Break up a long line into multiple ones, to make it easier to read. R…

…enee used this line for an example
lcolladotor · Jul 5, 2023 · 5b8d01f · 5b8d01f
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diff --git a/06_smokingMouse_plotting_basics.R b/06_smokingMouse_plotting_basics.R
@@ -1,4 +1,4 @@
-## ----download_data_biocfilecache_repeat------------------
+## ----download_data_biocfilecache_repeat------------------------------------------------------------
 ## Load the container package for this type of data
 library("SummarizedExperiment")
 
@@ -20,7 +20,7 @@ load(cached_rse_gene, verbose = TRUE)
 rse_gene_nic <- rse_gene[, which(rse_gene$Expt == "Nicotine")]
 
 
-## ----Data preparation, message=FALSE, warning=FALSE------
+## ----Data preparation, message=FALSE, warning=FALSE------------------------------------------------
 library("ggplot2")
 
 ## Histogram and density plot of read counts before and after normalization
@@ -43,7 +43,7 @@ plot <- ggplot(logcounts_data, aes(x = logcounts)) +
 plot + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
 
 
-## ----  message=FALSE, warning=FALSE----------------------
+## ----  message=FALSE, warning=FALSE----------------------------------------------------------------
 ## Retain genes that passed filtering step
 rse_gene_filt <- rse_gene_nic[rowData(rse_gene_nic)$retained_after_feature_filtering == TRUE, ]
 
@@ -59,7 +59,7 @@ plot <- ggplot(filt_logcounts_data, aes(x = logcounts)) +
 plot + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
 
 
-## ----QC_boxplots,  message=FALSE, warning=FALSE----------
+## ----QC_boxplots,  message=FALSE, warning=FALSE----------------------------------------------------
 library("Hmisc")
 library("stringr")
 library("cowplot")
@@ -156,31 +156,31 @@ multiple_QC_boxplots <- function(sample_var) {
 }
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_boxplots("Age")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_boxplots("Sex")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_boxplots("Group")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_boxplots("Pregnancy")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_boxplots("plate")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_boxplots("flowcell")
 
 
-## ----"QC scatterplots", message=FALSE, warning=FALSE-----
+## ----"QC scatterplots", message=FALSE, warning=FALSE-----------------------------------------------
 ## Scatterplots for a pair of QC metrics
 
 QC_scatterplots <- function(sample_var, qc_metric1, qc_metric2) {
@@ -254,33 +254,33 @@ multiple_QC_scatterplots <- function(qc_metric1, qc_metric2) {
 }
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_scatterplots("mitoRate", "rRNA_rate")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_scatterplots("mitoRate", "totalAssignedGene")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_scatterplots("sum", "detected")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_scatterplots("sum", "totalAssignedGene")
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 multiple_QC_scatterplots("detected", "totalAssignedGene")
 
 
-## ----exercise1_EDA, message=FALSE, warning=FALSE,  eval=FALSE, echo=FALSE----
+## ----exercise1_EDA, message=FALSE, warning=FALSE,  eval=FALSE, echo=FALSE--------------------------
 ## ## Solution
 ## multiple_QC_scatterplots("subsets_Mito_sum", "mitoMapped")
 ## ## Because in mitoMapped you take reads that mapped to the whole mt chr, in subsets_Mito_sum only reads that were aligned to mt genes. But there's almost a perfect correlation between these two metrics.
 
 
-## ----"QC sample filtering", message=FALSE, warning=FALSE----
+## ----"QC sample filtering", message=FALSE, warning=FALSE-------------------------------------------
 library("scater")
 library("rlang")
 library("ggrepel")
@@ -368,7 +368,7 @@ rse_gene_pups$Retention_after_QC_filtering <- as.vector(sapply(rse_gene_pups$SAM
 }))
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 ## Boxplots of QC metrics after sample filtering
 
 ## Boxplots
@@ -432,27 +432,27 @@ boxplots_after_QC_filtering <- function(rse_gene, qc_metric, sample_var) {
 }
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 ## Plots
 
 ## All samples together
 p <- boxplots_after_QC_filtering(rse_gene_filt, "mitoRate", "Age")
 p + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 ## Adult samples
 p <- boxplots_after_QC_filtering(rse_gene_adults, "mitoRate", "Group")
 p + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
 
 
-## ----message=FALSE, warning=FALSE------------------------
+## ----message=FALSE, warning=FALSE------------------------------------------------------------------
 ## Pup samples
 p <- boxplots_after_QC_filtering(rse_gene_pups, "rRNA_rate", "Group")
 p + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
 
 
-## ----exercise2_EDA, message=FALSE, warning=FALSE, eval=FALSE, echo=FALSE----
+## ----exercise2_EDA, message=FALSE, warning=FALSE, eval=FALSE, echo=FALSE---------------------------
 ## ## Solution
 ## p <- boxplots_after_QC_filtering(rse_gene_adults, "mitoRate", "Pregnancy")
 ## p + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
@@ -467,7 +467,7 @@ p + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
 ## p + theme(plot.margin = unit(c(2, 4, 2, 4), "cm"))
 
 
-## --------------------------------------------------------
+## --------------------------------------------------------------------------------------------------
 ## Why do we see log(CPM + 0.5) values smaller than -1?
 log2(0.5)
 
