{ "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)\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": 2, "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", " c2c <- reticulate::import(module = \"cell2cell\")\n", "})" ] }, { "cell_type": "markdown", "id": "cabd426f", "metadata": {}, "source": [ "### Directories\n", "\n", "Afterwards, specify the data and output directories:" ] }, { "cell_type": "code", "execution_count": 3, "id": "a8bc9c6b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "data_folder = '../../data/quickstart/'\n", "output_folder = '../../data/quickstart/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 lung [dataset](https://doi.org/10.1038/s41591-020-0901-9) of 63k immune and epithelial cells across three control, three moderate, and six severe COVID-19 patients21. We can download the data and store it in the SingleCellExperiment format:" ] }, { "cell_type": "code", "execution_count": 4, "id": "2baa2310", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "options(timeout=600)\n", "covid_data <- readRDS(url('https://zenodo.org/record/7706962/files/BALF-COVID19-Liao_et_al-NatMed-2020_SCE.rds'))" ] }, { "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": 5, "id": "c7a023f5", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "# basic filters\n", "covid_data <- scater::addPerCellQC(covid_data)\n", "min.features <- 200\n", "min.cells <- 3\n", "\n", "sce_counts <- counts(covid_data)\n", "\n", "if (min.features > 0) {\n", " nfeatures <- Matrix::colSums(x = sce_counts > 0)\n", " sce_counts <- sce_counts[, which(x = nfeatures >= min.features)]\n", "}\n", "# filter genes on the number of cells expressing\n", "if (min.cells > 0) {\n", " num.cells <- Matrix::rowSums(x = sce_counts > 0)\n", " sce_counts <- sce_counts[which(x = num.cells >= min.cells), ]\n", "}\n", "\n", "covid_data<-covid_data[rownames(sce_counts), colnames(sce_counts)]" ] }, { "cell_type": "markdown", "id": "62cc7303", "metadata": {}, "source": [ "We additionally remove high mitochondrial content:" ] }, { "cell_type": "code", "execution_count": 6, "id": "d8d1a069", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "is.mito <- grep(\"MT-\", rownames(covid_data))\n", "per.cell <- scuttle::perCellQCMetrics(covid_data, subsets=list(Mito=is.mito))\n", "colData(covid_data)[['subsets_Mito_percent']] <- per.cell$subsets_Mito_percent\n", "covid_data <- covid_data[, colData(covid_data)$subsets_Mito_percent < 15] " ] }, { "cell_type": "markdown", "id": "e11120c8", "metadata": {}, "source": [ "Which is followed by removing cells with high number of total UMI counts, potentially representing more than one single cell (doublets):" ] }, { "cell_type": "code", "execution_count": 7, "id": "1cd80250", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "covid_data <- covid_data[, colData(covid_data)$detected < 5500] " ] }, { "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": 8, "id": "4cc7d7be", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "scale.factor <- 1e4\n", "lib.sizes <- colSums(counts(covid_data)) / scale.factor\n", "assay(covid_data, 'logcounts') <- scuttle::normalizeCounts(covid_data, size.factors=lib.sizes, log=FALSE, center.size.factors=FALSE)\n", "assay(covid_data, 'logcounts') <- log1p(assay(covid_data, 'logcounts'))" ] }, { "cell_type": "markdown", "id": "af583cf3", "metadata": {}, "source": [ "Let's see what our data looks like:" ] }, { "cell_type": "code", "execution_count": 9, "id": "9b0e451c", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "DataFrame with 6 rows and 13 columns\n", " orig.ident sample sample_new disease\n", " \n", "AAACCCACAGCTACAT-1_1 C100 C100 HC3 N\n", "AAACCCATCCACGGGT-1_1 C100 C100 HC3 N\n", "AAACCCATCCCATTCG-1_1 C100 C100 HC3 N\n", "AAACGAACAAACAGGC-1_1 C100 C100 HC3 N\n", "AAACGAAGTCGCACAC-1_1 C100 C100 HC3 N\n", "AAACGAAGTCTATGAC-1_1 C100 C100 HC3 N\n", " hasnCoV cluster cell.type condition ident\n", " \n", "AAACCCACAGCTACAT-1_1 N 27 B Control C100\n", "AAACCCATCCACGGGT-1_1 N 23 Macrophages Control C100\n", "AAACCCATCCCATTCG-1_1 N 6 T Control C100\n", "AAACGAACAAACAGGC-1_1 N 10 Macrophages Control C100\n", "AAACGAAGTCGCACAC-1_1 N 10 Macrophages Control C100\n", "AAACGAAGTCTATGAC-1_1 N 9 T Control C100\n", " sum detected total subsets_Mito_percent\n", " \n", "AAACCCACAGCTACAT-1_1 3124 1377 3124 9.189882\n", "AAACCCATCCACGGGT-1_1 1430 836 1430 0.909727\n", "AAACCCATCCCATTCG-1_1 2342 1105 2342 6.319385\n", "AAACGAACAAACAGGC-1_1 31378 4530 31378 9.981516\n", "AAACGAAGTCGCACAC-1_1 12767 3409 12767 5.161745\n", "AAACGAAGTCTATGAC-1_1 2198 1094 2198 8.871702" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(colData(covid_data))" ] }, { "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": 10, "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": 11, "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": 12, "id": "27616d8a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "lr_pairs <- liana::select_resource('Consensus')" ] }, { "cell_type": "markdown", "id": "c2a468e2", "metadata": {}, "source": [ "" ] }, { "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": 13, "id": "9b2a85df", "metadata": { "scrolled": true, "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "`sample_new` was converted to a factor!\n", "\n", "Current sample: HC1\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: Plasma were removed!Cell identities with less than 5 cells: pDC were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“5711 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: HC2\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: NK were removed!Cell identities with less than 5 cells: pDC were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“6729 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: HC3\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: Plasma were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“5591 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: M1\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: Mast were removed!Cell identities with less than 5 cells: Neutrophil were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“3140 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: M2\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: Mast were removed!Cell identities with less than 5 cells: Neutrophil were removed!Cell identities with less than 5 cells: Plasma were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“3362 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: M3\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: Mast were removed!Cell identities with less than 5 cells: Neutrophil were removed!Cell identities with less than 5 cells: Plasma were removed!Cell identities with less than 5 cells: pDC were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“8374 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: S1\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Warning message in exec(output, ...):\n", "“2524 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: S2\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Warning message in exec(output, ...):\n", "“1629 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: S3\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: B were removed!Cell identities with less than 5 cells: Plasma were removed!\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in exec(output, ...):\n", "“4639 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: S4\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: B were removed!Cell identities with less than 5 cells: pDC were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“3792 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: S5\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: Plasma were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“4238 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: S6\n", "\n", "Running LIANA with `cell.type` as labels!\n", "\n", "`Idents` were converted to factor\n", "\n", "Cell identities with less than 5 cells: Mast were removed!Cell identities with less than 5 cells: pDC were removed!\n", "\n", "Warning message in exec(output, ...):\n", "“3449 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" ] } ], "source": [ "covid_data <- liana_bysample(sce = covid_data,\n", " sample_col = \"sample_new\", # context dimension column from SCE metadata\n", " idents_col = \"cell.type\", # 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", "# parallelize = TRUE,\n", "# workers = 40\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": 14, "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
sample_newsourcetargetligand.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>
HC1T NKB2MCD3D 3.983438e-110.01345424951.0000008.0598610.96102543.410586487.750.083276061.30085620.87197990
HC1MacrophagesNKB2MCD3D 3.186750e-100.01350724502.0000008.0595990.96102483.410500487.000.083273351.30055650.88321770
HC1NK NKB2MCD3D 4.979297e-090.01814754503.6666677.6143780.95994653.264099571.250.078673240.79091290.69787780
HC1T NKB2MKLRD11.366319e-080.00035887485.6666676.8652500.95790743.297900211.750.171293086.96092020.86240790
HC1B NKB2MCD3D 2.039520e-080.01923068775.3333337.5185430.95970233.232586595.000.077683050.68121070.69101790
HC1MacrophagesNKB2MKLRD12.039520e-080.00035887486.6666676.8650270.95790673.297814210.750.171287516.96062050.87364560
\n" ], "text/latex": [ "A tibble: 6 × 16\n", "\\begin{tabular}{llllllllllllllll}\n", " sample\\_new & 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 HC1 & T & NK & B2M & CD3D & 3.983438e-11 & 0.0134542495 & 1.000000 & 8.059861 & 0.9610254 & 3.410586 & 487.75 & 0.08327606 & 1.3008562 & 0.8719799 & 0\\\\\n", "\t HC1 & Macrophages & NK & B2M & CD3D & 3.186750e-10 & 0.0135072450 & 2.000000 & 8.059599 & 0.9610248 & 3.410500 & 487.00 & 0.08327335 & 1.3005565 & 0.8832177 & 0\\\\\n", "\t HC1 & NK & NK & B2M & CD3D & 4.979297e-09 & 0.0181475450 & 3.666667 & 7.614378 & 0.9599465 & 3.264099 & 571.25 & 0.07867324 & 0.7909129 & 0.6978778 & 0\\\\\n", "\t HC1 & T & NK & B2M & KLRD1 & 1.366319e-08 & 0.0003588748 & 5.666667 & 6.865250 & 0.9579074 & 3.297900 & 211.75 & 0.17129308 & 6.9609202 & 0.8624079 & 0\\\\\n", "\t HC1 & B & NK & B2M & CD3D & 2.039520e-08 & 0.0192306877 & 5.333333 & 7.518543 & 0.9597023 & 3.232586 & 595.00 & 0.07768305 & 0.6812107 & 0.6910179 & 0\\\\\n", "\t HC1 & Macrophages & NK & B2M & KLRD1 & 2.039520e-08 & 0.0003588748 & 6.666667 & 6.865027 & 0.9579067 & 3.297814 & 210.75 & 0.17128751 & 6.9606205 & 0.8736456 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 16\n", "\n", "| sample_new <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", "| HC1 | T | NK | B2M | CD3D | 3.983438e-11 | 0.0134542495 | 1.000000 | 8.059861 | 0.9610254 | 3.410586 | 487.75 | 0.08327606 | 1.3008562 | 0.8719799 | 0 |\n", "| HC1 | Macrophages | NK | B2M | CD3D | 3.186750e-10 | 0.0135072450 | 2.000000 | 8.059599 | 0.9610248 | 3.410500 | 487.00 | 0.08327335 | 1.3005565 | 0.8832177 | 0 |\n", "| HC1 | NK | NK | B2M | CD3D | 4.979297e-09 | 0.0181475450 | 3.666667 | 7.614378 | 0.9599465 | 3.264099 | 571.25 | 0.07867324 | 0.7909129 | 0.6978778 | 0 |\n", "| HC1 | T | NK | B2M | KLRD1 | 1.366319e-08 | 0.0003588748 | 5.666667 | 6.865250 | 0.9579074 | 3.297900 | 211.75 | 0.17129308 | 6.9609202 | 0.8624079 | 0 |\n", "| HC1 | B | NK | B2M | CD3D | 2.039520e-08 | 0.0192306877 | 5.333333 | 7.518543 | 0.9597023 | 3.232586 | 595.00 | 0.07768305 | 0.6812107 | 0.6910179 | 0 |\n", "| HC1 | Macrophages | NK | B2M | KLRD1 | 2.039520e-08 | 0.0003588748 | 6.666667 | 6.865027 | 0.9579067 | 3.297814 | 210.75 | 0.17128751 | 6.9606205 | 0.8736456 | 0 |\n", "\n" ], "text/plain": [ " sample_new source target ligand.complex receptor.complex magnitude_rank\n", "1 HC1 T NK B2M CD3D 3.983438e-11 \n", "2 HC1 Macrophages NK B2M CD3D 3.186750e-10 \n", "3 HC1 NK NK B2M CD3D 4.979297e-09 \n", "4 HC1 T NK B2M KLRD1 1.366319e-08 \n", "5 HC1 B NK B2M CD3D 2.039520e-08 \n", "6 HC1 Macrophages NK B2M KLRD1 2.039520e-08 \n", " specificity_rank magnitude_mean_rank natmi.prod_weight sca.LRscore\n", "1 0.0134542495 1.000000 8.059861 0.9610254 \n", "2 0.0135072450 2.000000 8.059599 0.9610248 \n", "3 0.0181475450 3.666667 7.614378 0.9599465 \n", "4 0.0003588748 5.666667 6.865250 0.9579074 \n", "5 0.0192306877 5.333333 7.518543 0.9597023 \n", "6 0.0003588748 6.666667 6.865027 0.9579067 \n", " cellphonedb.lr.mean specificity_mean_rank natmi.edge_specificity\n", "1 3.410586 487.75 0.08327606 \n", "2 3.410500 487.00 0.08327335 \n", "3 3.264099 571.25 0.07867324 \n", "4 3.297900 211.75 0.17129308 \n", "5 3.232586 595.00 0.07768305 \n", "6 3.297814 210.75 0.17128751 \n", " connectome.weight_sc logfc.logfc_comb cellphonedb.pvalue\n", "1 1.3008562 0.8719799 0 \n", "2 1.3005565 0.8832177 0 \n", "3 0.7909129 0.6978778 0 \n", "4 6.9609202 0.8624079 0 \n", "5 0.6812107 0.6910179 0 \n", "6 6.9606205 0.8736456 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "liana_res <- covid_data@metadata$liana_res %>% \n", " bind_rows(.id = \"sample_new\") %>%\n", " mutate(sample_new = factor(sample_new, levels = unique(sample_new)))\n", "\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": 15, "id": "54b8afcb", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 900 } }, "output_type": "display_data" } ], "source": [ "ligand_complex <- 'B2M'\n", "receptor_complex <- c('CD3D', 'KLRD1')\n", "source_labels <- c(\"B\", \"pDC\", \"Macrophages\")\n", "target_labels <- c(\"T\", \"Mast\", \"pDC\", \"NK\")\n", "sample_key <- 'sample_new'\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", "h_ = 7\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_new ~ lr.pairs, ncol = 12, scales='free_x') + \n", " theme_bw()" ] }, { "cell_type": "markdown", "id": "bc8981d5", "metadata": {}, "source": [ "Let's export it in case we want to access it later:" ] }, { "cell_type": "code", "execution_count": 16, "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": 17, "id": "382bdeb2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "saveRDS(covid_data, 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. For visualization purposes we sorted them according toby COVID-19 severity. Here, we can find the names of each of the samples in the `sample_new` column of the adata.obs information:" ] }, { "cell_type": "code", "execution_count": 18, "id": "0abc3dbe", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "sorted_samples = sort(names(covid_data@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": 19, "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" ] }, { "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 = covid_data@metadata[['liana_res']], \n", " outer_frac = 1/3, # Fraction of samples as threshold to include cells and LR pairs.