{ "cells": [ { "cell_type": "markdown", "id": "c1b37e61", "metadata": {}, "source": [ "# 5. Downstream Analyses of Factor Loadings\n", "\n", "Here we use the loadings obtained with Tensor-cell2cell for each element in their respective dimensions (contexts, ligand-receptor pairs, sender cells, receiver cells). These loadings represent the importance of each element in a given factor or latent pattern. We can use this information to gain more insights about the biological processes underlying each of the communication patterns." ] }, { "cell_type": "code", "execution_count": 1, "id": "c4732d99", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: ‘dplyr’\n", "\n", "\n", "The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "\n", "\n", "Attaching package: ‘rstatix’\n", "\n", "\n", "The following object is masked from ‘package:stats’:\n", "\n", " filter\n", "\n", "\n" ] } ], "source": [ "library(liana, quietly = TRUE)\n", "\n", "library(dplyr, quietly = TRUE)\n", "library(tibble, quietly = TRUE)\n", "\n", "library(patchwork, quietly = TRUE)\n", "library(ggplot2, quietly = TRUE)\n", "\n", "library(purrr, quietly = TRUE)\n", "library(forcats, quietly = TRUE)\n", "library(tidyr, quietly = TRUE)\n", "library(rstatix, quietly = TRUE)\n", "library(ggpubr, quietly = TRUE)\n", "\n", "library(reticulate, quietly = TRUE)\n", "use_condaenv('ccc_protocols')\n", "c2c <- reticulate::import(module = \"cell2cell\")" ] }, { "cell_type": "markdown", "id": "dd113e5c", "metadata": {}, "source": [ "## Directories" ] }, { "cell_type": "code", "execution_count": 2, "id": "9d81e635", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "output_folder <- file.path('..', '..', 'data', 'tc2c-outputs')\n", "data_path <- file.path('..', '..', 'data')" ] }, { "cell_type": "markdown", "id": "e571137c", "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", "id": "0c199d25", "metadata": {}, "source": [ "**Load the SingleCellExperiment object with the stored loadings obtained from the tensor factorization**" ] }, { "cell_type": "code", "execution_count": 3, "id": "3e3066d4", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "covid_data <- readRDS(file.path(data_path, 'covid_data_sce.rds'))" ] }, { "cell_type": "markdown", "id": "6a66d278", "metadata": {}, "source": [ "## Downstream Analyses\n", "\n", "### Boxplots to statistically compare group of samples/contexts\n", "\n", "The statistical test can be changed by modifying the parameters. An FDR correction is applied to the p-values. \n", "\n", "```statistical_test='t-test_ind'```\n", "\n", "```pval_correction='benjamini-hochberg'```" ] }, { "cell_type": "code", "execution_count": 4, "id": "2ff6bd52", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "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))" ] }, { "cell_type": "code", "execution_count": 5, "id": "0a718e3f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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ZsO/gwoiTbrlyj12LxZILnwIAAAAAiggODg4PDw8JCVE6iAqVK1cu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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": [ "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))\n", " " ] }, { "cell_type": "markdown", "id": "b0d67fb1", "metadata": {}, "source": [ "Here, we see that the communication patterns (or context loadings) identified statistically significant patterns when comparing each pair of COVID-19 severities.\n", "\n", "These factors thus represent differences in the ligand-receptor interactions as well as cell types participating in cell-cell communication among the different COVID-19 severities.\n", "\n", "### Correlation between Context Loadings & COVID-19 Severity\n", "\n", "We first define the ranking of the severity, where higher values represent the worst severity." ] }, { "cell_type": "code", "execution_count": 6, "id": "0c93b061", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "severity_rank<-function(x){\n", " if (x == 'Control'){\n", " ranking = 1\n", " }else if (x == 'Moderate COVID-19'){\n", " ranking = 2\n", " }else if (x == 'Severe COVID-19'){\n", " ranking = 3\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 7, "id": "17faebd4", "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 data.frame: 6 × 2
