recount quick start guide
6 November 2019
Package
recount 1.12.1
1 Basics
1.1 Install recount
R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. recount is a R package available via the Bioconductor repository for packages. R can be installed on any operating system from CRAN after which you can install recount by using the following commands in your R session:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("recount")
## Check that you have a valid Bioconductor installation
BiocManager::valid()1.2 Required knowledge
recount is based on many other packages and in particular in those that have implemented the infrastructure needed for dealing with RNA-seq data. That is, packages like GenomicFeatures and rtracklayer that allow you to import the data. A recount user is not expected to deal with those packages directly but will need to be familiar with SummarizedExperiment to understand the results recount generates. It might also prove to be highly beneficial to check the
- DESeq2 package for performing differential expression analysis with the RangedSummarizedExperiment objects,
- DEFormats package for switching the objects to those used by other differential expression packages such as edgeR,
- derfinder package for performing annotation-agnostic differential expression analysis.
If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.
1.3 Asking for help
As package developers, we try to explain clearly how to use our packages and in which order to use the functions. But R and Bioconductor have a steep learning curve so it is critical to learn where to ask for help. The blog post quoted above mentions some but we would like to highlight the Bioconductor support site as the main resource for getting help: remember to use the recount tag and check the older posts. Other alternatives are available such as creating GitHub issues and tweeting. However, please note that if you want to receive help you should adhere to the posting guidelines. It is particularly critical that you provide a small reproducible example and your session information so package developers can track down the source of the error.
1.4 Workflow using recount
We have written a workflow on how to use recount that explains more details on how to use this package with other Bioconductor packages as well as the details on the actual counts provided by recount. Check it at f1000research.com/articles/6-1558/v1 or bioconductor.org/help/workflows/recountWorkflow/ (Collado-Torres, Nellore, and Jaffe, 2017).
1.5 Citing recount
We hope that recount will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation('recount')##
## Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen
## KD, Jaffe AE, Langmead B, Leek JT (2017). "Reproducible RNA-seq
## analysis using recount2." _Nature Biotechnology_. doi:
## 10.1038/nbt.3838 (URL: https://doi.org/10.1038/nbt.3838), <URL:
## http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html>.
##
## Collado-Torres L, Nellore A, Jaffe AE (2017). "recount workflow:
## Accessing over 70,000 human RNA-seq samples with Bioconductor
## [version 1; referees: 1 approved, 2 approved with reservations]."
## _F1000Research_. doi: 10.12688/f1000research.12223.1 (URL:
## https://doi.org/10.12688/f1000research.12223.1), <URL:
## https://f1000research.com/articles/6-1558/v1>.
##
## Ellis SE, Collado-Torres L, Jaffe AE, Leek JT (2018). "Improving the
## value of public RNA-seq expression data by phenotype prediction."
## _Nucl. Acids Res._. doi: 10.1093/nar/gky102 (URL:
## https://doi.org/10.1093/nar/gky102), <URL:
## https://doi.org/10.1093/nar/gky102>.
##
## Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen
## KD, Jaffe AE, Langmead B, Leek JT (2019). _Explore and download data
## from the recount project_. doi: 10.18129/B9.bioc.recount (URL:
## https://doi.org/10.18129/B9.bioc.recount),
## https://github.com/leekgroup/recount - R package version 1.12.1,
## <URL: http://www.bioconductor.org/packages/recount>.
##
## Frazee AC, Langmead B, Leek JT (2011). "ReCount: A multi-experiment
## resource of analysis-ready RNA-seq gene count datasets." _BMC
## Bioinformatics_. doi: 10.1186/1471-2105-12-449 (URL:
## https://doi.org/10.1186/1471-2105-12-449), <URL:
## https://doi.org/10.1186/1471-2105-12-449>.
##
## Razmara A, Ellis SE, Sokolowski DJ, Davis S, Wilson MD, Leek JT,
## Jaffe AE, Collado-Torres L (2019). "recount-brain: a curated
## repository of human brain RNA-seq datasets metadata." _bioRxiv_.
## doi: 10.1101/618025 (URL: https://doi.org/10.1101/618025), <URL:
## https://doi.org/10.1101/618025>.
##
## Imada E, Sanchez DF, Collado-Torres L, Wilks C, Matam T, Dinalankara
## W, Stupnikov A, Lobo-Pereira F, Yip C, Yasuzawa K, Kondo N, Itoh M,
## Suzuki H, Kasukawa T, Hon CC, de Hoon M, Shin JW, Carninci P,
## FANTOM-consortium, Jaffe AE, Leek JT, Favorov A, Franco GR, Langmead
## B, Marchionni L (2019). "Recounting the FANTOM Cage Associated
## Transcriptome." _bioRxiv_. doi: 10.1101/659490 (URL:
## https://doi.org/10.1101/659490), <URL:
## https://doi.org/10.1101/659490>.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.2 Quick start to using to recount
Main updates:
- As of January 30, 2017 the annotation used for the exon and gene counts is Gencode v25.
