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craniolabcraniolab
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test
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‎.gitignore

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notebooks/
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figures_ignore/
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.ipynb_checkpoints/

‎notebooks_github/useful_code.ipynb

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‎notebooks_github/useful_code.ipynb.pdf

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‎notebooks_github/useful_code_R.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ea4f6f59-47f5-4890-9339-17d7be855b07",
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"metadata": {},
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"outputs": [],
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"source": [
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"custom_heatmap = function(object,signaling = NULL, pattern = c(\"outgoing\",\"incoming\",\"all\"), slot.name = \"netP\",\n",
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" color.use = NULL, color.heatmap = 'Blues', pathway.col = c(\"Cell-Cell Contact\" = \"red\", \"ECM-Receptor\" = \"green\", \"Mixed\" = \"blue\",\n",
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" \"MK PTN family\" = \"yellow\",\"Secreted Signaling\" = \"orange\"),\n",
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" title = NULL, width = 8, height = 20, font.size = 8, font.size.title = 10,\n",
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" row_clusters = NULL, column_clusters = NULL, seed = 99){\n",
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" pattern <- match.arg(pattern)\n",
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" set.seed(seed)\n",
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" ##############################\n",
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" #######construct matrix#######\n",
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" ##############################\n",
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" centr <- slot(object, slot.name)$centr\n",
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" outgoing <- matrix(0, nrow = nlevels(object@idents), ncol = length(centr))\n",
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" incoming <- matrix(0, nrow = nlevels(object@idents), ncol = length(centr))\n",
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" dimnames(outgoing) <- list(levels(object@idents), names(centr))\n",
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" dimnames(incoming) <- dimnames(outgoing)\n",
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" for (i in 1:length(centr)) {\n",
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" outgoing[,i] <- centr[[i]]$outdeg\n",
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" incoming[,i] <- centr[[i]]$indeg\n",
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" }\n",
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" if (pattern == \"outgoing\") {\n",
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" mat <- t(outgoing)\n",
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" legend.name <- \"Outgoing\"\n",
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" } else if (pattern == \"incoming\") {\n",
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" mat <- t(incoming)\n",
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" legend.name <- \"Incoming\"\n",
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" } else if (pattern == \"all\") {\n",
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" mat <- t(outgoing+ incoming)\n",
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" legend.name <- \"Overall\"\n",
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" }\n",
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" if (!is.null(signaling)) {\n",
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" mat1 <- mat[rownames(mat) %in% signaling, , drop = FALSE]\n",
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" mat <- matrix(0, nrow = length(signaling), ncol = ncol(mat))\n",
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" idx <- match(rownames(mat1), signaling)\n",
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" mat[idx[!is.na(idx)], ] <- mat1\n",
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" dimnames(mat) <- list(signaling, colnames(mat1))\n",
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" }\n",
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" mat.ori <- mat\n",
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" mat <- sweep(mat, 1L, apply(mat, 1, max), '/', check.margin = FALSE)\n",
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" \n",
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" ##############################\n",
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" ###setup legend and colors####\n",
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" ##############################\n",
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" if (is.null(title)) {\n",
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" title <- paste0(legend.name, \" signaling patterns\")\n",
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" } else {\n",
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" title <- paste0(paste0(legend.name, \" signaling patterns\"), \" - \",title)\n",
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" }\n",
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" if (min(mat, na.rm = T) == max(mat, na.rm = T)) {\n",
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" legend.break <- max(mat, na.rm = T)\n",
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" } else {\n",
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" legend.break <- c(round(min(mat, na.rm = T), digits = 1), round(max(mat, na.rm = T), digits = 1))}\n",
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"\n",
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" #heatmap color\n",
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" color.heatmap.use = grDevices::colorRampPalette((RColorBrewer::brewer.pal(n = 9, name = color.heatmap)))(100)\n",
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" color.heatmap.use[1] = '#ffffff' #set 0 point to white\n",
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" \n",
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" ##############################\n",
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" #####heatmap annotations######\n",
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" ##############################\n",
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" ###Rows###\n",
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" #Left annotation\n",
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" library(dplyr)\n",
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" pathway_annotations = distinct(data.frame(pathway = cellchat@DB$interaction$pathway_name,\n",
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" annotation = cellchat@DB$interaction$annotation))\n",
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" pathway_annotations[pathway_annotations$pathway %in% \n",
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" pathway_annotations[duplicated(pathway_annotations$pathway),]$pathway,]$annotation = 'Mixed'\n",
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" pathway_annotations = distinct(pathway_annotations)\n",
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" pathway_annotations = pathway_annotations[match(row.names(mat),pathway_annotations$pathway),]\n",
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" rownames(pathway_annotations) <- NULL\n",
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" \n",
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" pathway.col = pathway.col[pathway_annotations$annotation]\n",
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" \n",
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" row_anno = HeatmapAnnotation(\"Signaling Type\" = pathway_annotations$annotation,\n",
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" col = list(\"Signaling Type\" = pathway.col),\n",
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" which = \"row\",\n",
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" show_legend = TRUE, show_annotation_name = FALSE,\n",
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" simple_anno_size = grid::unit(0.1, \"cm\"))\n",
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"\n",
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" #right annotation\n",
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" pSum <- rowSums(mat.ori)\n",
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" pSum.original <- pSum\n",
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" pSum <- -1/log(pSum)\n",
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" pSum[is.na(pSum)] <- 0\n",
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" idx1 <- which(is.infinite(pSum) | pSum < 0)\n",
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" if (length(idx1) > 0) {\n",
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" values.assign <- seq(max(pSum)*1.1, max(pSum)*1.5, length.out = length(idx1))\n",
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" position <- sort(pSum.original[idx1], index.return = TRUE)$ix\n",
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" pSum[idx1] <- values.assign[match(1:length(idx1), position)]\n",
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" }\n",
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" ha1 = rowAnnotation(Strength = anno_barplot(pSum, border = FALSE), show_annotation_name = FALSE)\n",
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"\n",
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" ###columns###\n",
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" if (is.null(color.use)) {\n",
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" color.use <- scPalette(length(colnames(mat)))\n",
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" }\n",
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" df<- data.frame(group = colnames(mat)); rownames(df) <- colnames(mat)\n",
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" names(color.use) <- colnames(mat)\n",
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"\n",
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" #bottom annotation\n",
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" col_annotation <- HeatmapAnnotation(df = df, col = list(group = color.use),which = \"column\",\n",
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" show_legend = FALSE, show_annotation_name = FALSE,\n",
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" simple_anno_size = grid::unit(0.2, \"cm\"))\n",
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" #top annotation\n",
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" ha2 = HeatmapAnnotation(Strength = anno_barplot(colSums(mat.ori), border = FALSE,gp = gpar(fill = color.use, col=color.use)), show_annotation_name = FALSE) \n",
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" \n",
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" \n",
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" ##############################\n",
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" #######plotting heatmap#######\n",
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" ##############################\n",
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" ht1 = Heatmap(mat, col = color.heatmap.use, na_col = \"white\", name = \"Relative strength\",\n",
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" right_annotation = ha1,\n",
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" left_annotation = row_anno,\n",
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" bottom_annotation = col_annotation, top_annotation = ha2,\n",
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" #cluster_rows=hclust(dist(mat.noNA),method = 'average'),\n",
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" #cluster_columns =hclust(dist(t(mat.noNA)),method = 'average'),\n",
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" row_km = row_clusters, row_km_repeats = 5,\n",
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" column_km = column_clusters, column_km_repeats = 5,\n",
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" border = TRUE,\n",
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" row_names_side = \"left\",row_names_rot = 0,row_names_gp = gpar(fontsize = font.size),column_names_gp = gpar(fontsize = font.size),\n",
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" width = unit(width, \"cm\"), height = unit(height, \"cm\"),\n",
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" column_title = title,column_title_gp = gpar(fontsize = font.size.title),column_names_rot = 90,\n",
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" heatmap_legend_param = list(title_gp = gpar(fontsize = 8, fontface = \"plain\"),title_position = \"leftcenter-rot\",\n",
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" border = NA, at = legend.break,\n",
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" legend_height = unit(20, \"mm\"),labels_gp = gpar(fontsize = 8),grid_width = unit(2, \"mm\"))\n",
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" )\n",
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" return(ht1)\n",
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" }"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "cellchat",
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"language": "R",
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"name": "cellchat"
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},
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"language_info": {
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"codemirror_mode": "r",
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"file_extension": ".r",
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"mimetype": "text/x-r-source",
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"name": "R",
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"pygments_lexer": "r",
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"version": "4.3.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎notebooks_github/useful_code_R.ipynb.pdf