\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", "\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', # What to include across all samples\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 `covid_data@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": 20, "id": "fda5853f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "text/plain": [ "torch.Size([12, 1054, 10, 10])" ] }, "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": 21, "id": "19a1af6b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "reticulate::py_save_object(object = tensor, \n", " filename = file.path(output_folder, 'BALF-Tensor-R.pkl'))" ] }, { "cell_type": "markdown", "id": "04ba074b", "metadata": {}, "source": [ "Then, we can load it with:" ] }, { "cell_type": "code", "execution_count": 22, "id": "993ad1a2", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "tensor <- reticulate::py_load_object(filename = file.path(output_folder, 'BALF-Tensor-R.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": 23, "id": "7ac0fa7e", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Estimating ranks...\n", "\n", "Decomposing the tensor...\n", "\n" ] } ], "source": [ "tensor <- liana::decompose_tensor(tensor = tensor, \n", " rank = NULL, # Number of factors to perform the factorization. If NULL, it is automatically determined by an elbow analysis\n", " tf_optimization = 'robust', # 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": 24, "id": "84e7d2f8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"The estimated tensor rank is: 10\"\n" ] } ], "source": [ "print(paste0('The estimated tensor rank is: ', tensor$rank))" ] }, { "cell_type": "code", "execution_count": 25, "id": "4aea532a", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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9z3P/9TuOmm5uteN/W5zzW33Xb+nzI0NLRJ/kHcW/ObXuv1uu/ottdoNKanp2dn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\n4nQ6+5w66nSquVkPPqj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rCqBQAAAADg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\nADB52bZt27bruqUbwzD0fb+YmXqeF4bhqIc1NGj9eq1fH980s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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 900 } }, "output_type": "display_data" } ], "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": 26, "id": "e3e8b127", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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sSJE5cFJBSclurMgUNJAKqMX72sX+6oKJB69+6j3Idb+fpWAIBLBw7E5E3s7vf+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\n9L5rzNC2ceU7bRkMhgULFvhu4Xt4FRcX84kMQVN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HD8xK7ryQvcXou/T4kj41WEGABgyJUQCoIwTK+JQR8Smu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\nn06VS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1wBwgOJUQAAAGg4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EVSUXjdQ5rXdSYkKLnwwA+LRLZeU26pia6jxZ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\nHDH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9pw5xGScnUv20dPaX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\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3i5ObnvGm5ezrtjf+Y43rZeDp9eZH98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lf17Pj6GUtOpmvMqQRCCCGEEEIIIYQQQgiZ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\ncsdKFmotoz29asMpE8XZbtdfWD11xk8XRXEdOvdrZbx89ODqOWevvjbn3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alUBoOBiORyuVTq4G5E7+LByrhebXeE4jcal1D5+fnsvJkK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0kqlRIRl8t1/g1WVFTEDE708vJiJlQ6zPIGY2UKZH5+PvNDhefI0lwzqVRK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AAAAAACAwFQe+mvXc/77bX375BuvUPwe2E9Gid9/re+8Ls565OVNef9UDaLwC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kvH1o7S+HrLdqTm765SQ1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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 780, "width": 900 } }, "output_type": "display_data" } ], "source": [ "covid_data@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 = covid_data, group_col = 'condition', sample_col = 'sample_new')" ] }, { "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": 27, "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": 28, "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", "\n", "
A data.frame: 12 × 10
Factor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7Factor 8Factor 9Factor 10
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
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HC30.280845520.06400109830.33431330.389251440.44101950.259509030.29442040.210486990.067302530.13640665
M10.178115520.02433695640.27887510.542565050.26497680.519761200.39170960.315592050.297609600.25299737
M20.157660800.03674701230.28518600.500092390.26896720.506432950.40550260.302759650.295829470.22803977
M30.255313870.10383091120.28003230.247461100.19437550.352842210.36945250.237261700.153499860.11353971
S10.162276830.60680115220.29689220.163801850.27580420.096722410.15981140.262955930.310083090.16405572
S20.058484420.45251742010.29558280.273415830.31429510.252637390.29166000.351023020.421035920.36295748
S30.171097680.22141410410.31451120.299349340.31257990.125736580.28797740.292251230.169058220.30484933
S40.108372010.25052455070.30780770.052092810.34278180.323285880.27113940.402529980.430962380.47937286
S50.043106140.47058612110.25805550.162721530.21198630.204466120.22392440.350422890.433286990.40211794
S60.153561580.27739039060.30730830.106338800.35044980.194298570.26165480.363863050.331641640.43490598
\n" ], "text/latex": [ "A data.frame: 12 × 10\n", "\\begin{tabular}{r|llllllllll}\n", " & Factor 1 & Factor 2 & Factor 3 & Factor 4 & Factor 5 & Factor 6 & Factor 7 & Factor 8 & Factor 9 & Factor 10\\\\\n", " & & & & & & & & & & \\\\\n", "\\hline\n", "\tHC1 & 0.57167751 & 0.0040322351 & 0.2486676 & 0.06951163 & 0.1992737 & 0.09138259 & 0.2109948 & 0.09901118 & 0.08743461 & 0.10624311\\\\\n", "\tHC2 & 0.61345816 & 0.0009374141 & 0.2422200 & 0.07904458 & 0.1662704 & 0.01086045 & 0.1696340 & 0.05779721 & 0.05216325 & 0.09475538\\\\\n", "\tHC3 & 0.28084552 & 0.0640010983 & 0.3343133 & 0.38925144 & 0.4410195 & 0.25950903 & 0.2944204 & 0.21048699 & 0.06730253 & 0.13640665\\\\\n", "\tM1 & 0.17811552 & 0.0243369564 & 0.2788751 & 0.54256505 & 0.2649768 & 0.51976120 & 0.3917096 & 0.31559205 & 0.29760960 & 0.25299737\\\\\n", "\tM2 & 0.15766080 & 0.0367470123 & 0.2851860 & 0.50009239 & 0.2689672 & 0.50643295 & 0.4055026 & 0.30275965 & 0.29582947 & 0.22803977\\\\\n", "\tM3 & 0.25531387 & 0.1038309112 & 0.2800323 & 0.24746110 & 0.1943755 & 0.35284221 & 0.3694525 & 0.23726170 & 0.15349986 & 0.11353971\\\\\n", "\tS1 & 0.16227683 & 0.6068011522 & 0.2968922 & 0.16380185 & 0.2758042 & 0.09672241 & 0.1598114 & 0.26295593 & 0.31008309 & 0.16405572\\\\\n", "\tS2 & 0.05848442 & 0.4525174201 & 0.2955828 & 0.27341583 & 0.3142951 & 0.25263739 & 0.2916600 & 0.35102302 & 0.42103592 & 0.36295748\\\\\n", "\tS3 & 0.17109768 & 0.2214141041 & 0.3145112 & 0.29934934 & 0.3125799 & 0.12573658 & 0.2879774 & 0.29225123 & 0.16905822 & 0.30484933\\\\\n", "\tS4 & 0.10837201 & 0.2505245507 & 0.3078077 & 0.05209281 & 0.3427818 & 0.32328588 & 0.2711394 & 0.40252998 & 0.43096238 & 0.47937286\\\\\n", "\tS5 & 0.04310614 & 0.4705861211 & 0.2580555 & 0.16272153 & 0.2119863 & 0.20446612 & 0.2239244 & 0.35042289 & 0.43328699 & 0.40211794\\\\\n", "\tS6 & 0.15356158 & 0.2773903906 & 0.3073083 & 0.10633880 & 0.3504498 & 0.19429857 & 0.2616548 & 0.36386305 & 0.33164164 & 0.43490598\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 12 × 10\n", "\n", "| | Factor 1 <dbl> | Factor 2 <dbl> | Factor 3 <dbl> | Factor 4 <dbl> | Factor 5 <dbl> | Factor 6 <dbl> | Factor 7 <dbl> | Factor 8 <dbl> | Factor 9 <dbl> | Factor 10 <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|\n", "| HC1 | 0.57167751 | 0.0040322351 | 0.2486676 | 0.06951163 | 0.1992737 | 0.09138259 | 0.2109948 | 0.09901118 | 0.08743461 | 0.10624311 |\n", "| HC2 | 0.61345816 | 0.0009374141 | 0.2422200 | 0.07904458 | 0.1662704 | 0.01086045 | 0.1696340 | 0.05779721 | 0.05216325 | 0.09475538 |\n", "| HC3 | 0.28084552 | 0.0640010983 | 0.3343133 | 0.38925144 | 0.4410195 | 0.25950903 | 0.2944204 | 0.21048699 | 0.06730253 | 0.13640665 |\n", "| M1 | 0.17811552 | 0.0243369564 | 0.2788751 | 0.54256505 | 0.2649768 | 0.51976120 | 0.3917096 | 0.31559205 | 0.29760960 | 0.25299737 |\n", "| M2 | 0.15766080 | 0.0367470123 | 0.2851860 | 0.50009239 | 0.2689672 | 0.50643295 | 0.4055026 | 0.30275965 | 0.29582947 | 0.22803977 |\n", "| M3 | 0.25531387 | 0.1038309112 | 0.2800323 | 0.24746110 | 0.1943755 | 0.35284221 | 0.3694525 | 0.23726170 | 0.15349986 | 0.11353971 |\n", "| S1 | 0.16227683 | 0.6068011522 | 0.2968922 | 0.16380185 | 0.2758042 | 0.09672241 | 0.1598114 | 0.26295593 | 0.31008309 | 0.16405572 |\n", "| S2 | 0.05848442 | 0.4525174201 | 0.2955828 | 0.27341583 | 0.3142951 | 0.25263739 | 0.2916600 | 0.35102302 | 0.42103592 | 0.36295748 |\n", "| S3 | 0.17109768 | 0.2214141041 | 0.3145112 | 0.29934934 | 0.3125799 | 0.12573658 | 0.2879774 | 0.29225123 | 0.16905822 | 0.30484933 |\n", "| S4 | 0.10837201 | 0.2505245507 | 0.3078077 | 0.05209281 | 0.3427818 | 0.32328588 | 0.2711394 | 0.40252998 | 0.43096238 | 0.47937286 |\n", "| S5 | 0.04310614 | 0.4705861211 | 0.2580555 | 0.16272153 | 0.2119863 | 0.20446612 | 0.2239244 | 0.35042289 | 0.43328699 | 0.40211794 |\n", "| S6 | 0.15356158 | 0.2773903906 | 0.3073083 | 0.10633880 | 0.3504498 | 0.19429857 | 0.2616548 | 0.36386305 | 0.33164164 | 0.43490598 |\n", "\n" ], "text/plain": [ " Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 \n", "HC1 0.57167751 0.0040322351 0.2486676 0.06951163 0.1992737 0.09138259 0.2109948\n", "HC2 0.61345816 0.0009374141 0.2422200 0.07904458 0.1662704 0.01086045 0.1696340\n", "HC3 0.28084552 0.0640010983 0.3343133 0.38925144 0.4410195 0.25950903 0.2944204\n", "M1 0.17811552 0.0243369564 0.2788751 0.54256505 0.2649768 0.51976120 0.3917096\n", "M2 0.15766080 0.0367470123 0.2851860 0.50009239 0.2689672 0.50643295 0.4055026\n", "M3 0.25531387 0.1038309112 0.2800323 0.24746110 0.1943755 0.35284221 0.3694525\n", "S1 0.16227683 0.6068011522 0.2968922 0.16380185 0.2758042 0.09672241 0.1598114\n", "S2 0.05848442 0.4525174201 0.2955828 0.27341583 0.3142951 0.25263739 0.2916600\n", "S3 0.17109768 0.2214141041 0.3145112 0.29934934 0.3125799 0.12573658 0.2879774\n", "S4 0.10837201 0.2505245507 0.3078077 0.05209281 0.3427818 0.32328588 0.2711394\n", "S5 0.04310614 0.4705861211 0.2580555 0.16272153 0.2119863 0.20446612 0.2239244\n", "S6 0.15356158 0.2773903906 0.3073083 0.10633880 0.3504498 0.19429857 0.2616548\n", " Factor 8 Factor 9 Factor 10 \n", "HC1 0.09901118 0.08743461 0.10624311\n", "HC2 0.05779721 0.05216325 0.09475538\n", "HC3 0.21048699 0.06730253 0.13640665\n", "M1 0.31559205 0.29760960 0.25299737\n", "M2 0.30275965 0.29582947 0.22803977\n", "M3 0.23726170 0.15349986 0.11353971\n", "S1 0.26295593 0.31008309 0.16405572\n", "S2 0.35102302 0.42103592 0.36295748\n", "S3 0.29225123 0.16905822 0.30484933\n", "S4 0.40252998 0.43096238 0.47937286\n", "S5 0.35042289 0.43328699 0.40211794\n", "S6 0.36386305 0.33164164 0.43490598" ] }, "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": 29, "id": "dcf4793f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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ZsO/gwoiTbrlyj12LxZILnwIAAAAAiggODg4PDw8JCVE6iAqVK1cu\nPDy8TJkySgcBgH+LwujTGc3my/EJymaITkmNllRlMwAAAACACgQEBCgdQbXYtgDyFq3SAQAA\nAABkM+tlwlzQmkO4FhsAAHVgxCgAAACgNiEhIeHh4QzdyiHWa7GDg3P1Bq0AACDbURh9Oget\ntoiHe+581j+JSQaz+dHlbo6Ofq4uuRDAYrFcSUjMhQ8CAABAjqIqmqPYvAAAqACF0acr5ulx\n8v2uufNZa85d7PDHnxqNxnz3Jkg6jUav0+3o2qlcPt9cCJBmNHmP/zEXPggAAAAAAABQEHOM\n2pZWZUouebVlIff70xWVz59vwxuv5k5VFAAAAAAAALATjBi1OW0DSzcvVSIi6va1xMTS3t4h\nfr5ajUbpUAAAAAAAPFF8fPy6devOnz9fokSJFi1a+Pj4KJ0IAJ6OwqgtctLpQgsWCC1YQOkg\nAAAAAAA8xeLFiz/++OOYmBjrQ29v7/Hjx7/zzjvKpgKAp+JSegAAAAAA8JzWrVvXpUuXuLi4\ne0sSEhJ69OixcuVKBVMBwL9BYRQAAAAAADynUaNGaTQas9l8b4nZbNZoNKNGjVIwFQD8GxRG\nAQAAAADA87BYLPv3789cFbUym80HDx40Go2KpAKAf4nCKAAAAAAAeE6PVkWtLBaLyWTK5TAA\n8EwojAIAAAB52PVJL2k0r/6aennlV91aVCvu7ZG/VM1O32y4dq9QYfpn++RezSqX9nd39SoS\nWLPTl3+cTVMyMAA10Wg0FSpU0Gofri1otdqyZcs6OTkpkgoA/iUKowAAAEBeF72iR5txsfUH\n/bR+51/jm2as+ap951kXRUQkZfenjZv0W3DC5YW2fT5660W/G6tHdmj6yeYUhQMDUI8BAwY8\n9lL6AQMGKJIHAP49B6UDAAAAAPiPdmzSLr8wsZ2HiEjwj5P2r6g/ZfPfMb1K+lp2zp9zyth4\nevimXgVERMzvD6vy0qS/Vh+UxnWVjQxALd5+++0rV66MGDHCYDBYlzg6Og4ZMqRnz57KBgOA\np2LEKAAAAJDXebTrYq2KiohoAwKKiCQlJYuIWCxmsURs/utSqvW5yqOOJCZemkhVFEA2GjJk\nyKlTp8qXLy8i5cuXP3HixIgRI5QOBQBPx4hRAAAAIK8LKF480yONRnPvx8YDvm+38aPf3ym9\n9ovKderVqVu/aas2L1ctxLR/gFqdPXt2165diny0o6Oj9d+dO3fu3Lkz9wPUqFGjXLlyuf+5\nAPIuCqMAAABAXmctRzyOLuj95adb7Fr1x+p1G8P+XjTy5ylfflT8lYl/LesdwqkAoEZbt279\n4IMPFAxw+PDh7t27K/LREyZMoDAK4JlwKT0AAACgWknXjh85ct21Zqd+Y+b+ufvsrRtHF3YN\nuLyq/zerDUpHAwAAUFhOf01sidm/6Mdf/z55Ld23dNWWPXu1KPmYq3bi1w7rMv1opgV+r34/\n952gf/t2AAAAAI91Y1H3ysOu9Vh76afmriLikK9i2+YvOC+4fv9iewCq5KiVNTWUDpGLXjsg\nSUalQwDIe3K2MGo6tWDw6LW+bXsN7Opyed2cmUPGOf/4ZUOfh192MzJSAlsPal9Bd2eBvkih\nZ3g7AAAAgMcq8+pbNUcPnPNmtestG1Ut7R51fNemdX8bAz/s0ehJ194DwLPLMMvvN2T7bfkn\nTQo6y0u+0qmwOHGJKgBbl6OF0Yx9a9beKt/t2+4NvESqlHe/0WPw6i03GrYv9ODLTJGR0T5B\ntV+qXeG53g4AAADg8TRl+63c4PL1qNlrNy0MWyY+RUpX6T553pA+9TyVTgZANZKM0v+4XE69\n8/BSilxKkS3RMrGCeDGZMQCblqON1JnDh1ICO1b3sj7ShVSv6rb24KHE9oU8HnhZVORNc8Hy\nBU2pMdFJjr5+Ho6aZ3o7AAAAYL+K9Pvb0u/BRcX/t/l6u4tJLiIiovGv02fa2j4KJAOgIKNZ\nuhzKpc9KNEqK6eGF19Ok6yHxzK3C6KMBAOBfyMlGyhQbm6jx8/O9+1iT389XTsfFiTxY2YyM\njJTbW0Z3m3suwSxa91INu338QbNSzv/i7WPHjjWbzdaf09PTjUZjUlJSNq5BRkZGNv62vCUt\nLS17N6btMJnu/MlU6wraAuvxqHSK7JH1ipjN5mxveZRyrzlNSUnRMPNcdrNuXoPBoI69xabc\n23VVs23tp9kREYPBICImk0k1a2Q7NO75i+ozhFY9Z1hbnoyMjHtNUF731JbHYrGo7DhNS0tT\n6+leenq6iIhF5Ga6wlFSTZKaq/XK9PR0le2o99w7jU1OTlZHq66aE0bkdTlZGE1KTLQ4FXa5\nP6uIi4uLJCQkPPSyxJtR6U5OJZp/OrRugHNsxJ/TJ0wbpS/4Y+8ST3/7ihUr7h1LlStXdnNz\nS0tLy8Y1sOcD1WAwZO/GtEGqX0EFGQwG67muCmR9wmOxWMxms8r2dI2Y8wAAIABJREFUpTud\naeQAo9Foz39ZcojFYrH+oJojMetmx2w2q6zZCQsL8/FhCnnkPbGxsQ0aNFBNq/7Ulkej0aip\n5RFVD4JRTT/8ORiNRpXtqI9STV9dNV8sIa/LycKou4e7Jj0t1SJy59uM1NRU8fB4+EJ4j2Yj\nlze7+8C12hv/ax/+7q/bInpVePrbg4OD731t4uHhYbFYHByyc420WvudK1qr1WbvxrQd1lNK\nEVHrCirLenqg1WpVc/hk/X2sRqPRaDTq2JcsFou1RVXH6tgak8lksVjUdGjYjnsHqWp2Xftp\ndkRk8+bNHTt2VDoF8Jx+++23Jk2aKJ0ie9hVy2Ptr+p0OnUMu3uUPXc27OE0VjW7rjrWAiqQ\nk02GztvH3XI1JlbEejm85XZMnDx9SIBf0SL61Li4dJ3/U98+b968ez//9ttv+/bt8/b2zsY1\ncHZ2zsbflre4ubll78a0HfdmCVDrCiorOjpaRFxdXVVz+Dg6ZnXTXq1W6+joqI59yWg0xsXF\niYiHh4dOp1M6jtrExcUZjUYnJyc3Nzels6jNvdM/dRyJ8rRmR6fTqabZERE/Pz8RmT59evXq\n1ZXOojb3plzw9PS05ypJDtm3b1+fPn0KFCigmoPxqS2PTqdTx8paLJbbt2+LiJubm16vVzpO\njnB1dRUR0YgUcMqlj0wwPv6SeWeteGW1a2WnqAwxW1xcXNSxoz4qIyPDegGtl5eXOlr1rJsd\nINfk6HcpQZUru246dCilTWNXEbGcP3w4sVTzSg+NGM3YP/WDSTde+25Um0IaERHLtfMXMgqG\nFneWgH/zdgAAAORpQUFBoaGhSqdQG4PBEB8fLyI+Pj583ZXtrF8lAjbNQSsLq+TSZ0VnyEcR\ncjvT7AQaES9HmVoh94qzrx2QJJVMbQEgN+Xo9wxONds09zu0YMKKI5evntk+a+qa5GrtGhcR\nEZHrfy/46f/CLouIvlLjOm7HF38/deXfR08d37ty4tg/okJffyUwy7cDAAAAAAAb4KeXGS9I\nK39x04mIuOmkeQGZ+ULuVUUB4Hnl7OwbuuBuowdrflwydfCSjHylQt8f80GDO1fCRx9Zuyqs\nesV3GhQXfbl3vhqin/vLyhnbE8W7SNlaH3//5ku+Wb8dAABA4uLiLl++LCJnz57dvn17vXr1\nlE4EAIBd8nKQfiWlX0lJNt0pjwJAXpDT0xJr/Gp0+7JGt0eWV+r7y6q+dx/oCtboOqRG12d4\nOwAAsHerVq169913rVPFnTlzpn79+m3btl20aJG7u7vS0QAAsFdURQHkKWqYshcAANib06dP\nd+zYMTY2NvPClStX9u7dW6lIAAAAAPIWCqMAACDvmT59usFgMJvNDy1fvHjxzZs3FYkEAAAA\nIG+hMAoAAPKeo0ePajSaR5dbLJaIiIjcz4Pn4+rqKiJubm5KBwGeDbsuAADqkNNzjAIAAPW7\ndOlS27Ztc/MTL1y48OhwUav3338/N6cZbdy48fjx43Pt41SmXLly4eHhpUuXVjoI8GyCg4PD\nw8ODg4OVDgIAAP4TCqMAAOC/SktLO3r0qNIp7rhw4UJuflypUqVy8+PUJyAgQOkIwPNg1wUA\nQAUojAIAgGxTquabLl4Fc+GDjOnJZ/7+yWI2PbTcu0iFIuWb5kIAEbl6eFXCrXO581kAAAAA\nsh2FUQAAkG1CmvTLX7JGLnzQPyc2nt4285HFGgdH5xqdfsiFACKSGHWBwigAAACQd3HzJQAA\nkPec37NIo3m0G2OJvnQg4eYZBQIBAAAAyGsojAIAgLwnMeqiyP+zd5/xUVR9H4fP7KYX0imh\nJ7RQg1KU3gSxISBVRBFRFJSiIBY6FhSEm4cqiIKIiAjSq3SQXiJIgECEkEAo6WWzZeZ5sRhj\nCEFgM5NMvtcLPuTM7p7fJLtnz/53Zo6S96ab0SqHAQAAAFAUURgFAABFj4uHr5CkvDe5+6oc\nBgAAAEBRRGEUAAAUPWVrd1BkOVejJBlcPQMCKj6iSSQAAAAARQuFUQAAUPRUa9bfv1zdnC2S\nwagoSuNe/zMYnbVKBQAAAKAIoTAKAACKHqOzW8eRu8Lavp1dBi1Rskr7oRtCH3tR22DQBXnb\n6Nat3/klUY4/sHTaBwO6Ptvl5WFfrY/OzHGTtHOrvxoxoPdzTz3bo//wqWvPp2sWFgAAAA/K\nSesAAAAAD8LFw/exXjMadZ+aEn/ezTvQzbuk1omgG0r8qZ07rzdY2OOj70WnPm3aNT21/H8j\nn1m0dcHJjf3LCyFubXqt8TPfXBAlKtUL8766c/3yhbOXjNt/YOwjrlonBwAAwH3giFEAAFCE\nGYzOvsE1qYrC8Q5OX1Rh4f6fJ7/35pujZm1e+ka5xE0LVsYKIUTU/I+/uVC63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bicmYr5rDu0xiOZ764tH+/ftz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\n3UVUal77cy5cJSn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lUikhhMPhUJ2FhsRiMSFEQ0ODyaTJFcyYmJjM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\nowDsLrhcuXJCyBf2b169/3rbG1EYBeDKKIwCAFxfznf/t+3L3esHxui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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "sample_loadings <- liana::get_c2c_factors(covid_data,\n", " sample_col='sample_new',\n", " group_col='condition') %>%\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", " )\n", "\n", "# plot\n", "pwc <- pwc %>% add_xy_position(x = \"condition\")\n", "ggboxplot(sample_loadings, x = \"condition\", y = \"loadings\", add = \"point\", fill=\"condition\") +\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": 30, "id": "bdb255c8", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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W6bJo45TKqfd9F/y9rf2C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\nkKzIrXM8z6tMOv5vRNW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KA753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\n9xQU1xhQucQr64iNjU+59YU3p2bK9+8j+1tgYBQhhBBCC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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "h_ = 10\n", "w_ = 15\n", "options(repr.plot.height=h_, repr.plot.width=w_)\n", "\n", "liana::plot_lr_heatmap(sce = covid_data, 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. In this case the differences are reflected with an almost perfect clustering by COVID-19 severity, where moderate cases are more similar to control cases." ] }, { "cell_type": "markdown", "id": "6ae4821f", "metadata": {}, "source": [ "### Overall CCI potential: Heatmap and network visualizations of sender-receiver cell pairs" ] }, { "cell_type": "markdown", "id": "ebc4a231", "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 8, but this can be repeated for every factor obtained in the decomposition." ] }, { "cell_type": "code", "execution_count": 31, "id": "c5f32467", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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FEAAAAAIHKEUQAAAAAgcoRRAAAAACByhFEAAAAAIHKEUQAAAAAgcoRR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\n7MvONAAAAADNUfsx7NCuU5lmXt6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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "selected_factor <- 'Factor.8'\n", "liana::plot_c2c_cells(sce = covid_data,\n", " factor_of_int = selected_factor,\n", " name = \"Loadings \\nProduct\")" ] }, { "cell_type": "markdown", "id": "ddbab424", "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": 32, "id": "87a6fe15", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "threshold = 0.075" ] }, { "cell_type": "markdown", "id": "74ccafa7", "metadata": {}, "source": [ "Then, we can plot all networks we are interested in:" ] }, { "cell_type": "code", "execution_count": 33, "id": "231bcf07", "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 = covid_data,\n", " sample_col = \"sample_new\",\n", " group_col = \"condition\")\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.2', 'Factor.8', 'Factor.9'),\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_qs_r.png')\n", " )" ] }, { "cell_type": "markdown", "id": "e6dd5e97", "metadata": {}, "source": [ "![network_plot_qs](../../data/quickstart/outputs/network_plot_qs_r.png \"Network Plot QS\")" ] }, { "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 `KEGG Pathways`." ] }, { "cell_type": "markdown", "id": "c1a1c9b1", "metadata": {}, "source": [ "For the pathway enrichment analysis with GSEA, we use ligand-receptor pairs instead of individual genes. KEGG 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": 34, "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 = \"KEGG\"\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": 35, "id": "f7bd340d", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "factors <- liana::get_c2c_factors(sce = covid_data,\n", " sample_col = \"sample_new\",\n", " group_col = \"condition\")\n", "\n", "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": 36, "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>
52Factor.5 CHEMOKINE SIGNALING PATHWAY 1.5464060.0010000000.01444444
39Factor.4 ANTIGEN PROCESSING AND PRESENTATION 1.4797290.0010000000.01444444
117Factor.10ANTIGEN PROCESSING AND PRESENTATION 1.3647770.0010000000.01444444
78Factor.7 ANTIGEN PROCESSING AND PRESENTATION 1.3616380.0010000000.01444444
13Factor.2 ANTIGEN PROCESSING AND PRESENTATION 1.3560780.0010000000.01444444
0Factor.1 ANTIGEN PROCESSING AND PRESENTATION 1.3453140.0010000000.01444444
53Factor.5 CYTOKINE CYTOKINE RECEPTOR INTERACTION1.3197030.0010000000.01444444
40Factor.4 CELL ADHESION MOLECULES CAMS 1.3172780.0010000000.01444444
91Factor.8 CHEMOKINE SIGNALING PATHWAY 1.2656770.0050050050.04647505
14Factor.2 CHEMOKINE SIGNALING PATHWAY 1.2573630.0030030030.03549004
26Factor.3 ANTIGEN PROCESSING AND PRESENTATION 1.2295430.0040040040.04337671
104Factor.9 ANTIGEN PROCESSING AND PRESENTATION 1.2251970.0050050050.04647505
1Factor.1 CELL ADHESION MOLECULES CAMS 1.2207500.0010000000.01444444
16Factor.2 CYTOKINE CYTOKINE RECEPTOR INTERACTION1.1705830.0020020020.02602603
\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", "\t52 & Factor.5 & CHEMOKINE SIGNALING PATHWAY & 1.546406 & 0.001000000 & 0.01444444\\\\\n", "\t39 & Factor.4 & ANTIGEN PROCESSING AND PRESENTATION & 1.479729 & 0.001000000 & 0.01444444\\\\\n", "\t117 & Factor.10 & ANTIGEN PROCESSING AND PRESENTATION & 1.364777 & 0.001000000 & 0.01444444\\\\\n", "\t78 & Factor.7 & ANTIGEN PROCESSING AND PRESENTATION & 1.361638 & 0.001000000 & 0.01444444\\\\\n", "\t13 & Factor.2 & ANTIGEN PROCESSING AND PRESENTATION & 1.356078 & 0.001000000 & 0.01444444\\\\\n", "\t0 & Factor.1 & ANTIGEN PROCESSING AND PRESENTATION & 1.345314 & 0.001000000 & 0.01444444\\\\\n", "\t53 & Factor.5 & CYTOKINE CYTOKINE RECEPTOR INTERACTION & 1.319703 & 0.001000000 & 0.01444444\\\\\n", "\t40 & Factor.4 & CELL ADHESION MOLECULES CAMS & 1.317278 & 0.001000000 & 0.01444444\\\\\n", "\t91 & Factor.8 & CHEMOKINE SIGNALING PATHWAY & 1.265677 & 0.005005005 & 0.04647505\\\\\n", "\t14 & Factor.2 & CHEMOKINE SIGNALING PATHWAY & 1.257363 & 0.003003003 & 0.03549004\\\\\n", "\t26 & Factor.3 & ANTIGEN PROCESSING AND PRESENTATION & 1.229543 & 0.004004004 & 0.04337671\\\\\n", "\t104 & Factor.9 & ANTIGEN PROCESSING AND PRESENTATION & 1.225197 & 0.005005005 & 0.04647505\\\\\n", "\t1 & Factor.1 & CELL ADHESION MOLECULES CAMS & 1.220750 & 0.001000000 & 0.01444444\\\\\n", "\t16 & Factor.2 & CYTOKINE CYTOKINE RECEPTOR INTERACTION & 1.170583 & 0.002002002 & 0.02602603\\\\\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", "| 52 | Factor.5 | CHEMOKINE SIGNALING PATHWAY | 1.546406 | 0.001000000 | 0.01444444 |\n", "| 39 | Factor.4 | ANTIGEN PROCESSING AND PRESENTATION | 1.479729 | 0.001000000 | 0.01444444 |\n", "| 117 | Factor.10 | ANTIGEN PROCESSING AND PRESENTATION | 1.364777 | 0.001000000 | 0.01444444 |\n", "| 78 | Factor.7 | ANTIGEN PROCESSING AND PRESENTATION | 1.361638 | 0.001000000 | 0.01444444 |\n", "| 13 | Factor.2 | ANTIGEN PROCESSING AND PRESENTATION | 1.356078 | 0.001000000 | 0.01444444 |\n", "| 0 | Factor.1 | ANTIGEN PROCESSING AND PRESENTATION | 1.345314 | 0.001000000 | 0.01444444 |\n", "| 53 | Factor.5 | CYTOKINE CYTOKINE RECEPTOR INTERACTION | 1.319703 | 0.001000000 | 0.01444444 |\n", "| 40 | Factor.4 | CELL ADHESION MOLECULES CAMS | 1.317278 | 0.001000000 | 0.01444444 |\n", "| 91 | Factor.8 | CHEMOKINE SIGNALING PATHWAY | 1.265677 | 0.005005005 | 0.04647505 |\n", "| 14 | Factor.2 | CHEMOKINE SIGNALING PATHWAY | 1.257363 | 0.003003003 | 0.03549004 |\n", "| 26 | Factor.3 | ANTIGEN PROCESSING AND PRESENTATION | 1.229543 | 0.004004004 | 0.04337671 |\n", "| 104 | Factor.9 | ANTIGEN PROCESSING AND PRESENTATION | 1.225197 | 0.005005005 | 0.04647505 |\n", "| 1 | Factor.1 | CELL ADHESION MOLECULES CAMS | 1.220750 | 0.001000000 | 0.01444444 |\n", "| 16 | Factor.2 | CYTOKINE CYTOKINE RECEPTOR INTERACTION | 1.170583 | 0.002002002 | 0.02602603 |\n", "\n" ], "text/plain": [ " Factor Term NES P-value \n", "52 Factor.5 CHEMOKINE SIGNALING PATHWAY 1.546406 0.001000000\n", "39 Factor.4 ANTIGEN PROCESSING AND PRESENTATION 1.479729 0.001000000\n", "117 Factor.10 ANTIGEN PROCESSING AND PRESENTATION 1.364777 0.001000000\n", "78 Factor.7 ANTIGEN PROCESSING AND PRESENTATION 1.361638 0.001000000\n", "13 Factor.2 ANTIGEN PROCESSING AND PRESENTATION 1.356078 0.001000000\n", "0 Factor.1 ANTIGEN PROCESSING AND PRESENTATION 1.345314 0.001000000\n", "53 Factor.5 CYTOKINE CYTOKINE RECEPTOR INTERACTION 1.319703 0.001000000\n", "40 Factor.4 CELL ADHESION MOLECULES CAMS 1.317278 0.001000000\n", "91 Factor.8 CHEMOKINE SIGNALING PATHWAY 1.265677 0.005005005\n", "14 Factor.2 CHEMOKINE SIGNALING PATHWAY 1.257363 0.003003003\n", "26 Factor.3 ANTIGEN PROCESSING AND PRESENTATION 1.229543 0.004004004\n", "104 Factor.9 ANTIGEN PROCESSING AND PRESENTATION 1.225197 0.005005005\n", "1 Factor.1 CELL ADHESION MOLECULES CAMS 1.220750 0.001000000\n", "16 Factor.2 CYTOKINE CYTOKINE RECEPTOR INTERACTION 1.170583 0.002002002\n", " Adj. P-value\n", "52 0.01444444 \n", "39 0.01444444 \n", "117 0.01444444 \n", "78 0.01444444 \n", "13 0.01444444 \n", "0 0.01444444 \n", "53 0.01444444 \n", "40 0.01444444 \n", "91 0.04647505 \n", "14 0.03549004 \n", "26 0.04337671 \n", "104 0.04647505 \n", "1 0.01444444 \n", "16 0.02602603 " ] }, "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": 37, "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": 38, "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": 