RNA-seq workflow: gene-level exploratory analysis and differential expression
CSAMA 2019 version
19 June 2018
Contents
- 1 Abstract
- 2 Introduction
- 3 Preparing count matrices
- 4 The DESeqDataSet object, sample information and the design formula
- 5 Exploratory analysis and visualization
- 6 Differential expression analysis
- 7 Plotting results
- 8 Removing hidden batch effects
- 9 Session information
- References
1 Abstract
We walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were quantified with respect to a reference transcriptome, and prepare a count matrix which tallies the number of RNA-seq fragments mapped to each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis (DGE) and visually explore the results.
2 Introduction
Bioconductor has many packages which support analysis of high-throughput sequence data, including RNA sequencing (RNA-seq). The packages which we will use in this workflow include core packages maintained by the Bioconductor core team for storing genomic data sets and working with gene annotations (Huber et al. 2015). We will also use contributed packages for statistical analysis and visualization of sequencing data.
A conductor ensures the orchestra plays in harmony. (source)
Through scheduled releases every 6 months, the Bioconductor project ensures that all the packages within a release will work together in harmony (hence the “conductor” metaphor). The packages used in this workflow are loaded with the library function and can be installed by following the Bioconductor installation instructions.
A published (but essentially similar) version of this workflow, including reviewer reports and comments is available at F1000Research.
If you have questions about this workflow or any Bioconductor
software, please post these to the
Bioconductor support site.
If the questions concern a specific package, you can tag the post with
the name of the package, or for general questions about the workflow,
tag the post with rnaseqgene. Note the
posting guide
for crafting an optimal question for the support site.
2.1 Experimental data
The data used in this workflow is stored in an R package, airway2, containing quantification data for eight RNA-seq samples. The quantification files summarize an RNA-seq experiment wherein airway smooth muscle cells were treated with dexamethasone, a synthetic glucocorticoid steroid with anti-inflammatory effects (Himes et al. 2014). Glucocorticoids are used, for example, by people with asthma to reduce inflammation of the airways. In the experiment, four primary human airway smooth muscle cell lines were treated with 1 micromolar dexamethasone for 18 hours. For each of the four cell lines, we have a treated and an untreated sample. For more description of the experiment see the PubMed entry 24926665 and for raw data see the GEO entry GSE52778. An alternative version of the workflow uses the airway package, but this version uses the newer airway2 because it contains Salmon quantification files, which will be discussed later.
3 Preparing count matrices
As input, the count-based statistical methods, such as DESeq2 (Love, Huber, and Anders 2014), edgeR (Robinson, McCarthy, and Smyth 2009), limma with the voom method (Law et al. 2014), DSS (Wu, Wang, and Wu 2013), EBSeq (Leng et al. 2013) and baySeq (Hardcastle and Kelly 2010), expect data as obtained, e.g., from RNA-seq or another high-throughput sequencing experiment, in the form of a matrix of counts. The value in the i-th row and the j-th column of the matrix tells how many reads (or fragments, for paired-end RNA-seq) have been assigned to gene i in sample j. Analogously, for other types of assays, the rows of the matrix might correspond e.g., to binding regions (with ChIP-Seq), species of bacteria (with metagenomic datasets), or peptide sequences (with quantitative mass spectrometry).
The values in the matrix should be counts of sequencing reads/fragments. This is important for the statistical models used by DESeq2 and edgeR to hold, as only counts allow assessing the measurement precision correctly. It is important to not provide counts that were pre-normalized for sequencing depth/library size, as the statistical model is most powerful when applied to un-normalized counts and is designed to account for library size differences internally.
3.1 Transcript abundances and the tximport pipeline
In this workflow, we will use transcript abundances as quantified by the
Salmon (Patro et al. 2017)
software package. Salmon and other methods, such as
Sailfish (Patro, Mount, and Kingsford 2014),
kallisto (Bray et al. 2016), or
RSEM (Li and Dewey 2011),
estimate the relative abundances of all (known, annotated) transcripts
without aligning reads. Because estimating the abundance of the
transcripts involves an inference step, the counts are estimated.
Most methods either use a statistical framework called
Expectation-Maximization or Bayesian techniques to estimate the
abundances and counts.
Following quantification, we will use the tximeta (or r Biocpkg("tximport") (Soneson, Love, and Robinson 2015))
package for assembling estimated count and offset matrices for use
with Bioconductor differential gene expression packages.
The advantages of using the transcript abundance quantifiers in conjunction with tximeta/tximport to produce gene-level count matrices and normalizing offsets, are: this approach corrects for any potential changes in gene length across samples (e.g. from differential isoform usage) (Trapnell et al. 2013); some of these methods are substantially faster and require less memory and disk usage compared to alignment-based methods; and it is possible to avoid discarding those fragments that can align to multiple genes with homologous sequence (Robert and Watson 2015). Note that transcript abundance quantifiers skip the generation of large files which store read alignments (SAM or BAM files), instead producing smaller files which store estimated abundances, counts and effective lengths per transcript. For more details, see the manuscript describing this approach (Soneson, Love, and Robinson 2015) and the tximport package vignette for software details.
A full tutorial on how to use the Salmon software for quantifying transcript abundance can be found here, but in the next section we will provide the specific command line code that was used to quantify the eight samples that will be used in this workflow.
3.2 Salmon quantification
The Salmon software logo)
We begin by providing Salmon with the sequence of all of the reference transcripts, which we will call the reference transcriptome. We used version 27 of the GENCODE human transcripts, downloaded from the GENCODE website. As of writing, GENCODE has already released newer versions, but for computational reproducibility, all previous versions are available from the website. On the command line, creating the transcriptome index looks like:
salmon index -i gencode.v27_salmon_0.8.2 -t gencode.v27.transcripts.fa.gzThe 0.8.2 refers to the version of Salmon that was used. As of writing,
Salmon is up to version 0.14.1 and it’s always best to use the
latest version of software available.
To quantify an individual sample, sample_01, the following command
can be used:
salmon quant -i gencode.v27_salmon_0.8.2 -p 6 --libType A \
--gcBias --biasSpeedSamp 5 \
-1 sample_01_1.fastq.gz -2 sample_01_2.fastq.gz \
-o sample_01In simple English, this command says to “quantify a sample using this transcriptome index, with 6 threads, using automatic library type detection, using GC bias correction (the bias speed part is now longer needed with current versions of Salmon), here are the first and second read, and use this output directory.” The output directory will be created if it doesn’t exist, though if earlier parts of the path do not exist, it will give an error. A single sample of human RNA-seq usually takes ~5 minutes with the GC bias correction.
Rather than writing the above command on the command line, for quantifying these eight samples, a simple R script was written to perform the following Salmon calls from R, looping over the samples. It is also possible to loop over files using a bash loop, or more advanced workflow management systems such as Snakemake (Köster and Rahmann 2012) or Nextflow (Di Tommaso et al. 2017).
3.3 Importing into R with tximport
Following quantification, we can use tximport to import the data
into R and perform statistical analysis using Bioconductor packages.
Normally, we would simply point tximport to the quant.sf files on
our machine. However, because we are distributing these files as part
of an R package, we have to do some extra steps, to figure out where
the R package, and so the files, are located on your machine.
