## ----knitr, echo=FALSE, results="hide"----------------------------------- library("knitr") opts_chunk$set(tidy=FALSE,tidy.opts=list(width.cutoff=30),dev="png",fig.show="hide", fig.width=4,fig.height=7, message=FALSE) ## ----style, eval=TRUE, echo=FALSE, results="asis"--------------------------------------- BiocStyle::latex() ## ----options, results="hide", echo=FALSE-------------------------------------- options(digits=3, width=80, prompt=" ", continue=" ") ## ----install_countclust_bio, eval=FALSE--------------------------------------- # source("http://bioconductor.org/biocLite.R") # biocLite("CountClust") ## ----install_github, eval=TRUE------------------------------------------------ library(devtools) install_github("TaddyLab/maptpx") ## ----install_countclust_github, eval=FALSE------------------------------------ # install_github('kkdey/CountClust') ## ----load_countclust, cache=FALSE, eval=TRUE,warning=FALSE-------------------- library(CountClust) ## ----data_install_deng, eval=TRUE--------------------------------------------- library(devtools) read.data1 = function() { x = tempfile() download.file('https://cdn.rawgit.com/kkdey/singleCellRNASeqMouseDeng2014/master/data/Deng2014MouseEsc.rda', destfile=x, quiet=TRUE) z = get(load((x))) return(z) } Deng2014MouseESC <- read.data1() ## Alternatively # install_github('kkdey/singleCellRNASeqMouseDeng2014') ## ----data_install_gtex, eval=FALSE-------------------------------------------- # read.data2 = function() { # x = tempfile() # download.file('https://cdn.rawgit.com/kkdey/GTExV6Brain/master/data/GTExV6Brain.rda', destfile = x, quiet=TRUE) # z = get(load((x))) # return(z) # } # # GTExV6Brain <- read.data2() # # ## Alternatively # # install_github('kkdey/GTExV6Brain') ## ----data_load_deng, eval=TRUE------------------------------------------------ deng.counts <- Biobase::exprs(Deng2014MouseESC) deng.meta_data <- Biobase::pData(Deng2014MouseESC) deng.gene_names <- rownames(deng.counts) ## ----data_load_gtex, eval=FALSE----------------------------------------------- # gtex.counts <- Biobase::exprs(GTExV6Brain) # gtex.meta_data <- Biobase::pData(GTExV6Brain) # gtex.gene_names <- rownames(gtex.counts) ## ----topic_fit_gtex, eval=FALSE----------------------------------------------- # FitGoM(t(gtex.counts), # K=4, tol=0.1, # path_rda="../data/GTExV6Brain.FitGoM.rda") ## ----topic_fit_deng, eval=FALSE----------------------------------------------- # FitGoM(t(deng.counts), # K=2:7, tol=0.1, # path_rda="../data/MouseDeng2014.FitGoM.rda") ## ----prepare_deng_gom,eval=TRUE, warning=FALSE-------------------------------- data("MouseDeng2014.FitGoM") names(MouseDeng2014.FitGoM$clust_6) omega <- MouseDeng2014.FitGoM$clust_6$omega ## ----plot_topic_deng_annot, eval=TRUE, warning=FALSE-------------------------- annotation <- data.frame( sample_id = paste0("X", c(1:NROW(omega))), tissue_label = factor(rownames(omega), levels = rev( c("zy", "early2cell", "mid2cell", "late2cell", "4cell", "8cell", "16cell", "earlyblast","midblast", "lateblast") ) ) ) rownames(omega) <- annotation$sample_id; ## ----plot_topic_deng,eval=TRUE, warning=FALSE, fig.show="asis", dpi=144, fig.width=3, fig.height=7, out.width="3in", out.height="7in"---- StructureGGplot(omega = omega, annotation = annotation, palette = RColorBrewer::brewer.pal(8, "Accent"), yaxis_label = "Development Phase", order_sample = TRUE, axis_tick = list(axis_ticks_length = .1, axis_ticks_lwd_y = .1, axis_ticks_lwd_x = .1, axis_label_size = 7, axis_label_face = "bold")) ## ----prepare_gtex_gom, eval=TRUE---------------------------------------------- data("GTExV6Brain.FitGoM") omega <- GTExV6Brain.FitGoM$omega; dim(omega) colnames(omega) <- c(1:NCOL(omega)) ## ----annot_gtex, eval=FALSE--------------------------------------------------- # tissue_labels <- gtex.meta_data[,3]; # # # annotation <- data.frame( # sample_id = paste0("X", 1:length(tissue_labels)), # tissue_label = factor(tissue_labels, # levels = rev(unique(tissue_labels) ) ) ); # # cols <- c("blue", "darkgoldenrod1", "cyan", "red") ## ----plot_topic_gtex,eval=FALSE, warning=FALSE, fig.show="asis", dpi=144, fig.width=5, fig.height=7, out.width="5in", out.height="7in"---- # StructureGGplot(omega = omega, # annotation= annotation, # palette = cols, # yaxis_label = "", # order_sample = TRUE, # split_line = list(split_lwd = .4, # split_col = "white"), # axis_tick = list(axis_ticks_length = .1, # axis_ticks_lwd_y = .1, # axis_ticks_lwd_x = .1, # axis_label_size = 7, # axis_label_face = "bold")) ## ----extract_features_deng, eval=TRUE, warning=FALSE-------------------------- theta_mat <- MouseDeng2014.FitGoM$clust_6$theta; top_features <- ExtractTopFeatures(theta_mat, top_features=100, method="poisson", options="min"); gene_list <- do.call(rbind, lapply(1:dim(top_features)[1], function(x) deng.gene_names[top_features[x,]])) ## ----top_genes_clusters_deng, eval=TRUE--------------------------------------- xtable::xtable(gene_list[,1:5]) ## ----extract_features_gtex, eval=FALSE, warning=FALSE------------------------- # theta_mat <- GTExV6Brain.FitGoM$theta; # top_features <- ExtractTopFeatures(theta_mat, top_features=100, # method="poisson", options="min"); # gene_list <- do.call(rbind, lapply(1:dim(top_features)[1], # function(x) gtex.gene_names[top_features[x,]])) ## ----top_genes_clusters_gtex, eval=FALSE-------------------------------------- # xtable::xtable(gene_list[,1:3]) ## ----data_install_jaitin, echo=TRUE, eval=TRUE-------------------------------- read.data3 = function() { x = tempfile() download.file('https://cdn.rawgit.com/jhsiao999/singleCellRNASeqMouseJaitinSpleen/master/data/MouseJaitinSpleen.rda', destfile = x, quiet=TRUE) z = get(load((x))) return(z) } MouseJaitinSpleen <- read.data3() ## Alternatively # devtools::install_github('jhsiao999/singleCellRNASeqMouseJaitinSpleen') ## ----data_load_jaitin, echo=TRUE, eval=TRUE----------------------------------- jaitin.counts <- Biobase::exprs(MouseJaitinSpleen) jaitin.meta_data <- Biobase::pData(MouseJaitinSpleen) jaitin.gene_names <- rownames(jaitin.counts) ## ----non_ercc, eval=TRUE, echo=TRUE------------------------------------------- ENSG_genes_index <- grep("ERCC", jaitin.gene_names, invert = TRUE) jaitin.counts_ensg <- jaitin.counts[ENSG_genes_index, ] filter_genes <- c("M34473","abParts","M13680","Tmsb4x", "S100a4","B2m","Atpase6","Rpl23","Rps18", "Rpl13","Rps19","H2-Ab1","Rplp1","Rpl4", "Rps26","EF437368") fcounts <- jaitin.counts_ensg[ -match(filter_genes, rownames(jaitin.counts_ensg)), ] sample_counts <- colSums(fcounts) filter_sample_index <- which(jaitin.meta_data$number_of_cells == 1 & jaitin.meta_data$group_name == "CD11c+" & sample_counts > 600) fcounts.filtered <- fcounts[,filter_sample_index]; ## ----metadata, eval=TRUE, echo=TRUE------------------------------------------- jaitin.meta_data_filtered <- jaitin.meta_data[filter_sample_index, ] ## ----topic_fit_jaitin, eval=FALSE, echo=TRUE---------------------------------- # StructureObj(t(fcounts), # nclus_vec=7, tol=0.1, # path_rda="../data/MouseJaitinSpleen.FitGoM.rda") ## ----plot_topic_annot, eval=TRUE, echo=TRUE----------------------------------- data("MouseJaitinSpleen.FitGoM") names(MouseJaitinSpleen.FitGoM$clust_7) omega <- MouseJaitinSpleen.FitGoM$clust_7$omega amp_batch <- as.numeric(jaitin.meta_data_filtered[ , "amplification_batch"]) annotation <- data.frame( sample_id = paste0("X", c(1:NROW(omega)) ), tissue_label = factor(amp_batch, levels = rev(sort(unique(amp_batch))) ) ) ## ----plot_topic, eval=TRUE, echo=TRUE, warning=FALSE, fig.show="asis", dpi=144, fig.width=3, fig.height=7, out.width="3in", out.height="7in"---- StructureGGplot(omega = omega, annotation = annotation, palette = RColorBrewer::brewer.pal(9, "Set1"), yaxis_label = "Amplification batch", order_sample = FALSE, axis_tick = list(axis_ticks_length = .1, axis_ticks_lwd_y = .1, axis_ticks_lwd_x = .1, axis_label_size = 7, axis_label_face = "bold")) ## ----batch_correct, eval=FALSE, echo=TRUE------------------------------------- # batchcorrect.fcounts <- BatchCorrectedCounts(t(fcounts.filtered), # amp_batch, use_parallel = FALSE); # dim(batchcorrect.fcounts) ## ----session_info, eval=TRUE-------------------------------------------------- sessionInfo()