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Source  : 6a72c31
Branch  : main
Author  : Charlotte Soneson <charlottesoneson@gmail.com>
Time    : 2023-10-07 08:06:33 +0000
Message : Merge pull request #93 from carpentries-incubator/timings

Updates following the EuroBioC workshop

01-intro-to-rnaseq.md

@@ -1,8 +1,8 @@
 ---
 source: Rmd
 title: Introduction to RNA-seq
-teaching: 45
-exercises: 30
+teaching: 65
+exercises: 35
 ---
 
 
@@ -172,7 +172,7 @@ If you are mapping your reads to the transcriptome, you will instead need a file
 
 ## Challenge
 
-Download the latest mouse transcriptome fasta file from GENCODE and uncompress it. What do the entries look like? Tip: to read the file into R, consider the `Biostrings` package.
+Download the latest mouse transcriptome fasta file from GENCODE. What do the entries look like? Tip: to read the file into R, consider the `readDNAStringSet()` function from the `Biostrings` package.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 

02-setup.md

@@ -2,7 +2,7 @@
 source: Rmd
 title: RStudio Project and Experimental Data
 teaching: 20
-exercises: 20
+exercises: 10
 ---
 
 :::::::::::::::::::::::::::::::::::::: questions 

03-import-annotate.md

@@ -1,11 +1,11 @@
 ---
 title: "Importing and annotating quantified data into R"
 source: Rmd
-teaching: 75
+teaching: 80
 output:
   html_document:
     df_print: paged
-exercises: 30
+exercises: 40
 ---
 
 

04-exploratory-qc.md

@@ -1,8 +1,8 @@
 ---
 source: Rmd
 title: Exploratory analysis and quality control
-teaching: 60
-exercises: 45
+teaching: 120
+exercises: 60
 editor_options:
   chunk_output_type: console
 ---
@@ -326,33 +326,33 @@ Compare before and after variance stabilizing transformation.
 
 
 ```r
-pcaData <- DESeq2::plotPCA(vsd, intgroup = c("libSize"),
-                           returnData = TRUE)
-percentVar <- round(100 * attr(pcaData, "percentVar"))
-ggplot(pcaData, aes(x = PC1, y = PC2)) +
-    geom_point(aes(color = libSize/ 1e6), size = 5) +
+pcaDataVst <- DESeq2::plotPCA(vsd, intgroup = c("libSize"),
+                              returnData = TRUE)
+percentVar <- round(100 * attr(pcaDataVst, "percentVar"))
+ggplot(pcaDataVst, aes(x = PC1, y = PC2)) +
+    geom_point(aes(color = libSize / 1e6), size = 5) +
     theme_minimal() +
     xlab(paste0("PC1: ", percentVar[1], "% variance")) +
     ylab(paste0("PC2: ", percentVar[2], "% variance")) +
     coord_fixed() + 
-    scale_color_continuous("Total count in millions", type ="viridis")
+    scale_color_continuous("Total count in millions", type = "viridis")
 ```
 
 <img src="fig/04-exploratory-qc-rendered-pca-lib-1.png" style="display: block; margin: auto;" />
 
 
 
 ```r
-pcaData <- DESeq2::plotPCA(DESeqTransform(se), intgroup = c("libSize"),
-                           returnData = TRUE)
-percentVar <- round(100 * attr(pcaData, "percentVar"))
-ggplot(pcaData, aes(x = PC1, y = PC2)) +
-    geom_point(aes(color = libSize/ 1e6), size = 5) +
+pcaDataCts <- DESeq2::plotPCA(DESeqTransform(se), intgroup = c("libSize"),
+                              returnData = TRUE)
+percentVar <- round(100 * attr(pcaDataCts, "percentVar"))
+ggplot(pcaDataCts, aes(x = PC1, y = PC2)) +
+    geom_point(aes(color = libSize / 1e6), size = 5) +
     theme_minimal() +
     xlab(paste0("PC1: ", percentVar[1], "% variance")) +
     ylab(paste0("PC2: ", percentVar[2], "% variance")) +
     coord_fixed() + 
-    scale_color_continuous("Total count in millions", type ="viridis")
+    scale_color_continuous("Total count in millions", type = "viridis")
 ```
 