conditionseverity.ranking
<fct><dbl>
1Control 1
2Control 1
3Control 1
4Moderate COVID-192
5Moderate COVID-192
6Moderate COVID-192
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & condition & severity.ranking\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & Control & 1\\\\\n", "\t2 & Control & 1\\\\\n", "\t3 & Control & 1\\\\\n", "\t4 & Moderate COVID-19 & 2\\\\\n", "\t5 & Moderate COVID-19 & 2\\\\\n", "\t6 & Moderate COVID-19 & 2\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | condition <fct> | severity.ranking <dbl> |\n", "|---|---|---|\n", "| 1 | Control | 1 |\n", "| 2 | Control | 1 |\n", "| 3 | Control | 1 |\n", "| 4 | Moderate COVID-19 | 2 |\n", "| 5 | Moderate COVID-19 | 2 |\n", "| 6 | Moderate COVID-19 | 2 |\n", "\n" ], "text/plain": [ " condition severity.ranking\n", "1 Control 1 \n", "2 Control 1 \n", "3 Control 1 \n", "4 Moderate COVID-19 2 \n", "5 Moderate COVID-19 2 \n", "6 Moderate COVID-19 2 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "factors <- liana::get_c2c_factors(sce = covid_data,\n", " sample_col = \"sample_new\",\n", " group_col = \"condition\")\n", "sev_rank <- data.frame(condition = factors$contexts$condition, \n", " severity.ranking = sapply(factors$contexts$condition, function(x) severity_rank(x)))\n", "head(sev_rank)" ] }, { "cell_type": "markdown", "id": "88f21dd7", "metadata": {}, "source": [ "Number of factors from the tensor decomposition" ] }, { "cell_type": "code", "execution_count": 8, "id": "3f8f6f47", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [], "source": [ "nfactors <- dim(factors$contexts %>% dplyr::select(starts_with(\"Factor\")))[[2]]" ] }, { "cell_type": "markdown", "id": "7b043fb5", "metadata": {}, "source": [ "**Spearman correlation**" ] }, { "cell_type": "code", "execution_count": 9, "id": "76db196f", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"Factor.1 has a Spearman correlation of -0.85 with an associated p-value of 0.000\"\n", "[1] \"Factor.2 has a Spearman correlation of 0.88 with an associated p-value of 0.000\"\n", "[1] \"Factor.3 has a Spearman correlation of 0.41 with an associated p-value of 0.186\"\n", "[1] \"Factor.4 has a Spearman correlation of -0.10 with an associated p-value of 0.751\"\n", "[1] \"Factor.5 has a Spearman correlation of 0.42 with an associated p-value of 0.173\"\n", "[1] \"Factor.6 has a Spearman correlation of 0.02 with an associated p-value of 0.944\"\n", "[1] \"Factor.7 has a Spearman correlation of -0.15 with an associated p-value of 0.646\"\n", "[1] \"Factor.8 has a Spearman correlation of 0.79 with an associated p-value of 0.002\"\n", "[1] \"Factor.9 has a Spearman correlation of 0.85 with an associated p-value of 0.000\"\n", "[1] \"Factor.10 has a Spearman correlation of 0.83 with an associated p-value of 0.001\"\n" ] } ], "source": [ "for (i in 1:nfactors){\n", " factor <- paste0('Factor.', i)\n", " suppressWarnings({\n", " s.cor<-cor.test(factors$contexts[[factor]], sev_rank$severity.ranking, \n", " method = 'spearman') \n", " })\n", "\n", " s.r<-format(round(s.cor$estimate[['rho']], 2), nsmall = 2)\n", " s.p<-format(round(s.cor$p.value, 3), nsmall = 3)\n", " print(paste0(factor, ' has a Spearman correlation of ', s.r, \n", " ' with an associated p-value of ', s.p))\n", " \n", "}" ] }, { "cell_type": "markdown", "id": "c2eb095a", "metadata": {}, "source": [ "### Heatmaps\n", "\n", "**Cluster samples/contexts by their importance across factors**\n", "\n", "Here we provide a example on clustering samples given their respective loadings, colored by category of COVID-19 condition:" ] }, { "cell_type": "code", "execution_count": 10, "id": "5173afa9", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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Qmpp1Z/nChQuHMG/FNvZUSiyfNHzAldfc916D2dYAACAASURBVMGqZje+8WTXUtvz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\nx+3A+zW84d03lx5/eq+mwzJCSCpWv8ez4+5rW3AHfgDscMIoA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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "liana::plot_context_heat(sce = covid_data,\n", " sample_col = \"sample_new\",\n", " group_col = \"condition\")" ] }, { "cell_type": "markdown", "id": "a3818c56", "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.