diff --git a/404.html b/404.html index f00d01d..e32ea93 100644 --- a/404.html +++ b/404.html @@ -6,7 +6,7 @@ Page not found (404) • recountWorkflow - + @@ -33,7 +33,7 @@ recountWorkflow - 1.25.0 + 1.29.2 @@ -101,7 +101,7 @@

Page not found (404)

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.0.9.

diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html index 02fd4a0..c0b4d8d 100644 --- a/CODE_OF_CONDUCT.html +++ b/CODE_OF_CONDUCT.html @@ -1,5 +1,5 @@ -Contributor Covenant Code of Conduct • recountWorkflowContributor Covenant Code of Conduct • recountWorkflow @@ -17,7 +17,7 @@ recountWorkflow - 1.25.0 + 1.29.2 @@ -139,7 +139,7 @@

Attribution -

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.0.9.

diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html index fda58d5..aa0072b 100644 --- a/CONTRIBUTING.html +++ b/CONTRIBUTING.html @@ -1,5 +1,5 @@ -Contributing to recountWorkflow • recountWorkflowContributing to recountWorkflow • recountWorkflow @@ -17,7 +17,7 @@ recountWorkflow - 1.25.0 + 1.29.2 @@ -102,7 +102,7 @@

Code of Conduct
-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.0.9.

diff --git a/SUPPORT.html b/SUPPORT.html index 0d3af21..7465684 100644 --- a/SUPPORT.html +++ b/SUPPORT.html @@ -1,5 +1,5 @@ -Getting help with recountWorkflow • recountWorkflowGetting help with recountWorkflow • recountWorkflow @@ -17,7 +17,7 @@ recountWorkflow - 1.25.0 + 1.29.2 @@ -100,7 +100,7 @@

What happens next? -

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.0.9.

diff --git a/articles/create_report-1.png b/articles/create_report-1.png index 1422bb8..b2146be 100644 Binary files a/articles/create_report-1.png and b/articles/create_report-1.png differ diff --git a/articles/erdeanalysis1-1.png b/articles/erdeanalysis1-1.png new file mode 100644 index 0000000..483eb35 Binary files /dev/null and b/articles/erdeanalysis1-1.png differ diff --git a/articles/erdeanalysis2-1.png b/articles/erdeanalysis2-1.png new file mode 100644 index 0000000..f7cf7fd Binary files /dev/null and b/articles/erdeanalysis2-1.png differ diff --git a/articles/erdeanalysis3-1.png b/articles/erdeanalysis3-1.png new file mode 100644 index 0000000..db72a7f Binary files /dev/null and b/articles/erdeanalysis3-1.png differ diff --git a/articles/erdeanalysis4-1.png b/articles/erdeanalysis4-1.png new file mode 100644 index 0000000..7db2608 Binary files /dev/null and b/articles/erdeanalysis4-1.png differ diff --git a/articles/exondeanalysis1-1.png b/articles/exondeanalysis1-1.png index d6d2609..ff23955 100644 Binary files a/articles/exondeanalysis1-1.png and b/articles/exondeanalysis1-1.png differ diff --git a/articles/exondeanalysis2-1.png b/articles/exondeanalysis2-1.png index 214fb65..4d2b470 100644 Binary files a/articles/exondeanalysis2-1.png and b/articles/exondeanalysis2-1.png differ diff --git a/articles/geneexon-1.png b/articles/geneexon-1.png index 247e113..3159801 100644 Binary files a/articles/geneexon-1.png and b/articles/geneexon-1.png differ diff --git a/articles/geneexonmatch-1.png b/articles/geneexonmatch-1.png index fe8297c..1dda9a5 100644 Binary files a/articles/geneexonmatch-1.png and b/articles/geneexonmatch-1.png differ diff --git a/articles/goanalysis-1.png b/articles/goanalysis-1.png index def19db..71639fb 100644 Binary files a/articles/goanalysis-1.png and b/articles/goanalysis-1.png differ diff --git a/articles/index.html b/articles/index.html index e19a7bd..91ede6b 100644 --- a/articles/index.html +++ b/articles/index.html @@ -1,5 +1,5 @@ -Articles • recountWorkflowArticles • recountWorkflow @@ -17,7 +17,7 @@ recountWorkflow - 1.25.0 + 1.29.2 @@ -72,7 +72,7 @@

All vignettes

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.0.9.