The output directories from the above Salmon quantification calls has been
stored in the extdata directory of the R package called airway2.
library("devtools")
install_github("mikelove/airway2")The R function system.file can be used to find out where on your
computer the files from a package have been installed. Here we ask for
the full path to the extdata directory, where R packages store
external data, that is part of the airway2 package.
library("airway2")
dir <- system.file("extdata", package="airway2")
dir## [1] "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata"list.files(dir)## [1] "gencode.v27.annotation.tx2gene.csv.gz"
## [2] "multiqc_config.yaml"
## [3] "multiqc_report.html"
## [4] "quants"
## [5] "res_lfc-1_FDR-5.csv"
## [6] "SraRunTable.txt"The quantification directories are in the quants directory.
list.files(file.path(dir,"quants"))## [1] "SRR1039508" "SRR1039509" "SRR1039512" "SRR1039513" "SRR1039516"
## [6] "SRR1039517" "SRR1039520" "SRR1039521"The identifiers used here are the SRR identifiers from the
Sequence Read Archive. For this
experiment, we downloaded a table that links the SRR identifiers to
the sample information about each experiment. From this
Run Selector view
of the experiment, we clicked the button: RunInfo Table. This
downloads a file called SraRunTable.txt. We included this file in
the airway2 package, and we read it in now:
coldata <- read.delim(file.path(dir, "SraRunTable.txt"))
colnames(coldata)## [1] "AvgSpotLen" "BioSample" "Experiment"
## [4] "MBases" "MBytes" "Run"
## [7] "SRA_Sample" "Sample_Name" "cell_line"
## [10] "ercc_mix" "treatment" "Assay_Type"
## [13] "BioProject" "Center_Name" "Consent"
## [16] "DATASTORE_filetype" "DATASTORE_provider" "InsertSize"
## [19] "Instrument" "LibraryLayout" "LibrarySelection"
## [22] "LibrarySource" "LoadDate" "Organism"
## [25] "Platform" "ReleaseDate" "SRA_Study"
## [28] "cell_type" "source_name" "tissue"idx <- c("Run","cell_line","treatment")
coldata[,idx]## Run cell_line treatment
## 1 SRR1039508 N61311 Untreated
## 2 SRR1039509 N61311 Dexamethasone
## 3 SRR1039512 N052611 Untreated
## 4 SRR1039513 N052611 Dexamethasone
## 5 SRR1039516 N080611 Untreated
## 6 SRR1039517 N080611 Dexamethasone
## 7 SRR1039520 N061011 Untreated
## 8 SRR1039521 N061011 DexamethasoneFor simplicity we restrict to these three columns, and do a little cleanup of the column names and the levels of the factors.
coldata <- coldata[,idx]
colnames(coldata) <- c("run","cell","dex")
coldata$cell## [1] N61311 N61311 N052611 N052611 N080611 N080611 N061011 N061011
## Levels: N052611 N061011 N080611 N61311coldata$dex## [1] Untreated Dexamethasone Untreated Dexamethasone Untreated
## [6] Dexamethasone Untreated Dexamethasone
## Levels: Dexamethasone UntreatedWe can rename the levels of the dex column, but we have to be
careful that our new level names correspond 1-1 to the previous
levels. Here we replace Dexamethasone with trt and Untreated
with untrt. There is nothing wrong with the longer names, but
sometimes shorter factor levels are easier to work with, and as long
as the shorter levels can be understood by other analysts, it saves
keystrokes.
levels(coldata$dex)## [1] "Dexamethasone" "Untreated"levels(coldata$dex) <- c("trt","untrt")
levels(coldata$dex)## [1] "trt" "untrt"Note: it is prefered in R that the first level of a factor be the
reference level (e.g. control, or untreated samples), so we can
relevel the dex factor like so:
coldata$dex <- relevel(coldata$dex, "untrt")Now we can create a named vector that points to the Salmon
quantification files. Because we have gzipped the quants for
distribution, we point to quant.sf.gz. Normally would just be
quant.sf. (The importing software can import directly from gzipped
files.)
files <- file.path(dir,"quants",coldata$run,"quant.sf.gz")
names(files) <- coldata$run
all(file.exists(files))## [1] TRUEBecause we perform a gene-level analysis we need the mapping of transcripts to
genes for GENCODE v27. Since GENCODE uses Ensembl transcript identifiers, we use
the ensembldb package (Rainer, Gatto, and Weichenberger 2019) to extract these from one
of the available annotation databases. Pre-built EnsDb annotation databases
for all species for a large number of Ensembl releases are available through the
AnnotationHub resource. We thus load below an EnsDb database
with human annotations for Ensembl release 91, which corresponds to GENCODE
v27. The parameter localHub = TRUE ensures that we list and load only locally
stored resources, due to the very limited internet connectivity at the lab site.
library("AnnotationHub")
library("ensembldb")
ah <- AnnotationHub(localHub = TRUE)
ah_91 <- query(ah, "EnsDb.Hsapiens.v91")
ah_91## AnnotationHub with 1 record
## # snapshotDate(): 2019-05-20
## # names(): AH60773
## # $dataprovider: Ensembl
## # $species: Homo sapiens
## # $rdataclass: EnsDb
## # $rdatadateadded: 2017-12-21
## # $title: Ensembl 91 EnsDb for Homo Sapiens
## # $description: Gene and protein annotations for Homo Sapiens based on En...
## # $taxonomyid: 9606
## # $genome: GRCh38
## # $sourcetype: ensembl
## # $sourceurl: http://www.ensembl.org
## # $sourcesize: NA
## # $tags: c("EnsDb", "Ensembl", "Gene", "Transcript", "Protein",
## # "Annotation", "91", "AHEnsDbs")
## # retrieve record with 'object[["AH60773"]]'edb <- ah[[names(ah_91)]]We next define a data.frame that connects transcripts to genes. Note that the
information in tx2gene needs to directly match the reference transcriptome
used by Salmon.
tx2gene <- transcripts(edb, columns = c("tx_id_version", "gene_id", "tx_id"),
return.type = "data.frame")
colnames(tx2gene) <- c("TXNAME", "GENEID", "tx_id")
head(tx2gene)## TXNAME GENEID tx_id
## 1 ENST00000373020.8 ENSG00000000003 ENST00000373020
## 2 ENST00000496771.5 ENSG00000000003 ENST00000496771
## 3 ENST00000494424.1 ENSG00000000003 ENST00000494424
## 4 ENST00000612152.4 ENSG00000000003 ENST00000612152
## 5 ENST00000614008.4 ENSG00000000003 ENST00000614008
## 6 ENST00000373031.4 ENSG00000000005 ENST00000373031Finally the following line of code imports Salmon transcript
quantifications into R, collapsing to the gene level using the
information in tx2gene.
library("tximport")
library("jsonlite")
library("readr")
txi <- tximport(files, type="salmon", tx2gene=tx2gene)The txi object is simply a list of matrices (and one character
vector):
names(txi)## [1] "abundance" "counts" "length"
## [4] "countsFromAbundance"txi$counts[1:3,1:3]## SRR1039508 SRR1039509 SRR1039512
## ENSG00000000003 718.5683 471.6148 913.2719
## ENSG00000000005 0.0000 0.0000 0.0000
## ENSG00000000419 466.0003 515.0000 621.9999txi$length[1:3,1:3]## SRR1039508 SRR1039509 SRR1039512
## ENSG00000000003 2041.7517 2014.3074 2049.5581
## ENSG00000000005 156.5000 156.5000 156.5000
## ENSG00000000419 880.4449 880.7965 879.3954txi$abundance[1:3,1:3]## SRR1039508 SRR1039509 SRR1039512
## ENSG00000000003 26.61683 19.12435 27.99310
## ENSG00000000005 0.00000 0.00000 0.00000
## ENSG00000000419 40.02907 47.75923 44.43429txi$countsFromAbundance## [1] "no"3.4 Alternative: Importing into R with tximeta
In the previous section we showed how to import the quantifications from Salmon into a list of matrices in R, using the tximport package. The tximeta package provides a wrapper around tximport, which in addition to importing the quantifications also generates a SummarizedExperiment object (see below). Moreover, it automatically identifies the transcriptome that was used for the quantification, and downlaods the corresponding annotation information. Note that this requires an internet connection. Here, we will illustrate how to use the tximeta package to import the data above.