\n", "\n", "**Cluster LR pairs by their importance across factors**\n", "\n", "To consider important LR pairs in at least one factor, we can set a threshold to filter for the top n (fraction or number) of LR pairs. This value can be obtained in the way that user thinks is pertinent. Here we use ```n=05``` to filter for the top 5 LR pairs per factor" ] }, { "cell_type": "code", "execution_count": 11, "id": "af8e1c82", "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": [ "liana::plot_lr_heatmap(sce = covid_data, n = 5)" ] }, { "cell_type": "markdown", "id": "9b2d711b", "metadata": {}, "source": [ "In this case, LR pairs are grouped according to how important they for one factor versus the others. This can give an idea of the molecular mechanisms that are crucial in each communication pattern.\n", "\n", "**Cluster sender-receiver pairs for an specific factor**\n", "\n", "Factor 8 is associated with COVID-19 cases (see boxplots). Thus, it would be interesting to determine the cellular interactions underlying this communicatino pattern. To do so, we define its Loading Product between senders and receiver cells (outer product of loadings for that factor), which represents the joint importance of the cell-cell pair:" ] }, { "cell_type": "code", "execution_count": 12, "id": "563c848b", "metadata": { "vscode": { "languageId": "r" } }, "outputs": [ { "data": { "image/png": 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FEAAAAAIHKEUQAAAAAgcoRRAAAAACByhFEAAAAAIHKEUQAAAAAgcoRR\nAAAAACByhFEAAAAAIHKEUQAAAAAgcoRRAAAAACByhFEAAAAAIHKEUQAAAAAgcoRRAAAAACBy\nhFEAAAAAIHKEUQAAAAAgcoRRAAAAACByhFEAAAAAIHKEUQAAAAAgcoRRAAAAACByhFEAAAAA\nIHKEUQAAAAAgcoRRAAAAACByhFEAAAAAIHKEUQAAAAAgcoRRAAAAACByhFEAAAAAIHKEUQAA\nAAAgcoRRAAAAACByhFEAAAAAIHKEUQAAAAAgcoRRAAAAACByYhITE1N7BgAAAACAFOWOUQAA\nAAAgcoRRAAAAACByhFEAAAAAIHKEUUhxmxZN/HjAoBHfL1rvCb9whKycO3nyzKXbkt23buG0\nydMXbUjhieA/a+lffx3s/842/PLLwiM6C0THpmW/zZnz++r41J6DI2j5Kxdlz9dmWEJqzwHw\nn5YutQeA/7zEv77sfOeDb6xsPOSzdqXDlm87Vo99dMr6EEL6Epe/Nvytq0r61/BosnXtXys2\nHOwf8TNkL5A7S8wRnYdD9MW9lS9b1XvZyBvz7r1v5ouXVe4T+8XyPrEpPxcHtHX5T6M+mHf8\ndQ3L7dyyavCdLYZlqV27/qWNaxTPnJqzkbyv7o79/KovXrig4H7/c5i4cmKvm1veG99pw8Bi\nKTUZ/8DWNSs3Zc6dPf2Ol+tmDnn+uf7fzN2U66QK5zS95doaBdOm6njsbeIjZ9bsVbb7oq/v\nLJrao/BvbZk9rOfLQyfPXbk16dbEdbO+Grluc7P4rSFkTK3R+Ltt65cvX5f8X73vLV22vHmz\n+rPeUShx/YIJw77ZFtv83Pw7N333bNNn5p1Wu06jJhdVyOsGwojxbykcYbO7Xljnvm/js9S8\nPH0I4c/XHnhyypbiF9x9XeW5rz/93k13N2wwtGXu1J6R3cY9cErNXssOcnHNXstG35pMeCPV\nzB7WY9jsEEKYOiuEjaN699iU7W8rEjcvHT1obshQL0MqjMf+xS8e/tAVrbp8/de2KwYlCaPx\ny6YNe23MsNeeeqhsy27v9Lrh9BypOSTJ2PRz70tiEz8Y/WL9fbTRrYtGPNamzZOfL47P1CJT\nCg/HgWyZ99FDN7brO2b2yi3p8p9+eceXXrz11Lmd6tV4YNya7QuG9nu+58v3DfniyRrZU3dS\n+I9a8m6zqlcOXZ7criylmj57bz1V9Gjya5eazc8FlgAAIABJREFU5eN+PMjF5eN+nN7xlCM6\nD//Y+ul9rm/W7t1fNpSP+yVJGF0/b+yAXoMG9IrrWPO+196Mu6BY+v2dhP8WYRSOrIl9en27\nrVDLoT++2ShvCKuGDRmzLecVvYZ0aZgx/rQFuRv1e2vImpZt/Cn/6FG4WovrNq07yMVlyvmd\n6lHmh9fvumvQrlcDH7prYLLLYgrffPFZKTUTB2nZ4NaVmr39Z0y+Chc3b3xakh25m70wIuvH\nH/Xr1WdEvxvPW5151tCWBVNtSpJR69b2lT7t3OfS2IT3v+jToNDf2uiGGe+0b3nr85NXhdxV\nbnn+zWcaps6Q7MPGMXee16j3gphMeUucnGfjgh/evu381StvXv3ouMSK13R/9NoaRRPmfdH7\n4Y5vd2r5v5o/9an7979rAv61X/p2Hro85/nPDO93Q7FZTzep1bN4/0Wv19s4Z0yfO6/ulrZ+\nwwp+t3lUyX1G4+uuq3qQi4uc4QaYo0z8D4+fV/3hyRuyFj+vdZtaSX9Deea9Q98/Zdigl557\nZ8yTDc7f9u30pytJo5ERk5joKYdw5Gzud3HmVmNafbri9QvShhD/SatcDfvX7rdqSIssISzv\nc36+m+Y//MPMR0878ImAg/DX9FHT/wohhK8eqfPoujsHP3NRzr0Xpc1arEKV0rl9LPSosnns\n7WXP7bWkYrsPP+tSJ/nbDtf+8FTDs+//Oud1I2e9XCdLSg/Ifq3++uF6DR+fuKXs9YNH92m4\ns40mrpj03M0t7xswc1O64y54+NVXHji/iL+RP8osfK7GiXd+c/LtI