 <img src="fig/04-exploratory-qc-rendered-pca-lib-vst-1.png" style="display: block; margin: auto;" />
@@ -452,7 +452,7 @@ loaded via a namespace (and not attached):
 [49] miniUI_0.1.1.1          lattice_0.21-8          tibble_3.2.1           
 [52] shiny_1.7.5             withr_2.5.0             evaluate_0.21          
 [55] circlize_0.4.15         pillar_1.9.0            affyio_1.70.0          
-[58] BiocManager_1.30.22     renv_1.0.2              DT_0.29                
+[58] BiocManager_1.30.22     renv_1.0.3              DT_0.29                
 [61] foreach_1.5.2           shinyjs_2.1.0           generics_0.1.3         
 [64] RCurl_1.98-1.12         munsell_0.5.0           scales_1.2.1           
 [67] xtable_1.8-4            glue_1.6.2              tools_4.3.1            

md5sum.txt

@@ -3,17 +3,17 @@
 "LICENSE.md" "2fb7308e51efc6894f5fb9794505973e" "site/built/LICENSE.md" "2023-09-13"
 "config.yaml" "297622287fe4f84b2f8e1792cef52216" "site/built/config.yaml" "2023-09-13"
 "index.md" "6a1b0b9e8983481699e2e4ef54a4893a" "site/built/index.md" "2023-09-13"
-"episodes/01-intro-to-rnaseq.Rmd" "7cc3f4922347f6d5d9ef80dd31e84fbb" "site/built/01-intro-to-rnaseq.md" "2023-09-13"
-"episodes/02-setup.Rmd" "44ede89e11c54f154bacc1f5d4ee01f2" "site/built/02-setup.md" "2023-09-13"
-"episodes/03-import-annotate.Rmd" "2be226b2de49ad3ff72632563631c7a5" "site/built/03-import-annotate.md" "2023-09-23"
-"episodes/04-exploratory-qc.Rmd" "59d9cac581d1c736628534f8cf37f1b5" "site/built/04-exploratory-qc.md" "2023-09-18"
+"episodes/01-intro-to-rnaseq.Rmd" "b4d2d36be5042834578061b30665d493" "site/built/01-intro-to-rnaseq.md" "2023-10-07"
+"episodes/02-setup.Rmd" "e62af0b59eec704194484af83db5b859" "site/built/02-setup.md" "2023-10-07"
+"episodes/03-import-annotate.Rmd" "c43d8eab0fcc982993ea35a952d323c0" "site/built/03-import-annotate.md" "2023-10-07"
+"episodes/04-exploratory-qc.Rmd" "af033b6158f06de3f33d41673272a87d" "site/built/04-exploratory-qc.md" "2023-10-07"
 "episodes/05-differential-expression.Rmd" "29ab3939367a0b3521cfd764929e3aee" "site/built/05-differential-expression.md" "2023-09-17"
 "episodes/06-extra-design.Rmd" "e4859b8e76bc6d0c8d5c598e1d00b37b" "site/built/06-extra-design.md" "2023-09-13"
 "episodes/07-gene-set-analysis.Rmd" "e7b69b6f2af11dfab883303e9a50981d" "site/built/07-gene-set-analysis.md" "2023-09-17"
 "episodes/08-next-steps.Rmd" "88227f8b7b9ae91bd86a66a93d722946" "site/built/08-next-steps.md" "2023-09-13"
 "instructors/instructor-notes.md" "a59fd3b94c07c3fe3218c054a0f03277" "site/built/instructor-notes.md" "2023-09-13"
 "learners/discuss.md" "2758e2e5abd231d82d25c6453d8abbc6" "site/built/discuss.md" "2023-09-13"
 "learners/reference.md" "b9aea3dd8169bf1105bf4f462e8e38f5" "site/built/reference.md" "2023-09-13"
-"learners/setup.md" "ffaa01a8e1cbf5ea0acf405ecd867e07" "site/built/setup.md" "2023-09-13"
+"learners/setup.md" "29d12719c4fcebc031f726275706b330" "site/built/setup.md" "2023-10-07"
 "profiles/learner-profiles.md" "28ba7f4d04639862db131119f4dc4f4d" "site/built/learner-profiles.md" "2023-09-13"
 "renv/profiles/lesson-requirements/renv.lock" "9492694bdd3bcaaf34e2c727d6138dd3" "site/built/renv.lock" "2023-09-13"

setup.md

@@ -10,15 +10,16 @@ episode of the [Introduction to data analysis with R and Bioconductor](https://c
 Additionally, you will also need to install the following packages that will be used throughout the lesson. 
 
 ```r
-install.packages("BiocManager")
+install.packages(c("BiocManager", "remotes"))
 BiocManager::install(c("tidyverse", "SummarizedExperiment",
                        "ExploreModelMatrix", "AnnotationDbi", "org.Hs.eg.db", 
                        "org.Mm.eg.db", "csoneson/ConfoundingExplorer",
                        "DESeq2", "vsn", "ComplexHeatmap", "hgu95av2.db",
                        "RColorBrewer", "hexbin", "cowplot", "iSEE",
                        "clusterProfiler", "enrichplot", "kableExtra",
                        "msigdbr", "gplots", "ggplot2", "simplifyEnrichment",
-                       "apeglm", "microbenchmark", "Biostrings"))
+                       "apeglm", "microbenchmark", "Biostrings",
+                       "SingleCellExperiment"))
 
 ```