The input to tximeta is a data frame with sample information. This data frame
must contain at least two columns, named names and files, and containing the
sample IDs and the paths to the Salmon output files, respectively. Since we
already have the coldata table above, we just have to rename the Run column
and add the paths to the Salmon files.
coldata2 <- coldata
colnames(coldata2)[colnames(coldata2) == "run"] <- "names"
coldata2$files <- file.path(dir,"quants",coldata2$names,"quant.sf.gz")
all(file.exists(coldata2$files))## [1] TRUEcoldata2## names cell dex
## 1 SRR1039508 N61311 untrt
## 2 SRR1039509 N61311 trt
## 3 SRR1039512 N052611 untrt
## 4 SRR1039513 N052611 trt
## 5 SRR1039516 N080611 untrt
## 6 SRR1039517 N080611 trt
## 7 SRR1039520 N061011 untrt
## 8 SRR1039521 N061011 trt
## files
## 1 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039508/quant.sf.gz
## 2 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039509/quant.sf.gz
## 3 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039512/quant.sf.gz
## 4 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039513/quant.sf.gz
## 5 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039516/quant.sf.gz
## 6 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039517/quant.sf.gz
## 7 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039520/quant.sf.gz
## 8 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/airway2/extdata/quants/SRR1039521/quant.sf.gzNext, we call tximeta, using the sample information data frame as input argument.
library("tximeta")
(txim <- tximeta(coldata = coldata2))## class: RangedSummarizedExperiment
## dim: 200401 8
## metadata(5): tximetaInfo quantInfo countsFromAbundance txomeInfo
## txdbInfo
## assays(3): counts abundance length
## rownames(200401): ENST00000456328.2 ENST00000450305.2 ...
## ENST00000387460.2 ENST00000387461.2
## rowData names(3): tx_id gene_id tx_name
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(3): names cell dexBy default, tximeta returns a SummarizedExperiment object with
transcript-level quantifications. Since we are interested in gene-level
differential expression analysis, we summarize the abundances on the gene level.
(txim <- summarizeToGene(txim))## class: RangedSummarizedExperiment
## dim: 58288 8
## metadata(5): tximetaInfo quantInfo countsFromAbundance txomeInfo
## txdbInfo
## assays(3): counts abundance length
## rownames(58288): ENSG00000000003.14 ENSG00000000005.5 ...
## ENSG00000284747.1 ENSG00000284748.1
## rowData names(1): gene_id
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(3): names cell dex3.5 Branching point
At this point, we have quantified the RNA-seq fragments and summarized to gene-level counts and abundances. This is a branching point where we could use a variety of Bioconductor packages for exploration and differential expression of the count data, including edgeR (Robinson, McCarthy, and Smyth 2009), limma with the voom method (Law et al. 2014), DSS (Wu, Wang, and Wu 2013), EBSeq (Leng et al. 2013) and baySeq (Hardcastle and Kelly 2010). Schurch et al. (2016) compared performance of different statistical methods for RNA-seq using a large number of biological replicates and can help users to decide which tools make sense to use, and how many biological replicates are necessary to obtain a certain sensitivity. We will continue using DESeq2 (Love, Huber, and Anders 2014) and edgeR (Robinson, McCarthy, and Smyth 2009) below.
3.6 SummarizedExperiment
The DESeq2 package will store the data and intermediate results in an object called a DESeqDataSet. This object builds on top of a general Bioconductor class of object called the SummarizedExperiment. We will therefore first introduce the SummarizedExperiment and we begin with a diagram of the three component pieces:
Figure 1: The component parts of a SummarizedExperiment object
The ‘assay’ (pink block) contains the matrix of counts, the ‘rowRanges’ or ‘rowData’ (blue block) contains information about the genomic ranges and the ‘colData’ (green block) contains information about the samples. The highlighted line in each block represents the first row (note that the first row of ‘colData’ lines up with the first column of the ‘assay’).
The SummarizedExperiment container is diagrammed in the Figure above
and discussed in the latest Bioconductor paper (Huber et al. 2015).
In our case we have created a single matrix named "counts" that
contains the estimated counts for each gene and sample, which is
stored in assay. It is also possible to store multiple matrices,
accessed with assays. The rowData for our object will keep track
of the intermediate calculations for each gene: e.g. the mean of
normalized counts, the dispersion estimates, the estimated
coefficients and their standard error, etc. We can also include
information about the location of each gene, in which case, we would
use rowRanges to access the GenomicRanges or GenomicRangesList
associated with each gene.The component parts of the
SummarizedExperiment are accessed with an R function of the same
name: assay (or assays), rowRanges/rowData and colData.
4 The DESeqDataSet object, sample information and the design formula
Bioconductor software packages often define and use a custom class for storing data that makes sure that all the needed data slots are consistently provided and fulfill the requirements. In addition, Bioconductor has general data classes (such as the SummarizedExperiment) that can be used to move data between packages. Additionally, the core Bioconductor classes provide useful functionality: for example, subsetting or reordering the rows or columns of a SummarizedExperiment automatically subsets or reorders the associated rowData and colData, which can help to prevent accidental sample swaps that would otherwise lead to spurious results. With SummarizedExperiment this is all taken care of behind the scenes.
In DESeq2, the custom class is called DESeqDataSet. One of the
main differences with SummarizedExperiment is that we will use the
counts function to access the assay matrix, and it is required
that this matrix stores non-negative integers (this is to prevent
mistaken use of the package for non-count data).
A second difference is that the DESeqDataSet has an associated
design formula. The experimental design is specified at the
beginning of the analysis, as it will inform many of the DESeq2
functions how to treat the samples in the analysis (one exception is
the size factor estimation, i.e., the adjustment for differing library
sizes, which does not depend on the design formula). The design
formula tells which columns in the sample information table (colData)
specify the experimental design and how these factors should be used
in the analysis.
The simplest design formula for differential expression would be
~ condition, where condition is a column in colData(dds) that
specifies which of two (or more groups) the samples belong to. For
the airway experiment, we will specify ~ cell + dex
meaning that we want to test for the effect of dexamethasone (dex)
controlling for the effect of different cell line (cell).
We can construct a DESeqDataSet object from the tximport object using the function DESeqDataSetFromTximport:
library("DESeq2")
dds <- DESeqDataSetFromTximport(txi, coldata, design = ~cell + dex)For running DESeq2 or edgeR models, you can use R’s formula notation to
express any fixed-effects experimental design.
Note that DESeq2 and edgeR use the same formula notation as, for instance, the lm
function of base R. If the research aim is to determine for which
genes the effect of treatment is different across groups, then
interaction terms can be included and tested using a design such as
~ group + treatment + group:treatment. See the manual page for
?results for more examples.