77oXjt/mrBmSqeG5z4w\ndn3IfXG/X4e02HkfzYIeNUrd9V3jIavfvdifEo8eX96e30fp/wsSB12RsenwliOWv3J+mhAW\ndD3zhOdqfTvvqcohhDXvNT3++vVdZ35yTaHUnhL+Gxb1Ob/0TZ/nadR7RP8byyX7fKZti4dc\nf+6lry86p/uMr+8skdLzkUr8/hSOqLTp0yepL99/+dW6UOncc7f/iX7jxo0hxMd7aD4cNgVO\nrV07hBBCxgkXTFpXt07t2u7HPkaMe/edeaH4nT2f3kcVDSFkr9C+V7u3Kjz20cffhzrnpOh0\nHEjOcx8bOTxD/fodXm5cM3Hw6JcuLBy/8LPH2lzz5Kg/tmUr16J3v543npErtYckGT98PyU+\nxN75aO38aUIIIcfp7R9q3vWCvmvqXHlZ/t2rjr+gXrnw9YQJ08LFlVJrUpK3evb4L7/Mf+B1\nuUqdXcFfTByd1q9atTXkL1hw+zMNi5UqlXHBzJmbQuVMIeS4tEmdK1t+OGbjNVd4wvYx56/3\n21778WldX21TKrUnYbelH7zz+cZMteP67KOKhhDSFbmkW6dLB1/+wUcjVt55kzt+I8L/H+GI\nSleuXKkw+PMPv9xwQa34ka+8Nz+Uu7bBCSGEEFaPHv19CGcd70sojjaTHjnzqrdWFrp6YN8T\nXnlq1P4+Vl+2Rc97a/tc4VHpnAc//XSvjfHrly5PyF0gu//1HX1WzJq1ImS6qM7Z+31z0pxW\nvWq28MrMmWvCOZL30SZ7tYeHj0zfsN79fZvUXN+25qw+L323Kk2+c+7u/cYTTUr4IOhRasWC\nBetClqJFd1frtKVLlwhhbsGCezwMNleuXCEsWnewD5oh5Uzv1bRmr4NY56noR69shQpl/X97\ndxlVVdpHAfxPd0lKSyNKCIiBgNg6FiBgFzqKKHZgK7ZjYuuINUqZ2A2iIKigIh2CWLR03veD\nzjuICDjjveci+/eJe84ja6/FAu/Z9wkKS0urIGMhIh4trXas089f0igLIuITFRWqDo15Ra74\nTIJ7Fac/vBvyPKPoq8kuNflhB/cGZ4yfiWKUqyQlJREZ9e7d+Bxs6W7d2tO5xMQkos4cCgYM\nw9MhAHsZ/+5pt2XavsH6D1QrXiVmC3bePMqQ6HXQrMkLD98uE+7tOlSO6YhQT1lOWnJyTnlO\nxfv3Z3x9GzuIyc5iK4pRLlb49OQO30jN6TvHGxIVhCwb5LjhYQ6Ja/Rf8tcpr26YvcZV8vPz\niaRlZBo91pyIJCUliLKzs4lQjHIhMcvFV24LDu0z7/SmRBJqN2Sz78F5Noo42ZWLsVgsIkHB\nuofRiYpio4qWRM5sSG/9ZvzI2uviyEGu1dnWVvjYueWz/9JY7WiqoGdmJuZ99vTjNRadBWsj\nQ8LKSE4ODwvcK/+GZ49hu2LLGronZjh/jA2nA0Gj8vPziYxkmpoHKikpSRSTnc2RTMANUIwC\nsJnK1IDrOW4zfG7EFUoZj9x61FObiN48CLidyms04dDeidgziOsYu/td+61KSMOgu1pCzqrG\ntmEWEGvDsVTwgyrCFne235RYqTTbZed4qn3kPWHdw7w2He1NK59cWTpgml7GGacGth8Fpii1\nbUsUFRPzgbo3drDSx+jod0RmCgocCwY/SNRs7qU7AsN7z7rG0rbrb4lWFIC9DMbvPY09Rls4\nhdGrvfaFrdg/2rJAqOq0Y98pEzXPbOtv9sJG4+PD66l8JhMGqjMdEb4n8cDC3bFkPNFnxVDR\niG0LtqT023tkrGpO3NV93vtz3f/aYCPc9PcADmrbti1RTEwMUaOVdXR0DJEC3m62IihGAdiN\nR85m6fkXS2tra3l5/35CbO8e/HKBgYGyGI5/4UIyhvb9DL98LSvGaBT413KOr9meWGs0I/D0\nUksiijhzJo1M1jx6tlyv7NJo1SF+e09lO7k3Y1c24BAxO3srnksRQQEZ0z3UvzdtlPXaP/Ax\nkbG1tQRHw8GPETaeef6eoFOv6XPtB7NuX5prjK3xAAAaIWixPCTK8ujpmwLqRCTec4vf5tcT\nN12/8KpG0mjUwZPzDJtaTQFMKQoLjWHJT99+cIY9PznKRPraJlCn/oPl+w927Cxs3GvyBpeI\n5R3QuHARY3t72S1HrvrfK7ax++6qv+J7/lc/ksQoaxNORgNG4dcUgEN4qexdctqHgjJJXUst\nGV1z7OQMwEZx0dGV1Hmyl2NHOSJKuHMni4zchuvxEIn27N2V769n8UlEKEa5iPo4r3Hbhh2b\nP8zL7P6G7g0Vn2XP1o9ceLdEdvC88diui7vEnll+Jrb+xfY9jG4H3J5n3+fV7z3b1pk3auS6\n1tWIk+mgSWVRR5cvv/f3q+LINKKS8CPLl9/6Z0hxZBoDwQBaD2Gd/tOX9//7Ref5F2PnFufk\n87aRFcW8e2724cMHIm19/c+lioGBAV1MSCCSJxLu7j7JYPueY0+Wb7FiOCTUwd97zmKr0wv2\nuI7tEhEwRqOBNqzm/blpY/ek8xsumTUAO5C0HihGAdivIu2it+cin6vxBdVEch73sneLr+05\nP91h3ZaZ3bAUm6vVvL+zftbSkyFxb0tqGrjd44/0K1NxlAFXKi0tJWE1tc9LYPIfPIilNr/3\n+NzGiMrICFFBQQGT8eBbbYbsC1oc3WPDRlvtW6MXLPVw6GqgoSjBT6yK/KyU6OsH16zcey+r\nSmXUmT/HKTOdFb4WH+TtHfide7lhR9aH1b3gZIpilNtURJ/yjq53LeqEd1T9cdjmEICDeMXl\n8BaT68nIyBBl5ucTqRCRgpaWWFZsbAFZSxORqqoqvYmK+kBWje0RBBzGbzQ/8NAj89Fnx7bX\nP+3uNX9iX1MtFRlhXqou+fA67qH/tuUbz8R+Erba4LfGCvsgtCIoRgHYjPXmr1HWY86+FdHo\nNtyuOvR8KhGRhET54z9n9XqUeT1ysw0Wa3OtjD0ug1aElAvK6hqbKovzf7OQSV9BgIlY0Axa\nWlpUHhrymOXUjSfv/Nn7LNEh/bp//gnGRkaWkooKtmTjOiKW3hfP8c2cvf3i8YXDjy8k4hOV\nlRMuzc4rqyUi4pWznLzhwPYhKGe4jvm0w4f7Nz3sM00crcxNxHsuOHx4TDMHC7XXZ2sY+EEq\nNpM9SNEEbyN/BZUZ908H3X+VmV/ZwN72AlZTN7safnsdmCdrbKxMt05svzzj8CB5HjIyMqKg\n6zc+/e4sSRQdHU1kwId907iO6ijfKyWyHsuPXtnqdmUrEY+glLx0bf7HoioiIhLR+m2Zz76F\nHdGUtSo8LFZjJ4sAwH9UemWS+qCjgg5HQk9N0s7aZKaz1fpe9m5bqk7/y6Xb6Att18c9WYJV\noVwqc7uV+txXPbdEXp5vgD3yWprkrV0NFzzR6D+ln8jdE+fi+FwDM0878iYE7924YrXvM/Ep\n19MP9hViOiQ0pOZDuO/2fZci4xOTElPflomqaOvp6Rt1G+E519VYCvusAQDAr6X21bbuneeF\nl3zvvvS0W/n7enEyETRf6i4bY8/QEhGjFWEvV5vFbzLruOSdtce8oUpxpzYfjVJYHp24BhtV\ncqeSpAs+20/de5GYlJT8OpdkNfX09fRN+05dML2nKtbQtzooRgHY6+pk6YG+Foc+3nKTJUr5\npxglolBPVZtdWns+hLjjxDvuFLVY03Jbt6DSvxzwkWELVB53aOygGYFpVUT8KoMPh1wYr8WT\nsM7MYFm0YDvnY3f9XDWYTghNY7FYPDwoQwEA4Nd1a5pcH1/F8du3zLDRbSP87Z6ivFIq7eSw\nqJdrFcccXrr6+AuTP+6stKTq2K32XRaEFhMRSZjNP3tnS29ppgNC01gswtvN1g1P+wBsVfb+\nfSGpmZo2tEmQuYU5UfTr10QoRrlTe2NjvtrS0gr8qWyRhA2nBCQ4pUQ/z+LTMjNTk+AhojbW\