39, "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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t0MCIw6t2mlb5P/dav0IDPpzvnujxGLlm2b//vilDrP+zhv3XJj374b/Qdl9Z06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\n/QbVa/ycx19/XroZHJN1BWmuVyeaKKJTxdTUpFX7yi+8XM3W1qh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sbIqIODpali/vXKVKqUaNynl5lSrqCgLPmmKUS0YNXySKDwIhAAC5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\nZi8eoslk//vr7uA2TqFX7T9/moN0S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BgQiTMzc4lMZF1KQCAEAIBO\nIhazR/c/DA5IZoiRIlk0wHFcdaV466Y7b81xGT7KVi7lg4YKCyo3/uSfklRERKw0WfCF2nXT\nU0u+X379s6+G2/UxkFMJX1pZmaWpyflFhRV8Pk9PX8Palqen3/3Txf+WrMzSK+djoiKyxWKW\niHgMwxExDMU+LiCiM8cf9eipO3ZiH+cBpl1dUjni8XhaWlpCoZDjOF1deTTLz8/PJ+Lr6DR6\nLqSjo0OUm5vb/DY6OjoNl2ro6PCfb4NAKHMIhAAA0En+OhIZHJBMRBy1NQ8SvcgkJw5HqGko\nD3a3lHHhoKGqKtGGH/3SUorbvQeO48rKhRvW+v3w8xhTM4UYLb60pNr3n6cR4RkF+ZWN/mRq\nruU22GL4KzYqKrj16mI1Ney5k1HBAckcx9U96Kj9eqn/3CM9tejPP+5a99Kfu8jNwEi9K0oq\ndwzDnDlzZu3atT169Pj222/lcAQDAwOiuNLSSqL6j0SKi4uJeujpNb9Nemlpw6XlxcViIr1m\ntoGOwbcSAAB0Bv+bibdvPevoXjgihjm0J9zIWNPaFjcG8sJxtGf73bSU4rbUC0raD8tVVoo2\n/ui/+ucx6hoCGZXuZcRx3I2rCTeuxNfUiCWetOyMssvnY27fSpw8zWHwMDzO6DIlxVV//nEv\n+Vlhq2vWvo/JiQWb1vov+mCwXd8Wp837n+Xl5XXlyhW57d7E3JxHlJeXR1Tvqq8sKKgisrCw\nkLiNubk5UXxeXsMQWVBQQMSzsOjONbZdB4PKAACA3GVmlJ48EklM62u2iuM4luX2bAsVieQ+\nuICwWlxaIqxtUaZQbl6LD72T0sE0WIvjuOys8sP7H8hgXy+r6mrR3h1hl8/HCGuabVpbWyte\nXlZz9MDDMyeiWCl6z4LMVVWJfv/ljjRpsA7HUVVVzR+bQxLi8uVXsO6Lb2trRRR17179OnMu\nLOw+kYatreQO4ca2tppEYffu1f+QVIWFRRNZ2dpiWht5QA2hAhKX52XnFpSxmkZmJnpqTT5Y\n4sKkJ+llzW7N17Oxt9AgIqrKikvIE2pZOlrpNHOTxxUlR6eVCozs+pi0Z/zx2s2JVIx79zZu\nMg+ZKD8xJrPBkIOMkpq2gYm5kWaT1yTMTYjLrlI3t7fVb/pFUpkV+zSvRtvSqWdtc/WKzJjE\nfFGzpVI362dr0OiDw9WU5WdlFdRoGJkZSzilNXmJsVkV6qZ9bQ2Vm+5QXFmQnZNXUq2kpWtk\nbKwlYY2q7LiEXCFPz9rBQrPpX0tSo1KKVU362BlJfPrOFSZHp5cSMbpWjj0kttniilOiU0uI\nSMfSyVKn6d+FJdnZeUXlrKqOgYmJnqqEp0il6Y+TC6mZ8tVqx1klElcV5mbnFdeoGZqb6qsr\nNbnMqnLiE3KqtSwcrPSalqos/XFSvUK1frlKfeXXw1YV52RmlynpmZgYagnaGHZa+STWV5r+\nOLmQJSV9237mjdst1V7ARGqmfXoZSrwGai+gZj5JHXoJbXD+r2jiqF0NRSVgWa6goNLvxtNX\nX+stmz02lJFWcud20uPInMrKmtolZhba/d3MhnlbCVS65m4kL7c8KiIzN6ukpobV0hbYO4lt\n7PT5fLm8Y1WVojMnoxiG4WSSCImIuED/pNcm9O2Wc4ewLLd/1/3YxzlErY+6U3tKA3yfCavF\nM+f174TiQR2W5fbvDMvOLG/7hsQw3P4d9z7/1gd9QdtqyLz59htWXTp6vnjazOe3GNWBx8+m\nk9kHC0ZLrpfijZ7/ttm+HWeP+q8fPuL5/WPp38cvlZP9gvlDOqncCgaBUKEIU27t23XSNyqj\n4vnjbr6Wree0efMmuRn/ex9ZEfrnij8imt2Hxpg1x5b2JyJKu7J+xdmcwZ+d/m6EhBBDRFQd\nfnDFr/fM3vxl5zy7tpeWjTz63XdXi4j4Az86uPLVxlmm+M6uFbsfN9mKp2ExaOqHS2c469a7\nUSrw3bbicKLD4oM/T2h6N5J6Zd2Ks3nun51bMYJHRJR84acVl5p/eujw3r6fJ70YIIErT/T/\n68DRyw+zqp7fBfDULQdNnDV3mqeVel0BCgO3r9gb23fRng1TGzwMq0r1O7j7pN+jtLLnY7Mz\nqka93ScufGeqY4PQknZt/YpTOaQ57Is/vh7e5AVEHV/xc4Dt29t/nSGx6UV12L4VW8OJyGLW\n1u2zrJquwD05uWrF33lE5PnlueXe9b6d2cLIM38euBSakF/9/NXxtSz7vzLzvbe9LRrEithT\nq1bfpDGrTi5tdnKgtpxVIhLnBB/eefSf8OSS56eGp2YxaPLbC97w7FHv2ULmP5tXnEhzW3Z8\n1eimvTviTq1adZNGrTr5sRuRNJer1Fc+EVFNbsTFwwdO3054UUBGWa/vK9PnznzNuWmwbUKq\nT2I9yefXfnUyjYhsF+z6dVqj5jK1FzCRiseXh77ylvDopeT2tuXbIsREPd7a9secnrJ5CW3z\nNC4/8kGmbPfJMHT1fKzncGs19Wbe0XYRi9mLZ56EBqW+uHV/LiujJDO9JMD32Vtz+/fu16kt\nxwryKy6fi4mJyqn9L8MQx1FoYIaunuprk/s5yWHEi8sXnpSWVst6r9yJIxFffuMj6912vQun\nH8dE57R1q9A7KVY2usO8JXwpg5yEBqW0452qxXFcebnw9LFH7y5BIGkjp/c+Gb1h8ekPpq3W\n2faBt3HR/cPL52xPFQz8aenwF1/eOZd/WP5XEm/okj//bxARkbLPkqVuf36z6+1Z9sc2zHPV\nzgzYsWzxyWLNsRvfd+zCV9KdIRAqjJqsgN0/br2aVKNt6ejh3dfGmF+ekxR55+7tA6vuP/xw\n8w+vmTZ4TqPn9sabA/Ul7Eepp+QW3zInfugXVEQMw3Dih/5BJa++pi1pLcPBM14fUPvISVxZ\nkP7sacyjyLtHvvskZsE3y6f27sC8yDaj3ntV4jiG+o51tUTCuENfLz+VxGrZek0d1s9Cl1ee\nn/o4yPfeyfUPw+dtXP+mdQufr7K4v3/fdCAos0bNuLebm10vS53qzKdP42Ji/PateHj39Y8+\nnTvIuFFFRFnw7t0hrl+6t3NohvSAwKRZVtaNF3OxQcESJgKqyQ45tGnb+ZgSvo6lo3tfOxsT\npcLkhISYmPDzGz4Ju7fw8/8bb9f2LvbSnFViC8IP/bzpdEyZupn90CF9epmpVeWnRQffuXti\n3cP7s9avm2Ury/v/xqS68vP913266W6JwGTA2HEDbAzVa4qynoX73r6265uw2K+3fD5M4rX6\nXBs/iURECf7+acQwDMcl3r6dNm1GD4k7rg4LDKvy9mpy0ZcEBz1qOhtcR15C2926nlAbY2SI\n46iisiYkKGXk6F6y2qdYzO3feT8hVsInorbwFRXCfTvCZs7v7+JqJquDtiz5WeHhPferKv6t\nXq87jcXFVccPPPRKtn5tSj8ZHlEkYq9fjSdiZFafS0REHEeRDzMz0kvMLWR7cXWx2scE7dmS\nYS6cedzfzVxdQ57faPBCTY346t+xPIba3VaX4ygqIis+Nq93N+1MKDfm75/yLXh9wtcrxzus\nrF2iPfSrKxeXO9StUfro4v79YUpVE58HQiKnr69eLpowbcMnw899QkREjMno9f/89S46EMoJ\nAqGCED858P2Gq9k6TjNXLp9Vr/rpnYSTq1Ycjti97ozT5jfr32Zq9x05aVJPCXvqLML7/sGl\npO4z1TPy7D+R/kGFr42TNH6ErsOrkybV/37giiKPrVt/4v6+3867/fZWz3a3pzIfPGmSR8ur\nPDu1+XQSZzpm5cYlbtovDjRt1lshWz7/6daR3857/fJGc99cxf5bV+4OrtBxnP7157MHGv6b\n/MoT/t62YX/QmbVrVbZsntWzXjTgq6oyxQE79410+WhQ25OYoaFhXlpgQPIc60aPo7nYoDt5\njKGhXl5ewb9LxYnHVv98LlWph88HX34wzubf44lz7h7cuOWc346VYt0dX3q0NZtKcVa59FNr\n1px+KrCbtGLFoqH/nppFGVfWLd9+7/jPhwZsX2Qvv0Z7Ulz5Fbd3/X63RN35nfU/TOlZ9x36\n1uypJ7/58rD/1n0+Az8e1NzgGW3+JBJxMf63s4nvNnV83tm/E/1vp86Y3XQ8Cg1DQ3He/YC7\nlV7DGzVnKg0OimR1DA3L8+qlnI68hDarrhJFPsiSbRqsxWOYsJA0GQbCKxdiJKbBOhxLDEN/\nHY40MdMyMW22hbSsFORXHN5zv6pSLPHkcSwRUaBfkqaOqtcIa1kdNCoyq7xMKKu9NRISmDzt\nLWc57bxLXD4f285rm+OqKkU3r8VPmubQ+srQYfdD04qLqjq4E4Zh/G8kIhC2mfbgr64nzHoY\nGhz6KFe73xD3YW52evUfhOh7vb9q1USeU73PAmP06vqgpPfCQ0LuxpQaObsPc+9vpYXug3KD\nQWUUQ971g1eyyGDsf36Y3bAxoqrdjM9m9mFqnl679bTLSieJ8J5fcAVpeo6cM9JDn7jH/oEt\n3aXVw+i6zP5q0SABl3zuWGCbOwq0RWH0o3SO+k6c+W8aJCJi9NznT+1H4qfRMc399FSE/bk3\nuJxvO+e/a+fWT4NEpGE3afmGZe4a4mend17Jrv8XVY9Z0235+Te2H4xsx0+amaenLaUHBDZ+\nkM3FBAblMw6ew+rXiXGp5/44m8rqeH+24fP6aZCI+MZDFv60ZoYlUxrw56FHMm9TRlR258iZ\np2J1j6Vr3hva4NQIzMd9umiQMpd185/IpvVdnerpo6gq0vCcMblngydqAuvpb4/UoLLo6JRm\nN23HJ5F77Hc7h5RdR8wY72lFlOp/W1JthMDVc4ia8H5gaOOLozQk8JFY18PTocGXfQdeQtsl\nxOXLaVAWluOSnhXWdfProNzs8jv+ya2uxnGcWMxdPhcjk4O27Mq5mKoKUSsd+Rj652Jcx+90\n60RHZre+UrswDEVHyWvntUQ1bE1N540AVFFeE/0ouyM9LcNC0+TxrASainyQxTAd7XPLcVxM\ndI6wuot/hP4n8bWtBo6e+eFny94eP7RhGiQiPc/3Vq5c+d0b9g0XK+v3Hjp+7rLPPpw52g1p\nUL4QCBXCk3N/RdfwHd+c7iyhZYrxyFlzJ0wYpFlW0vkFa1ZlqN/dKtLxHNFf4OjloUfcE/8A\nydOXSqLjM22UPpXf8b0rh8RSJz8/n4h4vKZfUXrD5n68bOmrPZv5yUi5cMCvkEzGvfe6lcQP\noLbPwpl9lISPjhwPr78HJavpS9+w5OVe+f1QTNsf31t4edlQVkBAw7TBxQXeyWucB6tDjp6I\nE6s4v73Qo/EYKkRExLd9Y9EoPcq9dvCKzO/tMi//FVTBWE6Z5SXh0JrD3lgwYcJwg+quHelN\nmJ9fSqTEa/rmMX3Gf7xs2QzXZitO2/FJFEf6BxaSyqAR7hoWXp5WRJn+txMk7Frg6jVUXXg/\nMLTBQEtUEhwUJdbx8HJqUNoOvIR2SE0ukuHeGuFYLr0DE+XVdzc4Vcpbc47j4p7kFjaZaE62\n8vMqnkTltF4ijliWDQloPcpKKelZQYdvmyXjOHqWWCC7gWoaKC6sOn3k8a4tEbu3RJw69Fje\n706tx1EdSoNEVFJcLdcPCNQSidi4J7kyufZEIja+xXYEAP+L0GRUEZQkJuYR2Q0ZbCTxz3oD\n31w8sJOL1IqyYL/7QtIfM8KJT4yD5zC9i5fj/AOyXm88nEZz+L372NKVsKysbCK5tXs1t7YR\nUFb0yV9Omi+b2nAwECOnUaOdmtuuIDw8mcjQY4Rjcx1HGLMRI/r+GRcd/iCe3Op1DlKym7F0\ncuBX5y5uPe6zZV6ftn16zb29ex08GBiYOK9XXS8+LjYoOI9xesNDv+pW3Yrc4/AHVURuI3ya\nbRSjOnDkMK0bl+PCH5ZPHSsxNLaTODExmSOjgYOtJN6QqjpOWtz1/ckF1tbmFJZxa9dW+08X\n+tjWf2ipZu0+2rr5LdvxSRQ/8AsqIdXhI4aqEll6elkeTU697R83z65P4zMkGOjlru53O/Bu\nhc+Iulrd0uCgCLHOGC9H/vUOv4SHDx8mJibWX5Kenk5E1dXVVVUtVU9lZhTLvANhfRnpRT2s\nZNB6M+ZRdpt6zUVFZgweJscu1dERGVKuyRE92qUuRwAAIABJREFUeZTtM1o2w5NkppXI782q\nEbJZmUUyH6eR47gTBx5mZ5XXvoFpKSXH9ocv+MCNx5PfuLm1B2rD7AXNSUnONzbtQId3qeXn\nVkSG52hoKA/27NHxurKOy8ooj36Yq6OrMtRLRU6PCerk51XIcJaazIyiXn0kDMndVHW1PB9L\nA8gOAqEiyMjIIOKbmral0XvqmeVvX5JQPa/q/fmexa4yK5pkpUF+4SIyHO7jwBAR4+DloX/5\nUkvDaTQhMDLSIcrOzuLo326EsQeWvH2s6Y+gqKpUwh5Ct7z99h8SltvM/nXNeEMiInXvOfOu\nRe2Jiji86r1TRn1cB7kNGNB/gHNfs6bTXjSQmZVJRD0sW3opOj16aFF0YWZGNfWrP56nwH72\nktfufHvl7LbTwze/Zd2m9hMmXt69D+4PCEyYb/tizNfYoKA8xmG6hz79mwepJCuzgsjQ0rKl\nO5QePSyIYjIzs4ja0n+rtbOanZkhJjI168Je41Jc+dZT3x0X9NOV5Bu/fuL7p5XzIDfX/v37\n93e0MVBp5Rar7Z9E4X2/4FJSH+EzUIWIyNLTy+rIseSA2zGL+tg3Ppiym9dQ9VuBASEVI155\nnghLQ4MixXpjvRx51Q0CYbtewtWrV0+dOlV/iYuLCxGVl5eXlTU/XQdRaanMWjNKVFhY1nIB\npFTUllaXDEM52SVlZVLdFLZPTnaJtEGao8KCytLSMpnc5JfLqAluc/LyipUFMm5xV1hQlZX5\n7zXAcZSXU5GSlGdoLN8ZAgryyzs+OUdermwu4FZd/zupvLSGI47jaOAwk044YgvEYvbymYQa\nIctxpCTgObvKt1debrak3/n2KsiT9i0rL5drzxUAmUEgVABcYUERkbaOTlsaCAt0zK0kTR4o\nMGz7gCZtVRjoFykm0+Ej+tbe3TCOXh56ly42N5yGRGpqakTFZeU1RHVVdyr6FlYGTWvlKjJj\nnuY1uQPSMLLqKelez0y77kPD6zl5zRbra39f9g16EBcbfCU2+MoxYlSN+gwaOXbyxFH9dCXf\nnFVWVhIJ9HRbPJEGBvpEpeXl5UQN541TdZ7/4auhq/45ue2s54Y3e7Tl/s/U08tu/77AwLh5\ndn2IiLi4wKBcxnHaMD2qfyNcWVlJRHp6LU4XpmtgwGvHb11rZ7WwoJCIr6Mty1rHNpLmytce\n9MEvm5wv/f2P/92o5Ie+yQ99zxIpaVu6eL46YfK4wRbNROm2fxKr7vmFVpDWqz5uz6/iHl6e\nVseSkwP8o96xd2785iu5erlr+t4OCCl/5RUNouf9B3Ve83RkKLzRuu18Ce3CijlZj1jZgFjy\nkCttw7JcW/cjFsm3TkNUw0o/1CfLEsdxMqn2YWVxPlsgj/NWXSkhYVZVNj/zqYyIRGzHr+2a\nms7okMZxXGVFDUccw1B5mXwzvzRENZxQyBJHxFB5qdzLI9uT3DlvGUBnQiBUAIyxiRFRTk6O\nkBykHjjQZNTHa2d1zSijeQF+j1nS6WVUHhUVVbuINbRRpcJU/9vPZs+xkWon+QUFRLomJvVf\nsPWkFWslzEMYt2/RF2ebdAhwmr12eSvjYRIR39Bl/EKX8QvF5VlxkQ8eRkTGphaV5ob/9VvQ\nzTsf/vfbsRYSbv21tbSJ8jIzC4gkzW5ARERcZlY2EWlqSWgIp+62cLHPvZ/8j/9+0fPHSWZt\nuAE08fLqs39/YGDCgj52L/oPOr3poUuU1aB8WkSUmZlJ1HxFXU5mFkukqdnGhnqtnVVjY2Oi\n4pzcAiKDFlaTJymvfA1rrxnLvGZwwoKk6IiIiIiopzmlxc9u7Fvj6/f6N/9d2F9SH7w2fxIr\nQ/zuVpPAwUzpyYuPAulZ61FyYaB/5HvO/RtXZPJdvd01bvkGhpS+MkqLqCw4KEKsO6ZR/8F2\nvwRTU1N7+wY9/nV1dYlISUlJSamlnxJVNWXi5JgINdQFLRdASmrqShXl0t6Ychxp6ajI5LjN\n0dJRlb7qSU1dSSCQzewFKgJ+ZZUco5SGpuzPm6m5lpIyX1TvNp2vxDOz0FZSku8wFFraKlyH\n2yHq6qrJ9UKqM3CYadidLBVVvrObUeccsQVKmkqug00ehuVoaik7u8q9PLp6snyWraMn7VvW\n5ecZQEq4UhWBubkFUXZaejqRxDT17My3v/gW957+00fD5T6Meuty/P1iOKLioJ3fBDX6U6b/\n7YQ5NtLMcV+Yky0ksjWT41RhGaFngtO0Hca8aq9FxNcwtR82zn7YOCIiqorZ+8Xyc3v+vDX0\n+1ebBlBLWxs+5aUkJYlJv7mbleykpCoiCxsbiblBc9i77w8LXx986Pfr7v8d24YyG3l59d2/\nNzAgbqFdHyY26E4e4zh9WOPZPNRtbI0pPOdZUh4124hHnJSURqRsYyNlE15pGVpYCCg+Iy2N\nIwNJSTfn+ro1f6eZjV+xYly9t7aZm2Y55Y/SmBvXo0ss3acNsSBiBPo2riNtXEdOJSJic2/8\n9+Pfzm79y3P3oiZ9/KjNn8TarrREDw5996DRmiV3bkf8X3+3xl/gSgO8h2nd8A0IKRs1WrM0\nJChCrPeal0OTkrTvJSxYsGDBggX1l1y+fPn27dva2tq1ybA5JibakZwcx5a0sDRouQBSsu1t\nEB3RhjFC+jmYyeS4zbF3YoP906RZk2HIrq+hrApjaKKZllIkpy5dDMNY25goK8s+p02d4Xj2\n+KPaal4en5n8poOJqdwfK5mZ6RJJ29WzORY99OV6IdXpP5D6Oenx+TwNDQ01Nfk2ppXGUG/O\ndagRn8/T0tJSUVFpfYMOEAhk2e7ExFRPyres5f7VAC8PjDKqCNQcnewYyvznUrjE3s3pYbcj\nk5NZg54vQRokSvf3S+BI333+Zw3NHapLlHXbP06a25RMX79YIn07Ozn+yApjrx84sPt6vITy\nqPYb2l+bqp/Ep0hqVqI20HuQGlX4n7zU3D1yRdiJS4nE6zncs7lBInS8Fi8apF4VuX/7zcK2\n3LcZe3n1YfICA2M5igsKyuE5eXo0nd2xl6e3OUNxF05GNPNLxqWf+yuomjTch7vJbK66Wkwf\nJycBlQZc8pc4bmTZg8Dg5OQCPYvnVZeGhoZElJsnacC30tzcqudVjrKlkn3v0IH9f93Lafon\nnpH7YFuinPgEyUP2tu2TWBzkFyEi9f5vNvoovD/Cgqjsjn+4hEocvouXu7Y4IjCktLb/oK6H\np1PTZNqBl9Ae5j3kOxG5mbls9j9goLmUaZDhMTq6qlY2kqZGlR1rWz09AzVGil9pjiO3ITJ7\nOGPZU4chOY04whibaMgjDRKRg4vJwiWuPmMsfUZbLvhggLOrHB8H1unn2NEvGD6f16t357WH\n4PNfrru+TiuPurqyqbmWrIbSsbVrtnkPwP+ol+urAeTEYtL80fpUcGPv4ajSxvc7pSFH/04k\nMho8WDYj1HVQ8m3/ZCKTEW++MaKhN9/wNiTKDbgd09odG1cUePh8AqvkPGNaPzmOo2bZy1ZA\nlcHnrzcdF57LioktJjI3MZZ446Pj895cJxXh46PbziRWNPmrKCdox45bhYzp5CVvtPCW6I/6\ncH5/1fKwvbsC2zJkuaGnd18mNzAgNjYoKJdx8vKQ0KOP6fXWh+OMKP/ajl1385pEWq487sS2\n43E1aq4LFnnK/BmCzqh5r/dgKkMO/xlS0PjQVdHHTkewpDlo8Iv3VauXnTFRWrB/YuN5OLgM\nX79YIjU7O5mPAimwtbUgirt6LrppXq6KeZJMpGps3MysDW35JBYF+keKScNjyqxGH4WJM0dZ\nE5WH+N+XMPsIv7/3MC1xZGBwRkjgQ7Geh5ejhM9AR15CO9jJbRJnhkhPX83ASDaNwRycTSws\ndaS5Y+RYbuzEPvIewZLHY8ZN7sfVdiRsHsNQH3sju74yCxX9HIxZ+dQPMgxn7yjHsUy0dVQc\n+xs6DjDU0ZVvdVMdU3MtI2ONdscMhiF7R2MVVbTV6gz9Xc06PpYpjyHzHtoGnTCYAkDnQiBU\nDCr957zrrUcp57/7aM3xoJi0wmqOOGFp5qPLW777NaCIMZ3w0UxJTdw63TN//xQi8xEj+jT+\nC9PPx9uEqCDAP6q5b3ROWJIZH3L8h082BhTyzSbOHSPXUcv47rPm9lOteLBrxeqD/nHZJUKW\niMRVhUmhZzf+cCSOdIZMHt5MHzzGeMKSBU4alRH7P/9o9bGA6OS8CjFx1UXpcXf//u3TZev8\ncpQtpyydbd9y9Zvha0vm2gtKg4IetaXYBp7e9kxe0N4/g/IYJ68m7UVrqbrM+3CcKT/9xn+X\nfv7HlfvxGcVCjkTlOc8e+R38funyo9FCHbd3Pxgjj8fafNs33htvrpTj99NHK/b5RiXnVYqJ\nqynPjbm15/ufL2ZxOl5LFg6qOzF2E98eqsMln16z5oBfXF6lmCMSVxYkBh1bu3J/tFDNad6M\ngbK/rC0nLHzVmDIu/fj1lr/DUwoqRBwRV1OeFX3rzx+2+BUr2Uwc79LcN6v0n8S8AL9olnS8\nRrg26RhmPtzHjqHKu/73JFTh8p29hmmLI679ei1CrOchob1oR19C2xkYqltYavPkMMw9R8yA\nQeay2hvD0KwFA1RVlVqtlBswyNx1sBwnnKjj4GIyfJRt7cAbEjEM6RuqT3/bRYYHdR1oLqes\ny3E0cEhnnLfONGZCn47EjNHje8uwMNCCQe4ymGyD5cjdq2uGVwCQKzyXUhR6Xl/8pme7edPh\nsKPrwo4SIxDwa4QijojR7DVqyScL+zcaVbC5wfeJlL0/31dv4onwPxa8vafJd6z9/N+/Gf28\n8inrwrdvX29ypWm98u32Rf0aLuPi/G9nEFmNGGEt4cB9fHxMz57MajicxrPjn719tvb2ja0u\nL6sWExGj13/Of76Y0a9DV3dzEyQQ2bz165pJhkR8yyn/+SJ/0x8X75/adP8UMUoqKoywqoYj\nIlXTQfM//az5efyIsZiw5nfL039sPX732IawY0R8gTIrrOGIiNHuN+Wjj+d6WrTaGpMxnbh0\nlv/HB+LaNP6Doad3vz07n8QQz3nasGab1KoP/OC3zX33b9lz5er2H64S8ZQFTI1QTEQkMPdc\n9Mn/Temn0+hdr/Jd/3ZI03M+ZMnhj4a9+E/rZ5VI1XXxLxt6/r7hz4Czm1ecJUZZoCwSCjki\nUrXwXPTxEs/6hdYf8fFXOb9sORl2+peI08QIVJVFVUKWiJSN+s9Y8tkE80a39q1frlJc+ZqD\nFn/1buXmI3du7l51czcxAlWBuKpaTEQ8nd5jP/3P3L4tvHdSfhKzbvvFcmTgPcJZQlmMh4/o\ndzDhyV3/kErPEY17AvGdvT10rl+NiSPdiRLrB6mjL6EdvEbYnDgUIdNdEhExDOfhLcumDQaG\n6u8uHXpgV1hJsYRWvbUTDLgNsXj9rWanGZW5MRP7aGmrXLsY22jQ0drC9O5nNP1tFzV12Qwn\nU0tXT22Am/mD++myrSZkGNLTU3Pu3xktOTvTwCEW/jeepqeVtjUWMgy5DrLoad0ZvQeBiIxM\nNId5W925ndzuDuY8htHVV/Ucbi3TcgG8FBAIFQej4/jGyq1DwgICw5+k5BRX8rVNzM0t7AaN\n9LTTqn/XyNe3cXZuYdw0pRdtJdRM+zo7N9P8x0SLT0Q8XStn52b6oakbNG3Tk5lUYOLs7DRy\nhORnyLYjpno9CSouTU2l/takbGDr3Ohmma+qrW/e2234KO/eDdOKwNjO2VnDykDi9a5m2tfZ\n2cxK78UW6ub9nJ1bmGHITKduN4ZDFv00YOJDX//IlKzsrPxKga6xsYmZ3aARHr116hVNYNjL\n2VlgadjgHpuv7zLj263DI+4E3o96llVQKlTS0DHq2c91mOdga+2Gt/FqJn2dnassmrbhYyyn\nLl2QsDu0hMyMm2sixdOzdnausfl3/Bp9j/Fj7ihlkOW4f9uLCozsnJ3VejaYKEPV6pX/2zhw\nTEhgcERsal5xBauqZWDay2mwl4eTcaO0oNXD0dlZQvtFIiKj2malbTirROq9xn35q+vYoIC7\nUc+yCys4TSMzc3Mb1xE+9npN4pGm44zvt4+K9vd/+CwzMzOvUqBvam5h4+Lt42LU4Ca59ctV\n6iufSMVu0vJtIxKDboXEZWRn5xSLNQxNTEx6Onr7uLU+wbQ0n8TSp7mqzs5Dhr4quYbPyHvi\nmFClDGFmopgc1Uz7Ojuz5nVDJvCcX5kyMP2BUMVt5L9ba1k6Ojvr1ZtPo0Mvoc08faxvXo0v\nyKuUYVtEhmEGDe1hYSnjmQDNe2h/vNz71vWE0KBUUcPB5Q2N1MdM7OPUv7OnyRw23Mqpv2lI\nYPLjR9mF+ZViMaeuqWzXx9BtiEWvPnLpfvbmLOcH9zNkOzATx9Gbs1z4/JehKYosMQyzYPHg\nX366XVUhkv7yZhjG2ERj+hxZVu1Cq16b3DciPKOyooZl23Ntc8RNm+nMV+o+betqamr8/f0f\nPHiQkZGhoaFhYWExatSoPn2aNNECBdDRCVUBAEAxXb58+fvvv7948aKpaesZKSwkbe+Oe7I6\nNMMwPB6z6ufRsupA2FRNjTj2cXZGeqGwmtXSFvS1Nzc1l13HynapqqoqKyvjOFJWVpL3uJS7\nfg8N9H8mqxsEhsdYWGiv3fiaXDtelpWV1Q7qqKKioqXVqW/Ws6cFu38PraoSSTMLBcOQnr76\nB5+4Gxp16pSrRUVFIpGIiF6SUUYLCwvFYjERdcIoo3USEwq2bbrDsVw77n7HTuw7bnLfNm2S\nlZU1ceJEIlqxYsW0adPaekT5KS8v37x588aNG4uLGw/hNmDAgB9//HHcuHFdUjDoKt3nOQcA\nALy0Bg7t4eRiKquOhBzHTZpmL780SETKyvze/Qzchpi4e5s5uxp1eRqsI4fOmBK8Nae/to4q\nI4v8xjAMj0fv/N9geQ/D04Vseul/9vVwExNNavENqj2fdn2NPlvh3clpEGrZ2unPe9eNz2fa\ncDUyDBF5DLd+bVLb0uBLKz4+3tXV9bvvvispkTCcdGRk5Pjx499//32hsJmGP9AdockoAADI\nHcPQog8Gr1vtm5NV1vF6p8HulqPHo12THOnoqn7+1fA1390QEXHtal9Xh+O4dxYPtesj11G+\nup6hkcYX3/qEBKZcuxRXWlJNxDA8otpTx2Nqz6GhofqEqfYurmadk+pBogEDzfX01f78425J\ncXX9frmSMQxDNG2Ws/dIibPH/u9JTEx0d3cvKioiIonVpCzLEtHu3btzc3NPnz7N48mm6kic\nEXzs5OWg4Mhc7X5D3Ue/MedV2xZ7J3B3fhr7zfWaxot1Xv/13Ef9ZVIiaACBEAAAOoOqmtKH\nn3j88tPtkuLqjvRW6NXH8O1FrrilljebXvr/99GwP34NZnntzIQMQxxHU6Y5eI/oJjfTLePz\neZ4+1sO8rZ49LXgSlZOZXlRSUs0wpKunbt5Dx8nFtEdPGXd5hfaxstFbsfqVm9cS/P55WlPD\n1Q7RVH+FuiUuA0wnvm5vbPpSzNPccUKhcOLEiUVFRbWpr2Xnzp378ccfv/32244ft+rx7rfG\nfnAhTczXMtCsunB6z/p1B9dcuvDt0OY/EOl3L/3jF9RksaFTW2baAukhEAIAQCcxMtH4atXI\nHb+FJCcWtnljhogjD2+rmfMHKHWjcR1eZkPcLXVWqm5eH1BRLmxrhOfxGIZhFrw3cMSoXvIp\n3UuKx2N69Tbo1dugpKSkttGdmpqahgYaiL5cVNWUJ0y19xnV69HDzEcPs5ISCyrKn1dHKQt4\npmbaTgNMXQaYmUkYzu1/2M6dO588eSLlygzDrF27duHChRYWHZsthotcPe39Cxk95xy48scc\nB82q5L+/mjBj23eTvxyStmtMc0Mkx8XFEbmtjb79ccORpJW7vvdr94RACAAAnUdHV/Wzr71P\nH48K8H1GbRnZQVVFacp0R59RtvIsHTTW197ov+vH7twWGvM4h8djpBmesbZ2xchY4/0lQ/v0\nM+qEQgK0j6aWYJi31TBvKyLKyckrLalWVVMyNtZXVpblVC4vj02bNvF4PGmqB4mI47iqqqqd\nO3euXr26IwcV3tr+ZywZv7d73zwHZSLSsJry27Ev/nH58djOc5vGTJecuIvi4nJJY5yrvYYG\nGoN0CjxkBQCATqWszJ85t/93/x3l7Pp8VrrmJoyuXczn814Za7dm41ikwS5haKTxzQ+vfP71\n8NraEoYhauYWjccwRKSpJVjw3qB1v05AGoT/IXw+o6OroqIicR7a7iAyMjI5OVnKNFiLx+Od\nO3eug8cNPH06h/SnTn/l35DNOE9/sy+VXTh9rUknwediY2OJ7B3shTlPQv65eMnv4bP8asyK\nIE+oIQQAgC5gaq71fx+5Z2aU3gtOffwoJy2lqFHtk5Iy366PgVN/k0HultranTQqPTRngJt5\nf1ezx1E5oXdSoiKzcnPKG62gqSVwdDYdNLSH2yALgaDb3lUD/I+Kiopq6yYsyz558oRl2Q4M\nLVOdmppL5OHk1CBxODg6EsWmpWU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C3NTYkKi3tYRxoj35jc4r7popiTh+uefp4Lq0vz7kWHxRZqDFq05jVbDhFRSWFhA5GR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\nYVBgIkuaw/xc2ugMlXP19Va7cj4rKCjNf3b7kykoDpi7yO/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SR0z1dj8f2knMywbas3XkqM/GXFW2uO3RcScZzc3RQo9/Lx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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 480, "width": 600 } }, "output_type": "display_data" } ], "source": [ "h_ = 8\n", "w_ = 10\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": 40, "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 = covid_data,\n", " resource = net)" ] }, { "cell_type": "markdown", "id": "81d63114", "metadata": {}, "source": [ "Next, we can run the footprint enrichment analysis:" ] }, { "cell_type": "code", "execution_count": 41, "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": 