Consistent naming of Rmd code chunks

scripts/DRIMSeq_dtu.Rmd

@@ -12,7 +12,7 @@ output:
     keep_md: true
 ---
 
-```{r setup, include=FALSE}
+```{r DRIMSeq-setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
@@ -23,7 +23,7 @@ on abundance estimates from Salmon. It supports testing one or more contrasts.
 
 # Load packages
 
-```{r}
+```{r DRIMSeq-load-pkg}
 suppressPackageStartupMessages({
   library(dplyr)
   library(tximport)
@@ -39,14 +39,14 @@ suppressPackageStartupMessages({
 
 We will use the transcript-level quantifications.
 
-```{r}
+```{r DRIMSeq-print-se}
 sg <- se$sg
 st <- se$st
 ```
 
 # Create dmDSdata object
 
-```{r dmDSdata}
+```{r DRIMSeq-dmDSdata}
 counts <- data.frame(feature_id = rownames(st),
                      gene_id = unlist(rowData(st)$gene_id),
                      assays(st)[["counts"]],
@@ -62,43 +62,43 @@ plotData(d)
 
 # Filter ************** MODIFY ************** 
 
-```{r data-filter}
+```{r DRIMSeq-data-filter}
 d <- dmFilter(d, min_samps_gene_expr = 3, min_samps_feature_expr = 3,
               min_gene_expr = 10, min_feature_expr = 5)
 plotData(d)
 ```
 
 # Define design. ************** MODIFY ************** 
 
-```{r define-design}
+```{r DRIMSeq-define-design}
 # (des <- model.matrix(~ XXXX, data = samples(d)))
 (des <- model.matrix(~ 0 + group, data = samples(d)))
 ```
 
 # Calculate precision
 
-```{r calculate-precision}
+```{r DRIMSeq-calculate-precision}
 set.seed(123)
 d <- dmPrecision(d, design = des)
 plotPrecision(d)
 ```
 
 # Fit model
 
-```{r fit-model}
+```{r DRIMSeq-fit-model}
 d <- dmFit(d, design = des, verbose = 1)
 ```
 
 # Define contrasts. ************** MODIFY ************** 
 
-```{r define-contrasts}
+```{r DRIMSeq-define-contrasts}
 #(contrasts <- as.data.frame(makeContrasts(XXXX, levels = des)))
 (contrasts <- as.data.frame(makeContrasts("groupN61311-groupN052611", levels = des)))
 ```
 
 # Perform tests ************** MODIFY **************
 
-```{r result-genes}
+```{r DRIMSeq-result-genes}
 level <- "gene" ## set to "feature" if transcript-level results are desired
 signif3 <- function(x) signif(x, digits = 3)
 DRIMSeq_fits <- lapply(contrasts, function(cm) {
@@ -113,7 +113,7 @@ DRIMSeq_res <- lapply(DRIMSeq_fits, function(dr) {
 ```
 
 
-```{r result-transcripts}
+```{r DRIMSeq-result-transcripts}
 level <- "feature" ## set to "feature" if transcript-level results are desired
 DRIMSeq_feature_fits <- lapply(contrasts, function(cm) {
   dr <- dmTest(d, contrast = cm, verbose = 1)
@@ -128,7 +128,7 @@ DRIMSeq_feature_res <- lapply(DRIMSeq_feature_fits, function(dr) {
 
 # Write results to text files
 
-```{r save-result}
+```{r DRIMSeq-save-result}
 if (class(DRIMSeq_res) == "data.frame") {
   write.table(DRIMSeq_res %>% dplyr::arrange(pvalue), 
               file = "DRIMSeq_dtu_results.txt", 
@@ -147,7 +147,7 @@ if (class(DRIMSeq_res) == "data.frame") {
 
 The `SummarizedExperiment` object on the transcript level
 
-```{r se-gene}
+```{r DRIMSeq-se-gene}
 ## add rows (NA) for genes that are filtered out (if any)
 DRIMSeq_resA <- lapply(seq_along(DRIMSeq_res), FUN = function(x) {
     
@@ -217,7 +217,7 @@ for (i in seq_along(DRIMSeq_resA)) {
 
 The `SummarizedExperiment` object on the transcript level
 
-```{r se-tx}
+```{r DRIMSeq-se-tx}
 ## add rows (NA) for genes that are filtered out (if any)
 DRIMSeq_resB <- lapply(seq_along(DRIMSeq_feature_res), FUN = function(x) {
     
@@ -289,14 +289,14 @@ for (i in seq_along(DRIMSeq_resB)) {
 
 The output is saved as a list.
 
-```{r save-se}
+```{r DRIMSeq-save-se}
 analysis_se <- list(sg = sg, st = st)
 saveRDS(analysis_se, file = "DRIMSeq_dtu.rds")
 ```
 
