|
84 | 84 | "library(ggpubr)\n",
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85 | 85 | "library(reshape2)\n",
|
86 | 86 | "library(jcolors)\n",
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87 |
| - "library(ALPACA)\n", |
88 | 87 | "library(gridExtra)\n",
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89 | 88 | "library(extrafont)\n",
|
90 | 89 | "library(grid)\n",
|
|
115 | 114 | "metadata": {},
|
116 | 115 | "outputs": [],
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117 | 116 | "source": [
|
118 |
| - "\n", |
119 | 117 | "load(paste0(ppath,\"data/networks/finalEgret_v1_banovich_LCL_allModels_smart1_07032020_1_panda.RData\"))\n",
|
120 | 118 | "net <- melt(regnet)\n",
|
121 | 119 | "colnames(net) = c(\"tf\",\"gene\",\"score\")\n",
|
|
489 | 487 | "metadata": {},
|
490 | 488 | "outputs": [],
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491 | 489 | "source": [
|
492 |
| - "\n", |
493 | 490 | "indivs_high_erg_disrup <- cad_tfs_degree_hard_thresh_melted[which((cad_tfs_degree_hard_thresh_melted$tf_diff_degree>3) & (cad_tfs_degree_hard_thresh_melted$cellType == \"CM\")),]\n",
|
494 |
| - "indivs_high_erg_disrup\n", |
495 |
| - "\n" |
| 491 | + "indivs_high_erg_disrup" |
496 | 492 | ]
|
497 | 493 | },
|
498 | 494 | {
|
|
504 | 500 | },
|
505 | 501 | {
|
506 | 502 | "cell_type": "code",
|
507 |
| - "execution_count": 1, |
508 |
| - "metadata": {}, |
509 |
| - "outputs": [ |
510 |
| - { |
511 |
| - "ename": "ERROR", |
512 |
| - "evalue": "Error in eval(expr, envir, enclos): object 'cad_tfs_degree_hard_thresh_melted' not found\n", |
513 |
| - "output_type": "error", |
514 |
| - "traceback": [ |
515 |
| - "Error in eval(expr, envir, enclos): object 'cad_tfs_degree_hard_thresh_melted' not found\nTraceback:\n" |
516 |
| - ] |
517 |
| - } |
518 |
| - ], |
| 503 | + "execution_count": null, |
| 504 | + "metadata": {}, |
| 505 | + "outputs": [], |
519 | 506 | "source": [
|
520 | 507 | "top_indiv <- cad_tfs_degree_hard_thresh_melted[which((cad_tfs_degree_hard_thresh_melted$tf_diff_degree== max(cad_tfs_degree_hard_thresh_melted$tf_diff_degree)) & (cad_tfs_degree_hard_thresh_melted$cellType == \"CM\")),]\n",
|
521 | 508 | "\n",
|
522 |
| - "\n", |
523 | 509 | "# get tf disruption scores for ERG for all individuals in iPSC-CMs\n",
|
524 | 510 | "tf_disrup_erg_cm <- cad_tfs_degree_hard_thresh_melted[which((cad_tfs_degree_hard_thresh_melted$tf == \"ERG\") & (cad_tfs_degree_hard_thresh_melted$cellType == \"CM\")),c('indiv','tf_diff_degree')]"
|
525 | 511 | ]
|
|
638 | 624 | "merged_alpaca_table_qq <- cbind(merged_alpaca_table[,c(1)],as.data.frame(normalize.quantiles(as.matrix(merged_alpaca_table[,c(2:358)]))))\n",
|
639 | 625 | "colnames(merged_alpaca_table_qq) <- colnames(merged_alpaca_table)\n",
|
640 | 626 | "merged_alpaca_table_qq_sc <- cbind(merged_alpaca_table_qq[,c(1)],as.data.frame(t(scale(t(merged_alpaca_table_qq[,c(2:358)]), center = TRUE, scale = TRUE))))\n",
|
641 |
| - "colnames(merged_alpaca_table_qq_sc)[1] <- \"node\"\n", |
642 |
| - "\n" |
| 627 | + "colnames(merged_alpaca_table_qq_sc)[1] <- \"node\"" |
643 | 628 | ]
|
644 | 629 | },
|
645 | 630 | {
|
|
854 | 839 | "k <- k+1\n",
|
855 | 840 | "}\n",
|
856 | 841 | "\n",
|
857 |
| - "grid.draw(g)\n" |
| 842 | + "grid.draw(g)" |
858 | 843 | ]
|
859 | 844 | },
|
860 | 845 | {
|
|
877 | 862 | "\n",
|
878 | 863 | "[7] Padi, Megha, and John Quackenbush. \"Detecting phenotype-driven transitions in regulatory network structure.\" NPJ systems biology and applications 4.1 (2018): 1-12."
|
879 | 864 | ]
|
880 |
| - }, |
881 |
| - { |
882 |
| - "cell_type": "code", |
883 |
| - "execution_count": null, |
884 |
| - "metadata": {}, |
885 |
| - "outputs": [], |
886 |
| - "source": [] |
887 | 865 | }
|
888 | 866 | ],
|
889 | 867 | "metadata": {
|
|
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