- As of January 12, 2018 transcripts counts are available via recount2 thanks to the work of Fu et al, bioRxiv, 2018. Disjoint exon counts (version 2) were also released as described in detail in the recount website documentation tab.
- As of April 29, 2019 FANTOM-CAT/recount2 annotation quantifications area available via recount2 thanks to the work by Imada, Sanchez, et al., bioRxiv, 2019.
Here is a very quick example of how to download a RangedSummarizedExperiment object with the gene counts for a 2 groups project (12 samples) with SRA study id SRP009615 using the recount package (Collado-Torres, Nellore, Kammers, Ellis, et al., 2017). The RangedSummarizedExperiment object is defined in the SummarizedExperiment (Morgan, Obenchain, Hester, and Pagès, 2017) package and can be used for differential expression analysis with different packages. Here we show how to use DESeq2 (Love, Huber, and Anders, 2014) to perform the differential expresion analysis.
This quick analysis is explained in more detail later on in this document. Further information about the recount project can be found in the main publication. Check the recount website for related publications.
## Load library
library('recount')
## Find a project of interest
project_info <- abstract_search('GSE32465')
## Download the gene-level RangedSummarizedExperiment data
download_study(project_info$project)
## Load the data
load(file.path(project_info$project, 'rse_gene.Rdata'))
## Browse the project at SRA
browse_study(project_info$project)
## View GEO ids
colData(rse_gene)$geo_accession
## Extract the sample characteristics
geochar <- lapply(split(colData(rse_gene), seq_len(nrow(colData(rse_gene)))), geo_characteristics)
## Note that the information for this study is a little inconsistent, so we
## have to fix it.
geochar <- do.call(rbind, lapply(geochar, function(x) {
if('cells' %in% colnames(x)) {
colnames(x)[colnames(x) == 'cells'] <- 'cell.line'
return(x)
} else {
return(x)
}
}))
## We can now define some sample information to use
sample_info <- data.frame(
run = colData(rse_gene)$run,
group = ifelse(grepl('uninduced', colData(rse_gene)$title), 'uninduced', 'induced'),
gene_target = sapply(colData(rse_gene)$title, function(x) { strsplit(strsplit(x,
'targeting ')[[1]][2], ' gene')[[1]][1] }),
cell.line = geochar$cell.line
)
## Scale counts by taking into account the total coverage per sample
rse <- scale_counts(rse_gene)
## Add sample information for DE analysis
colData(rse)$group <- sample_info$group
colData(rse)$gene_target <- sample_info$gene_target
## Perform differential expression analysis with DESeq2
library('DESeq2')
## Specify design and switch to DESeq2 format
dds <- DESeqDataSet(rse, ~ gene_target + group)
## Perform DE analysis
dds <- DESeq(dds, test = 'LRT', reduced = ~ gene_target, fitType = 'local')
res <- results(dds)
## Explore results
plotMA(res, main="DESeq2 results for SRP009615")
## Make a report with the results
library('regionReport')
DESeq2Report(dds, res = res, project = 'SRP009615',
intgroup = c('group', 'gene_target'), outdir = '.',
output = 'SRP009615-results')The recount project also hosts the necessary data to perform annotation-agnostic differential expression analyses with derfinder (Collado-Torres, Nellore, Frazee, Wilks, et al., 2017). An example analysis would like this:
## Define expressed regions for study SRP009615, only for chromosome Y
regions <- expressed_regions('SRP009615', 'chrY', cutoff = 5L,
maxClusterGap = 3000L)
## Compute coverage matrix for study SRP009615, only for chromosome Y
system.time( rse_ER <- coverage_matrix('SRP009615', 'chrY', regions) )
## Round the coverage matrix to integers
covMat <- round(assays(rse_ER)$counts, 0)
## Get phenotype data for study SRP009615
pheno <- colData(rse_ER)
## Complete the phenotype table with the data we got from GEO
m <- match(pheno$run, sample_info$run)
pheno <- cbind(pheno, sample_info[m, 2:3])
## Build a DESeqDataSet
dds_ers <- DESeqDataSetFromMatrix(countData = covMat, colData = pheno,
design = ~ gene_target + group)
## Perform differential expression analysis with DESeq2 at the ER-level
dds_ers <- DESeq(dds_ers, test = 'LRT', reduced = ~ gene_target,
fitType = 'local')
res_ers <- results(dds_ers)
## Explore results
plotMA(res_ers, main="DESeq2 results for SRP009615 (ER-level, chrY)")
## Create a more extensive exploratory report
DESeq2Report(dds_ers, res = res_ers,
project = 'SRP009615 (ER-level, chrY)',
intgroup = c('group', 'gene_target'), outdir = '.',
output = 'SRP009615-results-ER-level-chrY')3 Introduction
recount is an R package that provides an interface to the recount project website. This package allows you to download the files from the recount project and has helper functions for getting you started with differential expression analyses. This vignette will walk you through an example.