diff --git a/articles/limmade1-1.png b/articles/limmade1-1.png index 8390548..4587912 100644 Binary files a/articles/limmade1-1.png and b/articles/limmade1-1.png differ diff --git a/articles/limmade2-1.png b/articles/limmade2-1.png index c38f20c..7e2ced7 100644 Binary files a/articles/limmade2-1.png and b/articles/limmade2-1.png differ diff --git a/articles/limmade3-1.png b/articles/limmade3-1.png index 7be7c7c..e3a2e04 100644 Binary files a/articles/limmade3-1.png and b/articles/limmade3-1.png differ diff --git a/articles/limmaplots1-1.png b/articles/limmaplots1-1.png index 8a85f47..26e5f5d 100644 Binary files a/articles/limmaplots1-1.png and b/articles/limmaplots1-1.png differ diff --git a/articles/limmaplots2-1.png b/articles/limmaplots2-1.png index fb88f92..8c7c381 100644 Binary files a/articles/limmaplots2-1.png and b/articles/limmaplots2-1.png differ diff --git a/articles/recount-workflow.html b/articles/recount-workflow.html index 5187355..f5bf0c6 100644 --- a/articles/recount-workflow.html +++ b/articles/recount-workflow.html @@ -6,7 +6,7 @@ recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor • recountWorkflow - + @@ -34,7 +34,7 @@ recountWorkflow - 1.25.0 + 1.29.2 @@ -132,9 +132,9 @@

Abhinav and a compendium of R code to use the data. -

R version: R version 4.3.0 (2023-04-21 ucrt)

-

Bioconductor version: 3.17

-

Package: 1.24.0

+

R version: R version 4.4.0 (2024-04-24)

+

Bioconductor version: 3.19

+

Package: 1.29.2

Introduction

@@ -253,7 +253,7 @@

Packages used in the workflowif (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } -BiocManager::install(c( +BiocManager::install(c( "recount", "GenomicRanges", "limma", "edgeR", "DESeq2", "regionReport", "clusterProfiler", "org.Hs.eg.db", "gplots", "derfinder", "GenomicState", "bumphunter", "derfinderPlot", "sessioninfo" @@ -265,7 +265,7 @@

Packages used in the workflowlibrary("GenomicRanges") library("limma") library("edgeR") -library("DESeq2") +library("DESeq2") library("regionReport") library("clusterProfiler") library("org.Hs.eg.db") @@ -579,14 +579,16 @@

Gene-level analysisif (!file.exists(file.path("SRP045638", "rse_gene.Rdata"))) { download_study("SRP045638", type = "rse-gene") }

-
## 2023-05-07 19:38:00.521397 downloading file rse_gene.Rdata to SRP045638
+
## 2024-05-21 18:39:57.328014 downloading file rse_gene.Rdata to SRP045638
 ## Check that the file was downloaded
 file.exists(file.path("SRP045638", "rse_gene.Rdata"))
## [1] TRUE
 ## Load the data
-load(file.path("SRP045638", "rse_gene.Rdata"))
+load(file.path("SRP045638", "rse_gene.Rdata"), verbose = TRUE) +
## Loading objects:
+##   rse_gene

The coverage count matrices are provided as RangedSummarizedExperiment objects (rse) (9). These objects store information at the feature level, the samples and the actual count matrix as shown in @@ -611,11 +613,11 @@

MetadataSeven Bridges’ Cancer Genomics Cloud and TCGAbiolinks (15).

-
+
 ## One row per sample, one column per phenotype variable
 dim(colData(rse_gene))

 
## [1] 72 21

-
+
 ## Mostly technical variables are included
 colnames(colData(rse_gene))

 
##  [1] "project"                                       
@@ -653,7 +655,7 @@ 
Technical variablesscale_counts().

-
+
 ## Input reads: number reported by SRA might be larger than number
 ## of reads Rail-RNA downloaded
 colData(rse_gene)[
@@ -674,17 +676,17 @@ 
Technical variables## SRR1554535                     106244496         91185969
 ## SRR1554558                     200687480        170754145
 ## SRR1554553                      90579486         51803404

-
+
 summary(
     colData(rse_gene)$proportion_of_reads_reported_by_sra_downloaded
 )

 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 ##  0.5719  0.9165  0.9788  0.9532  1.0000  1.0000

-
+
 ## AUC information used by scale_counts() by default
 head(colData(rse_gene)$auc)

 
## [1] 22950214241  7553726235 12018044330  7041243857 24062460144 45169026301

-
+
 ## Alternatively, scale_scounts() can use the number of mapped reads
 ## and other information
 colData(rse_gene)[, c(
@@ -713,11 +715,11 @@ 
Biological informationSHARQ beta tissue and
 cell type predictions, which are based on processing the abstract of
 papers. This information is available for some of the SRA projects.

-
+
 ## SHARQ tissue predictions: not present for all studies
 head(colData(rse_gene)$sharq_beta_tissue)

 
## [1] NA NA NA NA NA NA

-
+
 head(colData(rse_gene_SRP009615)$sharq_beta_tissue)

 
## [1] "blood" "blood" "blood" "blood" "blood" "blood"

 
For some data sets we were able to find the GEO accession IDs, which
@@ -729,7 +731,7 @@ 
Biological information
-
+
 ## GEO information was absent for the SRP045638 data set
 colData(rse_gene)[, c("geo_accession", "title", "characteristics")]

 
## DataFrame with 72 rows and 3 columns
@@ -746,29 +748,29 @@ 
Biological information## SRR1554535            NA          NA              NA
 ## SRR1554558            NA          NA              NA
 ## SRR1554553            NA          NA              NA

-
+
 
## [1] "GSM836270" "GSM836271" "GSM836272" "GSM836273" "GSM847561" "GSM847562"

-
+
 head(colData(rse_gene_SRP009615)$title, 2)

 
## [1] "K562 cells with shRNA targeting SRF gene cultured with no doxycycline (uninduced - UI), rep1." 
 ## [2] "K562 cells with shRNA targeting SRF gene cultured with doxycycline for 48 hours (48 hr), rep1."

