## ----setup, include=FALSE-------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----install package, eval=FALSE------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("blacksheepr") ## ----library call---------------------------------------------------------- library(blacksheepr) ## ----countdata example----------------------------------------------------- data("sample_phosphodata") sample_phosphodata[1:5,1:5] ## ----annotation example---------------------------------------------------- data("sample_annotationdata") sample_annotationdata[1:5,] ## ----read in data - phospho, echo = TRUE----------------------------------- data(sample_annotationdata) comptable <- sample_annotationdata comptable[1:5,] dim(comptable) ## -------------------------------------------------------------------------- data(sample_phosphodata) phosphotable <- sample_phosphodata phosphotable[1:5,1:5] dim(phosphotable) ## ----summarized experiment------------------------------------------------- suppressPackageStartupMessages(library(SummarizedExperiment)) blacksheep_SE <- SummarizedExperiment( assays=list(counts=as.matrix(phosphotable)), colData=DataFrame(comptable)) blacksheep_SE ## ----deva, fig.keep="none"------------------------------------------------- deva_out <- deva(se = blacksheep_SE, analyze_negative_outliers = FALSE, aggregate_features = TRUE, feature_delineator = "-", fraction_samples_cutoff = 0.3, fdrcutoffvalue = 0.1) ## -------------------------------------------------------------------------- names(deva_out) ## -------------------------------------------------------------------------- names(deva_out$pos_outlier_analysis) ## -------------------------------------------------------------------------- names(deva_out$significant_pos_heatmaps) ## -------------------------------------------------------------------------- deva_results(deva_out) ## -------------------------------------------------------------------------- subanalysis_Her2 <- deva_results(deva_out, ID = "Her2", type = "table") head(subanalysis_Her2) ## -------------------------------------------------------------------------- subanalysis_Her2 <- deva_results(deva_out, ID = "Her2", type = "table") head(subanalysis_Her2) ## ---- fig.width = 8, fig.height = 8---------------------------------------- subanalysis_Her2_HM <- deva_results(deva_out, ID = "Her2", type = "heatmap") subanalysis_Her2_HM ## ---- eval = FALSE--------------------------------------------------------- # ## NOT RUN # ## To output separately to pdf # pdf("outfile.pdf") # draw(subanalysis_Her2_HM) # dev.off() ## ----groupings - phospho--------------------------------------------------- groupings <- comparison_groupings(comptable) ## Print out the first 6 samples in each of our first 5 groupings lapply(groupings, head)[1:5] ## ----make outlier table - phospho------------------------------------------ ## Perform the function reftable_function_out <- make_outlier_table(phosphotable, analyze_negative_outliers = FALSE) ## See the names of the outputted objects names(reftable_function_out) ## Assign them to individual variables outliertab <- reftable_function_out$outliertab upperboundtab <- reftable_function_out$upperboundtab sampmedtab <- reftable_function_out$sampmedtab ## Note we will only use the outlier table - which looks like this now outliertab[1:5,1:5] ## ----groupingtablist - phospho--------------------------------------------- count_outliers_out <- count_outliers(groupings, outliertab, aggregate_features = TRUE, feature_delineator = "-") grouptablist <- count_outliers_out$grouptablist aggoutliertab <- count_outliers_out$aggoutliertab fractiontab <- count_outliers_out$fractiontab names(grouptablist) ## -------------------------------------------------------------------------- names(grouptablist$PAM50_Her2__Her2) ## -------------------------------------------------------------------------- head(grouptablist$PAM50_Her2__Her2$feature_counts) ## -------------------------------------------------------------------------- grouptablist$PAM50_Her2__Her2$samples ## ----outlier analysis - phospho-------------------------------------------- outlier_analysis_out <- outlier_analysis(grouptablist = grouptablist, fraction_table = fractiontab, fraction_samples_cutoff = 0.3) names(outlier_analysis_out) head(outlier_analysis_out$ outlieranalysis_for_PAM50_Her2__Her2_vs_PAM50_Her2__not_Her2) ## ----heatmap plotting - phospho, fig.keep="none"--------------------------- plottable <- comptable[do.call(order, c(decreasing = TRUE, data.frame(comptable[,1:ncol(comptable)]))),] hm1 <- outlier_heatmap(outlier_analysis_out = outlier_analysis_out, counttab = fractiontab, metatable = plottable, fdrcutoffvalue = 0.1) ## To output heatmap to pdf outside of the function #pdf(paste0(outfilepath, "test_hm1.pdf")) #hm1 #junk<-dev.off() ## ----hm, fig.width = 8, fig.height = 8, fig.cap = "Example outputted Heatmap"---- hm1$print_outlieranalysis_for_PAM50_Her2__Her2_vs_PAM50_Her2__not_Her2 ## ----format annotation data2 - phospho------------------------------------- dummyannotations <- data.frame(comp1 = c(1,1,2,2,3,3), comp2 = c("red", "blue", "red", "blue", "green", "green"), row.names = paste0("sample", seq_len(6))) dummyannotations ## Use the make_comparison_columns function to create binary columns expanded_dummyannotations <- make_comparison_columns(dummyannotations) expanded_dummyannotations ## ----normalize count data - phospho---------------------------------------- library(pasilla) pasCts <- system.file("extdata", "pasilla_gene_counts.tsv", package="pasilla") cts <- as.matrix(read.csv(pasCts,sep="\t",row.names="gene_id")) norm_cts <- deva_normalization(cts, method = "MoR-log")