From b455e67447692574a3c82f6eeea10d931f3f75bd Mon Sep 17 00:00:00 2001 From: YPP Date: Wed, 13 Mar 2024 23:02:08 -0700 Subject: [PATCH] update lect17 --- lect17-scRNA-topic-model/Setup.R | 231 +++ lect17-scRNA-topic-model/Util.R | 169 +++ .../lect17-scRNA-topic-model-combined.pdf | Bin 0 -> 8590771 bytes .../lect17-scRNA-topic-model.Rmd | 1272 +++++++++++++++++ 4 files changed, 1672 insertions(+) create mode 100644 lect17-scRNA-topic-model/Setup.R create mode 100644 lect17-scRNA-topic-model/Util.R create mode 100644 lect17-scRNA-topic-model/lect17-scRNA-topic-model-combined.pdf create mode 100644 lect17-scRNA-topic-model/lect17-scRNA-topic-model.Rmd diff --git a/lect17-scRNA-topic-model/Setup.R b/lect17-scRNA-topic-model/Setup.R new file mode 100644 index 0000000..ebb6081 --- /dev/null +++ b/lect17-scRNA-topic-model/Setup.R @@ -0,0 +1,231 @@ +options(stringsAsFactors = FALSE) + +`%&%` <- function(a,b) paste0(a, b) +`%r%` <- function(mat,rr) mat[rr, , drop = FALSE] +`%c%` <- function(mat,cc) mat[, cc, drop = FALSE] + +library(tidyverse) +library(data.table) +library(ggrepel) + +num.int <- function(...) format(..., justify="none", big.mark=",", drop0trailing = TRUE) + +num.sci <- function(...) format(..., justify="none", digits=2, scientific = TRUE) + +row.order <- function(mat) { + require(cba) + require(proxy) + + if(nrow(mat) < 3) { + return(1:nrow(mat)) + } + + D = proxy::dist(mat, method <- function(a,b) 1 - cor(a,b, method = 'spearman')) + D[!is.finite(D)] = 0 + h.out = hclust(D) + o.out = cba::order.optimal(D, h.out$merge) + return(o.out$order) +} + +col.order <- function(pair.tab, .ro, ret.tab = FALSE) { + + M = pair.tab %>% + dplyr::select(row, col, weight) %>% + dplyr::mutate(row = factor(row, .ro)) %>% + tidyr::spread(key = col, value = weight, fill = 0) + + co = order(apply(M[, -1], 2, which.max), decreasing = TRUE) + .co = colnames(M)[-1][co] + if(ret.tab) { + ret = pair.tab %>% + dplyr::mutate(row = factor(row, .ro)) %>% + dplyr::mutate(col = factor(col, .co)) + } else { + ret = .co + } + return(ret) +} + +order.pair <- function(pair.tab, ret.tab=FALSE) { + + require(tidyr) + require(dplyr) + + .tab = pair.tab %>% dplyr::select(row, col, weight) + + M = .tab %>% tidyr::spread(key = col, value = weight, fill = 0) + rr = M[, 1] %>% unlist(use.names = FALSE) + cc = colnames(M)[-1] %>% unlist(use.names = FALSE) + + ## log.msg('Built the Mat: %d x %d', nrow(M), ncol(M)) + ro = row.order(M %>% dplyr::select(-row) %>% as.matrix()) + + ## log.msg('Sort the rows: %d', length(ro)) + co = order(apply(M[ro, -1], 2, which.max), decreasing = TRUE) + + ## co = row.order(t(M %>% dplyr::select(-row) %>% as.matrix())) + ## log.msg('Sort the columns: %d', length(co)) + + if(ret.tab){ + ret = pair.tab %>% + dplyr::mutate(row = factor(row, rr[ro])) %>% + dplyr::mutate(col = factor(col, cc[co])) + } else { + ret = list(rows = rr[ro], cols = cc[co], M = M) + } + + return(ret) +} + + +.sort.matrix <- function(.X) { + as.matrix(.X) %>% + reshape2::melt() %>% + rename(row = Var1, col = Var2, weight = value) %>% + order.pair(ret.tab=TRUE) %>% + as.data.table %>% + dcast(row ~ col, value.var = "weight") %>% + dplyr::select(-row) %>% + as.matrix +} + +.rnorm <- function(d1, d2) { + matrix(rnorm(d1 * d2), d1, d2) +} + +############################################################### +.matshow <- function(.mat, .lab=0, .axis.lab=0, .lw=0, .scale=TRUE) { + + library(ggrastr) + + .mat <- as.matrix(.mat) + .cols <- colnames(.mat) + .rows <- rownames(.mat) + if(length(.cols) < ncol(.mat)){ + colnames(.mat) <- str_c("c", 1:ncol(.mat)) + } + if(length(.rows) < nrow(.mat)){ + rownames(.mat) <- str_c("r", 1:nrow(.mat)) + } + .cols <- colnames(.mat) + .rows <- rownames(.mat) + + .dt <- + as.data.frame(.mat) %>% + rownames_to_column("row") %>% + as.data.table %>% + melt(id.vars = "row", variable.name = "col") %>% + dplyr::mutate(row = factor(as.character(row), rev(.rows))) %>% + dplyr::mutate(col = factor(as.character(col), .cols)) + + ret <- + ggplot(.dt, aes(y = row, x = col, fill = value)) + + theme(legend.position = "none") + if(.axis.lab > 0){ + ret <- + ret + + theme(axis.text.x = element_text(size=.axis.lab, angle=90, vjust=1, hjust=1)) + + theme(axis.text.y = element_text(size=.axis.lab)) + } else { + ret <- + ret + + theme(axis.text = element_blank()) + + theme(axis.ticks = element_blank()) + } + ret <- ret + + theme(axis.title = element_blank()) + + if(.lw > 0){ + ret <- ret + + ggrastr::rasterise(geom_tile(linewidth = .lw, colour = "black"), dpi=300) + } else { + ret <- ret + ggrastr::rasterise(geom_tile(), dpi=300) + } + + if(.scale){ + ret <- ret + + scale_fill_distiller(palette = "RdBu", direction = -1) + } else { + ret <- ret + + scale_fill_distiller(palette = "Greys", direction = 1) + } + + if(.lab > 0) { + ret <- ret + + geom_text(aes(label = round(value,1)), size = .lab) + } + + return(ret) +} + +################################################################ +.gg.plot <- function(...) { + ggplot(...) + + theme_classic() + + theme(axis.title = element_text(size=8)) + + theme(axis.text = element_text(size=6)) + + theme(legend.spacing = unit(.1, "lines"), + legend.key.size = unit(.5, "lines"), + legend.text = element_text(size=5), + legend.title = element_text(size=6), + panel.background = element_rect(fill='transparent'), + plot.background = element_rect(fill='transparent', color=NA), + legend.background = element_rect(fill='transparent', linewidth=0.05), + legend.box.background = element_rect(fill='transparent')) +} + +as.dt <- function(x, col.names=NULL) { + .mat <- as.matrix(x) + if(is.null(col.names)) col.names <- str_c("V",1:ncol(.mat)) + colnames(.mat) <- col.names + as.data.table(.mat) +} + +################################################################ +if.needed <- function(.file, .code) { + if(!all(file.exists(unlist(.file)))){ .code } + stopifnot(all(file.exists(unlist(.file)))) +} + +################################################################ +setup.env <- function(fig.dir) { + + ## save figures here + dir.create(fig.dir, showWarnings = FALSE, recursive = TRUE) + knitr::opts_chunk$set(warning = FALSE, message = FALSE, fig.path = fig.dir) + knitr::opts_chunk$set(echo=FALSE, fig.align="center") + + ## allow the code to chunk set size="tiny" ## + hook.chunk <- knitr::knit_hooks$get("chunk") + + ## Default: normalsize -> scriptsize + ## This will redefine normalsize + knitr::knit_hooks$set(chunk = function(x, options) { + x <- hook.chunk(x, options) + ifelse(options$size != "normalsize", + paste0("\n \\", options$size, "\n\n", x, "\n\n \\normalsize"), + paste0("\n \\", "scriptsize", "\n\n", x, "\n\n \\normalsize")) + }) + + ## show plot one by one in beamer ## + hook.plot <- knitr::knit_hooks$get("plot") + + knitr::knit_hooks$set(plot = function(x, options) { + if (!is.null(options$onslide.plot)) { + bf <- paste0("\\onslide<", options$onslide.plot, ">{") + ret <- paste(c(bf, knitr::hook_plot_tex(x, options), "}"), + collapse = "\n") + return(ret) + } else if (!is.null(options$only.plot)) { + bf <- paste0("\\only<", options$only.plot, ">{") + ret <- paste(c(bf, knitr::hook_plot_tex(x, options), "}"), + collapse = "\n") + return(ret) + } + return(hook.plot(x, options)) + }) + + knitr::opts_chunk$set(tidy.opts = list(width.cutoff = 60), tidy = TRUE) + knitr::opts_chunk$set(dev = "cairo_pdf") +} + diff --git a/lect17-scRNA-topic-model/Util.R b/lect17-scRNA-topic-model/Util.R new file mode 100644 index 0000000..0726bb2 --- /dev/null +++ b/lect17-scRNA-topic-model/Util.R @@ -0,0 +1,169 @@ +sort.theta <- function(.dt){ + + .co <- order(apply(as.matrix(.dt[, -1]), 1, which.max)) + .to <- colnames(.dt)[-1] + + .melt <- .dt %>% + mutate(col = factor(1:n(), rev(.co))) %>% + melt(id.vars = c("col", "celltype")) %>% + mutate(variable = factor(variable, .to)) + + .cto <- + .melt[, + .(value = mean(value)), + by = .