-
+
 head(colData(rse_gene_SRP009615)$characteristics, 2)

 
## CharacterList of length 2
 ## [[1]] cells: K562 shRNA expression: no treatment: Puromycin
 ## [[2]] cells: K562 shRNA expression: yes, targeting SRF treatment: Puromycin, doxycycline

-
+
 ## Similar but not exactly the same wording used for two different samples
 colData(rse_gene_SRP009615)$characteristics[[1]]

 
## [1] "cells: K562"          "shRNA expression: no" "treatment: Puromycin"

-
+
 colData(rse_gene_SRP009615)$characteristics[[11]]

 
## [1] "cell line: K562"                      
 ## [2] "shRNA expression: no shRNA expression"
 ## [3] "treatment: Puromycin"

-
+
 ## Extract the target information
 target <- sapply(colData(rse_gene_SRP009615)$characteristics, "[", 2)
 target

@@ -784,7 +786,7 @@ 
Biological information## [10] "shRNA expression: expressing shRNA targeting ATF3"
 ## [11] "shRNA expression: no shRNA expression"            
 ## [12] "shRNA expression: expressing shRNA targeting ATF3"

-
+
 
##  [1] none SRF  none SRF  none EGR1 none EGR1 none ATF3 none ATF3
 ## Levels: none ATF3 EGR1 SRF

-
+
 colData(rse_gene_SRP009615)$target_factor <- target_factor

 
As shown in Figure @ref(fig:Figure2), we can expand the biological
 metadata information by adding predictions based on RNA-seq data (14). The predictions include information about
@@ -802,21 +804,21 @@ 
Biological informationadd_predictions() to expand the colData()
 slot.

-
+
 ## Before adding predictions
 dim(colData(rse_gene))

 
## [1] 72 21

-
+
 ## Add the predictions
 rse_gene <- add_predictions(rse_gene)

-
## 2023-05-07 19:38:03.426619 downloading the predictions to C:\Users\fellg\AppData\Local\Temp\Rtmp2vyc2o/PredictedPhenotypes_v0.0.06.rda

+
## 2024-05-21 18:39:59.370333 downloading the predictions to /tmp/Rtmpv3FCef/PredictedPhenotypes_v0.0.06.rda

 
## Loading objects:
 ##   PredictedPhenotypes

-
+
 ## After adding the predictions
 dim(colData(rse_gene))

 
## [1] 72 33

-
+
 ## Explore the variables
 colData(rse_gene)[, 22:ncol(colData(rse_gene))]

 
## DataFrame with 72 rows and 12 columns
@@ -888,13 +890,13 @@ 
Adding more informationSraRunTable.txt by default, then load it into R, sort it
 appropriately and then append it to the colData() slot.
 Below we do so for the SRP045638 project.

-
+
 ## Save the information from
 ## https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP045638
 ## to a table. We saved the file as SRP045638/SraRunTable.txt.
 file.exists(file.path("SRP045638", "SraRunTable.txt"))

 
## [1] TRUE

-
+
 ## Read the table
 sra <- read.csv(file.path("SRP045638", "SraRunTable.txt"),
     header = TRUE
@@ -958,7 +960,7 @@ 
Adding more information## 4          
 ## 5          
 ## 6

-
+
 ## Set all column names in lower case
 colnames(sra) <- tolower(colnames(sra))
 
@@ -981,7 +983,7 @@ 
Adding more information## Final dimensions
 dim(colData(rse_gene))

 
## [1] 72 40

-
+
 ## Explore result
 colData(rse_gene)[, 34:ncol(colData(rse_gene))]

 
## DataFrame with 72 rows and 7 columns
@@ -1013,7 +1015,7 @@ 
Adding more information## SRR1554553       DLPFC

 
Since we have the predicted sex as well as the reported sex via the
 SRA Run Selector, we can check whether they match.

-
+
 table(
     "Predicted" = colData(rse_gene)$predicted_sex,
     "Observed" = colData(rse_gene)$sex
@@ -1032,7 +1034,7 @@ 
DE setup(13) looked at differences between 6 age groups:
 prenatal, infant, child, teen, adult and late life. The following code
 creates these six age groups.