4 Sample DE analysis
This is a brief overview of what you can do with recount. In this particular example we will download data from the SRP009615 study which sequenced 12 samples as described in the previous link.
If you don’t have recount installed, please do so with:
install.packages("BiocManager")
BiocManager::install("recount")Next we load the required packages. Loading recount will load the required dependencies.
## Load recount R package
library('recount')Lets say that we don’t know the actual SRA accession number for this study but we do know a particular term which will help us identify it. If that’s the case, we can use the abstract_search() function to identify the study of interest as shown below.
## Find a project of interest
project_info <- abstract_search('GSE32465')
## Explore info
project_info## number_samples species
## 340 12 human
## abstract
## 340 Summary: K562-shX cells are made in an effort to validate TFBS data and ChIP-seq antibodies in Myers lab (GSE32465). K562 cells are transduced with lentiviral vector having Tet-inducible shRNA targeting a transcription factor gene. Cells with stable integration of shRNA constructs are selected using puromycin in growth media. Doxycycline is added to the growth media to induce the expression of shRNA and a red fluorescent protein marker. A successful shRNA cell line shows at least a 70% reduction in expression of the target transcription factor as measured by qPCR. For identification, we designated these cell lines as K562-shX, where X is the transcription factor targeted by shRNA and K562 denotes the parent cell line. For example, K562-shATF3 cells are K562 derived cells selected for stable integration of shRNA targeting the transcription factor ATF3 gene and showed at least a 70% reduction in the expression of ATF3 gene when measured by qPCR. Cells growing without doxycycline (uninduced) are used as a control to measure the change in expression of target transcription factor gene after induction of shRNA using doxycycline. For detailed growth and culturing protocols for these cells please refer to http://hudsonalpha.org/myers-lab/protocols . To identify the potential downstream targets of the candidate transcription factor, analyze the mRNA expression profile of the uninduced and induced K562-shX using RNA-seq. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Overall Design: Make K562-shX cells as described in the http://hudsonalpha.org/myers-lab/protocols . Measure the mRNA expression levels in uninduced K562-shX and induced K562-shX cells in two biological replicates using RNA-seq. Identify the potential downstream targets of the candidate transcription factor.
## project
## 340 SRP0096154.1 Download data
Now that we have a study that we are interested in, we can download the RangedSummarizedExperiment object (see SummarizedExperiment) with the data summarized at the gene level. The function download_study() helps us do this. If you are interested on how the annotation was defined, check reproduce_ranges() described in the Annotation section further down.
## Download the gene-level RangedSummarizedExperiment data
download_study(project_info$project)## 2019-11-06 22:17:32 downloading file rse_gene.Rdata to SRP009615## Load the data
load(file.path(project_info$project, 'rse_gene.Rdata'))
## Delete it if you don't need it anymore
unlink(project_info$project, recursive = TRUE)We can explore a bit this RangedSummarizedExperiment as shown below.