42, "id": "10bbc976", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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TGaHvq/KT//jRasZBqKaVpxfgNlGqc4C7/ak2Ka6Lpim+rLHz6NTCcARVUsWrA6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JO/3suXOs/17W8N/XNj5yrv3F1+yQu+CG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\ngD+EYZB05Ic61E3nNuafQ8juNWM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jwVXaNtL+GOYReuGoQ7aduDlqJoiskZbo7y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\ncT9H9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88L4byk+qV1NTM1RMmnTKPm0t\nDQbK6AI8SUZW0dNgzXPa/WgvsoIzq25Jl4/ebqKhbHpqhxdoBwN/aa7l+MvoaaO327RNqDqO\n4PTgMWk1m9uH00tHo5ljmECkF8hy1NHpRlGJhvIXEPZlDbVm2BhTbSgWUrH8B4QGegpZHW0f\ngmolFUNL522QsKsFmyO7eZ4DFVfS2USwI9NkSIZO00ETXj87TQcyPKD5IB6/+Yv9qqYhSTQf\nNDkgTMZRLZlNvLfaURQScdMDwp4mVBverFJlDcpdhM1Jd5OpAWGoB9WS5T2LvqV7XsI9kJek\nB33zCyyZr/iwuYl2k05gGYNlyYIgnJbOpIAwuef3X//yYwelwrrFSy/wKdGW3ZvXPHbf5r2J\nH3/7iv67TM/6+79870tNctmsBWevsAf3bVr9yHc37b7jx9+4wrv4vZ9wbHj25y8enPL2j18x\nwz/TCxDa9PMvfPeZxnRBzdwFK2ammndve/Ghja9vvO3b37qq9miTTW955t4XVncWzz+nbmHd\nYDdJrbm5DeWcate+fz21+UBH1FFaM2XO0qVTC8Ysx6uhIUkZ3CT6AkJdy/NC83gUR7Z9kjYH\niko8Yn5AGA0hSSY0gvuimmivaQFhpDfLzKJDKSiiq8XMAoF4pP+ryYaEw90fUuZHOo5qzWba\nncVBvNf8KXsD9CWmHf2PTlKQJIw05CkgjPbiNens7fudRjM+h7s7SeZ1/4BhtR7J8IBgZ14S\nI9kcuLz0dplZppZGS2cT21jsxCNmzn0AIB7CWWDyCJXTT8zcy0ksnGuXnLuASJBkPB971WpJ\nDAOLI+MDVTtI/cGkIAgT07gKCA+/+OPvbR/Y8vGe9ZE7Li4H9Def/vvBdOXV37/vtll9FzWj\n86mv3vbLrf/cGLjiSh+Q3Pzwz19qcp59539+4bJqC2AEXvvhZ773z988/MY7PnP2uZdPTm/6\n+YsHyxZcfnlfUhlt959//kyjUXfdt//jI3O9EpBuWfWjr/xo9W9/8/LKey719dcg+MaayLu/\n8z8fPTZuOEBrY5OGtO3nd6wJHd2dTnJPv+zjn7v9gooT25eHDh26/vrrBxzv8XiA7u5uVc3y\n+/LYCzU9Hewc/N5oGEZnZ+fxj0hIHkdRJBxNpKPZveKIJMMo0rVYKh058aVP1jnEE3zIxOOB\nkQ7PlDcSUhVLd9eo2l5dwzzNMIolKdYbiFjMqKGhFyfjMZ0RP66jenpG2PbKpUuOZLyzvd3E\nNXuFqURKtYVGqmQgMHj6dWcy7TT0ro52Iz/L9dRUka4kOjuH2+A8GAye/KCk2RQ8Pd1BQ8rj\n/o021WlTnYFgtz7E9i+hUCgUOiG/isPiUWVLoDMPm3MaRnEyOfpTLhwOh8NDfrCSoRdBLNAd\nsWX2c2g6pMDY5ng9TiiodXaOevs4XS9OxGKSOvofKRCNRqPRkS+zBRa71Ntj4uXOgl4g0RuJ\nJiNDbpoaj8fj8YF/tUu6GwJdXWlTJ5DEI4UOf6qzc7TpgxKJRCIxQleBLrkTEVtnpzmBtGTo\nRboWTWnRkb6FZDI51G3Llkx6INjVkcoibhuJrNkV3L3hgBEZciuJdDo9aN1U/PGYFklnE0B3\nd4/V5sDjkd65+emnV+9uM2ov/egNi/O/7EAQsjSuAsLggddfO2kGT2lpf/yUKF5+8ycWTlo2\n62gXl1Q0eZKHbcFgL/gg8q+/vdgl13301r5oEJB8K2+4bvGhFwMtjXDS2gVj49PPtFJ81S0f\n6osGAbXiwlvf+/L6n216+uXWS69/a26o+/z3f2DIaBCMpuYWSOtV7/x/H3vHolq/1rFv/eP/\n+9DLz913b0ntD95bOybjhNJQTcxBGRiAlM/cHjI6oOfQ7tclKR+7dEvppG7KwKgk6ZIia+bs\n8SRraQzDMDW5n65aATmd1M3rq5YNQ89hd9O+80E2dC0vAaEEElJW241LOoAukc8RQlmSgUx/\nqlJ+gmdZS4NhmLRG15BkQ1ZlLeNwOpU4laua4rEMXl3S04CR496DQzAUVTZ1G8C+k8aQJDL8\nQegGSMgSmR44PC0pqzZTSwTVbhi6pKclWTWhZFnXACO3SQK6pBwtynyGBBhktWWnZEhn7MbU\ne/7yrUe26zDnPf/x3rlj+iqpbfde/LYvrO4xgHmxy25Y7D3y/A9/vS4Mk6/894+cNVapq4fU\n8epP/+efndYlH/zSO6ed6roIp9q4Cgjn3/5/3xk6y6hjyorLpwBGOtrV2tLW2tJ4cOOzr/bA\nWz+5piOHdUoXLDhhxlLNNd944JrBC+xobkpinbdw1gnXf//ChTW82dTcDG8FhFVTpwzXaNKn\nXP3Fr7/NU7NoZmnfxz1p4RWfvsfSdcf9W/78+Kb3fHbZWDR4DDmT6E5CAox8bgWtIwFSJm3f\nASRD1/OwxtFQVDlpSsPLkA3NMCnZYF8rU0qbOTzV1zo3Mk6rPxxDkuUc2ol9x+ZpeBCMvgAq\niyP7m0rZBZOj1hcKSkjGqD9DKcO+ntHrO3WlzEO4IYrTJV3Loqsl/3u2Dfvq1ky+cVkFpPy0\n9SVNyzEUGcCQ+q7AmdcE6AsLTSVbDC1l8vVcS0qSjKyYU9e+7qpc7lm8dXrk0hM6bOkGgCFl\nc2M0pP5urzPQ7se+ec8jaXjPrHwGhIO8Suzxb35ldY9hm/GuT9965fJLyoHDz/3gnvta4cLi\nj58GAWH7Kz+559u73bcsFwGhML4CwpHED7306wf/snpnU1gDSfWU19WUODnc/1e9va0LZheO\nevZRV2cX+PyFAy6thX4/1Hd0hKA/OPW4h12GoZTMXHbS5rhF550/5/4tmw4cbGbZcfn5Kyoq\nfv/73w947l133QV4vV6fz0dW9JSkGpYBh6dSqUgkAkiSVFBwYhYQXdJTOJx2h5zPjTGiXQ6L\nah3sTaXT6aPTz4Z612q827DYsv5MhiL3uuR4aJhidV3v7e2fV1NQUCANteollcQwrK7sv7WB\nrHa7ZFiGLe34unm93uF7AZQmHYu1oNDURZixbqssDfqWDcM4OhvT4/Eog4XKcneMpOT15yup\nTKQDi2Jz+Aa57h2dxep2u0+em52KyIkk3gK3ZFLjcnCarKco8PqQT3iV3t7evo3pnU6n1XrC\nT1JPKEhD/kZypVrtMsOfcsPU7QSJKBg2j2/40k6WMnXZbKbcXjmzz9Zis6ON5j1GIpFUKgXY\n7Xa7feQheiWdwOUx8YuWdI1Qt9tp160D5y5Go9FkMglYrVanc+AuEHI8QiLq9haYuz2M1QFp\n64hvcPi6DdCmK1YHPr95v47uw3ZFHvSexXEzbC0Wi8s1eFtfDqboxe0rNPKwhjCdkBIB3C7v\nyWOtiUQiFosBiqL0LUI5gUG0Q1Gskrsgm8/q5HnFQp8Du3enwHblf/zxezeaP0VYEEyVRUAY\n3PDIH9Z3UXzOjf+27JQt7RhEw2Of//ffHfEuvOaWDy2bPbW2qtRrk/f/7va73woIZb/fC729\nvTC6FnBRUREcPmkhViAQAGth4bFMhZKcRXeczeO1gj5gEM5qtc6ePXhONFVVs15DqBloSRRZ\nPb5f8vjXHlCylgRQLHKeZqP1szmkeGTQN2UYx+5ng7/rVJJ0SnL7s/5MhuT00tWsJqM4B5/t\nr2nHRgBUVR0yIAx3A7LLI5tVQ4dbigSHf7/Hf6eKogwadB0TCeIYJPjJid1FJKDK8smNxeO/\nU0VRBn/dRAybw/zv9C0WO8nowB/CAIPWLZ5EVrDY8jlhFAwFPYVkKEPNOhxQN0NHN1AsKHn6\nxEZxyh09/2VZHu6ZwQhZ/RxKK1FUTJp5nbGyKimzs9FbKAW7RnPIaD+3PukUkSAVk039aajI\nspxOySfllRmhbloKRVWHCf6zYncT6Rnian+co51ckjTyVxMPYXONXGYG7E45Hh7qHB5V3eJh\nZEVxuPKxw4MikwhipBT1pGi0r/dhqLql4xgGVoesqtnc8fN3xTbJJT/cvvsewFM5xq+STqcB\nR1HRsWhwyZf+tfvjGjjLTMrXJQgmyeJn3PTkN+/61i4Kb5vxb8suNb9G2Tq0dlVD0nfVZ++5\naf7RRltfPoC3fnVVVVWwZ8fOHqYcC2SDr3z3s384dM6dP7196cC2XklVpZX9O7fu0c6Zeexv\nwa3bDkNFVeVor+bhf9x75y+3TrnxR/dcefw4YeOBA0msU6ZkuH13dmQVLYmWRB1Fp6RhoKWQ\nlfxvSeT00B0hHsWe+VbEkWB/Cabzl3JkN4H2oQLC0eppQ5JNS9IIFFdSv9W0TY3jEXq7Td5z\nAnB5CPcQ7cWdeU9zIkY6iTdfw4OAxUEyQjKKLZOPUEuTTmR2SHb6UgFrKWTrqH59fR03eUwF\nXFjOoZ3mnHJdzUhyFru5KCrl1TQ15Pr62anKdHeA8lp2rCPam+vVY4CWg+i6CRtCDmC1E4+i\npTNIC5xOkk7lYxs9XwU9TYS7cJs0ZSEeIhakdJE5pfXz+Gk/0tdvlc3hhk44iNuXp5urJPdv\nRWP3ZpYCNhkFaVTNg9OTETq4fs2mPfsPtqV8k6ZOW7TygtlFxzfobG0bHnlyP9Ov+eLME0NC\nLbB31XOvbj0UkHyTF7/9ygumeaRDz9770OvRGdd+9f0LFYB9T3zn4a36vPd97fqZEDn4z6df\n2tLQYxRPmbnkgksWlVmGfJV9f/vOw1sa/9kJxDf94RvfWEfphXfd8bYiR3DbY49sTZe//VMz\nJ534LrRQ445NGzZs3h8umDxz1pIV504drH2Tatmyat2OQ0eaOlOe8uqqqUsuPL/Oe8Lp1PPa\nL+9/qbn84rtuP7+IdOfm559Zt7sl4qyqm7f8kvOnH/3p7vrLtx7d3vrPTiDZV8HKt3/q1pWn\n0zCPMLayaEpMW7GilF3tboTUMAAAIABJREFU3Zs2HeJS83aRzVUiHgctkdToT/tghHf835/W\nxiGZSgLgu+DKc/9v59rHfrlq6d0XlqsAid1PPLG+M7Bo6vRjF49UOgU2QFpyxWXl/3zq+V89\ncv7XPzDLDZBu/eeDj2xK2hZeefFwgVyytyMQN1R3UaFTwb102bT7/7XhkV+8vOSLl/S9rBGt\nf/wXfztM6RWXLhiblDISigUthZ4eufmoJYBRbGGfO5ePQDuBNsozbHbpGr1d2Jz5yNyNzYnD\nTWcTFVOzz6ueTtHTRkFRthswDKa0hvqttB1ishmrINoOAZROGul5GXJ4UC0E2nFlnkK+L4TO\nIpIcNYsdxUI8hNWZwXcbD4A0FgEhoNjQo6TjI+eO19PoaRSLucn/T1RUwaGdtB+mdk5O5Whp\nulrwlWS3YeKMhacsIJy5MMMDqqezYx2N9cxYbGY9juxDUUzehBBwuEnEiPSOuhfGINKLJOMw\nf+1T0SQObqDriGkBYedhgEJzr3CeQjoa6Wyiano2h3e3oWvmb5V0HJuHSCeJUAZ706eTpGLY\n3Pm8kuRR77r777zjmw9v6To2O0byzLrunl//5u5z3wqokpv+cM89z3F59Wc+sPjoomTt8FP/\n/u4P3rfpWF5V/4ovPvzY25/5/j0/6bx68hf7A8K9j3/7nj+k37foy8v3fe76D923PnB0pour\n7vrvP/p/n1hkG/RV9v712/c81DeVNr7pD/dsgnn3vO+OtxX1FRhfZP/g7ccFX6HNP7/1vZ/5\nU/2xvLlK6fJbvveLH3x0wbEbj7b/kU/e/O+/+ldT8rj3L3nqrv7Sr/7wpfOPdkF1r37wnns2\nLFLfd6PnTx+57rNPHDz6dKXs3E/9+vEfXVkOsLN/yWN/zTfBMvsHRUA4kWXRWrVd/p1ff+xf\n1/1qw3/d9dCNT33U7AZltqasvKD6ycde/sGnO5YsnF6mBA5uWbstOWfhNOsb9c/f/x3rTZ96\n32LP+Td97B97Hlj9o8/Uv7BwTpUrdmjL67s7rXNvvvWSAgDJbrfCzifv/03L3LOuvXKeZ+77\nbr9803ef+9OX7nxj0fypvlTrzje3t8QK5t96y2XDXtG3/u8d31yVrP3gzx64oQrcF97+idf2\n/fj1H99123OL5lY5kx0Hdu44HLTWXvvpjy4csy45xYqukU6gDjOkYJBOoGso1jG5NygKBcX0\ntBPqyWxzp64WdI3CvK0uqpxG/Zu0H6Es2x6P5nq0NBVTzaxVQTFuHwd3MGlmrnGmpnFwB64C\n0/ZIPEqS8JfR0UigI7PhoHCAWBhfqZkh9GAcPsIdRLpxj+6tJ0Kk4tg9ed6T8y2SjGJDS5CO\n9W8ONig9RTrR/+Q8cvvwldC4j4ppWHN4pcZ9pBJZtqFh/jm8+rfsXzxrisqcpRkeU1yJw83+\nrWYGhMk4jfson5zZDvKjISs43ERDREOjmm0R7iWdwlWQj9uD3YOnmNa9VM/Nrt/gBLpGy26c\nBbjMbd9a7fhK6GmnsDzjYdJ0iq4mHO5cdzIclmrD4iARRrGOakNCXSPWjSRjy8Nsm/wzGh76\nwNWf/nsX9prz3/2ulTP8RufOlx99YsPuv/y/6x3Ttv7+XUNe5ff/7ztX3PZsh6GWLXzHZRct\nLA3vWv3882v+66qlf5o02OY50TVfueL+Ryd97Yk3brxwblF459/vvfOOH6//yyffM23Jru8t\nH+x8XfHll1ffsvfBG2/63WH31T947ovLcdYMsUVp6MVPn3XN/XvieOsuuuztZ89w9ex44c9/\n27ruFzdfHSvd8tsr+7prIms+/64P/2xnSvLMuPw9Vy6u9UnBI2+++MRz2/c99eVrb5m2+9Eb\nTshVoR/5/Y1v/3HTe/9n1SevXjZZbVrz+y/c9u9/XfvfN35s/q6nb6qAC+9ZtfquAw/e+OHf\nHXZc/YMXv7gcd+2YzFgTTldZtXHKrvrla6/OvuOmr35s4aJn77zrfefNqKooK3SpJ7VdrIWT\nJvnHYKQJwDLjA/d8zf7bR17ZvuHlek/VtLqlt/3w/RfXBFb/5qGndvSGEjoglV/y1Qeqnvrd\no6t37lmzP+Upm7Ti/R//4PVnl/fd3aSF77zp4q6nN6x/9rlY5ZVXzgPP0k/c99+zH374pa31\nm1fvspTUzLn0sus+9O5h9pgYjFR2yed/WPTco395eeu+Nw7GbKU1Uy/8wDs/cP2ykrGdeK/a\nSSdIJ5DTJ91xDXStf1tbxWrC/Xi0vMVEQ3S3oFpGe38NdBAJ4i3ClvlE01EqrKD1IE37KSjG\nnnlfeLiH9sP4y0zY3X6AGUvZ9DKHduY61fPQTuIRFl+cl5lLbh+RIIF2LNbRDvfFo3Q2YbXj\ny/uyCtWGvYB4kEgXzsIRPoBEiFiw/5Axo1jAQEuSiqJYBwaihoaWRNeQlDGZ4jV5HltepWEH\nM5ZkWUI8SuNefCUUlo/85MGc9TYsVlLJkZ9prsUrM5/MLknMOZuNr3BoF7WDrwbP2LY1JBPM\nXW5OaQM43KRTxMIYOk7vkL8HwyAcIBnH7sxmhv/oTF7Cthdo3E5tztF08y4SEWZfZEa1Biiu\npreHxn1MmcvoN2UxdBr3ouvZdzKOmsOPnibag8PAOux3paeJdKHruIrG6fDgnod+8Pcu5IVf\nXr32O0vfin+/+ttr5nz0qZY//vrZB9/1ocGD4tCTX//isx2Gc8lnH3/mh5eV9Z31wdf/89or\nv7Lq0CDPTz91708v+fnOp26bJAMULH3/fY93bZn8yX/U//Wp7d9bPtj56q9bsbLO9ZwTUMvn\nrVy5cqj3oG/70Wd/sieu1P7br5//7Ydn9nW8fe/bT3/srHc+dOh3d/3XJw58/xwg/fLPf7Yz\nhe8dD2565pYpb81pM771h+sWfOivnX//2+r0De8+/l6x7cHvRu969c37L+g7BWa8/TN//PX+\n6Rf9tOn5v66K3fR+B0UzV66c6XvOCSjDVlCYKLKJSHZ+Z+nSb+/E0JJ66s1Hv33ro0M+c949\n27f9hxnT26pueODJG0Z6klK65IbPLRnwNMd5N3/pvOP+X/LMuubOrw+x0YS17qrPfOeqEx+z\nTbropi8MeVtZ8bknn/zcSY8uu/uxJ+8+seTSRdfctWiIlx0rkoTFjpZES6FrgMVpK8AwJElO\nRqFvBYINU3Obj6JOpTW0HKD9MIXleIaduWQYdLf8f/buMzCKau0D+H/K9preK2mU0Jv0piBi\nF0QBxauI7VqwoegFKyIW7F1R5EUsgIIiIB2khR4gkBDS+ybZ3mfeDxtCCKm7s6Gd3xfI7MzZ\nJ5vZmXlOhbEGMqUfmwc9ErrjxG5kH0DngWjXwgx2K3IOQiT2tZddk0JjEBiGnMMIjID2gqlr\n26i2EjmHEBCGsGbqK30XEoPSXFQWweVsPU5TLaqKQTMIi+uYpxKpCuBhM8BYDpmm6Xp0txM2\nPZw2sFIo/DiqsWmMGBQDtx0uO2CHjFXz4ACKcrKeuSEYcYd06gagDkR4PMrOQKX1psXb7cKx\nf8Hz6NTezpfnKDXoOxy7NnhdgJdGeHfBTumNk/txaBuiktp36WiSsQanDiImGaHRvhbVHJUW\nJgNsFjjskCvRaNJRnoPdBqsRHAeZ0i/Dts/ShCMgEsXHERTrU8dRSy0Kj0IdiiB/dGBiRYhO\nRsEJFGQhJrVN66JwbhTnwGpCeII/hl82QlGQB8Gig7UGLjuk6ibu6TwPhwl2IwAoAsFe1MVd\nvOc8fPgEgKjrbu7T4KwNn/jcvN1RJxCmqQGaTAizPpq3XAd0evTLd85mgwA0/V/85oWVyc9k\nNLH6Bh//4Kv3xzS8PUWOGJGCrcdyc3MBXyowan+a++4xDuo7Fnx5NhsEQIfc8O7LY5c88PeZ\nzVvyMSAOyCs2p/bsKRn77PSEBn9OKuL22wZNW/WHNTe3HGjYwMdLxrz40rCGFQLSwSMGsp/8\n5srNLQBSfQiZuEL50ERFMWJpK6mD+MJGQ+JiY8SgReBdcDl5ChRF0zx4mgXNdmwqeC4gFhGJ\nqCiArhQmPQJCm2iU4zmYDaitgMsJVQACI5rtSycUmRKJ3ZF9ECd2I7l3W9sJzbXIPgi3G2l9\n/LWAWvdh2LUGBzfhmgnetF7azDi4CSIxegzzQ3Bn0TQiElBRiJpyWAwICGv6MchuRU05rCaI\npQiLE+Dpuc2kajAiWGth1oFmIZKCdkt5igNPOUy03QmXAxQFqRpSQScHaTuaAS0H56pruqc9\nQ6NpnhFRNOv/CZ8aSuoFixE5h8GKENKeR2yXEyd2w6xH5wFQ+NTGesv0jk4IQyIwcLRXRzIM\neo/EtlXY9ReG3uxTEE4Htq4ERaHXCJ/KaQUFpQZiCSwGmPSAQU5REgoAWKcF1UYAYFiotR2w\nKGTSNTj0J45vQo8bvOwC4rLj+Oa6ovxFrkJUEkpO40wmwhNaGYFpMaL0DBxWhMR4MamSd2gG\nihDY9HCY4bSAFYNnWJqTgufBs5bqumlFWTFkAR3UGd4vRBERQUBl0fJ3vrrnqwe6aeoui/Ih\nj382pIXDTDu3H+SAPg882KdRDWTiAzNGP5ux4cK1hSRDhg9o9HzUxOod3jiyY4cRCLhzxu2N\nbjWBt7/9u3K6iYn3VP0lPfzbwYcvPNyVnZ3XdMGdhw9vdLqJVCop0FSPWIKAdwlhlzn7rXME\nj4ToOBQFSgTwLrPFAICiqCChBvJ7h2ERngCDDvoqlOWBYRmxTOHmOFA0z6HCAqsZPAeRGKEx\nAk/f1wJtKJJ7IfcIju9GdDJCYlp6DOfcKMtD6WkwLFL7+vj42xKpAr1GYe/f2PUneo9q3yBA\nfRUOboLTgf5jvUkm24VmEB4HfTVqKzx/U0qukjtcHEVRHEfX2GC3wOkARUMbAm1Ix/dYEsnA\nSusemOwm0KhLWZ1O0CwkKkiUF6mKpAFPTY3BXLfWn0qlYjp+pXaaRpeBOLoDJ/bCbEBclzbl\noxYjju+CxYTEdIT42rqV0gPdB+LIbh+LaYeJD/kwmjU2FV0H4NgeqAPRY6iXhfAcdq5GbSWG\n3uzXqXfriKUQS+BwwGmD3caCB8DzFKRyiKUdkAp6SBToPAJH1+PYBnQZBWk7H7ntFpzYBLsJ\nXUZB7tdu3qpAxElQlI3ibFQroQ1tPDKQ42CuRW0VTDVgWESn+HXo4IUoCjItJErYzXBZwTlE\nDOpq3FwUWClEcogu22lFzxry+Owh3z+9o+CXB7v/9Wq/UWNHjxg2dOiIYX1jFS1dok7n5ABg\nU1IunM1Ok5ISig3lF2yPS0jwzx3KkJNTCSAxJeWCy402/YY70y84wG0oOHbk2Mmc3DOnc04e\ny9i8fseZJgtmEhL81gWIuDJdvlVDxJWFoqAJhioAZj0sRtpukXFnl/vjWMhVkKshV3Vsywig\nDUXnAcg9ivzjKMtDSDS0oY27VJkNqC1HZRGcdqgCkdjdL3OfnhdVCPqNxcHN2LMWKb0R27n1\nFaI5DgVZyN4PRoR+10HbMRXVFDRBUGlhqoXFCFOtvH4pQgcgkSMgAKoAf88i01J8FCRKSJTg\nOVTrasFTAK/SKMUScmE8n1iKniOQtQ8FWagsRkI3BDe/opfTjvwTKD0DmkbXgQgSZu2v+2dj\n1h0dtCBhpy4YfatvRfQcjtoqHP0Xdiv6jWl3fYfDhm2rUJaP9MGIS/MtlLajIJZALLFwtN1u\nByCVSJWKDpldtwF1KNKG4eQOHPoLacOgjWjrgYYKZG2Fy4HkwQjogNkxpAp06o6aclSVoDQX\npZSMFYlAgwLDcyhxgudB0QiMQHDkxbrK0SxkGkADq8VmMdsoUDRLabRqf/ew6Sh02qy/98e8\n/szcL9edKNq7+pu9q7+ZD0gj+oy/95k3X5qc2nS1Z35+AYDgsLAmKvwiIiKACxNCudxPI2fz\n8vLq37YVhsM/zHt+/vcbs6rrr4KMJnlo39gtGQUX7i2Sy8ltjGgXcsIQlxKagSoQqkCn02nQ\n11Icx1F0cIi3g+UEIVOh6zXQlaLkNIpOoegUw7ABjIijGJpzUfl1wzGhUCO+m/fj+torIBSD\nJuDQFmTtQ/4JdOqOsLima/GddpQXIPcILEZogtFzRAcMYjkPzUAdBHUQz/M1VZU0z3EUpdYG\nsqKO6yDaKooGT7k8D0kUfWF/IQJgWHQdiLJ85B/H8V2QyDwDWVmHCzQLzk27bdDZUV2C2irw\nHEKiEd9NwPUJYpNw2/345QuhymsWK8LD81qvY2kFRWHYrdi7DqcOQq/DwOuhavN6KhVF2PUn\nTHr0Hoku/X2L47IUFIse1+P4JmRugDYCCX1bmSzUbkb+QVTkQiRF1zHQ+HmA+TmefC8g3FPh\nxVnNlNNO8TzHimmVFgotFBqfzySBUDxPuXiAptgrJRv0UKRNnL9m4ms1J3esXbthy9atm7fu\nySndv+Ktu/76J2ffrpe6NfGQGxYWChTpKio4oPFfp7KyskPCPis0NBTIhk6nA1qqOnMfeXvs\nkOd3myCNHTb1zuuH9u3VNTU5JTU+RPTLRHZyEwkhQbSXTwmhW7f/x0WfrdmfnZt7+nRBrTg8\nMTk5OaXX2Idm3T8g9GL3tSIuczwo/qL32KtDISgSQZGwmlBbwVuMnNVMuV2cSMJogiBXQxvq\n5TrFvpAqMPAGlOYi+yAy/8Wx3QgMoxRaGQeeFVEuJ10BmGtRXQ6eg0yJHsMQntDRTazn4yia\n8zSVXNQwCG9RCI9HaAzK8lBVjLIzKM1t3KdPLEV4HMLjW5kgyit3PoJjGTi+X/CCz3Pv0+gk\nyIRQDINrxkMbgoNbsPorJPdEt2taqY6prcTh7SjMhliKkXcgUtB1ay4rigD0moDCoyg9iYNr\noA5GYCy0EXDxdZcOl50yO1FbhupCGCoAChFpiO1+MbpBUhQUaijUdovFYrEAEIvFavVFGnx8\nlWIDUkfcnTri7icBV/XBpU9Oun9JTsb8d9e9+N0NFybkKSkpQJEzJycfaNRr1JqbW9pBIXuE\nJyersNOYe/r0BQmhM2v1lxsLED70vtu702vmv77bBMWI9/atf6pzw8pUs83WoQETVy6vE0Lj\n7kUP3D/3l+OGBrXpZ45Wnjn67/oViz9ddOdr3331xIDLcmEbgmiOTAmZknO79TU1ng1BQUHU\nxc1tIhIRHg9dGSoKUFlE6UrPa5GRKRGdjLBYBEZcKhXVxOWOZhDZCZGd4HLApDfXVMFh5yla\nrNKI1Voo1P6b8Ilh8PwivDjNj+vUX3sHJkwVtMTO/RCdhEPbcPIATh1EcBRikhASTYGheAo8\nT1lNMFSiLA+FOdBXgWHQuR/SB/m95/klj5UgoS8iO6PkBHQFyKurCFBQtAIAf3YqSIkC4amI\n6tzuAYfE5Y5b91jc1OU26c2LT399Q/2sy2xgr3tfmvbukrlHLSUltcCFNVMBffsmYlPu3m++\nOvz0mz0a3hhLfvhuXRNzjPpTeu9e7OJt5cu+WTN/6ISG/VLd2z6477HPdcEPbXnkduQcP24E\n0GfS1M7nd61x79y5p0PjJa5cXiaEVb8/cttTP5cC0pjh0/5zW/+UuLhIlb0i/8zJvb9/s3hj\n/onlT94mTTyy+MaLOlUJQVwNKBrBkZ4xXZzbVVteSrscHCvWhIYzHThvJ3HVYcXQhjh41u12\nA2CVSkj9nsOoA/Dy53j5P6gsEb7wwWPx0P+ELxaqAAy9GV0G4MwxFGXjwBYATSQvgWHoPgSd\n0qEgjUvnSBRI6IuEvjDXwKSDqdZhM3M8D4mCUgdIFIFQdviqMMQlgu7SKbKqai9++ei7F6+f\nmVif2Vkzf/vrJID43r2bPjn6zfrfuE+m/33yo5kvjlkzf1SwpwrLkvnpzFe3dPR6p5EPzJ2x\nYPRnxUuffmxCly/uSKy7Z9sOv/HC9zpAO27cAAqIjImhcYTLOXLEitH13ZGcBb8++NC3lQBg\nNBq9j8FuMjkB8rRwtfMqIbT8Nev+H0vBpM5csfmjmyLOO4vu++/s/61+fORtn5/8/v6nJ+cv\nHtfhPekI4upF0ZxYxnmmvbk8VxomiJaFRWP+j3h1JgqyhSx2/N14YLY/vzRB4QgKR9/RqK1E\nTYVdV8E57ABYuVIUGIKgCJIHtkwRAEUAFCaHzWYDIJFIVKrLdPk8QiAxdz1x6/wpKyvXPdq/\n19qJ43vFqClTadaOVau2FzoQNHb2A80tEBg2bcHcL3Y8t2vPgnE9No6/fkSPMFvOrr9Xbz4t\nv+mesdt+WFcrFndYeiQbNe+dSaum/Hzqu0l991w3fnS/FK3lzI7fftqcb0PIrZ8vvFkKQDH6\n9nHaP/8q+eLOIeaZUwYnquzlpw9uWLZsS2lY/55hew+VH/t0+nTTI4+/fk/vdtXKyWQyAM5/\nFj31vq5HTJ+Jd/Rp81Bn4orjVUKYuWWLDujy3M8f3xRxYQGiiBs/+uW57d3fPLZ5cybG9fM5\nRoIgCII4KygUC3/C129iw28ClCaW4P7ZuG6iAEW1iTYE2hB7UIzD4QAgl8tF/prBkCCubOF3\nf/NbAfXQ/JXHj/z++ZHfz24WhV8z/aUPP5jZqdn6Hbr7sxv3hDx4y8M/Zmf8/k3G7wCgTH9g\nyS8fKl7T/gCZQtGB9amhk5cdCu9+75R5fx9ft/T4Os9GJvSahxd89tad4Z4fI+77dtmx2+/7\nYOeBJW8eWOLZJk+b8umuTx6O33hf37sX5+35/t2AoS+3MyGMH39rz1cOHqra8cmsHej71iiS\nEF7NvEkIaw4fLgTCRl7Xvbmj2fSxoyLePFZw6FAN+nXo2jsEQRDEFU8swSOvoPdQfPMWqsq8\nL6fXEMx4ERFkxS6CuBwFDJ39W+ZDp7bvOJRbUFhmEgXHxMd37je0Z0TD1mNx76nz5g1E0nnJ\nkqzL9CXHbnnl0O5de46WICx91E3XddHSxR/kO4CUmJizu6Xc9tK8JFf4kAs6n2qHPPbh14/H\n96lfaLSJdwkf9dg8tkraO6nBcU0VSIeOmLM296GcowcOHjiabw9K7Ny594B+8aqGw7HDxr2z\nI+fRnRt3HcspqGGCEroPv25oipYFcNt3p8tf3J1RKkvtrQYQOOTBefMmsN0vXK4meeL8z0aG\ndx5S/8uB7vnyjsyBv649WGKXh3QZ1WHz8xKXIm8SQs8KRSzb0rFisRiA0+n0LiyCIAiCaNnA\nMeg5GOt/xqrFqGnndPGde2PiTPQa7J/ICILoIJQ2ZdiElGHN7yDqNWVuw96jur0//XpQT3e6\n7oExif3GJfYbV/9K9ebNhwFxenrq2S3Jt86Z2+SipJoBU/47oKV3ARA28tG5Ixsd12yBkqCk\nvtcm9b22+d8DioTBNyU0ccmiNcmDRifX/RAweMbcpi9rieMfe+jCMpOvvTe5pTclrhbeJITh\nPXuGYWvxti05/JCkpueTy9uyNR+I6NmzY9a/JgiCIK5GUhluuhfj78aB7dj6J47sgsnQ0v6R\n8eg7HCNvQnxqS7sRBHGlUpb++fRDP5rDJoccW3bLuakP+aoNL8373QjJhFuvJ724iauOV2MI\newwerPhgxcE3Jr80avMbgy6YLc28b97kVzMA5aBB3X2PkCAIgiBaworQfxT6jwLP4cxJFJ5G\nST4qS+02CxiWV6rZ0Eg2Mg5J3RBEekURxNVNMvaJWb1/e+3AT3f1082Yeefg1GCqJv/Ev798\n+u2OCrDdn3vlTs3FDpEgOpxXCaFq4vsf37D5vj/3vzk04a9Jjz1y24CUhNhwua28IC9776rP\nP16WUemG9oaP3r+jxTV4CYIgCEJAFI3EzkjsDAC1tVaXywVAoVDIZF4vuksQxJVF2vfVP3+n\nZj7+/uoNH83e8FH9dnHE8Ce/WjavN7lYEFchL0/72OmLl5284753t5Ye+umVB3+6oNTwobO+\n/W56TFOHEgRBEARBEMRFEn7tK78fe/Lkjh2HcvIKKl3a2KSkpJRu3VNDyFomxFXK63qQ4LHz\nt+Q88OeiNz75c/+p07n55SYXqwiN7dQppff4R1586sZkhZBhEgRBEARBEIQw6IDUYTemtjAb\nDUFcRXxqGJd3uuHFb294EQBcZr1dolGQdnaCIAiCIAiCIIjLhTdrb1bt/GbBgo83FjXcxirO\nzwZN+5cuWLBg5QnOt/AIgiAIgiAIgiAIf/EmISz/Z9Hs2W+syW9pH3vG4tmzZ3+4qZ0rQxEE\nQRAEQRAEQRAdpa19PE2nd+7MMXn+n5djBOx5u9etMzW9s9tUvH7JXgAWi0WIIAmCIAiCIAiC\nIAjhtTUhzP/xoXHzMhtuWfXMuFWtHBTSq1e0d2ERBEEQBEEQBEEQ/tbWhFAcnJCW5vL8316V\ne6bKHRCbHCZvdn9aFtL91rmvjhH5HiJBEARBEARBEAThD21NCJMf/ePEo3X/P/ZKerd5Vff+\n34n3B/srLIIgCIIgCIIgCMLfvFknQtNt3OTJxl7BggdDEARBEARBEARBdBxvEsLo2xcuu73u\n/y5DfpkrLjqw7kdr9s49jqSBXcKklEABEgRBEARBEARBEP7hzbITAABe9+97d/eLDQiIf2qD\nu36rYf1LI7uFa+PGvrqxxN3C0QRBEARBEARBEMTF5mVCWP7rtB7Dn16WUWjiWFGDiWPEIdFh\nEtgL18+9tve9K3TCxEgQBEEQBEEQBEH4gVcJoXXz/55aWuwSxd/09vrTuiW3MfWvBExaUqo7\nufyxPiq+fOmTL22xChYoQRAEQRAEQRAEISyvEsLty5YVgUqb9duvz16bqGbOf5FSpEz66JeX\nB7Ao/PW3vULESBAEQRAEQRAEQfiBNwlh2alTRiD2hlt6N7vKYMKY0YlA1YkTVT7ERhAEQRAE\nQRAEQfiRNwmhZ9Sg0+lsYR+LxQKA53nvwiIIgiAIgiAIgiD8zZuEMLhv33igZPOG481NJMqd\n2rilCIju3TvEh9gIgiAIgiAI4gp0+tt7R46c+u3pix0HQXg5hjD9urGRwNH5tz/2R8mFzYTu\nyg3PTHzjEBA8alQVgZcNAAAgAElEQVR3nwMkCIIgCIIgiCuL6czeLVv2nDFd7DgIwruF6SEe\nufCnWVtGvZf1+c2Jfwy6895bBqTExkRoOF1R3umDaxcvXpdrAaKmf/fe9TKh4yUIgiAIgiCI\nFjkcjm+//fbff/81mUzp6ekzZ86MjIy82EERxCXKq4QQUA1d8MdSbubTn24p+veH+f/+cP6r\nlLb7lDe++XhCkO/xEQRBEARBEETb5eTkTJgw4eTJkxqNRqFQrFy58r333vvqq68mT54sSPmH\nP7z9yZXpr2ye1/XA0o+++D2jwKqK7jp86n/vHx7VcL5FrnLv/y3+Y/fho/n20LQePUdN+s/1\nKZ6mkt1vjn1h5ckCgF/ywMgdIePe/uv5foJERhBe8XJheoBNmfT+5uyTaxc9c9/E64f2Sg4P\nCIxK7TNiwuSZ/1u8//TBJY/0VQgZJ0EQBEEQBEG0guf5yZMnFxcXL1++vLa2tri4ODMzMykp\nafr06adPCzNirzb73y1bMo+svG/AzYtOiJL79gwrW/f+wyO63/59Yf0+um2vjek+aNpzb3zx\nR8aRbUvfffmx8b1637MkywUAEk1YeJCCARhFUHh4qEYsSFgE4S0vWwjrSOPHPbFw3BMCxUIQ\nBEEQBEEQPjhw4MD+/fsXLFgwadIkz5auXbv+9NNPaWlpixcvfu211wR6n/Uvzhn/7e7dd0Qx\nAPDEkHHJ961e+E3WvfPSANi2vTz1f5v1ne9f+tsHkzsraFflzkVT73huyYMzrxmx9eGYXo/+\nsGzIy53XHXfdtmDZaz0EioggvOV1C2ErCn97ftq0OavL/VQ8QRAEQRAEQTSWlZUFYOjQoQ03\npqamhoeHe14SiKnnQ+/UZYMAwseN6wGcbYIs//61rwqR9uSSL+/urKABsCGDn1n27i1K27bX\n39kuXAwEIQwfE0LOZW+Cterojx9/8OOPn/6VKUyUBEEQBEEQBNEquVwOwGAwNNzocrlMJpPn\nJYFEDRgQ3eBHmazBTIrHDh92IWXSXX0aPmcHTpx8LYOSI0d0wgVBEILwNiG0n/r+4VHdItUS\naRPkId1f3GKHqH//noIGSxAEQRAEQRDNGzRoEMuyX3zxBc/z9Rt//PFHk8k0fPhw4d4nLCys\nuZdMubmVQFxc3PmbRXFxEedaEQni0uHdGEL9r9MHTf+pxQoOdff73lt4J5lnlCAIgiAIgugo\nYWFhTz311MKFC8eMGTNjxgylUrl+/frPP/88PT196tSpwr0PRVHNvSTTaESA0WgE1A23m0wm\nIEKpFC4IghCEVy2Eed+8/bMOVNSt7/9zvKAoe+OcfhIwwxaeKCsrzTu68ceXxoRRTMToGXd1\nF7BhniAIgiAIgiBaNX/+/Lfeemvv3r133XXXjTfe+PHHH0+cOHHDhg1iccdM58kkJSUAWYcO\n2c7bnHfwUC3olJROHRIEQbSdNwmhc+/u/RzE18796snRnWOikka98Oi1jHv37syAsPC4bqOm\nvPbX5jd7nV407dmNJsHjJQiCIAiCIIjmMQzz/PPPl5SU7NixY+3atYWFhUuXLm2hi6fg0m+9\nLZmq/b833s+yn93EV/8x94MMKK6/ZYzk3I4cx3VYUATRLG8SwtKiIg6I69PnbIdQRXp6IhzH\njmXX/Szq/PjsO1SnP33hy3yBwiQIgiAIgiCINlOpVIMHDx43blxUVFQHvzXb94WF0yKcu168\nZvC0eR8uXvLV20+N73vHD0XSfi8tvCe8bh+WBQo2Lvlt3cYjFR0cH0Gcz/tZRhmGqf9/QkIC\nkHvqlOvsBnm/fl2BjI2bDE0eSxAEQRAEQRBXKPXN3+5fN298QNayV564754Hn/9gk7HXjG8z\nNs3ufPbRO/n6Sf202PP+HeNuef/gRY2VILyZVCYyJobGrvzMTBN6eMbFBiQlBWL9/v2ZuLVu\nXlGFQgHw5eUVjYbTEgRBEARBEMTlq+cTK7bcIekU3nCb4sa3NyRUhpybX5+JuHbun7kvWytz\ns/JtwSlpMerzH7rF/V/eW/X46cMnjcr4tI6JmyCa4U0LIdt/UH8G1tWvzVqV7/Rs6tGrF42T\nv/x0qK4ntCMj4whARUdHChYpQRAEQRAEQVxsmqRrhg/vHS1puI2N7DXmuut6hDbalZaFJPXq\n261xNliH0XTq3b9nSqjUb6ESRFt41WU07v7n7wqG++RXtyYGjfm0EEDg+JuHiHDqvTsmvvr9\nLz9//fLtMxZXgO4+sD+ZZ5QgCIIgCIIgCOIS5d06hMpbvtzxHX3bY0uOG3U1DgCInDH/6U9H\nvpW1Yu70FZ596JgZ8x9LESxQgiAuPw4HMg/g8D7knaYKz2hNRspk5OUKRqFCVCwSktC1F3r1\nh4zUHBEEQRAEQVwc3iWEgCx1+vfHpnxw5nC229ODWjrozS3/hM159btNx6vFMV0H3z57/tMj\nyMqbBHE14jj8uxlrfsHW9bBZ6zefd7k5knF2qwgDh2HCRIwaD5GoI8MkCIIgCIIgvE0IAQAi\nbULffvU/UWFDn/x6w5M+h0QQxGWL47B2Bb75EGeyW9/Zw+XEjo3YsRFhEbj3EdxxL0kLCR85\nbKgpQ2WpyGZlAGgC6OAIaEJAM60eShAEQRBXnTYlhJb8jH15ZlFkj0HJWn8HRBDE5Sr7OF57\nDkf3e3l4eSnefhk/f485C9B3kKCREVc+nkdhFk7uReEJVJd5tika7sCwiEhEfDekDYS28bQP\nBEEQBHH1alNCeGbxfSPmZQY/9E/lZ6MbbufdDoeLByOSsN6vZ0gQxJVgzS94/TnYbb6Wk5eD\nB+/A5Psx639gSVMh0TqXE0e3IWMt9FWgGYTFo+84BEWAkpgpxsW5wNIyc424qhiFWdixAjtW\nIq4LBkxAbOeLHTpBEARBXAJ86jJ6/PU+3ZpKFAmCuIpwbrz2LFYtE6xAnseyr5F/Gu98Teab\nIVqWexgbf4S+EkGRGD0NaQMgOzt4vbbW6XK5ACgUYpmsbmNVETJ34MhW/LwAnXpi1FRogi9S\n6JcSzg1dIaoKYayCqUbhtMkBiGWUMgDqEATHIjAKFKn4JQiCuEL5lBASBHG1c7vw/Exs/Ev4\nkv/djJkT8dlyKPw7O5XDApsRxgqJy07RLA8bpVBDpvbrexICcDmx+f9weDNUgZjwMFL7g6Ja\nPyo4GiMmY+CN2LMG+9fj+5cx9j6k9vd/uGe5nbDUQq9jrUaaZuBUM1wg5JqLNr7Rbkb2bhRm\nwmEDAIkCMjXPaNwUKM5F6wpRcrJue2w6kgdARJZLIwiCuOKQhJAgCG/xPF59xi/ZoMfRA5j1\nH3y81B/TzNiMqMiFrgAmnWeDyvNPCQBAokBQLILjoQkT/J3bgefhtMFuZF120AwvpigRS2ZG\nAQCbGSveR0kOug3B6KntzlKkCgy/E10HY/WnWP0pdCUYdIt/Aj3L5UBFLqoLUFsGngNwXtM3\nI0JAFIJiERLfcQ1xbidy9iJnL1wOhMQjpitC4iFVwmi02O12AFKpVKlUWgyoPIPCTGTvRv4h\nJF+DxD7kJGzMboJFz5j1EgAuOSMGJErSpkoQxGWDJIQEQXjrxy/wx3L/vsXe7Xh3Lma/KWCR\nDisKDqPsFHgOcg1i0qEIgNWlZ8Wcy0lJWZXDzFQXoTQLJScQEIn4vlAGCvj+bcBDXw59KQzl\ncDkA1LVXVgIUBUUgNOHQRkMk6dioLhl2C35egMoijLkHPUd5X05wNKbOxZrP8O8qOO0Yfqdw\nITbAuVGShcIjcNkhliE8GepQuCgLT9vBUwwvg0tSW4LqYlTloeAQ4nojJN4vkTRkrsWe32Cs\nQmAUuoxAUHSze8rViOuBuB6oLsbxLTi2GUXHMOB20ooOACYdaoqgL4PTBkAC1H0nSwFGBHUY\nAiKhCQfa0HZNEARxEZGEkLjkOK2wmminQUaBBs1ZGEiUYMj0IpeaY4fwoZB5WrOWf4d+QzB6\nvCCF6QpwcjvcToTEI7Yn5GcnTq6qcgIQAxoNJxIx0d3gtKHkBIqP4eBqxKQjvlcHPdUZylFy\nAjYDaBrKECiDYHeZKNbNc5SIkrtsrKEMxcdQmoXQTghNAn2VXcXdLqxchMpCjH8Qna/xtTSR\nBDf/F2s+x761UAagz3VChNiARY/jG2E1QBWM+OHQRtSdRQaDy+FwA5DL3XI5wlPAuVGVh7yD\nyNqC8kikDQfrt4S/Kh/7VoHj0O9mRKa19ajAKAyZgvzDOLIB25ag/60IiPRXhJc+qwElx2Eo\nB0VBGYTQTgBrd3JWigbFiUWU3FgJfSlqiiDXILIrVCEXM1qeB9wMzwMMyU0JgmjCVfYoQVzC\nODeM5bBUe5pEmPop43U1ACCWQxkMRRCpar00cG68/ixczg56u/kvYMAQKH1tkig8gryDkKmR\nOhSq1qYSEUkR1wsRacjZhcIjsNQgdZh/KyY4DoWHUFMEkRTR3REYU9cxr7rawXEcAJVKIpGw\nSIe5BmVZKDuF6kIk9IdM48eoLjXbf0XRKYy6W4Bs0INmcMNMWI3YuhwRiYhMEqZYADXFyNoK\nnkfacIQktB5GaCcEx6MoEwWHcOhPdBkNuR/+srVl2P0rxDJccxu04e0+PK4HVMHYuxL/LsfQ\nqVBf1DznYqk8g+KjoGiEJSM0CawYACwWt8XiAiAW02o1ghPAuaHLR9kp5PyLkAREpbdpmKuA\nHBZY9bAZ4XbIABkAJ2Arg0QJqRpSdUfHQxDEJYv0cL8quZywGFhzTQBvD+RtGs4Ggw42Mzju\nYkVkqkTpMRjKQIugjUJQJ5c0qloarZNG1YQmQxUGzo3qApSdgN10sWK8DHAuMBCzkDIQ825/\n3upXLEVWph/Lb0RXga8/8LGMwiPIO4CACPS8ofVssJ5Yhi6jkNAHuiIc+8cz+ssvXHbk7ERN\nEUIS0Xk0guNbGqalCECna5A4AJwb2TugL/VXVC3jXKB5MQsZAynnonne7+9YdAoZ65DaH70F\nbcpjWEx4CFIF/vpSsFoOfTmObQQrQc8bWs8G69EMYnug6xg4bTiyFjajMMHUsxqw+1ewEgyd\n6k026BEYhSF3gaKwdwUcVkHju/TxKDyMoiNQBKLLaER2qcsGm0QzCElElzEIjEHlGZzeBber\ng8J02VGdj6pcmHVgWEi1LkZlZlQmkdomVsBmRE0hKnNgM3RQPITgbJnf3TsoPkCu6P3akYsd\nC3ElIAnhVcblhL4SNWUw6ynOzYFyUTQoCg4bjNWoLoFZjw54rGuA51Gdj5pCsGKEpiAsFaow\niOQ8xXAUzVOMW6KCNgoRXRAYB86NymyYqjoywMsAz8FhhqUadgMthloEpRhqu4G2VMNh9kMO\n43Liu4+FLrQ1Py9Gjc7ro3UFyDuIgCh0HdPSA1xzotOR0Af6cuTu9TqElnAccvfCqkdMT0R1\na+uMHeowpAyDWI68/TB7/9m0G8/DYYZZB0sNGLeC5RUiXslZxeYqWPVw+63ZmOfxzw+QKTDm\nHuELV2gxagpqK7BvrQCl2Yw4sQliGXqMP9ctue0CotDtWridOL5ZyBSC57FvFZx2DLjN1xGA\nyiD0vQkWPTL+ECi4tqEpRsRKRKyEpi7OtDYlWajKQ2AskgZBJGt9fwAMi7jeiOoKYyXy9wP+\nv8HaDKg8DbsJymCEpyE4EbIgJ6OwMgqbSG0LjEV4Z2ijwHOoLoC+pCNCutrsfyGZoqiAGesE\nK3HdDBVFUckvHK77ufCzmTN+2GVLmzRz2kCycg4hgHZ0GXXVFJw8ebLhltwqe5Pb64mDExKC\n2v/wRfiJzQxTDQDI1ZAqnG7OYDAAoCgqKCgQDjusRlgMcFihDgbTQd2Jq/NgqYEiGAExLXZf\noaAIgkyDqlzUFAA8lBevqxLDMBRF8R2bOTfHZYfDBJ4HIwIt5c0WIw+OAi2XKXkX7bTCZYNY\nAVbAyeL/WYOSQuGKaxurBb8uwYwnvTjUYcXJ7ZCrkTbc+3n/orvBXIOSLGgjERTrZSHNKTwE\nSw1ierS7ZLEcna7BqW04sw8pwyFu2xOqL9wO2IzgOTAisHKYrUY35wIPmUzBQOyywVoLVgKJ\nSvjeaKcyUFWE0dPOrTQorLQBOLQJGX+j97WQ+PZJntgKzo30sd7/RVQhSB6Mk9twZh+SBOoc\nW3QcNaVIHy3M2L/QRKQORtYOFB1HdBcBCmwBz4NzgnNDRMvq51JyWkEzoNmOm8yzphjlp6CN\nQFzPdg9eCE2C24Wykyg5gUh/flyWatSWgJUgMK7Zyi+KgjwAMg30JTBXw+1EYCwZjnF5ycrM\ndKPPk8sWz46/2KEQV4Z2PPTXLv9PWlMTCja3HUC3eZlH53b1KrDzVW358P0N5c29GjTsv7PG\netv55aphNcJUC1YMTXBdA4Tb0eBlCmIpxFLYLTBWo7Yc2rAOyAkNpbDUQBUGbVSb9qdZhCSh\nMge1RRBJIVH5Ob4L8TxDUQEaTf2PAC7iOAyHBU4LaBZSJWgWHMe7LXV/VlbKMww4F+wm2E3g\n3BArBHrX1T8LVFD73/eBJ7z4tAsOwe1C6jBv2gYbSh4EfRnOZCAwWshnUGMFaooQnICgOG8O\nF0mR0B/ZO1CSifh+gkXVJKcNdiNoBlINGDEA8DYXDzco0CwnkUKsgMMMpxWcGzKNwE/qGX9D\noUX6MCHLbOSam/DLQhzdir7jvC+kMhemKiT2h8K3yWlDE1FThLJsRHYRYDAh50bWdigCEN/b\n16LqJQ9EYSaydiCqsx+vgpyrrtmZZmB32hwOOwCxWCIRSTk3ODdotiNmHXO7UHQUUjXi+niZ\nO0WkwWpARQ4CoyH1zxytdjP0pRDLEdSG9UsoGtpoMCIYK2Eoh/oKfYoyGo1mszk8vEN/vW7P\nbMx5wEmrIwQrceibh3Nm8yJtTN3PHMcBKlXHPwQRV6rLpMuovSL76NGjx0+ezm1KQbVdkHfh\njy6dM2fO0sP+bvcxHfrx1TlvrD7j57dpyGGDqRYiCbShrXRHk8ihDQXPw1Dl776jLjsMZZCq\noW1PdTVFIygRNIvqgo7t3Mrz4DjwPA+YLRajyWS2WEBR9ds7MJQ6LhucFjBiyDTNTjVJs5Bp\nwErgtHomRveZvha7twtRUPsVnsHJY+09yGpAWTZCE6AM8vX9aQZxveoKFAyPkhNgxYjo7H0Z\nci2C41FbCnO1cIFdwO2E3QiGhUxblw1eiKLq5qvgXAIPT6opQ+lppA8F689H/7iuCAjHsZ3e\nl8BzyDsIqaods3e2FE9vUBTyDwhQVGEmLHp0HgpauNs+zSB5IMw1gn4jzud2wu0ERYOVghGD\n410ut9Pldro5JyMGKwXFnMsY/ao8Gy47otvco7tJMd1B0Sg5IVxYDfAcaotAswiMbUddjCoM\nMg1MVXCY/RLVRZSZmTlixAi1Wh0REREbG/vTTz8JWrwpa+XrU4enJ4Qq5Zqo1P43P/f9wdqz\nr0m2PJ+UlPT4X+ceDAwHvnrs5mFdwrXByQNvnPHO1orsN3pR1NAP6po6fruLokL+u5Ur/eet\nu65JDFLINNFpI+57d1vZ2SEf1hX3JyX1nLtbDOi/uZ6ixi02AVseC6ao5Bf2C/p7EVepNjUB\nJT6wdPc4b0aOy6ITvTiqWV3u++qNG/xYHcLX5h89elRxLe/XrhO6rYve/TlDrw7ruIsvD1MN\naAbq4DbV4rJiqAJh0MFqhNyPS03piwEgIKbdHzfDQhOF6jyYqzqq4yjPg+dBUaAozu222upS\nK7lCQdH0uYSwA5sKeQ52M2gWUlVrHyAFiRI8B4cJrAi+Dr3Zvwuc27cifLBvB9K6teuIitPg\nOcT2FOb9Qzuh4DDKsxGRKkyBhgpY9YhK97U9PiwF1QWoyEFCf2ECa4yHzQCKhrQN7X6sBBIl\n7CY4rW0dZ9Wq7P0AkDZQmNJakNYfu/6Avgoarwbm6MtgMyJ5kDCto1IlQjuhPAdOu6/LThZm\nQqoUJk1tKLoLMjeh8BgiUgQuGQDnBucCzbRUAcGK4XaCc4Gi/LgEC8ehMheqEF9XjxBJEZyA\nihzYjJAK/TRj1sHtRGBcuz8HTSTsJhjKENxJ4JAuosrKypEjR1ZV1U05UFhYePfdd0ul0ltu\nuUWI4t2nPrl14GP/GFUxvYfcOJDK3r51zcLpmw+YD254pNOFN+TKvx4ddsenWYjuN3J8X3nZ\nvhWzx/z9U1cnkHzebtVrHxnxc9GkOe/PSBTlrn37xYXPjDvBHdr9bKPvlmzwfz//PPn7Jz7a\nFXfnO7NGRvYUegwDcVVq02VDFtV9QNt69F3dOLebYlpe5MddtHLhJ3v1HRWRh80CtwuqoHbU\nDEvkEJlhMUCm9NP4DLcLFj2UQV6utaUIhLEMpo5MCCmq2XyvPifswITQYQZ4SFrNBj0oiJWw\n1sBh8bmf7SH/TKvSRgf2YNpD7TqiKh+KQMEW0aYoBMehKBN2MyRCdMHVl4GiEdj8suBtxIqh\niUBtCTi3Ty0YzXHawHOQqtt6PRDJ4LLDYQYrFeZrUZAFpRbB/r8TxXXDrj9QcALpQ705XFcA\nihJylGlwPMpOoaYIoT48rDvtqC5GfC/hL1GMCGGdUH4aHCdk26OHp22wuWywYQw8B7cLNOOv\n2lxjBTiXl526GwmKRUUOaksRLnRCaNJBLPcmz6QZKINhKIfDArFc4KgulqVLl9Zngx48zy9a\ntEighDDjs7f+0Yfd/ceJpTcGAABf/MXoTg9t/Hxp9iP/a1w34tj1+mOfZklHvbf596d6KAG4\ni9c8MOKmxUWNEkLdsk/OfHXy7/sjKQCjRg6RZUdMXfXT72eeffb8aYrFaeNnpjG7n/loV+TQ\n6TNn+tz3hSCAK24dQt5SlLHhn72nS3UGp0QbHJE8cMzoXpHS8+4QtoI9/+w8kpNbapGHxqQN\nHn9dehADVG/79N2/j+UDOP7LS3M2xF7/7ENDPHPDOYr3b9xx+PSZYr0kNC4+td+o4ama+gKP\nLZ/zf7mDHnyuT+FX73y5/lTg5C8XTWq+o7o9a8mCH06GdEvTZ2b56RNois0CmoG0nZd5uQp6\nG+y2dh/YxqD0AA95gPclyAJgKIXL7sflm+t4VuNo+Umqg3NCHi4HWEk7Hv1pBqwELgfEPsZ4\nxm+dw9oir33vbjfBUitY86BHUCyKMlFTjHAhmkQMZVAGCTMCShOO6kIYK6Hxw2AZpw00277v\nmkgOmx5uhzDf0Ip8IVcIbEF4AigaFfmAVwlhdRHUYRAJN4eTNhysGNWFPiWENSXgeQT7pyEh\nOBbFJ2Co8H4diyZxLoBv61eDEcFlrxtP6A+eiht1qABFSVWQKKEvE+YCUs9hAeeCzNsIZVoY\nymEzXDkJYW5u7oUbT58+LUzpXHFxGRCqUJztAUFFTf1uZ7ciZ+iFfwHb74s+P4Nucz94skfd\ndFhM1IR3/nfj8nv+OL/rHR989zPTI+tvz+qePROxqqKiAmjzujUE4bUrKiGs+fe9WQu26nhW\nERyspq3HDuzc9PfKv258deGM9LpLnKPwnw/f/HxbsYORBwdJTAd3b/7r97/vePXte7rQUpVW\nqygBIJJrtVqVhAYAd8nmj978dFOBnRJrglXOA3s2//nLqg2Tnp09pZunEk5fcPRoVtiur1ct\n+6uCUQSnaVr4QA37Pn97ZVnqfe/dVTV3Tpaj+R0FxfNw2iFrf1uGWAKKhsPqp4TQbgJFQ+LD\nbIEyNQylsJv8nxCibX1BPeMJOyQhdDsBHkw7f3FWApcdnLP1GveWFOX5cLDPigra9SFbjQCg\n9G1uj0Y8YxGtQiwQ57LDaUeQQBW8nilMrAbhE0KeA+dq95MiKwJFCVNlY7fCYkCgcBM0tIAV\nQR2EmjJvjuU52MzCtCPVo2jItb6eb8YqAP5aRN6zqqdRJ3RC6AZFtbWLu2f5JP8lhDYjZGrB\npq5RBqKmWJii6nlGAHrdDZURQSSF/QoaRhgWFnbhRsGmlqEHT7g+4JfVX08cWH3f1JtGDx9y\nTc/EuD6Dm/zqnz5+3IHgESO7NbxvBY0c2R1/7Dl/z7SuXRue7yx7RT2iE5e4y+ps02Wu/xON\nK15liUNGddYAwLHln2/VKXrc//rzNyUqKcBRtffLl95Yv2blnmnpI6UA+OLViz7ZVhExatac\nh4dHSShX1cHvXn999W/v/XTN1/f2n/Zs3+1v7sjSJd/wzLMjPD1fKtd98smmAnmPafOevq2r\nluEdpTu+evODdcvfX9r984fSz94aDJuX7+x216tvTewZ1Pyti6/45733Nzr6PfnszTEV3zS7\nW2Fh4b333tv4WJ4HUFNTIxK1+3bEgNOCN9scNnuz65TxPK/TNfGqhgccNn1TL/nOblFTDK2r\nrm11zyZjA8C7aSDAqLfY4MelkcUikUqpNBgMTlcTy4FVV5+bxEMiFisVCr1e72pqT2ExvFQE\nhcFQw1PNLjJYU1NDnZ84UaAlCDAZLS4fPrEAvf5iTkXlclYXF/GytqYm+goJoLQ69DpdW/8o\nnrVYWsaIAo01Dp3O1MYym+M0s4DG7jLpdC1NjFW/wInRaDSZWnpTig4y1dp0OoEf62iIRFCb\nrUajrXFNVn1sJpPJbG78vmJo3HaYdb72kjdVM4AWjFmna8fMSPWxWSwWi8XS9gMlCo2xBrr2\nh+200uADXJxFp2vrV8xqtVqtre3Mqmx6VqeraW889Wqr5IDM6qy261qf/qr+c7PZbHZ761O2\n2d00EFBdYZGHC3kpVsoCOM6l152XCtfHZrfbHY7zzkaZRMnSYp3OLxMr2S0BIrlLp2tTXu50\nOpu7bXm4eDnnllWWV9OsYLORuYwKipLWGlq5X7tcruZi46Di7N6cZjU13p+Z/jNp0qRXX321\n0Ukybdo0gYoPu2fpdua1F9/+fs27z/72LkCrEgZPmPbkvOdvS2l8e8rPzwfiGqeiTeWmGo3P\nswkThLcuq4SwZOf3X1ww+1voramehNBsV6cPHdX1Vk82CEAc3H9Mv6D1v5eVVQCxgCNj+S/Z\n7uAJj/93RExyGycAACAASURBVBQDAGxwr/vvv3bbnL8y9hfemxzTuGScWLH8qEPa/7/PTeyq\nAgBKHDH0kVlnMp/4dd3yTXelj6375rrprnfNmtyzpc6P7vxf3/7ioGz0/54YFUShovkdOY67\n8HnUM7Ewz/NeLHxHgQfANbiPNqnJVzlQDM/x/lmzlnNRFM215Tdqdh/aDYB3035dD5CmaQBu\nrulQG250u90AqNY+akFQoAHwaOUDbPQqDzcA8JQvf1PK7sf0u02sZl7a1olKXHYKACNp05nm\n0ZY9GQnntgtw4rkdFACGFSw8WsS5Hf74RlBo//nmOYQC63s8TjsAsGJvroFNBtYyVszbzd58\njHXnm1jg840Vcy4HzXO81wPk3C4KAMVw7f2d2hIexXIA3A6BL300Rbv4lj7JRi+53W4RQ4GH\nP+5Zbgcl0bT1z9rqbpTI7SmTYpqt0Wsvzk2B8vGWyvG8N6f9JbIkbyPJyck///zzww8/XFpa\nCkAkEs