nM3YcU+03tL+BFNPxoFnQirYI8UHrguKbO9jAcamjATvTAAC0KGWZmbnKboF/TrfDOUstkriJ\n286zbl9e8BvNv//e9em9RxkiBl2tOyph5mHLgLebrR6e9gHYSkRPT5VuJiWVkoVo/Xtpqamf\nd0IELiVqP6yvmOfB/Qmu8/Txx7JFEpDRtrTV/ue1vK2bJ1H6DZ8/Su3nDGuPJxCuVJIY/Oex\nmzEpqSmpqa9zWXIa2lpaWjomfcdPGqSLvfS4UuyZZcu+d/jSN5wMUIwCAPxDxNBQQ4gXp8//\nMnjEVM0HjcB+2tyu+kPEqSMB4fGpqampqW9LxJS1tLW0tAy6jnAb1VkBD36tDpbSA7BXyfkx\nKsNPK06/8nBPP9nUf5bSV2Ucd+o0/pKs17OEddh5hotU5GW+L6r9+xXr460FzjNCFMYsnOdq\n3V5TUULgq88ThduoKUrgjSwXY5V9SE3LKa/7/xyrPG3/xCF7RDZlRCxQYywYNOzjg52L5q47\nEZld08BNPoXO45Zu2zSruzzHc0HjssKDwrMaG1Cb9+TImq3X31QRr/z0Wx/39uRUMgAA7lcR\nOlvf9d22J2cclDBtrWUofBEc/LyQt52tkxHv24KqRkYKSCsrS+GsVi5TkRiwYt4Sn+CU0obu\niukMnrnhj1VOujiLoDVBMQrAbh/9Rhi7BmYrWjqP7vbJf2eo/tw1PapD/I+ee1Wi7Xkneoet\nONMJoY77M+XtfHKaOdjOJ+fujIa2SQAuUPBwtYOD990P1Q3c42k370HK1m54/uAq1fE77TvP\nDi0SUrMdP8fDtbuBupqaigxPwduMzIyEsDM+233vZVRI2OyKvD0TU7hbkNJE/xVTZ+24/6FG\n0nj8+sPb3C3b4DcPAKCusphN/Wy2VTlMde6mqyBa/xhzIb0+TpbYd4ubxK7u2GHVS+Ex56N0\nlnVY9bKRkR1WvXyx0ohjwaAZ8q9OMf/tcBpJGw2fMXfyADMtNTU1RdGyj28yMlOjr/65bU/Q\ny3zSmnrlyYF+2B+29UAxCsB+JYlBG+Yt2BacVvbPNTG9YYu2b1s0sB22nuEuyQGLd4Z891zQ\nenRHbJxlg7W9XKk2cpF+583p6nbjHdrnXjtxPlndYd5gnZLMiOCz9/N6/ZlwYaIS5vpyleon\nXsZdN8RJDDoQcW6qTkOTK6pSDjtaTbn0yWhpeIx3p/rPjcCFqjKub3aftvZyeoWI9tAVB/bM\n76WCShsAoL680KUDB6+PKPzObTmPvw9uBS6RemT06MOpgv02nbF6sP7Ku0ZGth3o5TWgLceC\nQZPe/+VgNPpckaHnzYfbbaUb+KSWVRg6t3uvHbGSjqdjA10VOZ8QGIFiFIBDKvPTXr2MT0jK\nLBFX09Mz6NChnTQeEAHY5cVSfeP1vEtiYtcb81LCOjMD3/7RSRtMiApC5nTtFzL2cbhXR6xs\n4iI1t6cr9t5PLgHJZ5y+//l84dlRuo6nWe53PuzpiWKbq9VmP/KZO2Xpydhi/rb283YfWOmo\nI8J0JgAArpSwzsxgWYblJM+JDZ9KL6hrP7wTdpEB+Ane7+yhPPuB8dpXUcsMv/soXpOw3tJg\naYzNrrf3Z6IZbSVQzABwiKBMO9Me7Ux7MJ0D/gVW8ZsXL+JSUzNzSVZTR9fIuL0SHvG53Js3\nb0jCxdqYl4hIz8xMdPmLF2VkIkLSNisX9FSY4X1+gd8INKPc4/3Ll7nEM6h/30ZXLUn1HdCV\n9/TFFy/eU09lTkWDH1X0/LjXlLl7H+ey2lj8fvTwpgkmUkxHAgDgWpVRUS+kXf3uHHHE7loA\nbBb78iWLNHr3/34rSkR8+gP6aC199uJFLBGK0VYCxSgAW1W9eRqSkN/wPR4+IVEJaUVNbQ1Z\nEcx94laVKcGbl3pt83+R/8/sen7F7pOXb13r3kUeO+VxLSEhIaqu/rK/KE+7dpqs0IREIhMi\nkjY2Vi/3j3xBIzoxmRC+UlBQQKRmZCTZ+DBxIyMNupifn0+EYpQbladeWPO7x5Zbb6rFDEZu\nO7RjlrUCNj0AAGiMoIaGkpKEDlrRluLt5bVrg982c7Dyb8uXD8I7Fu5RUFBA1M2oqW1fjYyM\niC7lf+chHn5BKEYB2OrThXm9Pe41PoZfsfNIz0WLPRzaS3AkEzRf7lWP3kMPvRY3HOIxq7uh\nppJIcVZyzM2TJ+8c8Oj9sjTy3oJGP24EBmlpa1PZnYu3Sob0FiPS0tfnT3nw4D2ZKBFVpqVl\nUW5uLtMR4RuCgk1uuiwkhDNCuVT1u7vbZk1dFZhcJqgxcMW+vUsHaGAPbQCAplkOGlQwP+D5\nchNj/NVsCfKj/Pfvb+zApbo6KHmgGOU6Tb/fFMTbzdYGz/QAbCUx0PvcnkOLZh1LZMkY9R3Q\nzUBDQbjkfdqLkMt3k3gsPDbNNCpNi7l+1NfL8fLjky/Pjcbm3Fzl0Rq3Q2+N5ty9v9VW5p9J\nvYtXLTs+suv4IK/ZRydcn4I9n7iT5tjJvVZMPzLcLNPr8NklNja9rPlmbp65v4OXZWnw3hvl\nvB07tmc6IsCvgpUXdWj+lIVHowv5FLrPOXJo7UhDnEoHANBMQn02BzoOde0923ORS0On0vNK\nqbSTE2YkGjRAf+6tN27VzRzML4ml2AAtAIpRALYS1JR7fSowScnx0J1jbnr/PCnW5j3y/q3v\nxuNZEaErp81bMsO7h8Vyz1V3Rx/oyWBYqCczPPyt3OQTX7WiREQCGuMOrjl+dkrEo0jWlIFY\nTs+dlKeeupQx2m3XrTtx5UtslKbsWuvbecl0u0AiIn71acsmqTCdEOBXccG9y+9+NUQ8Mibd\n9YvubJt9p5HB5tMOTTPnWDQAAG6Xu6ennMc9Iro/LXRnQwNwKj134ZdQVMEqP4BfC4pRAPZ6\ndebww4qBJ47WbUWJiLdN1xX7Zp823bLr/qJDvQQ7LFowbM2Ily/fUU/MGeUexcXFpGtg0NAG\nsG3at1ei0OLiaiIc4MOleBX7rr+VsbYov1SUiAQ7Lroa0T7w7O0XJYpdRvw+pnMTe1kCE1I2\ndBbZ2viOy7XVFURN7QwFHFZTU0NERKz8p+f+fNrE4IL+KEYBAP4hYuoyf75FIwNEu6txLAz8\nK1VvQk6fvhTyNDW7UNZx78FxtfeCsvV/s2qL5djc6eJEMRG3xqe21FRyKAtwCRSjAGxV+fRp\nLCn31W/oc0VdXV3KDw19Rb06kICqqgI9zMwkQjHKPQxtbRU2hYUVzRrxzc/vzePId2RiYYFW\nlNvxSch8+enxyBkPmWY8hNk48D28whLS0s06u1xYlCSEcV4dV7HfFBW1uLmDZbTYGQUAoKUR\n7T5tS3emQ8C/xso6O7XP+MNxxZ9fduhUQvTm2IT+p5XHnbh+ZIQm+hauIiAqLS3dnIHCwkSi\neNBrPfCLCsBWgjo6anQpPDyLLOsv3K0MD39KpNtWiYgoJyYmixQVsQsNd+k2Z2Nv2xkOGxWP\nzbVR/v8u3RWZt1a5LI/UmXLVDQ/4AD+H4aKH+YuYDgH/koyWOeaAAgD8TNWfPhbwycmJ4YNA\nLpe01XH04QSJHvN8vCdJnuzt8IiIyHLapjGP5x0fNcLQLHKxDtMRoY4hx3DUPDQExSgAe3X6\nbbDK9j1eTgt1/lo1oJ3ol6u1edG+M90OZAlZze4tR1Uf720+9IDUpnZTZTQr1PP2YUipjYPe\n2iW27XaZ9rBq305JtOx9+svwkJj3lfy6AwWurJ1z5f+DdRy9Z1jjvBFG3fA09LzR3MF9d8bt\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": "1b469b98", "metadata": {}, "source": [ "### Overall CCI potential: Network visualization of sender-receiver cell pairs" ] }, { "cell_type": "markdown", "id": "67d65cb8", "metadata": {}, "source": [ "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. To do so, we need to get all the outer products between the loadings for the sender and receiver cells dimensions across all factors." ] }, { "cell_type": "code", "execution_count": 13, "id": "4fe83590", "metadata": {}, "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 × 11