rse_gene## class: RangedSummarizedExperiment
## dim: 58037 12
## metadata(0):
## assays(1): counts
## rownames(58037): ENSG00000000003.14 ENSG00000000005.5 ...
## ENSG00000283698.1 ENSG00000283699.1
## rowData names(3): gene_id bp_length symbol
## colnames(12): SRR387777 SRR387778 ... SRR389083 SRR389084
## colData names(21): project sample ... title characteristics## This is the sample phenotype data provided by the recount project
colData(rse_gene)## DataFrame with 12 rows and 21 columns
## project sample experiment run
## <character> <character> <character> <character>
## SRR387777 SRP009615 SRS281685 SRX110461 SRR387777
## SRR387778 SRP009615 SRS281686 SRX110462 SRR387778
## SRR387779 SRP009615 SRS281687 SRX110463 SRR387779
## SRR387780 SRP009615 SRS281688 SRX110464 SRR387780
## SRR389077 SRP009615 SRS282369 SRX111299 SRR389077
## ... ... ... ... ...
## SRR389080 SRP009615 SRS282372 SRX111302 SRR389080
## SRR389081 SRP009615 SRS282373 SRX111303 SRR389081
## SRR389082 SRP009615 SRS282374 SRX111304 SRR389082
## SRR389083 SRP009615 SRS282375 SRX111305 SRR389083
## SRR389084 SRP009615 SRS282376 SRX111306 SRR389084
## read_count_as_reported_by_sra reads_downloaded
## <integer> <integer>
## SRR387777 30631853 30631853
## SRR387778 37001306 37001306
## SRR387779 40552001 40552001
## SRR387780 32466352 32466352
## SRR389077 27819603 27819603
## ... ... ...
## SRR389080 34856203 34856203
## SRR389081 23351679 23351679
## SRR389082 18144828 18144828
## SRR389083 24417368 24417368
## SRR389084 23060084 23060084
## proportion_of_reads_reported_by_sra_downloaded paired_end
## <numeric> <logical>
## SRR387777 1 FALSE
## SRR387778 1 FALSE
## SRR387779 1 FALSE
## SRR387780 1 FALSE
## SRR389077 1 FALSE
## ... ... ...
## SRR389080 1 FALSE
## SRR389081 1 FALSE
## SRR389082 1 FALSE
## SRR389083 1 FALSE
## SRR389084 1 FALSE
## sra_misreported_paired_end mapped_read_count auc
## <logical> <integer> <numeric>
## SRR387777 FALSE 28798572 1029494445
## SRR387778 FALSE 33170281 1184877985
## SRR387779 FALSE 37322762 1336528969
## SRR387780 FALSE 29970735 1073178116
## SRR389077 FALSE 24966859 893978355
## ... ... ... ...
## SRR389080 FALSE 32469994 1163527939
## SRR389081 FALSE 21904197 781685955
## SRR389082 FALSE 17199795 616048853
## SRR389083 FALSE 22499386 806323346
## SRR389084 FALSE 21957003 787795710
## sharq_beta_tissue sharq_beta_cell_type biosample_submission_date
## <character> <character> <character>
## SRR387777 blood k562 2011-12-05T15:40:03.870
## SRR387778 blood k562 2011-12-05T15:40:03.897
## SRR387779 blood k562 2011-12-05T15:40:03.910
## SRR387780 blood k562 2011-12-05T15:40:03.923
## SRR389077 blood k562 2011-12-13T11:26:05.720
## ... ... ... ...
## SRR389080 blood k562 2011-12-13T11:26:05.787
## SRR389081 blood k562 2011-12-13T11:26:05.800
## SRR389082 blood k562 2011-12-13T11:26:05.817
## SRR389083 blood k562 2011-12-13T11:26:05.830
## SRR389084 blood k562 2011-12-13T11:26:05.847
## biosample_publication_date biosample_update_date avg_read_length
## <character> <character> <integer>
## SRR387777 2011-12-07T09:29:59.890 2014-08-27T04:18:20.530 36
## SRR387778 2011-12-07T09:29:59.890 2014-08-27T04:18:21.053 36
## SRR387779 2011-12-07T09:29:59.890 2014-08-27T04:18:21.800 36
## SRR387780 2011-12-07T09:29:59.890 2014-08-27T04:18:22.320 36
## SRR389077 2011-12-13T11:26:06.663 2014-08-27T04:22:14.857 36
## ... ... ... ...