2aNeuxxx4T7A1UXae8/fuUBebCA9u3bN++5e/ffv3p1dvXHV52fNXk89smIyIigPLy\ncqDhVGSVlZWChUIQArisEsLEG1964JrGdS/i4LrpGBS973723PpKnF1fdmbf6l1VwNnpGkqz\nsy0IHD04rUE7Ht3tP18sneJuciI8/Zm8GtDXDBvSsA8GlTBsSMyvy/PyCoGzVTkxPbq3OBTO\nlvntW0tPh97+1kN9OnzJGM912runCMqfaylQNM9zPvWurDu8+SYyQXhudVQb+ih69umoW2P9\nH7Ydb0fVLTPjU4Q8K7q4yxfz7Zls0VMBzzkpCLpiO+ekKJUQJx7DwfMkJxDORQnY5tAAD4Dy\n4kLCU6AEiIdieABu4T6olrld8O5j9Bzlyb4EjYdiWO+zQQAUzQP+mnCId1OA8H01eZ5v1ylX\ndwX2Tw0mxfCcgH9WN42zZ7VQKJrneN9uqTxFCfFtvXTcfPPNY8aMycjIMJvNPXv2jIxszwpX\nLctb+87SA6J+0564Ljamz7hpfcZNe/KVe/6bMOLjNau2uybfcd6XIaVzZxq/b9t2AsPOLS5k\n2LHjqGDREIQALquEUBHZuVu3llIqW/72lWu2H8nOKy6trLW6IZLJzt39uJLSMiAp5PxRFJRY\nrmpmQFVZWRkQGBra6AYaGhoCFJaVWdCtLjnValvKB2t3fbxwdVXaf96bktr6g2xYWNinn37a\naOMLL7wAQK1We9OdgHPDUCmXiCXSxsP1XC6Xp4sXRVFqdROTMLL6CrASjcIvfRhq9YzTTDf3\nG9XHhuY7UbjslA2QKyVyjR8XJvM8ZKiUyvqqdY7jjMa6jkNqtbo+V2RoGoBCqeyAnJBz0i4L\nlAp1o8dWnufrW5hVKhV9/qx/vJtymiCVSeRi7z8xSqmCofWOvv5CUerw8LYv0cAFUqWAmFFq\nNK38UfT6uv6BCoWi5ZEbPAe3g1ZqRL5373FKqEpAxMg1mpYmITEajRzHAZDL5S30G+dc4N2U\nXCVAYI3wbsphhEyqYCSNE+v62GQymVjc+Fpq17M0C9+vIZ6/B8VJNZp2jH81mUyednupVCqR\ntKMewWVn5Upvum+5FTgNMJC1GqfFYnE6nW2MrcTFiuU+dSdTamgAcrFWomz96lQfm1gslsla\nr0oxOCgAKq1EI+yl2A0RLdZIz/utrVarpxOgSCSSy8+vH3az4Hk/dbqrlFEUJ265cLvdbrPZ\nmo7tfOYihqIQEKwScMi52clYrJRKqWky56+PjWEYpbLpsft6A+tmvTnNbDZBlrj1C4VCMXz4\ncOHLpU/8/NJL+7rUDslY2KfuK2LS6zkgPCam8e1Dcesj94b//t2ipz69bcUjXeQA+MpNL7zy\nmxDj0M9xGUqLaxxQhMQFXynzAhEd67JKCFtWvefDOQv/KZMlDRgy8poJsVGR0XGJ9r+eeObX\nutdpiUQEOB1tXr9WrpAD1dbGI0+sNhtAS6Xn7vcM08KgKu7Q5m01lEa95/P/7avbZC7VA9zu\nr+cUKJB868vT+557FpRKpf37N72OGMuyXowhBERgRLTLQV9wbMO8pYmSXU7wHMRSr960dRI5\nbHpQbhHb2nR8zQVg1wOAVMmIRH6o9G6I4xiaZs4mV55HzPrYzjUechw6ahQ4z8BlAcWxjT4b\nz6O5B8uyDHPeJ+M598VShmJ8+MTCI1FS6P3hPgoOE7W5vygAhRoAnNbGH1QLWv2iWQ3geUiV\ntEjk62hKlgFNw2lu6znMMEwLsZlNAIQJrDERnGbw7ibiPFchckFsnlkiReJmv8Jtpw2CSApj\nVfu+7C3E1gKeh6ESEf29CVskAiuGw9z6n6A+NpqmW30juwkytU8fo2dNRZuBVbZhYuf6iqS2\nxAbAZvC8hcCXYjfAucCyooZZU/2Yxsax8XC6QLNg/HTDUsCsaxxMI54sGgBFUS1/bg4zRDKI\nfaiYazJCiw68QyRqaomd+pHtzcXGc3DaIPXqNLsa5z6JvevJWxdMWfnO0MS/+w3s3UlWfuTf\n3Yfz9cFjP5vZ94KdFde99sHdG6f+36MDuv7fmGGp8qqMf9bnp9x7W8q3K9pVU9Wi/M9uSpqd\nwU7+1bnsdoGKJK4uF3N6CGFV71z1TzHT//GP35s9866bxgzu0yUuWGo1N0jnoiKjgJK8vPOG\nGDuOrXjvvUWrs5pYaTs8IoKCOffM+UP+nLmnC4HQqMi2XgGlmtDQEIm1srxejcUNcJaa8vLy\n8lqr/3toSGRwOeBq57ymNnPdsf4h0wKAxYemJmstaFaYteBa4XkEaLndr2MXpqdo0Cxc9nZ0\n6uV5uGygWZ8Xpo/rkLn/mxPfvqn35QEQSVEtaALrKU0rxIyXFA1lCAzlwnTONpQBEGZm/Aux\nYrgd4Jq4UDbLaQXQ7rlwm0YhKAIVBUIU1ZqaMjgdCPJ2wUNNOGpLwAvXk91cA5vJ1/PNM/Gs\nTuiZLT2qi0BR0DQxp6NPPH1QubZV4bpdAPzSIdZDHQqXA2YhJqxxO2DWCf9xSZWgKFhbnxKr\naXZT3UKjRNtE3P3D1l9fndI30JS1cfmytUcNgQP+886fu1c8lNTUSRg16cf9m967b3Bwybbf\n/z7m6Dbjx73r748yglarfZhqnSCEdOXU65SUlADK6OhzvR34ml3r9+gBZd2YgtB+AxOXLN21\n4veC/hNj6+rAqrf99tOWjOjYuxp0LT37bCbqMaivfO++1f+397on+9f1VHUWrv55m5GKGN2/\nrROL0wMf/Xrg+ZtOfP2f5/9wjHr265ndvPlN202mhNUIsx6aNk867nbBaoJE1va+ee0lkkEk\nhakSqtC2LnXdkMMCmwHKEH8tQ3yehglhkykfz7eycr0fiOWwGeC0QNy2lNhpAc/5tM5HnS49\nsHKpz6V4q2v7lhSkKATGoDIXbqdgU8brCiFR1C0+4TtNOAzlMFVB5duSADyP2hLItWhyQLTv\nRHI47bCbIGtbhzLOBacNrFSwZ/ToVOxfB6sRMj+PxC44Xvd23gmOg64AtWUIEGi8kqcCIvDC\nWc/aQ6GFQouyHKQOEiSo85TlQBsh5NKLHhQFmgXnAtXa0EfODc4FmvHmVtJGmggUHkFtsQBf\n/NpS8Lzwa8NQNKQa2PRwhXiz0IuxAjSLC0aWEM1Tpt3+8o+3v9zMq7cv4/lldf+36goqTOLu\nj3/791Pndqj5+ngZElJS2Av3Pyt1zn7HMw547lxBMzfzMxu8OPY7I/9dg587Pb+Pf97734Yg\nrpwWwoRu3eSo/PuzRcvX7jxwYPemFZ/Nefr9TF4NlO787Y99hTZQMbfcPz6Uz17y/HOLlq3Z\ntG3znz/Mf/GLDHvwqLvHRgAAxTA0kLt7bcahk2V2QDNy+qQkcfWm+U/N+3rFus2b1ixb9Pxz\ni0+6tSPvvzOlpfvTka8ffuCBB+b+6dVKVsKjGcjVcNhgaVvlIc/DUAUKUGj9GpcmCm4njOXe\nHKsvBkVD7YcFuJtWnxNyHAXQNE1RFE3Tni0dnw0CYMRgJXBa4WrD8A2XHU4rWLFvKxB69Bvs\ncxG+vPuQ9h4REg/OjdKTwry/SQd9GYKFW2hOGwlGjLIsX8upKYRd6BXwGqIZiOVwO+qWO2sZ\nz8GqB0UL2YCfkA6eR/Z+wQpszqkMyFUIj/fy8IBo0AxKjgsTjOfUlWsh9/liHNUZtaXQtzDJ\ntVeqCmDRI6pz63t6gRGBouF2gGt+1RjODbcDFCVYjU+TRBJowqDLh8O3WZZ5DmWnIFFAGSxQ\nZA2oQgFA3/4HD3M1nDaoQvyYUV/Vyr+7LT4+ZeqPVec2GbfOe2cjnzZ1Sp8Wj6RFEqngIwAI\noklXTguhYvCDz08yfLRy09LPNgGUWNNp2JT5Lw01LHvpvTVbvluZMqhfjFSS/sC7bwZ9/NHy\nLcu+3AQATGD6zc88PLWvp8qZ6jxwSOi+7bu+fHVX6n++XnhLKBVz25vvar/84Nstfyw+AAC0\nNLzvlNmPTezR8mOOw1BZUeGQmdvTvcq/5Go4HTDr6/7fAs4NQxVcTqiD/Nc86CHTQKqCvgxi\nRft6quhLYDNCE+nfJ4DGPOkfz9MUFag9/+msw7NBD4kSnBt2EziupUXDnRY4LKBZiAVpWolL\nRHwS8nKEKKudlGr0Gdj6bucLiII6FIVHEJaM9kxQ2rQz+8GIEJ3uazn1GBHCklFyDDXFCPC2\nm6LbidIsSFUIjBUssAuJ5eBccNQ3NTdzyrudsBnA85BphHy+jO0MpRZHt6P7CMHKvJC+8v/Z\nu++4qqv/D+Cvz+d+7l7ABWQJCIoL3FrOXKVtrTSttN3X9rRtw29pZb8sm6btsq35rUxNDfdW\nFASVKeuyL3evz/j9cQEBL3CBe1HjPP/okXece/js9xnvg6KTGDql4zUXSxE9AEXpqNX7YVxx\naSacVgyY3NlyAMQPQc5BnNyBS27yQ2kNsveBkaDnQH+W2RgjAesC565bdL5xwmdPx6DAg6Ih\nkgR8tEjUAJwsR2km4lt/hm9VZR5cNsSPCEjoxUigCoO5AqZyaHwekuqywaSHRA5FiP+rRABA\n/O2Lbv/ohq/uG3LJb9MnjuijMp7a9uO3qcXhs9c8lHJ+k3YTxFkXSUAYNunRJQMcyqhWcydp\nh97231U31pSVVrvV0T3DFZ4+vLuXfznPKUikdWnqtANueOHjmS6jvqjSre4RGaaWNDodgy97\navXouwsLqoXgqLrmO1nc5Efemfygraq41CQOi4nUSpqcvslzliy5Uh3T7DGz7+xXl1whyHq0\nMAYs7ppnllwq6BLaswU6T6ODqRpWI1xOqLRgvHUVOSywmsDzUIdA2hWJqnS9UH4K1fnQ9fI1\nJjSVw1QGeVAXdg82oChQFM9xDoeDoiie5xUKBUWfv+Y7CjItnGa4beCcYORNMr8LPFg33Hbw\nHEQSSNX+C1qvuQkfvOGnstpj6jWQdmRcWq/hOPYX8g6g7/hO/X5FLmpLETsYEr8Oywztheoz\nKDoGuQay9gftgoAzh+F2oueQgLdLyDRwWuC2g3VBomg+Mo1zw20H6wRFQ671c3sNRWPwZOxe\ni8IsxAamPwrAgQ0AMLhzAVhMCspOI3c/hlzdqY1gN6EoHZow6PwR58s1SByO7P2oKkJo5wag\nNijPRUU++lzq5zOiCQqMFDwLjgXngoRWMgo5BVAUzbkAQCT2/4oXXsnU0MWjKh8qHULjO1KC\npRqlWVDpOt700yZ1OFgnLJXgOQRFth0kO80wFIMSITj2vLRqdhNh16/atXXE0re+2Lhh5Uaj\nKDy+d9K8//v2jcfGB6CfmCA66CIJCCVhvZN9mmEjkodEJzZr5hJJmncLUBJtVGILM2EkwbFJ\n5+RhEylC43p7O3M1PZOTz721qmMGJsd4+XQdRUTf5PMSzGhDYTPBZoKhHIxEJBLLwAqgaEGA\nuQYuO3gejBganR96UnxDMwhNRFUOKnOhjWxjPiHnRm0xbAbINNDFd00FvRAAm71u2JBC2QU5\nbVpDUZBpwDrhssFlAUDLoePB06CdRgoARUOq7siUktbMmItV78LZtanGKQqzb+/YVzU9ENUP\npSehDEFMR7syzFXI3gNlCHr6r3vQg6aRMAqndyJvP3qPaa2z91yCgJJ0mCoQ0S9Q6WSakaog\nksBlhdMCpwUMtCLwFCjOImIFABDLIVEEpANk6BQc+gs7fsKtLwXk4bW6FOk7kDQCus5N/2Mk\nSLwUJ7fj1E4MmNTBnivWhcytEAT09t+svz6X4swxHNuICfP8MOXPYUHaJsjUSBrtj8q1imZA\nM+A5uJxugfdM54ZUJglcFhmvYpJhN6I4HWJZuycBOkzIPwhGiviRgalcvaBoALDVgHVAE9li\nrM6zMFfAWgNGipDYrh1u0w2JoyY+9P7Eh853NQiiZWRscjej0CAkEgoNIIicVpXgVgsupeCG\n0wZGAnUIgiO6LBr0EMvQox9kKhhLoc+EucJLPlSXDbUl0J+AzQB1OMISyVSHJhgpFMGQacHI\nBQ4uARwHFyMXZFooQvwdDQLQhWPmLf4utC1jJ6P/oA5/O2EUgiJRcAhlpzvydXMVMrdCJMbA\nyQHpjpCqED8CrBOnd8BS7eu3ODfy96OqACE9EZHk/1q1hJFAEQx5ECQKgOYoCgJ4MJxUDaUO\nUlWgTk+ZEmNvQFk+jm7xf+ECj7+/BC3ChNl+KC2sF2IHo7oQ2Xs7knHU7UDmVthM6DseSh8W\nivCRWIbh18JqwMH1nc2DyrM4sA4uK4Zf6324SSDQIrC80+owWR0mlnd2cTQIgKKRMAoSOfIP\noKI9o+aNZTi1EwKPhFEBv8FSNIJ7QhMBtwNVuajOh7UarJMWOBo8LbhFdiMMRSg/DWsN5FqE\nJgTgHkEQxMXmIukhJPyIFkGphVLrcjqsJhNNQaDooBDdeRwvQjMI6wNbLYwlqC1GbTFEYkag\ntRQtCDxdWlqXUlymhja6ff0n3YpIDEokmO11q90qZcGizqw32Lp7HsOGX2EyBqr8ZkQMHn6u\nMwVQNPpNxIktyN4DqwEJI9sRtFTkIXs3RGIMnOKPNK0tUIehz3jk70fOHgRFImpAq4e6gJpi\n6LPgdiC8N6ICNoSyFSIxRGJYXRaO40BBJVOJZQF/Qh86BSf3Y/uPiO6DHvH+LHn3byg+jYlz\n2pGMuXVxQ+Cyo+w07Cb0n9iOHjmrAZnb4LQg8RL/DBZtLDwB/ScgczuO/ImhV3UwByzrwuHf\nYSjFkOl+G316sWCkSJqAgoMoOQFTBaIGtJHvx2WD/iRqiiFVIvGSAF5AmlGFQhEESxXsRhj1\nAKRAXdhn94wcUUEdFqikxARBXHRIQNiNUTRH0RxA4fzkRGnGk0nPZYO9Fi4bnHZK4GhKJEhU\nkKogD+q6dmiibbowPPw8Xu+qLNe33oukzqatEEsxaDpy9qI0C4YSxA9reyKQrRZn0lBVAGUw\nBkwJeE52uQZJE1CaiZpimMqg7gFtBFS6s505nIuyWGAsg1EPlw0yNWKHdna9iosLReOa+/HN\ny1j7Lm55wW/B24ld2Pc7EodgxDT/FAgAFPqMgToUOftwaC16piBqQBsBGOtEUQZKM0HRGDC5\ns0tNtKTPpXBYkHcYNiNGzWx3JlibEft/hakS/cYhbnBAaniBYyRIHIPy0yjPwant0IRDG9k8\niQvrgrkSRj1q9YAAXSyiB3b1sEyagSYCmgi47bCZXQ6bG6BEYqi1igAN6iYI4uJFAkLiwiJR\nQKKA280ajXW9T6GhZN71BenGedjzD/7ZGPAf6jMADzztl5JoEZLGISgSBUeQlQplMHRxCI2F\noumoPKcFNcWoLoRBD1qEnoPQc1Cgc+7WYaSIHYrwRJTnwFTuadoHEEIzgsBTdZEhBWUwIpIQ\n3PNCaMnpahodbngcP72FH9/ATQsR0un52Cd2YdMX6BGHqxf4P1NlRBIUQcg7iPzDKMlCaBx0\nsc0XJefcMJSiuhDVheDcCI1DrxEdSS/ku5SpUOuQvgX/fIHkyYgZ4NO3BAFnjiFrOzgWI64L\n1FITFwWKQkRf6OJRfhqGEpgqAICiFSKJDBA4F+05VWkGQVGISArs3myTWA4JODdtB8AwjFRF\nhtkQBNEcCQgJgugQisLLy5GfjYLcAP5KsA7vfN6x5KItCU9EaDxKs1Ceg8I0FKYBFBhJiEjK\n8yzNOWmeAwCJHFH90DPlPIxSlmkQNwwCD0s1HCaYDA7ORVGMIFcyCi2j6dDC0/8mkYmY+Rh+\nW4HvX8OV9yKho/1UPIcdP+PQJkT2wg2PQ+LvpdU9NOEYcjWqClCaBf1JlGYBACNViyQcQLEO\nmnMDAC1CcDR6pnRRl2/8UKjDkPYXDv+O7H1IGI6ofi1Ob3PZUZKF3EOwGqANx9Crmse03ZNY\nipgURCfDWgNLNexm1mnjBQHyYF6pESuDoQrDecxCTRAE4TsSEBIE0VHaIHz0A+64FhXtXwvZ\nFwol3v8WMf5fbZ0WISYZMclwmFFTDLsJZoObc9FiOauMZmQqOigKmtCAr2zWOoqGOgzqMIhq\nbDzPA1Cr1VIpuWgDQGx/zHkO//sAa9/FoAkYPwvydg7o1ediy9coP4O+IzH9noCn+giNR2g8\n3A7UFMNaA6uJddlA0YIyRFBoGE0YgqO6aPmEBroYTLoLBWnI2Y+0jTi+GcFR0PYAo5AIFACK\nEsRuK2r1qC2DIEAZhCFXIjaZjDZsgqKg0kGlg83mstlsACQSiUZDsnYSBHExIc8WBEF0QmQM\nPl+PB+fiTJ6fS9YG4b2vMXCIn4ttSqauy8hSVVWXjEer1YrF5IH3IhAei3mvYtt3OL4Dpw5i\n2FQMmQxlqxk+PPS5OLABOUcgkeOKOwK70n0zYhl69AYAk8nucrkAKBQKheK83YhpERKGo9cw\nVOSjLBtVhcg/AkE4m4CEoqEKQfxQRPWFrlsOUSYIgugOSEBIEETnRMfi8/V4dgEO7vZbmX0G\n4O1ViE3wW4HEv5FUjivvQcoE7F6Lvf/Dvj8QNwDxyYhOQkhkk0+aa1BVgqKTyDmCGj0YMQZP\nxpjrodCcp6pfSCgKPRLQIwEAeA6VpVa71Q1AqZHoeii6fnUHgiAIoouRgJAgiE4LCcUnP+Kz\n97H6XbicnSqKpjHrdjz+kn/nDRL/YjFJuPlZ6HNxYjdOH0RBRt3rYplWLBV4FqyLZt11L4b1\nxIRZGDjWp77EbogWQRHEi+QsAJmMIdEgQRBEd0ACQoIg/IEW4d7HMO06vLMYOzZDEDpSyLBL\n8OSrGNAtk9kTnROZiMhETJ2HymKU5cNQBkOV2+0QACjUopAeTEgkopOg1J7vihIEQRDEBYYE\nhARB+E9sAt79EqdP4LtV2PonrBafviWRYPzlmHs3ho8OcP2IfzsKYT0R1hMAamttLMsCUCqV\ncjm52REEQRCEd+QeSRCEvyUNxKvv4rml2JOKg7uQdgBn8mC3NfmMRILYBCQPw6ixGDsFGtJx\nQxAEQRAEcR6QgJAgiMCQyTH5Sky+0vMvoUJfW1pK2a2CTK4Oj2Aio0DmJxEEQRAEQZxvJCAk\nCKJLhEVwdP3aXEFBJBokCIIgCIK4EJDltgiCIAiCIAiCILopEhASBEEQBEEQBEF0UyQgJAiC\nIAiCIAiC6KZIQEgQBEEQBEEQBNFNkYCQIAiCIAiCIAiimyIBIUEQBEEQBEEQRDdFAkKCIAiC\nIAiCIIhuigSEBEEQBEEQBEEQ3RQJCAmCIAiCIAiCILopEhASBEEQBEEQBEF0UyQgJAiCIAiC\nIAiC6KZIQEgQBEEQBEEQBNFNkYCQIAiCIAiCIAiimyIBIUEQBEEQBEEQRDdFAkKCIAiCIAiC\nIIhuigSEBEEQBEEQBEEQ3RQJCAmCIAiCIAiCILopEhASBEEQBEEQBEF0UyQgJAiCIAiCIAiC\n6KZIQEgQBEEQBEEQBNFNkYCQIAiCIAiCIAiimyIBIUEQBEEQBEEQRDdFAkKCIAiCIAiCIIhu\nigSEBEEQBEEQBEEQ3RQJCAmCIAiCIAiCILopEhASBEEQBEEQBEF0UyQgJAiCIAiCIAiC6KZI\nQEgQBEEQBEEQBNFNkYCQIAiCIAiCIAiimyIBIUEQBEEQBEEQRDdFAkKCIAiCIAiCIIhuigSE\nBEEQBEEQBEEQ3RQJCAmCIAiCIAiCILopEhASBEEQBEEQBEF0UyQgJAiCIAiCIAiC6KZIQEgQ\nBEEQBEEQBNFNkYCQIAiCIAiCIAiim2LOdwUIgiAIohvhedhNtMPGgBIojpLLQVHnu07Ev5YA\ns5GuLJWYailBoGRy8NHQhkIkOt8VIwjiAkICQoIgCIIILJ5HeQ70p1FVCHMNBF7V8JaIgSYc\nYXGI7gddz/NYR+JfxO1EbgbyMlCSC6ddCkgbv0uL0KMn4vqh7zCog89XHQmCuHCQgJAgCIIg\nAsXtRO5B5OyHwwqaRnA0EoZBJHOCcUMAOKnLIq4pxem9OL0XmlD0HYvYQaTPkOgopx1HUpG+\nBy4HZArE9EZ4T5dcbecBgIGg5F2o0qMkF/s2Yv8mJKZg5OXQRZzvevuVe8vzV7y+FymPrl0x\n4yIOeGv/fHrOCvrRX964Un2+q+Krf8mWv1jZUl+98fWq+757f2Z4u79LAkKCIIgLkiDAVA2L\nUV5bTbmdAs2IVBqotQgOByM535W7CAg8OCdNndeZ8sWZSNsIhwUh0UiZiuj+dbvOZHK6XC4A\nCgWtUIgB2M0oykDuQRxcj+z9GHY1QqLPZ82Ji1J2Gnash92CmEQMHo+4fqBFADi73W21AhAY\nBkFBdR+uLMGJfcg6hLwMDB6P0Vd6PvxvwJenp6amgr3J1aU/m7163n3fBbcWCzmLd/745Y9/\np+VV2hRhvQZPmTX/5olxcu+fNW1++j/LTsz8Z9JFEw3ivG35AOEP/d/1C/8Y/tLWVyYF7kbC\nlu76/vutx06dPFXsCIpPSup/6YzbZ6ZoWqiRIXPjd9+s33uyqFYU2Xfg8Ol33HVFL1nD24px\nVw7IQlr84wAAIABJREFUv+T+xydP/G5me+NxEhASxL+CzYSaMspm1tosFMcKIoauUUGhRkgE\nFC1cWM4HiueE8/uEflGwGnHmJCpL4LIDOPu0UA4AoGiE9EBkL0TEk46kZtxOmMpgLIe9Fqyr\n7sG3jIJYBlUYtD2gDkPXHICCgIytOLUHqhCMuwURvdv4vFyNpNHocwnyjyJjG1K/QMpU9Lm0\nK6pK/BvwHLb/hhP7EByOabcipq0DDkBYNCbeiOFTsHM9jm5HeRGumg+ZMvB1/bfK+nb5t6mZ\n41qKhfgzv/7nmjtWZ1gaXvn124/eeGv+539+fnOvc0Jx247nFqw2XLHyhYmy5m8RXYTb/sX/\n/ZGqly0QAvUL9hNfPHzb45+lGZu8+upro+9f+f2KGXHN7u+V216+ee5r/1Twdf/e8Ovny5ct\nv+m939bcO1DseYkZ9dwr13867+Gn509ZNa19z34kICQuYIJwYT7vUoJACzxPXwCBjSCgoggl\n2XBYAVCMmBaJeZqhOTdVW4mqEhSehEyJ6N4Ijz0/G5Nzw1gNYxVlM4Wyrrp9WiGGTAVtKLSh\nEEvbLqT7cNqRexyluQCFkB4IS0ZQWK3dwdJiiufUYpGEdaKyGJUlqN6DgkwkDYMu8nxX+oLA\nulB+GtWFEHgwEqhCwVNOSsQKAkULEtbBGIpRUwixDBFJCO4Z2LOBdWP/r9CfRswAjLweIrGv\nX6RoJAxHZBIOrMOxzbDWYsj0ANaT+JfgOPz1FQqyMGAkJswE4/MBB0AdhKtux7Gd2P0HflqB\na+9GcPtHmxFwZy5/YHlmK+8ffeW6W1dnOBEy/LYF868aEVZ56K+vP/nmcNbX86+JSji6dGTT\nYR9nPn1+Zb7unk/v+HeN5b2YWPe99NhqfSB/gUtbOveez9J5OnTcgmfvmZLSU1GbveuHZe+s\ny937wW239zn+zyMJZ+9T7vS3Zl63eLcVuuFz7px71bjeooKtq9/6+J+cXx6Y1Tvl8NJL65qO\nQ295+s5nx374/IfPTnsusT21IQFhd+S2w2GGy8Zw7mCBByUSah2QKCBTgz6/RwTPw2aC1cg4\nbDrWTUEQKBr2ashVUGogP6/jJjgWFgMsRpHTquPr22dMeshVUGqhCjoPg23MBuSmwWaGXIW4\n/giJ5KUKQ02N583g4GCRy46aMlQUIfcY9HlIHNKl+QN4DhWFqCgCz0HEQKl18OApESVwMpqi\nbCaYa1CSDV0UIuLJGEgAMFYhbQfcDoT3RJ+hkNflHeHdNeB5gRYJCjWkoQiLhsCjJA95x3Fk\nG2L7Iml4F0f7PAuHBS4rXHY1eBqUYLFQbjmkakgUXVmROrV6FB8Dx0IbibBeUASDolBba2dZ\nFoBSScnlDMfCVI7ybBQdR9UZxA8PYFUP/gb9aQy4DAMu68jX5WqMvw2H1iPnAEQMUqb6u35E\nYNDw9CQErD+hJam/oiALl1yBkZd3sITB4xHcAxu/wfpPcdNDUAW1/RUCgFOftvvA8YxD2375\n8oedxc6WP6j/8tllx52QDX7uf/8sGasGgJlz77wqZvzUpccylz//xUN//6fRGHH+8Acf7Oai\nH54/uT2xPeEH1jMH9xzKSN+/6fsvfz1UyQbyp8q/XLgsnYds8oqDfz8Y7+lhmDx99tzx9w6Y\nvrpk+0tv/P3wp1fU39mLVz/80m4r1OOWbNr83HBP8Hf93OsHThuy4O+sZS9/+/Sme+se7+gx\n825NfP+tjz7YvXD52HY805OAsHtxWmCuAOsEAFpMgeIoRoBAu6xwmGAqg1wLVVg72rP9xjNd\nylAOjgVNQ6pwUoxA07TAS2nK08UEiQy6yPMwAJLnUVuO2grwPBixoNDa3ZxAUTQEOSOC3Qyr\nEdWlCO4BbVjXPZdXFiM3DSIxEgef7f1riFQ95CpE90ZUIioKUXgSJ3YjYTDCuySPoc2M/HS4\nnVAHIzwWqmABsFRXe94UBwUxjAhWEyqLUVUCQzniBkCj64qKXbDKzyBjDxgpRlyOoLA2PkzR\niOmNiDic2IvCU7BbMWg8uqTLmmdhqYTNCAigRQANiFiAEjjGWgNrDURiqMMh68LTtOw0yk9D\npkbCYChafo4VMQiORlAUagpRmonsXYgfAWWI/+tz4h+UZKHfuA5Ggx40jZEzwHM4tQeacMQN\n8l/9CP/iWNgtcFoVbrfCEwqygMsMqQIyFcSBb+pK24Gsg0ge3fFo0CM2CVffif+twl9f44YH\n/6XrUjjyUtf+tSct7WhWpTQuefDgoZddPWNUlJc/lSvb+93q7zYfya1ya3qmTJx99x2XJ5Z/\nddedX+YPuP+HD2f3qPtYwefzpryY0fbv6n/86m8HEDx3yeKxZ5u21WMXL71l5VWf12z5+qeS\n/zzeEBHa/lzxWS4Snp43ptFF/eDb1z79p2Xyq/8smmDL2/LNZz+lppfYQ5KGjxw5/qqZE3u1\nMBGxNeU/LZjz8Slorn9n/WNDm//9u5dc++LfdmrwI2vfnVl/WXUW71zz5fqDOUVFZWaxLjom\ntv+EubfPHKRr7VDh/3llyuLtSHlo7YobmzZIF3111/wv8+VXv7XhqZFNv1J19Jev12zcf7rU\nRIfF9R40cc6dNw0P7aJ4Je3t6674oMznj7Olu9d89fPWIznlVllEr6Shl8+747r+Wp/uxcKh\nXXscQMjcZ+6Lb/wFzbRH7+i7+vVTxmPHCnBFL8+LB99/e7sT1MgXv6mPBgGAir930e2v/r1C\nv/3PTZZ759Tnrh4577a+b736+XtrF4+d7XtHCgkIuwuBh1EPhwk0A3UPyNTgBLfJZAJAUZRO\np3PZYa+FvRYOEzSRkGu7sHIci7ICOKyQyhEWA4WGZVmLsW5MtTQ0FBwLSy0M5dDnQ6NDaHTX\nxV1uJ/R5cDkgV0MXAZmK5zibweB5U6bTURRgM6Naj6oSWI2I6AVR4E+r8jPIPQalFv1GQdrW\nfYCi0CMOweHIOoCco+A5RMQHtnrGSpzJBC1CwqCzYZ7QrNWcglILpRa2njiTifx0RCUirLsm\n3a+tRMYeKDQYOgkyn/utGDEGT0DOMeRn4ORBDLgkkFUEAIcZxlIIPORayIMgUcBgMHMcB0Cl\nUkkYmcMMazVqSyCpRVB0V/Sal2ej/DTUYYgb7tOZR1HQxUERjIKDyNuPxNGtxZAdUJaDrJ2I\n6ouBkzpbFEVh5AyYq3F0A8LioOjKazLhC4GHxQCbGYIARsxKZC6WB8DQlIQSYDXCaoRMCXVI\nAG8K5YXY/SdiemPC9X4oLToB469H6q84tAWXTPNDgRcUS8bnD8995MsMa/0L678DqJDRj6/6\nbukNvRpF7kLN7iWzb1q0taz+pvXHT6vfX/XAF4uDDqamZjiucZz9aPjlz7yrqmvpLN/8xtIN\n3kMJ89+b9wqA8qrrJjc9FJjJM65Rf/61ed/mLabHb69rR3P9teZXAyLmX9skTqo5tSs1tTZC\nn7dm/tV3f3