@@ -497,7 +497,7 @@ log2(0.01578469)
 1 / 0.03156938
 
 
-## --------------------------------------------------------
+## --------------------------------------------------------------------------------------------------
 ## Check the documentation
 ## ?edgeR::cpm
 ## > If log-values are computed, then a small count, given by prior.count but scaled to be proportional to the library size, is added to y to avoid taking the log of zero.

diff --git a/13_expression_heatmaps.R b/13_expression_heatmaps.R
@@ -1,11 +1,11 @@
-## ---- include = FALSE-----------------------------------------------------------------------
+## ---- include = FALSE------------------------------------------------------------------------------
 knitr::opts_chunk$set(
     collapse = TRUE,
     comment = "#>"
 )
 
 
-## ----vignetteSetup_expheatmap, echo=FALSE, message=FALSE, warning = FALSE-------------------
+## ----vignetteSetup_expheatmap, echo=FALSE, message=FALSE, warning = FALSE--------------------------
 ## For links
 library(BiocStyle)
 
@@ -22,20 +22,20 @@ bib <- c(
 options(max.print = 50)
 
 
-## ---- echo=FALSE----------------------------------------------------------------------------
+## ---- echo=FALSE-----------------------------------------------------------------------------------
 suppressPackageStartupMessages(library("SummarizedExperiment"))
 suppressPackageStartupMessages(data(airway, package = "airway"))
 suppressPackageStartupMessages(library("ComplexHeatmap"))
 suppressPackageStartupMessages(library("circlize"))
 
 
-## -------------------------------------------------------------------------------------------
+## --------------------------------------------------------------------------------------------------
 library("SummarizedExperiment")
 library("ComplexHeatmap")
 library("circlize")
 
 
-## ----download_data_biocfilecache_expheatmap-------------------------------------------------
+## ----download_data_biocfilecache_expheatmap--------------------------------------------------------
 ## Download and cache the file
 library("BiocFileCache")
 bfc <- BiocFileCache::BiocFileCache()
@@ -54,9 +54,12 @@ load(cached_rse_gene, verbose = TRUE)
 rse_gene
 
 
-## -------------------------------------------------------------------------------------------
+## --------------------------------------------------------------------------------------------------
 ## Extract genes and samples of interest
-rse_gene_pup_nic <- rse_gene[rowData(rse_gene)$DE_in_pup_brain_nicotine == TRUE, rse_gene$Age == "Pup" & rse_gene$Expt == "Nicotine"]
+rse_gene_pup_nic <- rse_gene[
+    rowData(rse_gene)$DE_in_pup_brain_nicotine == TRUE,
+    rse_gene$Age == "Pup" & rse_gene$Expt == "Nicotine"
+]
 
 ## Extract logcounts and add name columns
 logs_pup_nic <- assay(rse_gene_pup_nic, 2)
@@ -86,7 +89,7 @@ logs_pup_nic <- cleaningY(logs_pup_nic, model, P = 2)
 logs_pup_nic <- (logs_pup_nic - rowMeans(logs_pup_nic)) / rowSds(logs_pup_nic)
 
 
-## -------------------------------------------------------------------------------------------
+## --------------------------------------------------------------------------------------------------
 ## Prepare annotation for our heatmap
 ## For this heatmap I want to be able to see the Group to which each sample belongs
 ## as well as the Sex of the pup
@@ -115,7 +118,7 @@ left_ans <- rowAnnotation(
 )
 
 
-## ---- out.width = "1000px"------------------------------------------------------------------
+## ---- out.width = "1000px"-------------------------------------------------------------------------
 ## Finally, let's plot!
 Heatmap(logs_pup_nic,
     name = "logcounts",
@@ -131,7 +134,7 @@ Heatmap(logs_pup_nic,
 )
 
 
-## -------------------------------------------------------------------------------------------
+## --------------------------------------------------------------------------------------------------
 ## For a) we need to use he function anno_barplot() to generate the barplot and gpar() to fill the bars
 
 top_ans <- HeatmapAnnotation(
@@ -167,7 +170,7 @@ left_ans <- rowAnnotation(
 )
 
 
-## ---- out.width = "1000px"------------------------------------------------------------------
+## ---- out.width = "1000px"-------------------------------------------------------------------------
 Heatmap(logs_pup_nic,
     name = " ",
     show_row_names = FALSE,

diff --git a/13_expression_heatmaps.Rmd b/13_expression_heatmaps.Rmd
@@ -122,7 +122,10 @@ rse_gene
 
 ```{r}
 ## Extract genes and samples of interest
-rse_gene_pup_nic <- rse_gene[rowData(rse_gene)$DE_in_pup_brain_nicotine == TRUE, rse_gene$Age == "Pup" & rse_gene$Expt == "Nicotine"]
+rse_gene_pup_nic <- rse_gene[
+    rowData(rse_gene)$DE_in_pup_brain_nicotine == TRUE,
+    rse_gene$Age == "Pup" & rse_gene$Expt == "Nicotine"
+]
 
 ## Extract logcounts and add name columns
 logs_pup_nic <- assay(rse_gene_pup_nic, 2)