-
+
 ## Create the original 6 age groups
 age_bins <- cut(colData(rse_gene)$age, c(-1, 0, 1, 10, 20, 50, Inf),
     include.lowest = TRUE
@@ -1045,7 +1047,7 @@ 
DE setupMost of the DE signal from the original study was between the
 prenatal and postnatal samples. To simplify the analysis, we will focus
 on this comparison.

-
+
 ## Create prenatal factor
 colData(rse_gene)$prenatal <- factor(
     ifelse(colData(rse_gene)$age_group == "prenatal", "prenatal",
@@ -1058,7 +1060,7 @@ 
DE setuprse
 object with the output of scale_counts(rse).

-
+
 ## Scale counts
 rse_gene_scaled <- scale_counts(rse_gene)
 
@@ -1066,7 +1068,7 @@ 
DE setuprm(rse_gene)

 
Having scaled the counts, we then filter out genes that are lowly
 expressed and extract the count matrix.

-
+
 ## Extract counts and filter out lowly expressed geens
 counts <- assays(rse_gene_scaled)$counts
 filter <- rowMeans(counts) > 0.5

@@ -1085,7 +1087,7 @@ 
DE analysis
+
 library("limma")
 library("edgeR")
 
@@ -1102,14 +1104,14 @@ 
DE analysis
+
 plotMDS(dge, labels = substr(colData(rse_gene_scaled)$sex, 1, 1))

 

 Multi-dimensional scaling plot of the gene-level data by sex.

 Multi-dimensional scaling plot of the gene-level data by sex.
 

 

-
+
 tapply(
     colData(rse_gene_scaled)$rin, colData(rse_gene_scaled)$prenatal,
     summary
@@ -1121,13 +1123,13 @@ 
DE analysis## $postnatal
 ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 ##   5.300   8.100   8.300   8.197   8.700   9.100

-
+
 ## Specify our design matrix
 design <- with(
     colData(rse_gene_scaled),
     model.matrix(~ sex + rin + prenatal)
 )

-
+
 ## Run voom
 v <- voom(dge, design, plot = TRUE)

 

@@ -1135,7 +1137,7 @@ 
DE analysis
+
@@ -1143,7 +1145,7 @@ 
DE analysis
+
 

@@ -1152,7 +1154,7 @@ 
DE analysis
+
 limma::volcanoplot(fit, coef = 4)

 

 Volcano plot of the gene-level data. Testing for prenatal and postnatal DE adjusting for sex and RIN.

@@ -1176,7 +1178,7 @@ 
DE report(20), which is another package in the
 ReportWriting biocView category.

-
+
 ## Extract data from limma-voom results
 top <- topTable(fit,
     number = Inf, sort.by = "none",
@@ -1184,7 +1186,7 @@ 
DE report)
 
 ## Build a DESeqDataSet with the count data and model we used
-library("DESeq2")
+library("DESeq2")
 dds <- DESeqDataSet(rse_gene_scaled[filter, ], ~ sex + rin + prenatal)

 
## converting counts to integer mode

 
## Warning in DESeqDataSet(rse_gene_scaled[filter, ], ~sex + rin + prenatal): some
@@ -1193,7 +1195,7 @@ 
DE report##   standard deviation larger than 5 (an arbitrary threshold to trigger this message).
 ##   Including numeric variables with large mean can induce collinearity with the intercept.
 ##   Users should center and scale numeric variables in the design to improve GLM convergence.

-
+
 ## Add gene names keeping only the Ensembl part of the Gencode IDs
 rownames(dds) <- gsub("\\..*", "", rownames(dds))
 
@@ -1223,7 +1225,7 @@ 
DE report
+
 library("regionReport")
 ## This takes about 20 minutes to run
 report <- DESeq2Report(dds,
@@ -1236,7 +1238,7 @@ 
DE reportbrowseURL(). A pre-computed version is available as
 Supplementary File 1.

-
+
 browseURL(file.path("SRP045638", "gene_report.html"))

 

 

@@ -1246,14 +1248,14 @@ 
GO enrichment
+
 library("clusterProfiler")
 library("org.Hs.eg.db")
 
 ## Remember that limma_res had ENSEMBL IDs for the genes
 head(rownames(limma_res))

 
## [1] "ENSG00000000003" "ENSG00000000005" "ENSG00000000419" "ENSG00000000457" "ENSG00000000460" "ENSG00000000938"

-
+
 ## Perform enrichment analysis for Biological Process (BP)
 ## Note that the argument is keytype instead of keyType in Bioconductor 3.5
 enrich_go <- enrichGO(
@@ -1296,20 +1298,20 @@ 
Exon and exon-exon junctions
+
 ## Download the data if it is not there
 if (!file.exists(file.path("SRP045638", "rse_exon.Rdata"))) {
     download_study("SRP045638", type = "rse-exon")
 }

-
## 2023-05-07 19:42:35.79495 downloading file rse_exon.Rdata to SRP045638

-
+
## 2024-05-21 18:44:04.331859 downloading file rse_exon.Rdata to SRP045638

+
 ## Load the data
 load(file.path("SRP045638", "rse_exon.Rdata"))
 