lrFactor.1Factor.2Factor.3Factor.4Factor.5Factor.6Factor.7Factor.8Factor.9Factor.10
<fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
A2M^LRP1 2.014718e-046.177029e-070.00010184092.616977e-031.016893e-065.631589e-071.216602e-013.608764e-021.469622e-041.185005e-04
ACTR2^ADRB2 3.939710e-023.842080e-020.01412205304.564203e-021.393886e-023.178997e-021.182422e-021.927887e-022.016811e-051.158559e-08
ACTR2^LDLR 1.093640e-041.604342e-160.01161694432.514870e-024.178628e-103.704964e-022.194405e-101.038034e-091.593195e-036.626642e-02
ADAM10^AXL 4.680507e-051.702355e-140.00155329483.183815e-021.170693e-024.584032e-025.565283e-028.909291e-081.652256e-118.912689e-11
ADAM10^CADM12.598824e-043.258244e-080.00115001104.352961e-103.441869e-021.577997e-044.419620e-041.123850e-031.777034e-051.144090e-06
ADAM10^CD44 5.699971e-032.898187e-020.02882838256.858978e-036.697052e-021.096958e-021.019256e-015.999368e-023.157115e-028.152719e-03
\n" ], "text/latex": [ "A tibble: 6 × 11\n", "\\begin{tabular}{lllllllllll}\n", " lr & Factor.1 & Factor.2 & Factor.3 & Factor.4 & Factor.5 & Factor.6 & Factor.7 & Factor.8 & Factor.9 & Factor.10\\\\\n", " & & & & & & & & & & \\\\\n", "\\hline\n", "\t A2M\\textasciicircum{}LRP1 & 2.014718e-04 & 6.177029e-07 & 0.0001018409 & 2.616977e-03 & 1.016893e-06 & 5.631589e-07 & 1.216602e-01 & 3.608764e-02 & 1.469622e-04 & 1.185005e-04\\\\\n", "\t ACTR2\\textasciicircum{}ADRB2 & 3.939710e-02 & 3.842080e-02 & 0.0141220530 & 4.564203e-02 & 1.393886e-02 & 3.178997e-02 & 1.182422e-02 & 1.927887e-02 & 2.016811e-05 & 1.158559e-08\\\\\n", "\t ACTR2\\textasciicircum{}LDLR & 1.093640e-04 & 1.604342e-16 & 0.0116169443 & 2.514870e-02 & 4.178628e-10 & 3.704964e-02 & 2.194405e-10 & 1.038034e-09 & 1.593195e-03 & 6.626642e-02\\\\\n", "\t ADAM10\\textasciicircum{}AXL & 4.680507e-05 & 1.702355e-14 & 0.0015532948 & 3.183815e-02 & 1.170693e-02 & 4.584032e-02 & 5.565283e-02 & 8.909291e-08 & 1.652256e-11 & 8.912689e-11\\\\\n", "\t ADAM10\\textasciicircum{}CADM1 & 2.598824e-04 & 3.258244e-08 & 0.0011500110 & 4.352961e-10 & 3.441869e-02 & 1.577997e-04 & 4.419620e-04 & 1.123850e-03 & 1.777034e-05 & 1.144090e-06\\\\\n", "\t ADAM10\\textasciicircum{}CD44 & 5.699971e-03 & 2.898187e-02 & 0.0288283825 & 6.858978e-03 & 6.697052e-02 & 1.096958e-02 & 1.019256e-01 & 5.999368e-02 & 3.157115e-02 & 8.152719e-03\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 6 × 11\n", "\n", "| lr <fct> | 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", "| A2M^LRP1 | 2.014718e-04 | 6.177029e-07 | 0.0001018409 | 2.616977e-03 | 1.016893e-06 | 5.631589e-07 | 1.216602e-01 | 3.608764e-02 | 1.469622e-04 | 1.185005e-04 |\n", "| ACTR2^ADRB2 | 3.939710e-02 | 3.842080e-02 | 0.0141220530 | 4.564203e-02 | 1.393886e-02 | 3.178997e-02 | 1.182422e-02 | 1.927887e-02 | 2.016811e-05 | 1.158559e-08 |\n", "| ACTR2^LDLR | 1.093640e-04 | 1.604342e-16 | 0.0116169443 | 2.514870e-02 | 4.178628e-10 | 3.704964e-02 | 2.194405e-10 | 1.038034e-09 | 1.593195e-03 | 6.626642e-02 |\n", "| ADAM10^AXL | 4.680507e-05 | 1.702355e-14 | 0.0015532948 | 3.183815e-02 | 1.170693e-02 | 4.584032e-02 | 5.565283e-02 | 8.909291e-08 | 1.652256e-11 | 8.912689e-11 |\n", "| ADAM10^CADM1 | 2.598824e-04 | 3.258244e-08 | 0.0011500110 | 4.352961e-10 | 3.441869e-02 | 1.577997e-04 | 4.419620e-04 | 1.123850e-03 | 1.777034e-05 | 1.144090e-06 |\n", "| ADAM10^CD44 | 5.699971e-03 | 2.898187e-02 | 0.0288283825 | 6.858978e-03 | 6.697052e-02 | 1.096958e-02 | 1.019256e-01 | 5.999368e-02 | 3.157115e-02 | 8.152719e-03 |\n", "\n" ], "text/plain": [ " lr Factor.1 Factor.2 Factor.3 Factor.4 Factor.5 \n", "1 A2M^LRP1 2.014718e-04 