## SRR389080 2011-12-13T11:26:06.663 2014-08-27T04:22:15.933 36
## SRR389081 2011-12-13T11:26:06.663 2014-08-27T04:22:16.290 36
## SRR389082 2011-12-13T11:26:06.663 2014-08-27T04:22:16.647 36
## SRR389083 2011-12-13T11:26:06.663 2014-08-27T04:22:17.037 36
## SRR389084 2011-12-13T11:26:06.663 2014-08-27T04:22:17.473 36
## geo_accession bigwig_file
## <character> <character>
## SRR387777 GSM836270 SRR387777.bw
## SRR387778 GSM836271 SRR387778.bw
## SRR387779 GSM836272 SRR387779.bw
## SRR387780 GSM836273 SRR387780.bw
## SRR389077 GSM847561 SRR389077.bw
## ... ... ...
## SRR389080 GSM847564 SRR389080.bw
## SRR389081 GSM847565 SRR389081.bw
## SRR389082 GSM847566 SRR389082.bw
## SRR389083 GSM847567 SRR389083.bw
## SRR389084 GSM847568 SRR389084.bw
## title
## <character>
## SRR387777 K562 cells with shRNA targeting SRF gene cultured with no doxycycline (uninduced - UI), rep1.
## SRR387778 K562 cells with shRNA targeting SRF gene cultured with doxycycline for 48 hours (48 hr), rep1.
## SRR387779 K562 cells with shRNA targeting SRF gene cultured with no doxycycline (uninduced - UI), rep2.
## SRR387780 K562 cells with shRNA targeting SRF gene cultured with doxycycline for 48 hours (48 hr), rep2.
## SRR389077 K562 cells with shRNA targeting EGR1 gene cultured with no doxycycline (uninduced - UI), rep1.
## ... ...
## SRR389080 K562 cells with shRNA targeting EGR1 gene cultured with doxycycline for 72 hours (72 hr), rep2.
## SRR389081 K562 cells with shRNA targeting ATF3 gene cultured with no doxycycline (uninduced - UI), rep1.
## SRR389082 K562 cells with shRNA targeting ATF3 gene cultured with doxycycline for 24 hours (24 hr), rep1.
## SRR389083 K562 cells with shRNA targeting ATF3 gene cultured with no doxycycline (uninduced - UI), rep2.
## SRR389084 K562 cells with shRNA targeting ATF3 gene cultured with doxycycline for 24 hours (24 hr), rep2.
## characteristics
## <CharacterList>
## SRR387777 cells: K562,shRNA expression: no,treatment: Puromycin
## SRR387778 cells: K562,shRNA expression: yes, targeting SRF,treatment: Puromycin, doxycycline
## SRR387779 cells: K562,shRNA expression: no,treatment: Puromycin
## SRR387780 cells: K562,shRNA expression: yes targeting SRF,treatment: Puromycin, doxycycline
## SRR389077 cell line: K562,shRNA expression: no shRNA expression,treatment: Puromycin
## ... ...
## SRR389080 cell line: K562,shRNA expression: expressing shRNA targeting EGR1,treatment: Puromycin, doxycycline
## SRR389081 cell line: K562,shRNA expression: no shRNA expression,treatment: Puromycin
## SRR389082 cell line: K562,shRNA expression: expressing shRNA targeting ATF3,treatment: Puromycin, doxycycline
## SRR389083 cell line: K562,shRNA expression: no shRNA expression,treatment: Puromycin
## SRR389084 cell line: K562,shRNA expression: expressing shRNA targeting ATF3,treatment: Puromycin, doxycycline## At the gene level, the row data includes the gene Gencode ids, the gene
## symbols and the sum of the disjoint exons widths, which can be used for
## taking into account the gene length.
rowData(rse_gene)## DataFrame with 58037 rows and 3 columns
## gene_id bp_length symbol
## <character> <integer> <CharacterList>
## ENSG00000000003.14 ENSG00000000003.14 4535 TSPAN6
## ENSG00000000005.5 ENSG00000000005.5 1610 TNMD
## ENSG00000000419.12 ENSG00000000419.12 1207 DPM1
## ENSG00000000457.13 ENSG00000000457.13 6883 SCYL3
## ENSG00000000460.16 ENSG00000000460.16 5967 C1orf112
## ... ... ... ...
## ENSG00000283695.1 ENSG00000283695.1 61 NA
## ENSG00000283696.1 ENSG00000283696.1 997 NA
## ENSG00000283697.1 ENSG00000283697.1 1184 HSFX3
## ENSG00000283698.1 ENSG00000283698.1 940 NA
## ENSG00000283699.1 ENSG00000283699.1 60 MIR4481## At the exon level, you can get the gene Gencode ids from the names of:
# rowRanges(rse_exon)4.2 Finding phenotype information
Once we have identified the study of interest, we can use the browse_study() function to browse the study at the SRA website.