 ## Scale and add the metadata (it is in the same order)
 identical(colData(rse_exon)$run, colData(rse_gene_scaled)$run)

 
## [1] TRUE

-
+
 colData(rse_exon) <- colData(rse_gene_scaled)
 rse_exon_scaled <- scale_counts(rse_exon)
 ## To highlight that we scaled the counts
@@ -1321,7 +1323,7 @@ 
Exon and exon-exon junctions## filter_exon
 ## FALSE  TRUE 
 ## 32.76 67.24

-
+
 ## Build DGEList object
 dge_exon <- DGEList(
     counts = assays(rse_exon_scaled)$counts[filter_exon, ]
@@ -1337,7 +1339,7 @@ 
Exon and exon-exon junctions
+
 ## Run remaining parts of the DE analysis
 fit_exon <- lmFit(v_exon, design)
 fit_exon <- eBayes(fit_exon)
@@ -1350,7 +1352,7 @@ 
Exon and exon-exon junctions
+
 ## Get p-values and other statistics
 top_exon <- topTable(fit_exon,
     number = Inf, sort.by = "none",
@@ -1366,7 +1368,7 @@ 
Exon and exon-exon junctions
+
 ## Get the gene IDs for genes that are DE at the gene-level or that have at
 ## least one exon with DE signal.
 genes_w_de_exon <- unique(
@@ -1403,7 +1405,7 @@ 
Exon and exon-exon junctions
+
 ## Keep only the DE exons that are from a gene that is also DE
 top_exon_de <- top_exon[top_exon$adj.P.Val < 0.001 &
     top_exon$ID %in% attr(vinfo, "intersections")[["genes:exons"]], ]
@@ -1464,35 +1466,73 @@ 
Base-pair resolutioncoverage_matrix().

-
-## Define expressed regions for study SRP045638, only for chromosome 21
+
+## Normally, one can use rtracklayer::import() to access remote parts of BigWig
+## files without having to download the complete files. However, as of
+## 2024-05-20 this doesn't seem to be working well. So this is a workaround to
+## issue https://github.com/lawremi/rtracklayer/issues/83
+download_study("SRP045638", type = "mean")
+
+## Define expressed regions for study SRP045638, only for chromosome 21
 regions <- expressed_regions("SRP045638", "chr21",
     cutoff = 5L,
-    maxClusterGap = 3000L
-)
-
-## Explore the resulting expressed regions
-regions
-summary(width(regions))
-table(width(regions) >= 100)
-
-## Keep only the ones that are at least 100 bp long
+    maxClusterGap = 3000L,
+    outdir = "SRP045638"
+)

+
+## Explore the resulting expressed regions
+regions

+
## GRanges object with 3853 ranges and 6 metadata columns:
+##        seqnames            ranges strand |     value      area indexStart  indexEnd cluster clusterL
+##           <Rle>         <IRanges>  <Rle> | <numeric> <numeric>  <integer> <integer>   <Rle>    <Rle>
+##      1    chr21   5026549-5026630      * |   6.48181   531.509    5026549   5026630       1     1677
+##      2    chr21   5027935-5027961      * |   6.19690   167.316    5027935   5027961       1     1677
+##      3    chr21   5028108-5028225      * |   8.99329  1061.208    5028108   5028225       1     1677
+##      4    chr21   5032053-5032117      * |   7.06828   459.438    5032053   5032117       2     8283
+##      5    chr21   5032148-5032217      * |   6.48833   454.183    5032148   5032217       2     8283
+##    ...      ...               ...    ... .       ...       ...        ...       ...     ...      ...
+##   3849    chr21          46695774      * |   5.02902   5.02902   46695774  46695774     708     5708
+##   3850    chr21 46695784-46695843      * |   5.38047 322.82838   46695784  46695843     708     5708
+##   3851    chr21 46695865-46695869      * |   5.11283  25.56414   46695865  46695869     708     5708
+##   3852    chr21 46696463-46696486      * |   5.25689 126.16540   46696463  46696486     708     5708
+##   3853    chr21 46696508-46696534      * |   5.22988 141.20686   46696508  46696534     708     5708
+##   -------
+##   seqinfo: 1 sequence from an unspecified genome

+
+summary(width(regions))

+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+##     1.0     6.0    68.0   186.2   151.0 11709.0

+
+table(width(regions) >= 100)

+
## 
+## FALSE  TRUE 
+##  2284  1569

+
+## Keep only the ones that are at least 100 bp long
 regions <- regions[width(regions) >= 100]
 length(regions)

+
## [1] 1569

 
Now that we have a set of regions to work with, we proceed to build a
 RangedSummarizedExperiment object with the coverage counts, add
 the expanded metadata we built for the gene-level, and scale the counts.
 Note that coverage_matrix() scales the base-pair coverage
 counts by default, which we turn off in order to use use
 scale_counts().