 # Session info
 
-```{r}
+```{r DRIMSeq-session-info}
 date()
 sessionInfo()
 ```

scripts/edgeR_dge.Rmd

@@ -12,7 +12,7 @@ output:
     keep_md: true
 ---
 
-```{r setup, include=FALSE}
+```{r edgeR-setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, dev = c("png", "pdf"))
 ```
 
@@ -23,7 +23,7 @@ abundance estimates from Salmon.
 
 # Load packages
 
-```{r load-pkg}
+```{r edgeR-load-pkg}
 suppressPackageStartupMessages({
   library(dplyr)
   library(tximport)
@@ -40,7 +40,7 @@ We load the `SummarizedExperiment` objects prepared using `tximeta`, containing
 gene- and transcript-level counts and feature lengths. In this report, we will
 use the gene-level quantifications.
 
-```{r print-se}
+```{r edgeR-print-se}
 ## List of SummarizedExperiment objects (gene/transcript level)
 se
 
@@ -53,7 +53,7 @@ sg
 
 # Create DGEList and include average transcript length offsets
 
-```{r dge-generate}
+```{r edgeR-dge-generate}
 ## Extract count matrix and average transcript lengths
 cts <- assay(sg, "counts")
 normMat <- assay(sg, "length")
@@ -74,7 +74,7 @@ dge0$genes <- as.data.frame(rowRanges(sg))
 
 # Calculate logCPMs and add as an assay
 
-```{r add-logcpm}
+```{r edgeR-add-logcpm}
 logcpms <- edgeR::cpm(dge0, log = TRUE, prior.count = 2)
 stopifnot(all(rownames(logcpms) == rownames(sg)),
           all(colnames(logcpms) == colnames(sg)))
@@ -83,7 +83,7 @@ assay(sg, "logcpm") <- logcpms
 
 # Define design. ************** MODIFY ************** 
 
-```{r define-design}
+```{r edgeR-define-design}
 stopifnot(all(colnames(dge0) == metadata$names))
 #(des <- model.matrix(~ XXXX, data = metadata))
 #e.g.
@@ -92,7 +92,7 @@ stopifnot(all(colnames(dge0) == metadata$names))
 
 # Filter out lowly expressed genes
 
-```{r filter-genes}
+```{r edgeR-filter-genes}
 dim(dge0)
 keep <- edgeR::filterByExpr(dge0, design = des)
 dge <- dge0[keep, ]
@@ -102,7 +102,7 @@ dim(dge)
 
 # Estimate dispersion
 
-```{r estimate-disp}
+```{r edgeR-estimate-disp}
 ## Estimate dispersion and fit model
 dge <- estimateDisp(dge, design = des)
 qlfit <- glmQLFit(dge, design = des)
@@ -113,7 +113,7 @@ plotBCV(dge)
 
 # Define contrasts ************** MODIFY ************** 
 
-```{r define-contrasts}
+```{r edgeR-define-contrasts}
 #(contrasts <- as.data.frame(makeContrasts(XXXX, levels = des)))
 #e.g.
 (contrasts <- as.data.frame(makeContrasts(
@@ -123,7 +123,7 @@ plotBCV(dge)
 
 # Perform tests
 
-```{r perform-tests}
+```{r edgeR-perform-tests}
 signif3 <- function(x) signif(x, digits = 3)
 edgeR_res <- lapply(contrasts, function(cm) {
   qlf <- glmQLFTest(qlfit, contrast = cm)
@@ -134,7 +134,7 @@ edgeR_res <- lapply(contrasts, function(cm) {
 
