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lcolladotor · Jul 3, 2023 · 6084f4d · 6084f4d
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commit 6084f4d
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Showing 3 changed files with 26 additions and 26 deletions.
diff --git a/01_SPEAQeasy.R b/01_SPEAQeasy.R
@@ -1,4 +1,4 @@
-## ----download_data_biocfilecache_speaqeasy_example-------------------
+## ----download_data_biocfilecache_speaqeasy_example---------------
 ## Load the container package for this type of data
 library("SummarizedExperiment")
 
@@ -23,7 +23,7 @@ rse_gene
 lobstr::obj_size(rse_gene)
 
 
-## --------------------------------------------------------------------
+## ----------------------------------------------------------------
 class(rse_gene$trimmed)
 
 ## logical vectors can take 2 values (plus the third `NA` if it's missing)

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) {
@@ -254,23 +254,23 @@ 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")
 
 
@@ -280,7 +280,7 @@ multiple_QC_scatterplots("detected", "totalAssignedGene")
 ## ## 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
@@ -432,21 +432,21 @@ 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"))

diff --git a/index.R b/index.R
@@ -1,4 +1,4 @@
-## ----install, eval = FALSE-------------------------------------------
+## ----install, eval = FALSE---------------------------------------
 ## ## For installing Bioconductor packages
 ## if (!requireNamespace("BiocManager", quietly = TRUE)) {
 ##     install.packages("BiocManager")
@@ -43,7 +43,7 @@
 ## BiocManager::install("LieberInstitute/smokingMouse")
 
 
-## ----session_packages, eval = TRUE, message = FALSE------------------
+## ----session_packages, eval = TRUE, message = FALSE--------------
 ## Load the package at the top of your script
 library("sessioninfo")
 
@@ -92,7 +92,7 @@ library("ggrepel")
 library("spatialLIBD")
 
 
-## ----session_info----------------------------------------------------
+## ----session_info------------------------------------------------
 ## Reproducibility information
 options(width = 120)
 session_info()