevHmf//znjTFDmG5UWGtfe+21I488Mp/PD23JQLUQ\ncAAAAABALXrrrbdOOeWU4Ug3Kqy1//znP88+++zhKBwYeQQcAAAAAFCLT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Onqhv2yV5SRkWu9ULZH2V5FE4q1y9ArblQp5pVNqlSISBFJ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njd0IVDViNm62l00BvhsEGhgPGk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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 720 } }, "output_type": "display_data" } ], "source": [ "res %>% # sig/isnig flag\n", " mutate(significant = if_else(p_adj <= 0.05, \"signif.\", \"not\")) %>%\n", " ggplot(aes(x=source, y=condition, shape=significant,\n", " colour=score, size=-log10(p_value+1e-36))) +\n", " geom_point() +\n", " scale_colour_gradient2(high = \"red\", low=\"blue\") +\n", " scale_size_continuous(range = c(3, 12)) +\n", " scale_shape_manual(values=c(21, 16)) +\n", " theme_bw(base_size = 15) +\n", " theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +\n", " labs(x=\"Pathway\",\n", " y=\"Factor\",\n", " colour=\"Activity\"\n", " )" ] }, { "cell_type": "markdown", "id": "ac5b493e", "metadata": {}, "source": [ "Let's zoom in on Factor 5 which is associated with interferon-beta stimulation. " ] }, { "cell_type": "code", "execution_count": 50, "id": "67a65397", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“\u001b[1m\u001b[22mUsing an external vector in selections was deprecated in tidyselect 1.1.0.\n", "\u001b[36mℹ\u001b[39m Please use `all_of()` or `any_of()` instead.\n", " # Was:\n", " data %>% select(condition_col)\n", "\n", " # Now:\n", " data %>% select(all_of(condition_col))\n", "\n", "See .”\n" ] }, { "data": { "image/png": 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xsbCw0C4oKTU1NaWlpQUF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\n2q1T65JVrad6H/bDK/Zu12X17Vrv9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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 720 } }, "output_type": "display_data" } ], "source": [ "selected_factor = 'Factor.5'\n", "sample_loadings <- liana::get_c2c_factors(sce,\n", " sample_col=sample_col,\n", " group_col=condition_col) %>%\n", " purrr::pluck(\"contexts\") %>% \n", " pivot_longer(-c(condition_col, 'context'),\n", " names_to = \"factor\",\n", " values_to = \"loadings\") %>% \n", " mutate(factor = factor(factor))\n", "\n", "res.factor <- res %>% \n", " filter(condition == selected_factor) %>%\n", " mutate(p_adj = p.adjust(p_value, method = \"fdr\")) %>%\n", " arrange(desc(score)) %>%\n", " mutate(source = factor(source, levels = source))\n", "\n", "ggplot(data=res.factor, aes(x=score, y=source, fill = score)) +\n", " geom_bar(stat=\"identity\", width=0.5) + theme_bw() + scale_fill_gradient2(trans = 'reverse') + \n", "ylab('') + xlab('Activity')" ] }, { "cell_type": "code", "execution_count": null, "id": "3ededea8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R [conda env:ccc_protocols]", "language": "R", "name": "conda-env-ccc_protocols-r" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.2.3" }, "vscode": { "interpreter": { "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" } } }, "nbformat": 4, "nbformat_minor": 5 }