-
-## Compute coverage matrix for study SRP045638, only for chromosome 21
+
+## Normally, one can use rtracklayer::import() to access remote parts of BigWig
+## files without having to download the complete files. However, as of
+## 2024-05-20 this doesn't seem to be working well. So this is a workaround to
+## issue https://github.com/lawremi/rtracklayer/issues/83
+download_study("SRP045638", type = "samples")
+
+## Compute coverage matrix for study SRP045638, only for chromosome 21
 ## Takes about 4 minutes
 rse_er <- coverage_matrix("SRP045638", "chr21", regions,
-    chunksize = 2000, verboseLoad = FALSE, scale = FALSE
-)
-
-## Use the expanded metadata we built for the gene model
+    chunksize = 2000, verboseLoad = FALSE, scale = FALSE,
+    outdir = "SRP045638"
+)

+
+## Use the expanded metadata we built for the gene model
 colData(rse_er) <- colData(rse_gene_scaled)
 
 ## Scale the coverage matrix
@@ -1505,7 +1545,7 @@ 
Base-pair resolution
+
 ## Build DGEList object
 dge_er <- DGEList(counts = assays(rse_er_scaled)$counts)
 
@@ -1514,34 +1554,77 @@ 
Base-pair resolution## Explore the data
 plotMDS(dge_er, labels = substr(colData(rse_er_scaled)$prenatal, 1, 2))

-
+

+Multi-dimensional scaling plot of the expressed regions level data by age group.

+Multi-dimensional scaling plot of the expressed regions level data by
+age group.
+

+

+
 plotMDS(dge_er, labels = substr(colData(rse_er_scaled)$sex, 1, 1))

-
+

+Multi-dimensional scaling plot of the expressed regions level data by sex.

+Multi-dimensional scaling plot of the expressed regions level data by
+sex.
+

+

+
 ## Run voom
-v_er <- voom(dge_er, design, plot = TRUE)
-
-## Run remaining parts of the DE analysis
+v_er <- voom(dge_er, design, plot = TRUE)

+

+voom mean-variance plot of the expressed regions level data.

+voom mean-variance plot of the expressed regions level data.
+

+

+
+## Run remaining parts of the DE analysis
 fit_er <- lmFit(v_er, design)
 fit_er <- eBayes(fit_er)

-
+
 ## Visually explore the results
-limma::volcanoplot(fit_er, coef = 4)
-
-## Number of DERs
+limma::volcanoplot(fit_er, coef = 4)

+

+Volcano plot of the expressed regions level data. Testing for prenatal and postnatal DE adjusting for sex and RIN.

+Volcano plot of the expressed regions level data. Testing for prenatal
+and postnatal DE adjusting for sex and RIN.
+

+

+
+## Number of DERs
 top_er <- topTable(fit_er,
     number = Inf, sort.by = "none",
     coef = "prenatalpostnatal"
 )
 table(top_er$adj.P.Val < 0.001)

+
## 
+## FALSE  TRUE 
+##   609   960

 
Having identified the differentially expressed regions (DERs), we can
 sort all regions by their adjusted p-value.

-
+
 ## Sort regions by q-value
 regions_by_padj <- regions[order(top_er$adj.P.Val, decreasing = FALSE)]
 
 ## Look at the top 10
-regions_by_padj[1:10]
-width(regions_by_padj[1:10])

+regions_by_padj[1:10]

+
## GRanges object with 10 ranges and 6 metadata columns:
+##        seqnames            ranges strand |     value       area indexStart  indexEnd cluster clusterL
+##           <Rle>         <IRanges>  <Rle> | <numeric>  <numeric>  <integer> <integer>   <Rle>    <Rle>
+##   2998    chr21 44441692-44442678      * |  34.73978  34288.160   44441692  44442678     607    14072
+##   2144    chr21 38822674-38824916      * |  85.56379 191919.577   38822674  38824916     435    14882
+##   3033    chr21 44458772-44459070      * |   8.44090   2523.830   44458772  44459070     608     4968
+##   3029    chr21 44458526-44458644      * |   5.80784    691.133   44458526  44458644     608     4968
+##   3505    chr21 46250498-46250780      * |   5.68433   1608.666   46250498  46250780     678    30649
+##   3045    chr21 44461331-44461480      * |   5.82022    873.033   44461331  44461480     608     4968
+##   1356    chr21 33070821-33072413      * | 190.20982 303004.244   33070821  33072413     292     2261
+##   1714    chr21 36225565-36225667      * |  11.56453   1191.146   36225565  36225667     375     9845
+##   3773    chr21 46598568-46599629      * | 301.85950 320574.784   46598568  46599629     704     6544
+##   2254    chr21 39928983-39929390      * | 233.01399  95069.710   39928983  39929390     464     3344
+##   -------
+##   seqinfo: 1 sequence from an unspecified genome

+
+width(regions_by_padj[1:10])

+
##  [1]  987 2243  299  119  283  150 1593  103 1062  408

 

 

 
Visualize regions
@@ -1558,7 +1641,7 @@ 
Visualize regions
+
+names(bws) <- colData(rse_er_scaled)$run
+
+## Workaround to https://github.com/lawremi/rtracklayer/issues/83: use the local
+## files we already downloaded
+bws <- gsub("http://duffel.rail.bio/recount/", "", bws)

 
We visualize the DERs using derfinderPlot, similar to
 what was done in the original publication (13). However, we first add a little padding to
 the regions: 100 base-pairs on each side.