PSIVKHalxVG9Z/BaiH3P/l2neb/A2+6JHSoyJ1ZSaKfjz02NARTd7itn/17l+p\nlfSEG76pu0C6M1dcN+WpjWXuJp97/41F/R9eu3PFtBbbc4WKE6mpqWBnnDOz0lZwMDU1Q9W7\npvGLfHnq0ttueXmLnjv72sq3X1405+M/vry9bxdMMOl14+J3e9s8/5/3y6IVu8wtftR95vdn\n59zxzr5Gf8BXH72xaND93/zx/vU92wwKi06dsgFIGjiweR9MbGwscApFRUWAJyDMWL8+D6DG\n3jo3vulH6fHLsmsXs6CljR8ckq+7rtery9av+dM2e47PDxQXwCQoIvB4DjVn4DBBGYKwRChD\nvPQBSuTQRiI0AYwUxlJYqrqqcqwLxdlw2hAajZgkKLVegj0RA20oYvtDEwJTNfT550QXgeFy\noPg0WBci4hHdGzKVtw9RUGjQsy9CY+CwovgUWLe3j/mPqRp56VAHI3lc29FgA4kcKeOgDkF+\nBoyB3LtWIwoyIZEjaYRPnX4KDZKGQxmEkhzUVgSwYi0QeHAsNKpgrVqnUQVTYHiu7W/5k9OG\nYzsglrYvGmzQezCie6MkB0WnA1C5s2w1qC2GSAxdL2ijvIy3pBkoghGaCFUYXFbUFIAL6Hgb\nwFiGslNQh6PXqPY9css1SBwNWoSCw3UjJvyC55D2F+RqjJzhnzYrEYNRM8GxOLbZD6X5QhAg\nZqRKhVqpUDMiSddcaC9KHItqPawmSOUIjUZojEumsdESGy1xSJTQRSEsFgoNHDZUl8LtaLvA\njhCQuhYSKa64xW+tL8mXIrYvjqQG9jZxHlR8cfOYu7/MsIqipz7y5uq1m/766ZPX7hkdItTs\nfefGsQs2NESJ4NMXT5784tYyIXjUPa+v/GXjXz98+Motg3H0o1k3vn3ONTZ41G2P1rt9TGhL\nP34qM5MHMHT06OYZYqSjRw8DwGdmnqp/Sdi3dZsV4ktHD/dS0tH/XnfHt7VTlm0rqDFU1hgr\nM9c/OybYnPbxjWMWbLK1d5v0nz07GUDuut+adXJyqT+vqwSYSfPmxHhe2PvCjCc2lrllidc+\n8+FPf27dvm3Dz58sunGACras929+fIOfnnocO58aPeXFLXo+bOx9b6z+ZdOmXz9dct/oUMqe\n88Md42/5qX0PCMeWz5g0acH35e2rQtTEexv26OwhraRYql1/+8jr3tlXI465/PF3vly/+a8f\nPnrllsEawXz8oxvHP/y3peVv1gmesTw1NTV11W1Rzd4QsrNzAaBXr7ruQVTv3n0KwMDp089t\nNJcotVqtVi1rEs8NGj1aAVfq1p3teJq5SHoIq1JXLP+7xZ2qm/DwE9PIvNsWCagtgdsBbVTb\n/X6MFCFxqC2BpRIiceD7CXke+nxwLCITGmZMtYimEdYTjBQ1elQWB3zoI8dCnwdBQHQfSH14\nTA8Kg0QKfR7K8hHdO1CpDDkWpw9DLEW/Ue0e0kOL0G8kju/A6cMYNiUgjdasCwUZYBgkDm5H\nthiRGAkpOH0YhVmQKbsszZ3Ag+fq2hZ4gec5lqIoiqZ5DjwHWtRV00JzjsHtxMgrOhINevQb\nCUstctLQIw6SgLShOs0wlUOiQHDPNg5tioIqFGIZaotRW4SQgGVC5dwoPg6ZCnHDOvITEgXi\nhiNvH0oyEdd8uFQHnTkGiwGjZvozU5I2HIkjkHMAtWUICuR9TuDB84AAMSMRBAEARVE8C1Cg\n6S5KzdomkUgkk8k8/3M+68FzqNGD5xAU3uL1SiSCRge5CoZy1JQhJNL/CbTyM1FZgnHXQuHX\nCfbjr8eat3FoG6bM9mex55UjdfFLG8yget/zvx0rr4r0HM7TZ901Z9zNY+9Yp//6+fcWXvl8\nfwpA1fcvvX3MhZBpH+9dvyDJs8um3zz/ur5XjHt5b7tDLg/hzJkiAAgNPbeNNCQ0lAKEooIC\nHiNpAMjYurUCGDl6tLf23lMnTo5akv7bU/0ZABCF9r9u6dbN7NCRb5/8+qUPn5u2sE+7KjZw\n9uwBr2Rknl63Luu15P4NL3P//Ly2CpBdOX9W3fSF9PVrsznIpi5PXb8gpu5yO+nKm25KmB51\n5ybjzp3HcZW38LWd8t5f+EE+j9j5vx74YmYPz0664oa77rjqjktnfFu49okXN1/76RU+t4LX\nnt6dmho/PTCNMeyBpc98XwnRwMe27Vo+ztOLevn0m++aN+HGUQv+PPPJo0vuTV8ypNWrlDrh\nkssSmr/orjq+/rUFH+QB2mseuLX+KTcrKwsAoqOjYc/b8tXnv2zPKDRLYvoPTrnkyltuHKY7\n5wbIXHrpCKzZsWXrYUwb5eOfdGFc49vkrMhOT0/PPJWb501hjX9aeIX071544YXvjgWkSdRe\nuHvd1yvfXbr4v2+9v2rNpvSqADedn2WphssKdbiv0R1FIygaYhlMenCBXkmmuhQuB8J7th0N\nNggOh0YHcw0stYGsGVBVDLcLEfE+RYMeCg3CYuCwosb3EejtVJoLl6N94VZjYikSB8PtRGmu\nv2sGACgrgNuF+OR2V88zvhQUSnICUrFz8Dw4FgJAM2AksFiNNrvZajOBYkViUBR4LuAdXABg\nqYU+H1EJ0LbYuNw2mkbfEWDdyEv3X83O4lnUloKRICjG18BAqoImEm4HzO1snvVdRQ5YF6KT\nO96yodIhJBa1JbAb2/6wL7L3QxWMnsn+Ka1B37GgRcjZ7+diG/M0ggCgRbA7LbWm6lpTtcNl\n9bSJNLx7PgmAALlMrlKqVEqVXCb3vHJ+qmIoB88hOKLt1iuxFLooUDRqy/2/EY/thkyJ5NF+\nLjY4DL0H4fQRODoY/1x4bD8v/7QYUF370pv10SAAQJx4+9tPjBSBO7Z8xTYAwOmP3vzNAiQ/\nvOw/SY3uYqqhz7x2ew90kM1i4QFoQkLOvVjRISFBAASLpa6T0nTw4GlAOWBArNeyNDc881D/\nJsXIRjz9zNVycAfeWbG7vTXrP2t2MoDMdesadX5yO35eVwEor59/Q91jo5tNvPaxxx57/YWb\nY5rEHrphw+IAVFX5ozuZ//vNpfvdCLrpnffqo0EAgCjy+hXLZmmAkp9+2Mm3/P2uZP759RWn\ngJ73f7h0XONJB9KE/6xcPJ4Bn/XDT8faU2DZD3eNGjKgd3Ro1OBZ7x1ikma8s+m7W+vT/Qpl\nZRUAFGHCvucmDrv8/tdX/rD+rz9/XvX2i4/MGtl/3KM/5ZzzrB6ZnBwCFBw8WO1zFS6SHkKP\nAXeuev3qACaaFGrPpKenKy8XAP+2aPNFGxe/tPJINccoQnQq7vihXX//vva3aQvffPCSQGde\n4FlYqyFRQtmehB2emLAyD+ZKBAVucWS3E+YaqILbndAsNBp2M2r03seX+oXTDrMB2tB257DR\nhMJiRG0ltKH+z5zJsSjNhTa0U2nBg8KhDUVpDiIT2pedvE1OO6pLERQOZYd6liUyhMWg/AzM\nhkBnQxV48Cwoynv+JM/rnodgT1dhAJ3JAkUhodM5Q7Q6hPdESTZ6D/bzbgUsVRB4aNs5J1Cu\nhdMCWy2UIRAF4FSoKoA6HKpOxNEAIpJgKEZ5NuJHtP3h1hn0MFUiebL/r0lyNSL7oDgLQ68O\nSL8+z0HgQdF1+1eoHycqCAJFQ0TXfYDH+Vu3vD7wczgddrsdgEwmk8vkdW918Xo6NgvcTmh0\nkPi2QJyIQVA4avSw1PozdZbFiJIcDBrr9/MdAAZeguw05Bzzf7R5fuSePOkG6Glzbz4njVTC\nnDmjnjm4t+rUqRpMCWGPH88C0H/W7JRmh5V08uwZoR+v7FDk43a7AcDTu92cUqkEDHA6nYAa\nQFlZGYDQ0BYubWOnTzvnOThs2rSh+HNPWWamAWPbd/PsN2tWyisZ6Ud/++3M80/HAQC4Hb+s\nqwCCZs67tr6lXjzi3uXnXiLZit0//X2mXT/XmsJjxwwAM/7aK895JAyeevkI/LzNuHdvFq4Y\n6Ldf7LhTx445AO3Ua8efs0ujL798AHYez9+7txzDfG5DcJakHzyWBYCiAElYQu8Qydn2I6vF\nIgBw//rglXY+aurDz88a0z9Gasg9+PvHK37J2rPilhtUvQ693nThEp1OB9To9XrAx4vORRUQ\nXuh4jqNEonNvTRUbPlp9pDp47EMvP3JFnBxwVx7+4vUlf2xa8dnIzx4fFdglR20GCDzU7Q8f\nRBIogmAzgAsPWNLR2koACGn/KCiKQnAEKgphNQYqO3ZtBSgawR0aoaWLQtFJGKv9v0CcoaJu\neG0nRSUiaz9qyxEa449q1TOUQxA6lbEmPBaVRTCUBTog5LgWo8EGtAiCAJ4DRQcsh5EgoLIE\nusiODxZtLCoRFUWoKkVEnB9Kq8dzsNVCpoG4/dcqdRgcZlhroPH3WEdzOXgOoZ3+QxkptJEw\n6v0Q+ZflAEDMgM5WyavoASg5iapC9Oj02d+MIDSJBr2iReABgYcQuHOhFZ5okAIAlmU9SYxY\nlq2LA4UujgkFWAxgJO1rK5TIIFPCZoZS67eYvug0BAGJKf4prZnoRMgUOHPqAg8IH580Ka3x\nv0WTX9uyaOy5n+NycvIBRPXylnYlOj6ewV42NzcXCCnIyWEBxMd7ubbEx8cBHQoIFQoFANTW\nehvX5HA4gLq4EKgPCHU674/xoXFxXnqlI3r1kmGPIz8/H2jnzXPA7NkDX04/cWjdb8VPPxoD\ngN/+89pKIHz2/GnNBvqwtdl7t27bl3bydG5eXl72yczTpWY/9npnZ2cDEB199+pJX5zzZnUW\nDfD+6YrsPGd2dhEALvWlKZOWNn+T0xcDdd2mPWD6/fHr32n7KI2+e13ODE6hiwhjSnZ98tDN\nC+8YuzHjz13LJmkB0J4U4m47P3zRnp2Lh9WPmp05/z+3LL1s+PP70t+8f8Vdh55qvOygTqcD\nssvKygAfx6z8ywJCwVZ86O8tB3L11Sa3NCg0ss+lU6cMjZI1uVU4Cvdv2X08J09vU4T37Df2\nqitSdCKgZsdH/7fxxBkAmT+/+MLfsVcuXFDXDewqObx117Hc/BKjNDwuvu/IyZf11TYUeOLH\nF9bkjbnv6eFFq97+dPPpkDmfvju7+ZOP8fDuEy5q0KwHrojz7ERx2PC7b5uwdfHW48fyMCow\njw8Nf60ZYllHnuQAyINgM8BhDkhmdgCwmTqelVsVhKriQAWEggCrEQp1B1tepXJI5bDW+j8g\nrNGDFrW9JkGbtGEQMagp83NAaKyETNGpGYAiBuoQGKvr1q8PDM9EKV+W3BSJwPJ1yygGRG0l\n3E6E+Wkv6CIgYlBZ7N+A0GkGhA6m4hRJIFXAYfZ/QGgsB81A3elTAYC2BwzFMFdC27lKVhdB\nroYqMFfL8HgAqA5EQFg/UrR1tAgcD4EDdV6eGlq5GFBdO3DU5QTPdaTFSqmFwwqnzW8rJ+kL\nIGLQw/u4ws6iKEQloDQ/IIX7T1pqamrjfzMR3uMFW22tG4BG423bi4KDNUCNJ9aoqKgAwKjV\nXtrogoI6+rghCQlRAlZXTY0VaHaH5KurawFIg4PrHvNtNhta6k0E5HJvc+goqVQKOGpqary8\n2YZ+s2anLHo5ff9v6/WPPhgJfufPa8uAqFvmT2l0rgv6za/ccd+bm8/UzdASKcPj+46aPTel\nds27f3Vw8Xahac4ql15fA8BZenxXmbcTnmYY2mZtLVdL1U8Pzvo4s+GftaeNwKlP50za2LAp\nRZNf3bJoQseq21i5Xs8DsJ45tKvIW10ZhoHNagYAd6lPRykTFJNYd3glTHzq29e3hN276Z0n\nP/zPked7A4qoKC1gRPg9b788rMn+lyQvfPXWZdO+MBzducfyVGKjuVeeRgirpe3kNg1V8PmT\nFwHDnneeeHN7tcAoQ0M1tP3Ekd3bNq7bcO3iZfem1J3arqItK5Z8sqPEJVKE6qSWo/v+2bB+\n402L35o/gJapg4KUpQDEiqCgILWUBgCu9J/3l3y0rdBJSbShaveR/f/8+fNvf89e+OytyZ4u\ne2NhevrJHntX//b9hgqRMrSf1ssGNbjlffuOGNm3cSe/SKNRAuZWD+3O41mwzo4PqRLLIBLD\nZQ1MQOhygHUjqKNDHykKcjXsgdmATht4roPjHj2UWtSUgXP7uXfVaoQ6xA/DtmgamhA/T8Lk\nOdgtflg3Qh0CYxUc1nZMK20ngQco3+bCUaBoCIGbtGAxAOj4WdAMLYJG5/e5tU4rKLrja7hL\n1XBawTrA+HUwhN0EZZB/Mp14rpB2U2cDQlMltB2eY9QWmQoSOUx+bx0XIAi+bsbAngst8THY\n67JOQqcNQDsmljcQSyFi/BkQGioQFBbABS10EcjLgMPmn/ELgbG2rKzp5CmZ95BN3auXDqgu\nKioC+jd/01pYWAMgPj6+/r96tqSkHGh+QSgpKeloRZOSkoCjnnUE+jV9Kz87mwXQr1/96+Hh\n4YDRYDAAXu6npYWFLMY03+s1+fnGhr+hvfrOmjXo5fTjO9etr3pwQfCOn9eWAwm3zR979rog\nZL51zXWLjzjFPSc++MT9N066dNiAWK0YAE4s+rPDAaFe3+SLktjYHkB51EP/lLw3vkMFOouP\n7dp1dq61wLGAu+DgrsKGa4Moyj/X0MjYWAZ72ZRXso4taiONT8j8tWUzvB2lheteWrbNgMF3\nvHvP8OYPdSFDhvTEpjNH9+53orfUk00GRiQPHnzO4x+TnNwP2MtnZZ0Ghp193dM4EN7D95vS\nRRUQVmds/hPNHyrkCeMm99cCwIkfP9lerRx892vPXJegogBX1YFPX3x98x/r9s9LmSQDIJT8\n/u6HOyoiJz/xwv2XRUspturoF6+99vuv7/wwevXto+YtHLFzya6T1X2ufmrhRM95ULnpww+3\nFSoGz3vlyRsGBokEl37XqiXvbfpx+XeDPlmQUv+kb/rnx93Jcxe/MWuI98U54697adl1jV8Q\nHPrtf+yugnLciOZXppKSkgceeKDZizzPAzAajVJp+xJ1CG4GUDvcVrfBS3KYxhNFDPUL6zX/\nDK12OSiDwdSu3/WF2G1XARan2+3tpxs3HLVUNzknyDi2tqZG8HdXksRlUwJmh4vlvP90A+8D\nQACJi1UC5poa1q/TCIOcdrdUZW1hgzRjNBqplreMQqAlTnutb0X5gnY7tYCN5Z0+lGkymVqq\nm9jFqgCLodrtCNTqHSpFEMu5zRar13fNZnPjujGMRC5VmkxmLgAZZuRGgwyotTsFV2sbzXMF\nAGC1Wj2Nxy1R0iKxw+rH3QrA7VBTIspQ6/0i4Bm/B8Bms3kmdzXDu8SAylhroaX+3KEue5BY\n4TYYvO/EZnWz2+11o7JaQNFBFqPLYOhUCg2HJTgoxuljIQ371OFwOJ0+pUWTqjRmg2AwtLwq\nVvuJGYlcprRYmxzeDXVzOp11c58AAIxIrJCrzGYLG+iVdepRFBWkDXI4HHaHvVndXC5Xw63B\nM5/QaDQ2vBs4KtYuAmU0ejkdGm5bbrfb621LJUDkchn9dHpqzbWcRmfxrbSGurEs29IttRno\n36HJAAAgAElEQVSpSKIATPoirq0WK6PRT0mZ2i/Y18fd3r17A9XmtLQ8XNGsk51PS0sHQPfp\nkwAgKilJhb2WrLQ0F6Y3vX1bMjIKOlrR+JEjw3C08sSmTcUv9msyJET/11/HAOhGjqyvV0RE\nBJBdXe09Gwh35FAaP2dE00Yc665daQBEiYkdGh3Sf/bs5JeOZ2xf93vNffE/r60ABs6b1zj1\n8v7PPz7iBD16yc6tT8U1+Wkfj6ZGp26DypMnm/Zn9unTBygvzcgwYHzzLnjeYTRYWYhVOk3L\nz8HRT+xyP3H2n9vvD5v4SfyS3IPP+HPMDABA3KdPPJBzKiODRZ/mgRRnM9TaeUg1OpUYoOTB\nPbwmRg2qPvDhB5uElLD77xnefKggr9dXAJBrtZ5jsGevXgwy2ZKSknOHBHs6tdGrV3yTlz09\n3hERvrdzXlQBYenur1aek0IpfGZfT0BodWpSxk8eONMTDQKQhI6aOlK3eX1ZWQUQC7gO/fhz\nNhd6zSMPT4wWAQATOvTuuy/f8cKGQ4eLbu9zbktM1tof012yUQ8/PWugGgAoSeT4B57Iz3j0\nl00/bpubMq2uA4mjB859Ys4QH0aQOPavevGH46YqfZmR6X3NM4vvHNt8ajDLsue2QanVagAc\nxzU83/hIcIsACGDb/GKLH6A48JL2/q4vxBwLgBVa/um26sZRFACBdXG+DP5rD4F1dbJurFBf\nN8pvSRgonqd4jqNFPu6O1p+NOJqhBIF3OQU/NTDTrAsAR/lUvVbqRnm2GOsKxFEHgKIoiqJ4\nvsWzqVndKLCQQuCFgNTH5RBoEQsK/tinADiRRMK6eZb1YysJz1EU3fbFp+W6UQA4FgLjtw0o\n8JTAURTj6yWxze1Gi3nOSXVmF/M8eA40w7e3EN9jGFrMs52r5LnEDABwLMt5S4ApCM0OewoI\n2LngjWdtCZbzcgtrXDeWZeGltoHBczzVxl5oqSacAEZo9328RW4XLxJ3oDQfv+K5sQpOe8ef\nHy4g0ZMm98X+UwdXvL3jsY8mNA70an5867MzgHjMpLFSABg1dYrmq/U1a979esn0exqHwvmr\nVvze4RUM6PEzrtV9+nn13p9+KnrkiZ5nL8/6X37eLQAh182cUB9ohffqpcDO6uJiB87pAgGQ\nv+rttYt+uKnRCCYh/6Ml3xsA+eRp4zs2Jqnv7NmDXsw4/s9v//sjeq0eGDr/tibzzvT6MgBh\nw0Y0jQbh3L9+U1t5pCmGoQE+Ly8PaDSLRjj+4cc7m34yasJlvaldOds/WnHs3pcHN3ky0X8+\nI/HeTfaUxaeOL0pq958XAH0vu6zHaznlv69YmT/zwV6N3xFOLRs78Lk0evKnRVvvba29QjNi\nRBI2ncr85Yf05xenNPlzjX/8/LcdQPKgQZ5DRTXjjhuD/vzx9JrPdj2zfFzj+NKxf826PAAJ\nI0Y0GcvHlpZWApL4+OaLHLbsIll2wiPh2heXnOPpK+saW5TDblm48LGrenu2Hu80lp7c8vve\nRr3D+uxsG0JGj+3X6BGdTr5r5XffLZnhba6XMb/AAHrohHGNozaq14RxPcEVFBSdfa3n4EE+\nzifgBUqsiUkeOSxOXJm578gZL+3pfkUJAASh44+GgkB5CvE7ARQAqhPLHnu+KwRieSxKBIDq\nxMQUz3f923UpUBQoivLTmC1POQLtt63n2RGdrx4lcAjQbgVQ31JO+Ty8zNNbKARmfW6BElEC\nD/8VTvEcaNq/Bx5FdWo0nueIoPx7GaEEioLA++3PFDiKEnWqhjQN/1bpXAJP+T3JZ92B7dsB\nE9BzwauG5RBb/1hXVkxAx2+JVP2Nzz9okb9uB95xHADhvCWW9S9q+NNLZoUAJSvnXPfWfkN9\nf2nJX09d+cD/zECvB9683/NYH3LLa0+l0LBveuLqJ/8srW9zKFz/0IxFezpxx2SmP/XEYBG4\nPS/c8PL++lEE1n0vXffMDhaiwU8uvLIhlGPGTxxHgztw8Ij3okw/3jvt+c16T9V48/GVs6c8\nv58FEh9+7a6ODnvvM2v2EMC9+eWHfykDPXb+rb2bvN2/fz8A5Zt/3XV2gIKrZOfym274vxwA\nsBcVtTgUk+7fvy+Asq9efiej/g9356998LZlJ0RMk+1JDX162S3h4DKW3jxv5aGa+vPMWbL5\nxRue2mSHbMoDd14Q0SAA2dRXX5umgD31uRsfXJPZMFLFmvvrAzctTuMQfMP9c9rqvR50+4LR\nMnDpb936n6/TG8YcuIo2vTb7P9/ogfA5z9xZP0VYNfPxe+IhZK+Ye9Mb2/R14zn4it3vzZnz\nzmkBuhmLH2m6mu6xAwecwJjJE30fWHhR9RAqo/onJ7e27ITjzM51f+w8nl1Qoq+stXMQy+Vn\nr2V8qb4M6B3WNAsBJVGoWxjUV1ZWBoSEhze7HIaHhwFFZWU2JNcNrA8K8jEelI2+b5knY5dQ\n/ufzD658iQ1a/cy4xsPzw8LC3njjjWZfW7JkCQCVSuXpKvQd56JMRsgkCqnayyHBsqxniBdF\nUSqV9/laZpNYYIT2/q4vaBcDW41CKua9rajLcVzD0LiWfp1hraAopaYTM/1aqpuDhq1aKZXw\nci8/zfO81Vp3+qtUKq8PKyKLG1bIVRqZX1ciFhiJBHwru0MQBEv9FGKlUkm3fPcSVwkCI1H7\nb+tRnAzlkDGUuIXqNa6bQqFoaV1p2ugEIFNrJd42vp8IYrFELW5Svtls9lo3CiIAcoXca1tt\nJ4lUGlQJGplYkLS21q7FYvE87MpkMrG4tRZgMc8KEpl/T1iLleZcVEtlWq1WTx9XS3VzWWgr\noFDJGJk/h09XSASKl7T+lzbUTSqVSiQt/rrAg2dpuYrp5HZjpADXRpUa2O12T79W63VrjHOK\nFFo/X40pigagUqp44Wwnj8Ph8IwUFYvFjTNb0JQIgEwuk8HfC6y3TBAEuUzOMEwrdRMzYjQk\ncgwwkZUTue1e94LL5fKM/mUYxmvmD4nZCbR4KrUXJZMzrMvH0hrqRtN0QzbL1okoHoAiWCe0\n9ROtj2O/UATd8NaH89Lu/CZ70zOX9ngjYVByFFuQllFk4YCg4U9/umhM/VlIJz/xySubrnlp\n96F3roldFTtocJxQmJZeaBanPLL8si2PfpDpPa1LW6j+T3215K9xz+w69N8xkd+MmjAspPzQ\nzsOFZh7qCW9881T/Rg8SQVOmDMfmg/v2FWLMOTmDosZN1hzYtnRazIqoAQN6WE+n5xtZAJoR\nT698dlTHH+n7zZqV8mJaemGhA6Kp8+Y2W26s7/zHpr9z58bsDyYlpE66bHAPUW1h1uH96WWK\nkQseG/7bu7+XbXpoYMrvi35Y/5CXJSH63/bgpP976B/T1ieH9/l20ti+isrMfQfS9cy4Za/F\nvPrs940/qpnx5sq7jtz2edYPCy5Z/1LigP6xKlthxtHsahZ0r/lrvl3g1zR4nRN914oVf057\n4LejH9066OuFSQP7RklMeceP5ht5SFMe/23lTW2emXSfRz577ZcRT+1O//z2Id8/Gd+nd4TE\nUHAqt9TMAtI+//n8oxvPBiyiS5758L7/Xf/p6Q3PTYldEtOvfzRdfurkmVoXIO991+crb20a\nflbt25cDDJwypR2pDS+qgLB1NftXvLBsS5m89yXjJo2+JjY6KiYuwbnh0ad+qXuflkrFgNvl\n8+QHhVIB1NibX+nsDgdAyxo93IhErTUa1WRs3p0v9BozLblRDmGqx6iRcStP7N11lB/XaOIu\nFArF1KlTm5WwdOlSABKJpN1zCCUw0+BdjFTqZUdTFNUw58dryTwH1gVFENXe3/WJmAFFiVkn\nvBXeeOJKi7/udkAiC0jdRDQqIeackHpJyMNxXENAKJVKvbdeG5ygaYlS7edUmTIl5bC28ic3\nHn4mkUhaCroAwGGBTOnXrSeBiGFcdqaFMhsHhBKJpOEJrznWAUCs1HQw/awPeA48R0nF0saN\n9Q0BoVgsbhzYcG4AkPr2yN5uKg0AidsJdWvZ66xWqycgFIvFbewyhxVylX9PCpccVhsYWuo1\nQVLDsyDDMF5/12kAKMhVYv/2NEgVcNnauDTZ7XbPGdFS3eo+ZgIAuUoklXaqiqoQ2Ay0jxu/\nYd6gSCTy5Ss8B5sREYn+vxpzbohoRtzofHS5XF7rxrMQAKk0UCemdwIo6uxf7Xa7PXcHmm60\nqQWglTuFf3EKuGxSSjh3EcKGkZNN6taA58G6oVD7rZ7B4VR5oY+l8TzfEBD6WgFLLWiRRNej\nzTRmPrZonHfxc74+mjL1yQf++8uB3LzDO/MAkbLHwMvufPPjxVfHNr66Kccs+udQ/4V3PfP1\nnrzCo7sKwYQOm79k1YqHDM+GAVCrO5bwTDL46Y37Ip576OXPtxfs+6MAAKXqNenu/3645Nb+\nTbdg7PTp/Z85mLZ9u/GJec0bbTUT396xdNtjT77/x5GMg6UAJCGJl97y2uq35/Tp1IGVNGv2\n4BfSjwHSafNnN+/bomLv+O4v29OPvPLlgYy/f80AwAT1veqlLz5aND26doZ83hOfbMnKza/2\nPs+eSnjwxw2Oxx98bc2x0qObfj4KyKLHLfjy07dv17/8RXw8dI3j6+gZnx0+evmzD7z8/e7s\n7MMV2QAoRfyUh1547bk7L21n+rUhT6xPnSNL8HeK63p00t3rjg379ImH3lh3qCBrf2kWAFrT\n9/qHXv7vwjkpPjW20/2f3Jo5cPlTC5etz6jKO16VB4CWh6dc8+CSt5++JrHpJSb0qo93b09+\nduGy7/cWFWccKAYoWWi/q+55fcVLNyQ2a6Ow79h+EIifPr1ZBqNW/XsCwprdv20pEY16/IMX\nJ53dD8esNs9CnwAQHRUNlBQUuBB99txznVj7wabCPlc9fG2/5te8iMhICkV5+RUY1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xbWGxzdrMX4vez7kEiIaQtTyIo9nWx9m1iUxefr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\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k5iJANdtJ2kq4lAF+H+watuioLbT24x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\nAxh/PCirbmjYcVhuXi6ApCSZJe+ErNdfJ3Kmw5b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EPgsTQoS6ryDqZnSz9RA3/S6KjL5NfMgu1PQaaElaj0SakrGrr7Gr70xuSXJkKDss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\nhBBCCIkoTAgRQgghhBBCSERhQogQQgghhBBCIgrbTiCEEAGyb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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 480, "width": 600 } }, "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 8 which is associated with COVID-19 cases. " ] }, { "cell_type": "code", "execution_count": 43, "id": "67a65397", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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n/3+99JPF2zuD50yfnMZSRWvfnYI0++vrC6qM/wPb5ywLAusfasGAAAYJPp2IHw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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 480, "width": 600 } }, "output_type": "display_data" } ], "source": [ "selected_factor = 'Factor.8'\n", "sample_loadings <- liana::get_c2c_factors(covid_data,\n", " sample_col='sample_new',\n", " group_col='condition') %>%\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 = forcats::fct_relevel(factor, \"Factor.10\", after=9))\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": "c3a96579", "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 }