## Browse the project at SRA
browse_study(project_info$project)The SRA website includes an Experiments link which further describes each of the samples. From the information available for SRP009615 at NCBI we have some further sample information that we can save for use in our differential expression analysis. We can get some of this information from GEO. The function find_geo() finds the GEO accession id for a given SRA run accession id, which we can then use with geo_info() and geo_characteristics() to parse this information. The rse_gene object already has some of this information.
## View GEO ids
colData(rse_gene)$geo_accession## [1] "GSM836270" "GSM836271" "GSM836272" "GSM836273" "GSM847561" "GSM847562"
## [7] "GSM847563" "GSM847564" "GSM847565" "GSM847566" "GSM847567" "GSM847568"## Extract the sample characteristics
geochar <- lapply(split(colData(rse_gene), seq_len(nrow(colData(rse_gene)))), geo_characteristics)
## Note that the information for this study is a little inconsistent, so we
## have to fix it.
geochar <- do.call(rbind, lapply(geochar, function(x) {
if('cells' %in% colnames(x)) {
colnames(x)[colnames(x) == 'cells'] <- 'cell.line'
return(x)
} else {
return(x)
}
}))
## We can now define some sample information to use
sample_info <- data.frame(
run = colData(rse_gene)$run,
group = ifelse(grepl('uninduced', colData(rse_gene)$title), 'uninduced', 'induced'),
gene_target = sapply(colData(rse_gene)$title, function(x) { strsplit(strsplit(x,
'targeting ')[[1]][2], ' gene')[[1]][1] }),
cell.line = geochar$cell.line
)4.3 Predicted phenotype information
Shannon Ellis et at have predicted phenotypic information that can be used with any data from the recount project thanks to add_predictions(). Check that function for more details (Ellis, Collado-Torres, Jaffe, and Leek, 2018).
4.4 DE analysis
The recount project records the sum of the base level coverage for each gene (or exon). These raw counts have to be scaled and there are several ways in which you can choose to do so. The function scale_counts() helps you scale them in a way that is tailored to Rail-RNA output. If you prefer read counts without scaling, check the function read_counts(). Below we show some of the differences.
## Scale counts by taking into account the total coverage per sample
rse <- scale_counts(rse_gene)
##### Details about counts #####
## Scale counts to 40 million mapped reads. Not all mapped reads are in exonic
## sequence, so the total is not necessarily 40 million.
colSums(assays(rse)$counts) / 1e6## SRR387777 SRR387778 SRR387779 SRR387780 SRR389077 SRR389078 SRR389079
## 30.26702 29.07199 35.38355 34.65798 23.36050 22.62014 34.74629
## SRR389080 SRR389081 SRR389082 SRR389083 SRR389084
## 35.20971 33.41459 36.78133 34.05013 33.95469## Compute read counts
rse_read_counts <- read_counts(rse_gene)
## Difference between read counts and number of reads downloaded by Rail-RNA
colSums(assays(rse_read_counts)$counts) / 1e6 -
colData(rse_read_counts)$reads_downloaded / 1e6## SRR387777 SRR387778 SRR387779 SRR387780 SRR389077 SRR389078 SRR389079
## -8.839994 -12.892541 -7.536284 -6.497571 -13.239233 -15.816168 -14.775568
## SRR389080 SRR389081 SRR389082 SRR389083 SRR389084
## -6.274001 -5.054220 -2.328694 -5.265160 -4.421728## Check the help page for read_counts() for more detailsWe are almost ready to perform our differential expression analysis. Lets just add the information we recovered GEO about these samples.
## Add sample information for DE analysis
colData(rse)$group <- sample_info$group
colData(rse)$gene_target <- sample_info$gene_targetNow that the RangedSummarizedExperiment is complete, we can use DESeq2 or another package to perform the differential expression test. Note that you can use DEFormats for switching between formats if you want to use another package, like edgeR.
In this particular analysis, we’ll test whether there is a group difference adjusting for the gene target.
## Perform differential expression analysis with DESeq2
library('DESeq2')
## Specify design and switch to DESeq2 format
dds <- DESeqDataSet(rse, ~ gene_target + group)## converting counts to integer mode## Perform DE analysis
dds <- DESeq(dds, test = 'LRT', reduced = ~ gene_target, fitType = 'local')## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingres <- results(dds)We can now use functions from DESeq2 to explore the results. For more details check the DESeq2 vignette. For example, we can make a MA plot as shown in Figure 1.
## Explore results
plotMA(res, main="DESeq2 results for SRP009615")