 # Make MA plots
 
-```{r}
+```{r edgeR-ma-plots}
 if (is(edgeR_res, "data.frame")) {
   print(ggplot(edgeR_res, aes(x = logCPM, y = logFC, color = FDR <= 0.05)) + 
           geom_point() + theme_bw() + 
@@ -151,7 +151,7 @@ if (is(edgeR_res, "data.frame")) {
 
 # Write results to text files
 
-```{r save-results}
+```{r edgeR-save-results}
 ## Write results to text files and make MA plots
 if (is(edgeR_res, "data.frame")) {
   write.table(edgeR_res %>% dplyr::arrange(PValue), 
@@ -168,7 +168,7 @@ if (is(edgeR_res, "data.frame")) {
 
 # Output results as list of `SummarizedExperiment` objects
 
-```{r se}
+```{r edgeR-se}
 ## add rows (NA) for genes that are filtered out (if any)
 edgeR_resA <- lapply(seq_along(edgeR_res), FUN = function(x) {
   
@@ -227,14 +227,14 @@ for (i in seq_along(edgeR_resA)) {
 
 The output is saved as a list.
 
-```{r save-se}
+```{r edgeR-save-se}
 analysis_se <- list(sg = sg, st = se$st)
 saveRDS(analysis_se, file = "edgeR_dge.rds")
 ```
 
 # Session info
 
-```{r session-info}
+```{r edgeR-session-info}
 date()
 sessionInfo()
 ```

scripts/prepare_shiny.Rmd

@@ -14,7 +14,7 @@ editor_options:
   chunk_output_type: console
 ---
 
-```{r setup, include=FALSE}
+```{r shiny-setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, dev = c("png", "pdf"), root.dir = "../")
 #knitr::opts_knit$set(root.dir = "../")
 ```
@@ -25,7 +25,7 @@ This script prepares the data to be used for shiny app.
 
 # Load packages
 
-```{r load-pkg}
+```{r shiny-load-pkg}
 suppressPackageStartupMessages({
   library(dplyr)
   library(tidyr)
@@ -40,15 +40,15 @@ suppressPackageStartupMessages({
 
 # Load data
 
-```{r load-data}
+```{r shiny-load-data}
 options(ucscChromosomeNames = FALSE)
 sg <- se$sg
 st <- se$st
 ```
 
 # Gene models
 
-```{r gene-model}
+```{r shiny-gene-model}
 create_genemodels <- function(genemodels) {
     idx <- match(c("transcript_id", "gene_id", "exon_id"), 
                  colnames(mcols(genemodels)))
@@ -66,7 +66,7 @@ if (!is.null(gtffile)) {
 
 # Vector with bigWig file names and condition information
 
-```{r bigwig}
+```{r shiny-bigwig}
 metadata <- colData(sg)
 if (!is.null(bigwigdir)) {
     bwfiles <- normalizePath(list.files(bigwigdir, pattern = "\\.bw$", 
@@ -85,7 +85,7 @@ if (!is.null(bigwigdir)) {
 
 # edgeR - gene-level MDS
 
-```{r MDS}
+```{r shiny-MDS}
 logcpms <- assay(sg, "logcpm")
 mds <- limma::plotMDS(logcpms, top = 500, labels = NULL, pch = NULL,
                       cex = 1, dim.plot = c(1, 2), ndim = min(7, ncol(logcpms) - 1),
@@ -108,7 +108,7 @@ and condition information
 
 The multidimensional scale data is stored in `reducedDims`.
 
-```{r sce_gene}
+```{r shiny-sce-gene}
 ## column data
 nam <- colData(sg)$names
 colData(sg)$bwFiles <- bwfiles[nam]
@@ -137,7 +137,7 @@ and condition information.
 
 The multidimensional scale data is stored in `reducedDims`.
 
-```{r sce_tx}
+```{r shiny-sce-tx}
 ## column data
 nam <- colData(st)$names
 colData(st)$bwFiles <- bwfiles[nam]
@@ -151,15 +151,15 @@ sce_tx <- SingleCellExperiment(assays = assays(st),
 
 # Output results
 
-```{r output}
+```{r shiny-save-sce}
 saveRDS(list(sce_tx = sce_tx, 
              sce_gene = sce_gene),
         file = "shiny_sce.rds")
 ```
 
 # Session info
 
-```{r session-info}
+```{r shiny-session-info}
 date()
 sessionInfo()
 ```