4.1 Creating a DGEList for use with edgeR
The edgeR package uses another type of data container, namely a DGEList object. It is just as easy to create a DGEList object using the count matrix and information about samples. We can additionally add information about the genes:
genetable <- data.frame(gene.id = rownames(txi$counts))Because we are using counts from tximport, we have a slightly different procedure to import them, which involves calculating a statistical offset that will account for differences in gene length across samples. The following code is from the tximport vignette in the edgeR section.
library("edgeR")
cts <- txi$counts
normMat <- txi$length
normMat <- normMat/exp(rowMeans(log(normMat)))
o <- log(calcNormFactors(cts/normMat)) + log(colSums(cts/normMat))Actually creating the DGEList object simply entails providing the counts, sample information, gene and information. The second line adds an appropriate offset.
dge <- DGEList(counts = cts,
samples = coldata,
genes = genetable)
dge <- scaleOffset(dge, t(t(log(normMat)) + o))
names(dge)## [1] "counts" "samples" "genes" "offset"A much simpler approach with respect to the amount of code is to generate counts-from-abundance using tximport, which correct for the gene length changes directly. Example for this code is:
txi2 <- tximport(files, type="salmon", tx2gene=tx2gene,
countsFromAbundance="lengthScaledTPM")
dge2 <- DGEList(counts = txi2$counts,
samples = coldata,
genes = genetable)
names(dge2)## [1] "counts" "samples" "genes"The counts + offset method and the counts-from-abundance method are not statistically identical (obviously), but tend to output roughly similar groups of genes. In this dataset, using counts-from-abundance has 96% of genes in common with count + offset at a target 5% FDR.
Just like the SummarizedExperiment and the DESeqDataSet the DGEList contains all the information we need: the count matrix, information about the samples (columns of the count matrix), and information about the genes (rows of the count matrix).
5 Exploratory analysis and visualization
There are two separate paths in this workflow; the one we will see first involves transformations of the counts in order to visually explore sample relationships. In the second part, we will go back to the original raw counts for statistical testing. This is critical because the statistical testing methods rely on original count data (not scaled or transformed) for calculating the precision of measurements.
5.1 Pre-filtering the dataset
Our count matrix with our DESeqDataSet contains many rows with only zeros, and additionally many rows with only a few fragments in total. In order to reduce the size of the object, and to increase the speed of our functions, we can remove the rows that have no or nearly no information about the amount of gene expression. Here we apply the most minimal filtering rule: removing rows of the DESeqDataSet that have no counts, or only a single count across all samples. Additional weighting/filtering to improve power is applied at a later step in the workflow.
nrow(dds)## [1] 58243dds <- dds[ rowSums(counts(dds)) > 1, ]
nrow(dds)## [1] 32748Here, we also filter the DGEList for edgeR in the same way. Note, however, that edgeR does not apply filtering internally, and for this reason, typically a stronger prefiltering criterion is used at this stage for edgeR.
dge <- dge[rowSums(round(dge$counts)) > 1, ]
all(rownames(dge) == rownames(dds))## [1] TRUEWe use the above code mostly for aiding comparison with DESeq2 in later sections. The recommended filtering code for edgeR (here not evaluated) is as follows:
dge <- dge[filterByExpr(dge),]5.2 Variance stabilizing transformations
Many common statistical methods for exploratory analysis of multidimensional data, for example clustering and principal components analysis (PCA), work best for data that generally has the same range of variance at different ranges of the mean values. When the expected amount of variance is approximately the same across different mean values, the data is said to be homoskedastic. For RNA-seq counts, however, the expected variance grows with the mean. For example, if one performs PCA directly on a matrix of counts or normalized counts (e.g. correcting for differences in sequencing depth), the resulting plot typically depends mostly on the genes with highest counts because they show the largest absolute differences between samples. A simple and often used strategy to avoid this is to take the logarithm of the normalized count values plus a pseudocount of 1; however, depending on the choice of pseudocount, now the genes with the very lowest counts will contribute a great deal of noise to the resulting plot, because taking the logarithm of small counts actually inflates their variance. We can quickly show this property of counts with some simulated data (here, Poisson counts with a range of lambda from 0.1 to 100). We plot the standard deviation of each row (genes) against the mean:
lambda <- 10^seq(from = -1, to = 2, length = 1000)
cts <- matrix(rpois(1000*100, lambda), ncol = 100)
library("vsn")
meanSdPlot(cts, ranks = FALSE)
Figure 2: SD over mean plot for Poisson-distributed counts
And for logarithm-transformed counts after adding a pseudocount of 1:
log.cts.one <- log2(cts + 1)
meanSdPlot(log.cts.one, ranks = FALSE)
Figure 3: SD over mean plot for log(x+1) transformed counts
The logarithm with a small pseudocount amplifies differences when the values are close to 0. The low count genes with low signal-to-noise ratio will overly contribute to sample-sample distances and PCA plots.
As a solution, DESeq2 offers two transformations for count data that stabilize the variance across the mean: (1) the the variance stabilizing transformation (VST) for negative binomial data with a dispersion-mean trend (Anders and Huber 2010), implemented in the vst function, and (2) the regularized-logarithm transformation or rlog (Love, Huber, and Anders 2014).
For genes with high counts, the VST and rlog will give similar result to the ordinary log2 transformation of normalized counts. For genes with lower counts, however, the values are shrunken towards the genes’ averages across all samples. The VST or rlog-transformed data then becomes approximately homoskedastic, and can be used directly for computing distances between samples, making PCA plots, or as input to downstream methods which perform best with homoskedastic data.
Which transformation to choose? Mike tends to prefer the VST. The VST is much faster to compute via the function vst and is less sensitive to high count outliers than the rlog. Perhaps further development on the rlog would make it less sensitive to outliers. (Why have the rlog at all then? In the DESeq2 paper, the rlog sometimes outperformed the VST in terms of recovering clusters when there was a large range of sequencing depth across samples e.g. x10 from lowest to highest sequencing depth.)
Note that the two transformations offered by DESeq2 are provided for applications other than differential testing. For differential testing we recommend the DESeq function applied to raw counts, as described later in this workflow, which also takes into account the dependence of the variance of counts on the mean value during the dispersion estimation step.
Both transformation functions return an object based on the SummarizedExperiment class that contains the transformed values in its assay slot.
vsd <- vst(dds, blind = FALSE)
head(assay(vsd), 3)## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003 10.221687 9.978170 10.277697 10.113774 10.521661
## ENSG00000000419 9.851450 10.042274 9.945383 9.946456 9.892338
## ENSG00000000457 9.615346 9.497622 9.538941 9.603572 9.447866
## SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003 10.312734 10.409570 10.121840
## ENSG00000000419 10.019459 9.830019 9.980094
## ENSG00000000457 9.572822 9.639053 9.613115meanSdPlot(assay(vsd), ranks = FALSE)
Figure 4: SD over mean plot for VST values from counts used in this workflow
rld <- rlog(dds, blind = FALSE)
head(assay(rld), 3)## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003 9.501184 9.184020 9.571559 9.364104 9.867477
## ENSG00000000419 8.907946 9.163535 9.035810 9.037123 8.963851
## ENSG00000000457 8.391302 8.217315 8.279013 8.373579 8.139702
## SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003 9.615245 9.732977 9.373479
## ENSG00000000419 9.134549 8.878498 9.082016
## ENSG00000000457 8.329449 8.425159 8.387897meanSdPlot(assay(rld), ranks = FALSE)
Figure 5: SD over mean plot for rlog-transformed counts from this workflow
In the above function calls, we specified blind = FALSE, which means
that differences between cell lines and treatment (the variables in
the design) will not contribute to the expected variance-mean trend of
the experiment. The experimental design is not used directly in the
transformation, only in estimating the global amount of variability in
the counts. For a fully unsupervised transformation, one can set
blind = TRUE (which is the default).