-
+
 ## Add 100 bp padding on each side
 regions_resized <- resize(regions_by_padj[1:10],
     width(regions_by_padj[1:10]) + 200,
@@ -1591,7 +1678,7 @@ 
Visualize regions
+
 ## Get the bp coverage data for the plots
 library("derfinder")
 regionCov <- getRegionCoverage(
@@ -1606,7 +1693,7 @@ 
Visualize regions(27) package comes into play as it has done the
 heavy lifting for us already.

-
+
 ## Import the Gencode v25 hg38 gene annotation
 ## using GenomicState
 library("GenomicState")
@@ -1615,23 +1702,42 @@ 
Visualize regionsgencode_v25_hg38_txdb <- GenomicStateHub(
     version = "25", genome = "hg38",
     filetype = "TxDb"
-)[[1]]
-
-## Explore the TxDb object
+)[[1]]

+
## loading from cache

+
## Loading required package: GenomicFeatures

+
+## Explore the TxDb object
 gencode_v25_hg38_txdb

+
## TxDb object:
+## # Db type: TxDb
+## # Supporting package: GenomicFeatures
+## # Data source: ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_25/gencode.v25.annotation.gtf.gz
+## # Organism: Homo sapiens
+## # Taxonomy ID: 9606
+## # miRBase build ID: NA
+## # Genome: hg38
+## # transcript_nrow: 198093
+## # exon_nrow: 678580
+## # cds_nrow: 270623
+## # Db created by: GenomicFeatures package from Bioconductor
+## # Creation time: 2019-10-07 09:59:57 -0400 (Mon, 07 Oct 2019)
+## # GenomicFeatures version at creation time: 1.36.4
+## # RSQLite version at creation time: 2.1.2
+## # DBSCHEMAVERSION: 1.2

 
Now that we have a TxDb object for Gencode v25 on hg38 coordinates,
 we can use bumphunter’s (28)
 annotation functions for annotating the original 10 regions we were
 working with as well as the annotated genes that we can download using
 GenomicState.

-
+
 ## Download annotated transcripts for gencode v25
 ann_gencode_v25_hg38 <- GenomicStateHub(
     version = "25", genome = "hg38",
     filetype = "AnnotatedGenes"
-)[[1]]
-
-## Annotate the regions of interest
+)[[1]]

+
## loading from cache

+
+## Annotate the regions of interest
 ## Note that we are using the original regions, not the resized ones
 library("bumphunter")
 nearest_ann <- matchGenes(regions_by_padj[1:10], ann_gencode_v25_hg38)

@@ -1641,22 +1747,25 @@ 
Visualize regionsmakeGenomicState() that we can download
 with GenomicState.

-
+
 ## Download the genomic state object for Gencode v25
 gs_gencode_v25_hg38 <- GenomicStateHub(
     version = "25", genome = "hg38",
     filetype = "GenomicState"
-)[[1]]
-
-## Annotate the original regions
+)[[1]]

+
## loading from cache

+
+## Annotate the original regions
 regions_ann <- annotateRegions(
     regions_resized,
     gs_gencode_v25_hg38$fullGenome
 )

+
## 2024-05-21 18:45:45.155842 annotateRegions: counting

+
## 2024-05-21 18:45:45.326422 annotateRegions: annotating

 
We can finally use plotRegionCoverage() to visualize the
 top 10 regions coloring by whether they are prenatal or postnatal
 samples. Known exons are shown in dark blue, introns in light blue.

-
+
 library("derfinderPlot")
 pdf("region_plots.pdf")
 plotRegionCoverage(
@@ -1668,9 +1777,11 @@ 
Visualize regions= 1, ylab = "Coverage (RP40M, 100bp)",
     ask = FALSE, verbose = FALSE
 )
-dev.off()
-
-## Visualize DER #2
+dev.off()

+
## agg_png 
+##       2

+
+## Visualize DER #2
 plotRegionCoverage(
     regions = regions_resized, regionCoverage = regionCov,
     groupInfo = colData(rse_er_scaled)$prenatal,
@@ -1680,6 +1791,11 @@ 
Visualize regions= 1, ylab = "Coverage (RP40M, 100bp)",
     ask = FALSE, verbose = FALSE, whichRegions = 2
 )

+

+Base-pair resolution plot of differentially expressed region 2.