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‎required_files/regev_lab_cell_cycle_genes.txt

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Mcm5
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Pcna
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Tyms
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Fen1
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Mcm2
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Mcm4
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Rrm1
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Ung
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Gins2
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Mcm6
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Cdca7
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Dtl
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Prim1
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Uhrf1
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Cenpu
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Hells
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Rfc2
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Rpa2
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Nasp
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Rad51ap1
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Gmnn
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Wdr76
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Slbp
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Ccne2
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Ubr7
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Pold3
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Msh2
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Atad2
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Rad51
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Rrm2
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Cdc45
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Cdc6
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Exo1
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Tipin
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Dscc1
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Blm
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Casp8ap2
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Usp1
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Clspn
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Pola1
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Chaf1b
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Brip1
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E2f8
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Hmgb2
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Cdk1
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Nusap1
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Ube2c
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Birc5
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Tpx2
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Top2a
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Ndc80
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Cks2
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Nuf2
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Cks1b
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Mki67
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Tmpo
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Cenpf
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Tacc3
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Pimreg
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Smc4
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Ccnb2
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Ckap2l
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Ckap2
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Aurkb
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Bub1
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Kif11
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Anp32e
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Tubb4b
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Gtse1
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Kif20b
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Hjurp
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Cdca3
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Jpt1
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Cdc20
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Ttk
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Cdc25c
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Kif2c
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Rangap1
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Ncapd2
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Dlgap5
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Cdca2
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Cdca8
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Ect2
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Kif23
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Hmmr
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Aurka
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Psrc1
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Anln
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Lbr
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Ckap5
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Cenpe
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Ctcf
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Nek2
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G2e3
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Gas2l3
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Cbx5
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Cenpa

‎notebooks/save_notebooks_commit_push.sh ‎save_notebooks_commit_push.sh

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#!/bin/bash
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cd notebooks
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for filename in *.ipynb; do
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jupyter nbconvert $filename --to webpdf --allow-chromium-download
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jupyter nbconvert $filename --to webpdf --allow-chromium-download \
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--output ../notebooks_github/$filename
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jupyter nbconvert $filename --to notebook \
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--ClearOutputPreprocessor.enabled=True --output ../github_notebooks/$filename
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--ClearOutputPreprocessor.enabled=True --output ../notebooks_github/$filename
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done

‎test.txt

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