To show the effect of the transformation, in the figure below we plot the first sample against the second, first simply using the log2 function of normalized counts (after adding 1, to avoid taking the log of zero), and then using the VST and rlog-transformed values.
library("dplyr")
library("ggplot2")
library("hexbin")
ntd <- normTransform(dds)
df <- bind_rows(
as_data_frame(assay(ntd)[, 1:2]) %>% mutate(transformation = "log2(x + 1)"),
as_data_frame(assay(vsd)[, 1:2]) %>% mutate(transformation = "vst"),
as_data_frame(assay(rld)[, 1:2]) %>% mutate(transformation = "rlog"))
colnames(df)[1:2] <- c("x", "y")
ggplot(df, aes(x = x, y = y)) + geom_hex(bins = 80) +
coord_fixed() + facet_grid(. ~ transformation)
Figure 6: Scatterplot of transformed counts from two samples
Shown are scatterplots using the log2 transform of normalized counts (left), using the VST (middle), and using the rlog (right). While the rlog is on roughly the same scale as the log2 counts, the VST has a upward shift for the smaller values. It is the differences between samples (deviation from y = x in these scatterplots) which will contribute to the distance calculations and the PCA plot.
We can see how genes with low counts (bottom left-hand corner) seem to be excessively variable on the ordinary logarithmic scale, while the VST and rlog compress differences for the low count genes for which the data provide little information about differential expression.
5.3 Sample distances
A useful first step in an RNA-seq analysis is often to assess overall similarity between samples: Which samples are similar to each other, which are different? Does this fit to the expectation from the experiment’s design?
We use the R function dist to calculate the Euclidean distance between samples. To ensure we have a roughly equal contribution from all genes, we use it on the VST data. We need to transpose the matrix of values using t, because the dist function expects the different samples to be rows of its argument, and different dimensions (here, genes) to be columns.
sampleDists <- dist(t(assay(vsd)))
sampleDists## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517
## SRR1039509 37.50000
## SRR1039512 30.33886 42.43052
## SRR1039513 48.29300 35.04028 39.50752
## SRR1039516 33.17285 44.70749 32.27577 48.94303
## SRR1039517 48.23758 39.22630 43.95400 38.24421 37.54620
## SRR1039520 30.42424 44.44568 28.41660 45.46122 34.74551 47.58855
## SRR1039521 48.59103 35.80141 44.45068 29.90182 49.61229 39.00323
## SRR1039520
## SRR1039509
## SRR1039512
## SRR1039513
## SRR1039516
## SRR1039517
## SRR1039520
## SRR1039521 40.63466We visualize the distances in a heatmap in a figure below, using the function pheatmap from the pheatmap package.
library("pheatmap")
library("RColorBrewer")In order to plot the sample distance matrix with the rows/columns
arranged by the distances in our distance matrix,
we manually provide sampleDists to the clustering_distance
argument of the pheatmap function.
Otherwise the pheatmap function would assume that the matrix contains
the data values themselves, and would calculate distances between the
rows/columns of the distance matrix, which is not desired.
We also manually specify a blue color palette using the
colorRampPalette function from the RColorBrewer package.
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$dex, vsd$cell, sep = " - ")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette(rev(brewer.pal(9, "Blues")))(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows = sampleDists,
clustering_distance_cols = sampleDists,
col = colors)
Figure 7: Heatmap of sample-to-sample distances using the VST values
Note that we have changed the row names of the distance matrix to contain treatment type and patient number instead of sample ID, so that we have all this information in view when looking at the heatmap.
Another option for calculating sample distances is to use the
Poisson Distance (Witten 2011), implemented in the
PoiClaClu package.
This measure of dissimilarity between counts
also takes the inherent variance
structure of counts into consideration when calculating the distances
between samples. The PoissonDistance function takes the original
count matrix (not normalized) with samples as rows instead of columns,
so we need to transpose the counts in dds.
library("PoiClaClu")
poisd <- PoissonDistance(t(counts(dds)))We plot the heatmap in a Figure below.
samplePoisDistMatrix <- as.matrix(poisd$dd)
rownames(samplePoisDistMatrix) <- paste(vsd$dex, vsd$cell, sep = " - ")
colnames(samplePoisDistMatrix) <- NULL
pheatmap(samplePoisDistMatrix,
clustering_distance_rows = poisd$dd,
clustering_distance_cols = poisd$dd,
col = colors)
Figure 8: Heatmap of sample-to-sample distances using the Poisson Distance
5.4 PCA plot
Another way to visualize sample-to-sample distances is a principal components analysis (PCA). In this ordination method, the data points (here, the samples) are projected onto the 2D plane such that they spread out in the two directions that explain most of the differences (figure below). The x-axis is the direction that separates the data points the most. The values of the samples in this direction are written PC1. The y-axis is a direction (it must be orthogonal to the first direction) that separates the data the second most. The values of the samples in this direction are written PC2. The percent of the total variance that is contained in the direction is printed in the axis label. Note that these percentages do not add to 100%, because there are more dimensions that contain the remaining variance (although each of these remaining dimensions will explain less than the two that we see).
plotPCA(vsd, intgroup = c("dex", "cell"))
Figure 9: PCA plot using the VST values
Each unique combination of treatment and cell line is given its own color.
Here, we have used the function plotPCA that comes with DESeq2.
The two terms specified by intgroup are the interesting groups for
labeling the samples; they tell the function to use them to choose
colors. We can also build the PCA plot from scratch using the
ggplot2 package (Wickham 2009).
This is done by asking the plotPCA function
to return the data used for plotting rather than building the plot.
See the ggplot2 documentation
for more details on using ggplot.
pcaData <- plotPCA(vsd, intgroup = c("dex", "cell"), returnData = TRUE)
pcaData## PC1 PC2 group dex cell name
## SRR1039508 -13.733856 -1.8922672 untrt:N61311 untrt N61311 SRR1039508
## SRR1039509 7.850058 0.2823076 trt:N61311 trt N61311 SRR1039509
## SRR1039512 -8.854522 -3.9485044 untrt:N052611 untrt N052611 SRR1039512
## SRR1039513 13.978649 -3.6144982 trt:N052611 trt N052611 SRR1039513
## SRR1039516 -11.919822 10.3630468 untrt:N080611 untrt N080611 SRR1039516
## SRR1039517 8.798182 13.9783088 trt:N080611 trt N080611 SRR1039517
## SRR1039520 -10.334421 -8.2904258 untrt:N061011 untrt N061011 SRR1039520
## SRR1039521 14.215733 -6.8779675 trt:N061011 trt N061011 SRR1039521percentVar <- round(100 * attr(pcaData, "percentVar"))We can then use these data to build up a second plot in a figure below, specifying that the color of the points should reflect dexamethasone treatment and the shape should reflect the cell line.
ggplot(pcaData, aes(x = PC1, y = PC2, color = dex, shape = cell)) +
geom_point(size = 3) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
coord_fixed()
Figure 10: PCA plot using the VST values with custom ggplot2 code
Here we specify cell line (plotting symbol) and dexamethasone treatment (color).