6.177029e-07 0.0001018409 2.616977e-03 1.016893e-06\n", "2 ACTR2^ADRB2 3.939710e-02 3.842080e-02 0.0141220530 4.564203e-02 1.393886e-02\n", "3 ACTR2^LDLR 1.093640e-04 1.604342e-16 0.0116169443 2.514870e-02 4.178628e-10\n", "4 ADAM10^AXL 4.680507e-05 1.702355e-14 0.0015532948 3.183815e-02 1.170693e-02\n", "5 ADAM10^CADM1 2.598824e-04 3.258244e-08 0.0011500110 4.352961e-10 3.441869e-02\n", "6 ADAM10^CD44 5.699971e-03 2.898187e-02 0.0288283825 6.858978e-03 6.697052e-02\n", " Factor.6 Factor.7 Factor.8 Factor.9 Factor.10 \n", "1 5.631589e-07 1.216602e-01 3.608764e-02 1.469622e-04 1.185005e-04\n", "2 3.178997e-02 1.182422e-02 1.927887e-02 2.016811e-05 1.158559e-08\n", "3 3.704964e-02 2.194405e-10 1.038034e-09 1.593195e-03 6.626642e-02\n", "4 4.584032e-02 5.565283e-02 8.909291e-08 1.652256e-11 8.912689e-11\n", "5 1.577997e-04 4.419620e-04 1.123850e-03 1.777034e-05 1.144090e-06\n", "6 1.096958e-02 1.019256e-01 5.999368e-02 3.157115e-02 8.152719e-03" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(factors[['interactions']])" ] }, { "cell_type": "code", "execution_count": 14, "id": "2d9a1923", "metadata": {}, "outputs": [], "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", "py_factors<-unname(lapply(factors, function(df) format_py_df(df)))\n", "py_factors = reticulate::py_dict(keys = names(factors), values = py_factors)" ] }, { "cell_type": "code", "execution_count": 15, "id": "0e00daf2", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 600, "width": 900 } }, "output_type": "display_data" } ], "source": [ "# Get all outer products as adjacency matrices, one per factor\n", "networks <- c2c$analysis$tensor_downstream$get_factor_specific_ccc_networks(py_factors,\n", " sender_label = 'senders',\n", " receiver_label = 'receivers')\n", "\n", "# Then, flatten the adjacency matrices\n", "network_by_factors = c2c$analysis$tensor_downstream$flatten_factor_ccc_networks(networks, orderby='receivers')\n", "\n", "# And we can plot the distributions of the weights for each factor-specific network\n", "ggplot(data.frame(weights = unlist(unname(as.vector(network_by_factors)))), aes(x=weights)) + \n", "geom_histogram(bins = 50) + theme_bw() + ylab('')\n" ] }, { "cell_type": "markdown", "id": "e937cec5", "metadata": {}, "source": [ "With this distribution we can set a reasonable threshold. We can also apply some mathematical methods to select one, as for example choosing a percentile. Here we set a threshold of 0.075." ] }, { "cell_type": "code", "execution_count": 16, "id": "6d8c1ebe", "metadata": {}, "outputs": [], "source": [ "threshold = 0.075" ] }, { "cell_type": "markdown", "id": "c3d398c0", "metadata": {}, "source": [ "Then, we can plot all networks we are interested in:" ] }, { "cell_type": "code", "execution_count": 17, "id": "9c136f3e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[1]]\n", "
\n", "\n", "[[2]]\n", "[[2]][[1]]\n", "\n", "\n", "[[2]][[2]]\n", "\n", "\n", "[[2]][[3]]\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "c2c$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(data_path, 'tc2c-outputs', 'network_plot_05_r.png')\n", " )" ] }, { "cell_type": "markdown", "id": "0e52564e", "metadata": {}, "source": [ "![network_plot_05](../../data/tc2c-outputs/network_plot_05_r.png \"Network Plot\")" ] }, { "cell_type": "markdown", "id": "7786440c", "metadata": {}, "source": [ "### Gini coefficients of the factor-specific cell-cell communication\n", "\n", "The Gini coefficient quantifies the extent to which sender-receiver cell pairs differ within a factor according to their importance as measured by their loadings. Here it is use to measure the imbalance of communication for a given factor. In other words, it identifies whether a few cells are key for an identified pattern (low Gini coefficient) or whether the pattern corresponds to a general process that involves most of the cell types (high Gini coefficient). Gini coefficients range between [0, 1].\n", "\n", "**Note that here we show ginis seperately for sender and receiver cell types per factor, while in Python we show the combined gini for sender and receiver cell types**" ] }, { "cell_type": "code", "execution_count": 18, "id": "1a6e4a16", "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", "\n", "