(celltype, variable)] %>% + dcast(celltype ~ variable, value.var = "value") %>% + (function(x){ + .mat <- as.matrix(x[,-1]) + .o <- order(apply(.mat, 1, which.max), decreasing = T) + rev(x$celltype[.o]) + }) + + .melt[, celltype := factor(celltype, .cto)] + return(.melt) +} + +read.theta <- function(.file){ + return(sort.theta(fread(.file))) +} + +plot.theta <- function(.file, with.ct = F){ + .dt <- read.theta(.file) + .thm <- + theme(axis.text.y = element_blank()) + + theme(axis.ticks.y = element_blank()) + + theme(legend.title = element_blank()) + + theme(legend.position = c(1,1)) + + theme(legend.justification = c(1,1)) + + ret <- + ggplot(.dt, aes(variable, col, fill = value)) + + ggrastr::rasterise(geom_tile(), dpi=300) + + scale_fill_distiller(direction=1) + + .thm + + if(with.ct){ + ret <- ret + + theme(strip.text.y = element_text(angle=0, hjust=0)) + + facet_grid(celltype ~ ., scales="free", space="free") + } + return(ret) +} + +################################################################ + +sort.beta <- function(.dt, genes.selected){ + + .genes <- rownames(.dt) + .mat <- as.matrix(.dt) + colnames(.mat) <- 1:ncol(.mat) + rownames(.mat) <- .genes + + .mat <- .mat[rownames(.mat) %in% genes.selected, , drop = F] + .melt <- reshape2::melt(.mat) %>% as.data.table() + .melt <- .melt[order(`value`, decreasing = T), head(.SD, 1), + by = .(Var1, Var2)] # remove redundancy + + .mat <- dcast(.melt, Var1 ~ Var2, value.var="value", fun.aggregate = max) + .rows <- unlist(.mat[,1], use.names = F) + .mat <- .mat[,-1] + + .ro <- apply(t(.mat), 2, which.max) %>% + order(decreasing = F) %>% + (function(x) .rows[x]) + .to <- colnames(.mat) + + .melt[, row := factor(`Var1`, .ro)] + .melt[, variable := factor(`Var2`, .to)] + + return(.melt) +} + +read.beta <- function(.file, ...){ + return(sort.beta(fread(.file), ...)) +} + +################################################################ +make.gs.lol <- function(.dt) { + .dt <- as.data.table(.dt) %>% unique() + .list <- + .dt[, .(gene = .(gene_symbol)), by = .(gs_name)] %>% + as.list() + .names <- .list$gs_name + .ret <- .list$gene + names(.ret) <- .names + return(.ret) +} + +run.beta.gsea <- function(.dt, .db){ + + vars <- unique(.dt$Var2) + ret <- data.table() + for(v in vars){ + + .scores <- as.vector(scale(.dt[Var2 == v]$value)) + names(.scores) <- .dt[Var2 == v]$Var1 + + .gsea <- fgsea::fgsea(pathways = .db, + stats = .scores, + scoreType = "pos") %>% + as.data.table() + .gsea[, Var2 := v] + ret <- rbind(ret, .gsea) + } + return(ret) +} + +read.panglao <- function(.organs = NULL, .file = "../data/PanglaoDB_markers_27_Mar_2020.tsv.gz"){ + + if(is.null(.organs)){ + .dt <- fread(.file) + } else { + .dt <- fread(.file)[organ %in% .organs] + } + + ret <- .dt %>% + rename(gene_symbol = `official gene symbol`, gs_name = `cell type`) %>% + mutate(gs_name = gsub(pattern="[ ]*cells", replacement = "", gs_name)) %>% + make.gs.lol() +} + +select.gsea.beta <- function(.gsea, .beta, ntop = 3, nmax = 30, sd.cutoff = 1e-4, padj.cutoff = .1){ + + ## cell types (pathways) to show + .cts <- unlist(.gsea[order(pval), + head(.SD, ntop), + by = .(Var2)][padj < padj.cutoff, + .(pathway)]) + + .gsea.show <- .gsea[pathway %in% .cts] + .gsea.show[, variable := factor(`Var2`, 0:100)] # 100 should be enough + + .temp <- dcast(.gsea.show, variable ~ pathway, + value.var = "pval", + fun.aggregate = min) + + .po <- order(apply(-log10(1e-20 + .temp[,-1]), 2, which.max)) + .po <- colnames(.temp)[-1][.po] + .gsea.show[, ct := factor(pathway, .po)] + + ## genes to show + .sd <- .beta[, .(s = sd(`value`)), by = .(Var1)] + + .genes.show <- intersect(unlist(.gsea.show$leadingEdge), + .sd[s > sd.cutoff]$Var1) + .beta.show <- .beta[Var1 %in% .genes.show] + + ## leading edges to show + .temp <- copy(.gsea.show) + .temp[, r := as.character(.I)] + .leading.edges <- .temp[, leadingEdge[[1]], by=r] %>% + left_join(.temp[, .(r, ct)]) %>% + mutate(row = factor(V1, sort(unique(.beta.show$row)))) + + .leading.genes <- .beta.show[row %in% .leading.edges$row][order(value, decreasing = T), head(.SD, 10), by = 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