From the PCA plot, we see that the differences between cells (the
different plotting shapes) are considerable, though not stronger than the differences due to
treatment with dexamethasone (red vs blue color). This shows why it will be important to
account for this in differential testing by using a paired design
(“paired”, because each dex treated sample is paired with one
untreated sample from the same cell line). We are already set up for
this design by assigning the formula ~ cell + dex earlier.
5.5 MDS plot
Another plot, very similar to the PCA plot, can be made using the multidimensional scaling (MDS) function in base R. This is useful when we don’t have a matrix of data, but only a matrix of distances. Here we compute the MDS for the distances calculated from the VST data and plot these in a figure below.
mds <- as.data.frame(colData(vsd)) %>%
cbind(cmdscale(sampleDistMatrix))
ggplot(mds, aes(x = `1`, y = `2`, color = dex, shape = cell)) +
geom_point(size = 3) + coord_fixed()
Figure 11: MDS plot using VST values
In a figure below we show the same plot for the PoissonDistance:
mdsPois <- as.data.frame(colData(dds)) %>%
cbind(cmdscale(samplePoisDistMatrix))
ggplot(mdsPois, aes(x = `1`, y = `2`, color = dex, shape = cell)) +
geom_point(size = 3) + coord_fixed()
Figure 12: MDS plot using the Poisson Distance
6 Differential expression analysis
6.1 Performing differential expression testing with DESeq2
As we have already specified an experimental design when we created the DESeqDataSet, we can run the differential expression pipeline on the raw counts with a single call to the function DESeq:
dds <- DESeq(dds)This function will print out a message for the various steps it
performs. These are described in more detail in the manual page for
DESeq, which can be accessed by typing ?DESeq. Briefly these are:
the estimation of size factors (controlling for differences in the
sequencing depth of the samples), the estimation of
dispersion values for each gene, and fitting a generalized linear model.
A DESeqDataSet is returned that contains all the fitted parameters within it, and the following section describes how to extract out results tables of interest from this object.
6.2 Building the results table
Calling results without any arguments will extract the estimated
log2 fold changes and p values for the last variable in the design
formula. If there are more than 2 levels for this variable, results
will extract the results table for a comparison of the last level over
the first level. The comparison is printed at the top of the output:
dex trt vs untrt.
res <- results(dds)
res## log2 fold change (MLE): dex trt vs untrt
## Wald test p-value: dex trt vs untrt
## DataFrame with 32748 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## ENSG00000000003 748.016996954203 -0.353757056733999 0.106951396185109
## ENSG00000000419 522.868589367106 0.205250588223682 0.124923884026677
## ENSG00000000457 320.347867978525 0.0279097457757637 0.156868246893547
## ENSG00000000460 80.8581320979381 -0.216903040608035 0.302150308730343
## ENSG00000000938 0.308650211610144 -1.37447285564756 3.50375579763067
## ... ... ... ...
## ENSG00000284738 4.40253030246072 0.665763550870547 0.98942622304473
## ENSG00000284740 2.95721859427428 -2.34387920763935 1.43542703692369
## ENSG00000284744 0.989456372823932 -0.216737576253391 2.65649387440176
## ENSG00000284747 14.2137963831892 1.27009488804022 0.68686593415504
## ENSG00000284748 0.669382049804924 -0.0528044401203826 3.08832551896903
## stat pvalue padj
## <numeric> <numeric> <numeric>
## ENSG00000000003 -3.30764318515042 0.000940846012638153 0.00652127155153376
## ENSG00000000419 1.64300517729542 0.100381862078792 0.269633053284747
## ENSG00000000457 0.17791838902046 0.858787068904449 0.939497621921653
## ENSG00000000460 -0.717864699590996 0.472840715549353 0.711209893537231
## ENSG00000000938 -0.392285574404761 0.694847221158491 NA
## ... ... ... ...
## ENSG00000284738 0.672878417171736 0.501024644033436 NA
## ENSG00000284740 -1.6328793782947 0.102494368051487 NA
## ENSG00000284744 -0.0815878321203359 0.934974477595501 NA
## ENSG00000284747 1.84911614462675 0.0644410436338607 0.196342147214049
## ENSG00000284748 -0.0170980810785808 0.986358369766931 NAWe could have equivalently produced this results table with the
following more specific command. Because dex is the last variable in
the design, we could optionally leave off the contrast argument to extract
the comparison of the two levels of dex.
res <- results(dds, contrast = c("dex", "trt", "untrt"))As res is a DataFrame object, it carries metadata
with information on the meaning of the columns:
mcols(res, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized counts for all samples
## log2FoldChange results log2 fold change (MLE): dex trt vs untrt
## lfcSE results standard error: dex trt vs untrt
## stat results Wald statistic: dex trt vs untrt
## pvalue results Wald test p-value: dex trt vs untrt
## padj results BH adjusted p-valuesThe first column, baseMean, is a just the average of the normalized
count values, divided by the size factors, taken over all samples in the
DESeqDataSet.
The remaining four columns refer to a specific contrast, namely the
comparison of the trt level over the untrt level for the factor
variable dex. We will find out below how to obtain other contrasts.
The column log2FoldChange is the effect size estimate. It tells us
how much the gene’s expression seems to have changed due to treatment
with dexamethasone in comparison to untreated samples. This value is
reported on a logarithmic scale to base 2: for example, a log2 fold
change of 1.5 means that the gene’s expression is increased by a
multiplicative factor of \(2^{1.5} \approx 2.82\).
Of course, this estimate has an uncertainty associated with it, which
is available in the column lfcSE, the standard error estimate for
the log2 fold change estimate. We can also express the uncertainty of
a particular effect size estimate as the result of a statistical
test. The purpose of a test for differential expression is to test
whether the data provides sufficient evidence to conclude that this
value is really different from zero. DESeq2 performs for each gene a
hypothesis test to see whether evidence is sufficient to decide
against the null hypothesis that there is zero effect of the treatment
on the gene and that the observed difference between treatment and
control was merely caused by experimental variability (i.e., the type
of variability that you can expect between different
samples in the same treatment group). As usual in statistics, the
result of this test is reported as a p value, and it is found in the
column pvalue. Remember that a p value indicates the probability
that a fold change as strong as the observed one, or even stronger,
would be seen under the situation described by the null hypothesis.