A tibble: 10 × 2
factorgini
<chr><dbl>
Factor.1 0.2662005
Factor.100.1829162
Factor.2 0.1913923
Factor.3 0.4460056
Factor.4 0.4506584
Factor.5 0.6984962
Factor.6 0.6161417
Factor.7 0.2631348
Factor.8 0.0631097
Factor.9 0.6547246
\n" ], "text/latex": [ "A tibble: 10 × 2\n", "\\begin{tabular}{ll}\n", " factor & gini\\\\\n", " & \\\\\n", "\\hline\n", "\t Factor.1 & 0.2662005\\\\\n", "\t Factor.10 & 0.1829162\\\\\n", "\t Factor.2 & 0.1913923\\\\\n", "\t Factor.3 & 0.4460056\\\\\n", "\t Factor.4 & 0.4506584\\\\\n", "\t Factor.5 & 0.6984962\\\\\n", "\t Factor.6 & 0.6161417\\\\\n", "\t Factor.7 & 0.2631348\\\\\n", "\t Factor.8 & 0.0631097\\\\\n", "\t Factor.9 & 0.6547246\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 10 × 2\n", "\n", "| factor <chr> | gini <dbl> |\n", "|---|---|\n", "| Factor.1 | 0.2662005 |\n", "| Factor.10 | 0.1829162 |\n", "| Factor.2 | 0.1913923 |\n", "| Factor.3 | 0.4460056 |\n", "| Factor.4 | 0.4506584 |\n", "| Factor.5 | 0.6984962 |\n", "| Factor.6 | 0.6161417 |\n", "| Factor.7 | 0.2631348 |\n", "| Factor.8 | 0.0631097 |\n", "| Factor.9 | 0.6547246 |\n", "\n" ], "text/plain": [ " factor gini \n", "1 Factor.1 0.2662005\n", "2 Factor.10 0.1829162\n", "3 Factor.2 0.1913923\n", "4 Factor.3 0.4460056\n", "5 Factor.4 0.4506584\n", "6 Factor.5 0.6984962\n", "7 Factor.6 0.6161417\n", "8 Factor.7 0.2631348\n", "9 Factor.8 0.0631097\n", "10 Factor.9 0.6547246" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get loadings for source/sender Cell types\n", "calculate_gini(factors$senders)" ] }, { "cell_type": "code", "execution_count": 19, "id": "4a874007", "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", "\n", "
A tibble: 10 × 2
factorgini
<chr><dbl>
Factor.1 0.08451066
Factor.100.47924770
Factor.2 0.10965274
Factor.3 0.13653756
Factor.4 0.09516597
Factor.5 0.14736680
Factor.6 0.10340692
Factor.7 0.50656809
Factor.8 0.69806214
Factor.9 0.05691588
\n" ], "text/latex": [ "A tibble: 10 × 2\n", "\\begin{tabular}{ll}\n", " factor & gini\\\\\n", " & \\\\\n", "\\hline\n", "\t Factor.1 & 0.08451066\\\\\n", "\t Factor.10 & 0.47924770\\\\\n", "\t Factor.2 & 0.10965274\\\\\n", "\t Factor.3 & 0.13653756\\\\\n", "\t Factor.4 & 0.09516597\\\\\n", "\t Factor.5 & 0.14736680\\\\\n", "\t Factor.6 & 0.10340692\\\\\n", "\t Factor.7 & 0.50656809\\\\\n", "\t Factor.8 & 0.69806214\\\\\n", "\t Factor.9 & 0.05691588\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 10 × 2\n", "\n", "| factor <chr> | gini <dbl> |\n", "|---|---|\n", "| Factor.1 | 0.08451066 |\n", "| Factor.10 | 0.47924770 |\n", "| Factor.2 | 0.10965274 |\n", "| Factor.3 | 0.13653756 |\n", "| Factor.4 | 0.09516597 |\n", "| Factor.5 | 0.14736680 |\n", "| Factor.6 | 0.10340692 |\n", "| Factor.7 | 0.50656809 |\n", "| Factor.8 | 0.69806214 |\n", "| Factor.9 | 0.05691588 |\n", "\n" ], "text/plain": [ " factor gini \n", "1 Factor.1 0.08451066\n", "2 Factor.10 0.47924770\n", "3 Factor.2 0.10965274\n", "4 Factor.3 0.13653756\n", "5 Factor.4 0.09516597\n", "6 Factor.5 0.14736680\n", "7 Factor.6 0.10340692\n", "8 Factor.7 0.50656809\n", "9 Factor.8 0.69806214\n", "10 Factor.9 0.05691588" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get loadings for source/sender Cell types\n", "calculate_gini(factors$receiver)" ] }, { "cell_type": "markdown", "id": "50b0b026", "metadata": {}, "source": [ "For senders, we observe that the lowest Gini coefficient was obtained by Factor 2, which seems to have more balanced cell loadings. Similarly, for receivers, the lowest Gini coefficient is in Factor 10. For each of these factors respectively, senders and receivers are more similar or homogeneous." ] } ], "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": "a89d9df9e41c144bbb86b791904f32fb0efeb7b488a88d676a8bce57017c9696" } } }, "nbformat": 4, "nbformat_minor": 5 }