+Base-pair resolution plot of differentially expressed region 2.
+

+

 
In plots like Figure @ref(fig:regionplots) we can see that some DERs
 match known exons (DERs 2, 8, 10), some are longer than known exons
 (DERs 1, 7, 9), and others are exon fragments (DERs 3, 4, 5, 6) which
@@ -1706,11 +1822,11 @@ 
Session information(29). The session information is available in
 Supplementary File 2. The most recent version of this workflow is
 available via Bioconductor at http://bioconductor.org/help/workflows/.

-
+
 ## Final list of files created
 dir("SRP045638")

 
## [1] "gene_report.bib"  "gene_report.html" "rse_exon.Rdata"   "rse_gene.Rdata"   "SraRunTable.txt"

-
+
 ## Pandoc information
 library("rmarkdown")

 
## 
@@ -1718,250 +1834,250 @@ 
Session information## The following objects are masked from 'package:BiocStyle':
 ## 
 ##     html_document, md_document, pdf_document

-
+
-
## [1] '2.19.2'

-
+
## [1] '3.1.13'

+
 ## Time for reproducing this workflow, in minutes
 round(proc.time()[3] / 60, 1)

 
## elapsed 
 ##     6.2

-
+
 
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────
 ##  setting  value
-##  version  R version 4.3.0 (2023-04-21 ucrt)
-##  os       Windows 11 x64 (build 22621)
-##  system   x86_64, mingw32
-##  ui       RTerm
+##  version  R version 4.4.0 (2024-04-24)
+##  os       Ubuntu 22.04.4 LTS
+##  system   x86_64, linux-gnu
+##  ui       X11
 ##  language en
-##  collate  English_United States.utf8
-##  ctype    English_United States.utf8
-##  tz       America/New_York
-##  date     2023-05-07
-##  pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
+##  collate  en_US.UTF-8
+##  ctype    en_US.UTF-8
+##  tz       UTC
+##  date     2024-05-21
+##  pandoc   3.1.13 @ /usr/bin/ (via rmarkdown)
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@@ -2204,7 +2320,7 @@ 
References
   

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Site built with pkgdown 2.0.7.

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Site built with pkgdown 2.0.9.

 

 
       
diff --git a/articles/regionplots-1.png b/articles/regionplots-1.png
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+++ b/authors.html
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@@ -17,7 +17,7 @@
       
       
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@@ -54,7 +54,7 @@
   

     

       

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Citation

   doi = {10.12688/f1000research.12223.1},
   url = {https://f1000research.com/articles/6-1558/v1},
 }

-    
Collado-Torres L, Nellore A, Jaffe AE (2023).
+    
Collado-Torres L, Nellore A, Jaffe AE (2024).
 recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor.
-doi:10.18129/B9.bioc.recountWorkflow, https://github.com/LieberInstitute/recountWorkflow - R package version 1.25.0, http://www.bioconductor.org/packages/recountWorkflow. 
+doi:10.18129/B9.bioc.recountWorkflow, https://github.com/LieberInstitute/recountWorkflow - R package version 1.29.2, http://www.bioconductor.org/packages/recountWorkflow. 
 

     
@Manual{,
   title = {recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor},
   author = {Leonardo Collado-Torres and Abhinav Nellore and Andrew E. Jaffe},
-  year = {2023},
+  year = {2024},
   url = {http://www.bioconductor.org/packages/recountWorkflow},
-  note = {https://github.com/LieberInstitute/recountWorkflow - R package version 1.25.0},
+  note = {https://github.com/LieberInstitute/recountWorkflow - R package version 1.29.2},
   doi = {10.18129/B9.bioc.recountWorkflow},
 }

 
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+++ b/index.html
@@ -5,29 +5,15 @@
 
 
 
-recount workflow: accessing over 70,000 human RNA-seq samples with
-    Bioconductor • recountWorkflow
-
+recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor • recountWorkflow
+
 
 
 
 
 
-
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 Changelog • recountWorkflowChangelog • recountWorkflow
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Changelog 

       Source: NEWS.md
     

 
+    

+
recountWorkflow 1.29.2

+
SIGNIFICANT USER-VISIBLE CHANGES

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recountWorkflow 1.11.2

 
SIGNIFICANT USER-VISIBLE CHANGES

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recount
 

 
 

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- • recountWorkflow • recountWorkflow
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@@ -69,7 +69,7 @@ 
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index b05c6bd..7290af7 100644
--- a/reference/recountWorkflow-package.html
+++ b/reference/recountWorkflow-package.html
@@ -1,5 +1,5 @@
 
-recountWorkflow: recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor — recountWorkflow-package • recountWorkflowrecountWorkflow: recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor — recountWorkflow-package • recountWorkflow
@@ -17,7 +17,7 @@
       
       
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@@ -88,7 +88,7 @@ 
Author

 

 
 

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Site built with pkgdown 2.0.7.

+  
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