We can also summarize the results with the following line of code, which reports some additional information, that will be covered in later sections.
summary(res)##
## out of 32748 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 2345, 7.2%
## LFC < 0 (down) : 1920, 5.9%
## outliers [1] : 0, 0%
## low counts [2] : 16508, 50%
## (mean count < 11)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsNote that there are many genes with differential expression due to dexamethasone treatment at the FDR level of 10%. This makes sense, as the smooth muscle cells of the airway are known to react to glucocorticoid steroids. However, there are two ways to be more strict about which set of genes are considered significant:
- lower the false discovery rate threshold (the threshold on
padjin the results table) - raise the log2 fold change threshold from 0 using the
lfcThresholdargument of results
If we lower the false discovery rate threshold, we should also
inform the results() function about it, so that the function can use this
threshold for the optimal independent filtering that it performs:
res.05 <- results(dds, alpha = 0.05)
table(res.05$padj < 0.05)##
## FALSE TRUE
## 13383 3492If we want to raise the log2 fold change threshold, so that we test
for genes that show more substantial changes due to treatment, we
simply supply a value on the log2 scale. For example, by specifying
lfcThreshold = 1, we test for genes that show significant effects of
treatment on gene counts more than doubling or less than halving,
because \(2^1=2\).
resLFC1 <- results(dds, lfcThreshold = 1)
table(resLFC1$padj < 0.05)##
## FALSE TRUE
## 19227 188Sometimes a subset of the p values in res will be NA (“not
available”). This is DESeq’s way of reporting that all counts for
this gene were zero, and hence no test was applied. In addition, p
values can be assigned NA if the gene was excluded from analysis
because it contained an extreme count outlier. For more information,
see the outlier detection section of the DESeq2 vignette.
6.3 Independent hypothesis weighting within DESeq2
The results function in DESeq2 integrates with a new method for dealing with genes that have low counts and therefore low power for detecting differential gene expression. This method is called Independent Hypothesis Weighting (IHW) (Ignatiadis et al. 2016), and generalizes the idea of filtering low count genes to reduce the multiple testing burden. While the specifics of the method are beyond the scope of this workflow, we demonstrate its usage here:
library("IHW")
resIHW <- results(dds, filterFun=ihw)
summary(resIHW)##
## out of 32748 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 2405, 7.3%
## LFC < 0 (down) : 1921, 5.9%
## outliers [1] : 0, 0%
## [1] see 'cooksCutoff' argument of ?results
## see metadata(res)$ihwResult on hypothesis weightingThe results are similar but not identical to the results table built above, which uses a simple optimization of a filter over mean normalized counts. The new procedure results in a gain of 50 genes.
table(old=res$padj < .1, IHW=resIHW$padj < .1)## IHW
## old FALSE TRUE
## FALSE 11813 162
## TRUE 118 4147We can plot the weighting function which is fit by the IHW package:
plot(metadata(resIHW)$ihwResult)
Figure 13: IHW weighting function
6.4 Other comparisons
In general, the results for a comparison of any two levels of a
variable can be extracted using the contrast argument to
results. The user should specify three values: the name of the
variable, the name of the level for the numerator, and the name of the
level for the denominator. Here we extract results for the log2 of the
fold change of one cell line over another:
results(dds, contrast = c("cell", "N061011", "N61311"))## log2 fold change (MLE): cell N061011 vs N61311
## Wald test p-value: cell N061011 vs N61311
## DataFrame with 32748 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## ENSG00000000003 748.016996954203 0.26405210396441 0.152270063295407
## ENSG00000000419 522.868589367106 -0.0729290429325449 0.177572255294167
## ENSG00000000457 320.347867978525 0.139028755688018 0.222510619516051
## ENSG00000000460 80.8581320979381 -0.0494079532724753 0.423446388387593
## ENSG00000000938 0.308650211610144 0 4.9975798104491
## ... ... ... ...
## ENSG00000284738 4.40253030246072 -1.05831123904053 1.38923738352172
## ENSG00000284740 2.95721859427428 -1.3711188072016 2.00626873053414
## ENSG00000284744 0.989456372823932 0 3.97321970079478
## ENSG00000284747 14.2137963831892 0.226234777609869 0.983124140103056
## ENSG00000284748 0.669382049804924 2.45829279074629 4.39131190913349
## stat pvalue padj
## <numeric> <numeric> <numeric>
## ENSG00000000003 1.73410385633152 0.0828996574187213 0.403806369670076
## ENSG00000000419 -0.410700662734336 0.681292040920636 0.934174081426105
## ENSG00000000457 0.624818518731368 0.53209017498986 0.882460596343736
## ENSG00000000460 -0.116680540033915 0.907113211730804 0.983806501837068
## ENSG00000000938 0 1 NA
## ... ... ... ...
## ENSG00000284738 -0.761792945967021 0.446183591154616 NA
## ENSG00000284740 -0.6834173240773 0.494343176131897 NA
## ENSG00000284744 0 1 NA
## ENSG00000284747 0.230118220458053 0.817999906910469 0.969365306934029
## ENSG00000284748 0.559808285454123 0.575610211594061 NAThere are additional ways to build results tables for certain
comparisons after running DESeq once.
If results for an interaction term are desired, the name
argument of results should be used. Please see the
help page for the results function for details on the additional
ways to build results tables. In particular, the Examples section of
the help page for results gives some pertinent examples.
6.5 Annotation of RNA-seq results
One of the central steps in the analysis of genomic data is the annotation of
the quantified entities to biologically more relevant representations such as
transcripts, genes or proteins. Such annotations enable for example pathway
enrichment analyses and ease the interpretation of the results. Bioconductor
provides a large variety of annotation packages and resources, most of them
supporting the AnnotationDbi interface which enables a unified
way to retrieve annotations for given identifiers. The annotation resources from
Bioconductor range from web-based tools such as biomaRt to
packages working with pre-built databases containing all identifier mappings for
a certain species (the *.org packages such as org.Hs.eg.db) or providing
gene and transcript models, such as databases build by
GenomicFeatures (TxDb databases/packages) and
ensembldb (EnsDb databases/packages) (Rainer, Gatto, and Weichenberger 2019).
In this section we use the ensembldb package to annotate all tested genes
which are identified by Ensembl gene IDs. The Salmon quantification used in the
analysis leading to this table was based on GENCODE v27 transcripts which is
linked to Ensembl release 91. We thus load below an EnsDb database with human
annotations for Ensembl release 91 from the AnnotationHub
resource. The parameter localHub = TRUE ensures that we list and load only
locally stored resources (due to the very limited internet connectivity at the
lab site).
library("AnnotationHub")
library("ensembldb")
ah <- AnnotationHub(localHub = TRUE)
ah_91 <- query(ah, "EnsDb.Hsapiens.v91")
ah_91## AnnotationHub with 1 record
## # snapshotDate(): 2019-05-20
## # names(): AH60773
## # $dataprovider: Ensembl
## # $species: Homo sapiens
## # $rdataclass: EnsDb
## # $rdatadateadded: 2017-12-21
## # $title: Ensembl 91 EnsDb for Homo Sapiens
## # $description: Gene and protein annotations for Homo Sapiens based on En...
## # $taxonomyid: 9606
## # $genome: GRCh38
## # $sourcetype: ensembl
## # $sourceurl: http://www.ensembl.org
## # $sourcesize: NA
## # $tags: c("EnsDb", "Ensembl", "Gene", "Transcript", "Protein",
## # "Annotation", "91", "AHEnsDbs")
## # retrieve record with 'object[["AH60773"]]'#' Load the database
edb <- ah[[names(ah_91)]]If we had a working internet connection we could simply list all available
EnsDb databases with query(ah, "EnsDb") (after loading AnnotationHub with
localHub = FALSE). We can now use the edb variable to access the data in
the database. With the listColumns function we can for example list all of the
available annotation fields (columns) in the database.
listColumns(edb)## [1] "seq_name" "seq_length" "is_circular"
## [4] "gene_id" "entrezid" "exon_id"
## [7] "exon_seq_start" "exon_seq_end" "gene_name"
## [10] "gene_biotype" "gene_seq_start" "gene_seq_end"
## [13] "seq_strand" "seq_coord_system" "description"
## [16] "gene_id_version" "symbol" "name"
## [19] "value" "tx_id" "protein_id"
## [22] "protein_sequence" "protein_domain_id" "protein_domain_source"
## [25] "interpro_accession" "prot_dom_start" "prot_dom_end"
## [28] "tx_biotype" "tx_seq_start" "tx_seq_end"
## [31] "tx_cds_seq_start" "tx_cds_seq_end" "tx_support_level"
## [34] "tx_id_version" "tx_name" "exon_idx"
## [37] "uniprot_id" "uniprot_db" "uniprot_mapping_type"We could get data from any of these database columns by passing the respective
column name(s) along with the column parameter to any function retrieving
annotations from an EnsDb database. Gene-related annotations can be retrieved
with the genes function, that by default (and similar to the genes function
from the GenomicFeatures package) returns a GRanges object with
the genomic coordinates of the genes and additional annotation columns in the
GRanges’ metadata columns. Below we use the genes call to retrieve
annotations for all human genes. With the parameter return.type = "DataFrame"
we tell the function to return the result as a DataFrame instead of the
default GRanges.
anns <- genes(edb, return.type = "DataFrame")
rownames(anns) <- anns$gene_idWe can now combine the RNA-seq result with the annotation.
resAnn <- BiocGenerics::cbind(anns[BiocGenerics::rownames(res), ], res)
resAnn## DataFrame with 32748 rows and 18 columns
## gene_id gene_name
## <character> <character>
## ENSG00000000003 ENSG00000000003 TSPAN6
## ENSG00000000419 ENSG00000000419 DPM1
## ENSG00000000457 ENSG00000000457 SCYL3
## ENSG00000000460 ENSG00000000460 C1orf112
## ENSG00000000938 ENSG00000000938 FGR
## ... ... ...
## ENSG00000284738 ENSG00000284738 AL358472.5
## ENSG00000284740 ENSG00000284740 AL645728.2
## ENSG00000284744 ENSG00000284744 AL591163.1
## ENSG00000284747 ENSG00000284747 AL034417.4
## ENSG00000284748 ENSG00000284748 AL513220.1
## gene_biotype gene_seq_start
## <character> <integer>
## ENSG00000000003 protein_coding 100627109
## ENSG00000000419 protein_coding 50934867
## ENSG00000000457 protein_coding 169849631
## ENSG00000000460 protein_coding 169662007
## ENSG00000000938 protein_coding 27612064
## ... ... ...
## ENSG00000284738 antisense_RNA 153923337
## ENSG00000284740 transcribed_unprocessed_pseudogene 1503250
## ENSG00000284744 lincRNA 6767954
## ENSG00000284747 antisense_RNA 7991134
## ENSG00000284748 bidirectional_promoter_lncRNA 37596126
## gene_seq_end seq_name seq_strand seq_coord_system
## <integer> <character> <integer> <character>
## ENSG00000000003 100639991 X -1 chromosome
## ENSG00000000419 50958555 20 -1 chromosome
## ENSG00000000457 169894267 1 -1 chromosome
## ENSG00000000460 169854080 1 1 chromosome
## ENSG00000000938 27635277 1 -1 chromosome
## ... ... ... ... ...
## ENSG00000284738 153935240 1 1 chromosome
## ENSG00000284740 1509452 1 1 chromosome
## ENSG00000284744 6770038 1 1 chromosome
## ENSG00000284747 8005312 1 1 chromosome
## ENSG00000284748 37607336 1 1 chromosome
## description
## <character>
## ENSG00000000003 tetraspanin 6 [Source:HGNC Symbol;Acc:HGNC:11858]
## ENSG00000000419 dolichyl-phosphate mannosyltransferase subunit 1, catalytic [Source:HGNC Symbol;Acc:HGNC:3005]
## ENSG00000000457 SCY1 like pseudokinase 3 [Source:HGNC Symbol;Acc:HGNC:19285]
## ENSG00000000460 chromosome 1 open reading frame 112 [Source:HGNC Symbol;Acc:HGNC:25565]
## ENSG00000000938 FGR proto-oncogene, Src family tyrosine kinase [Source:HGNC Symbol;Acc:HGNC:3697]
## ... ...
## ENSG00000284738 NULL
## ENSG00000284740 NULL
## ENSG00000284744 NULL
## ENSG00000284747 NULL
## ENSG00000284748 NULL
## gene_id_version symbol entrezid baseMean
## <character> <character> <list> <numeric>
## ENSG00000000003 ENSG00000000003.14 TSPAN6 7105 748.016996954203
## ENSG00000000419 ENSG00000000419.12 DPM1 8813 522.868589367106
## ENSG00000000457 ENSG00000000457.13 SCYL3 57147 320.347867978525
## ENSG00000000460 ENSG00000000460.16 C1orf112 55732 80.8581320979381
## ENSG00000000938 ENSG00000000938.12 FGR 2268 0.308650211610144
## ... ... ... ... ...
## ENSG00000284738 ENSG00000284738.1 AL358472.5 101928059 4.40253030246072
## ENSG00000284740 ENSG00000284740.1 AL645728.2 NA 2.95721859427428
## ENSG00000284744 ENSG00000284744.1 AL591163.1 NA 0.989456372823932
## ENSG00000284747 ENSG00000284747.1 AL034417.4 NA 14.2137963831892
## ENSG00000284748 ENSG00000284748.1 AL513220.1 NA 0.669382049804924
## log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric>
## ENSG00000000003 -0.353757056733999 0.106951396185109 -3.30764318515042
## ENSG00000000419 0.205250588223682 0.124923884026677 1.64300517729542
## ENSG00000000457 0.0279097457757637 0.156868246893547 0.17791838902046
## ENSG00000000460 -0.216903040608035 0.302150308730343 -0.717864699590996
## ENSG00000000938 -1.37447285564756 3.50375579763067 -0.392285574404761
## ... ... ... ...
## ENSG00000284738 0.665763550870547 0.98942622304473 0.672878417171736
## ENSG00000284740 -2.34387920763935 1.43542703692369 -1.6328793782947
## ENSG00000284744 -0.216737576253391 2.65649387440176 -0.0815878321203359
## ENSG00000284747 1.27009488804022 0.68686593415504 1.84911614462675
## ENSG00000284748 -0.0528044401203826 3.08832551896903 -0.0170980810785808
## pvalue padj
## <numeric> <numeric>
## ENSG00000000003 0.000940846012638153 0.00652127155153376
## ENSG00000000419 0.100381862078792 0.269633053284747
## ENSG00000000457 0.858787068904449 0.939497621921653
## ENSG00000000460 0.472840715549353 0.711209893537231
## ENSG00000000938 0.694847221158491 NA
## ... ... ...
## ENSG00000284738 0.501024644033436 NA
## ENSG00000284740 0.102494368051487 NA
## ENSG00000284744 0.934974477595501 NA
## ENSG00000284747 0.0644410436338607 0.196342147214049
## ENSG00000284748 0.986358369766931 NAIf we had only a reduced set of genes to annotate, e.g. the set of the most
significant genes, it is faster and more elegant to extract annotations for the
subset of genes using the filter framework from ensembldb. Below we create a
top table by selecting the 20 genes with the smallest p-values.
resTop20 <- res[order(res$pvalue), ][1:20, ]To get only annotations for the selected genes we pass the filter expression
~ gene_id == rownames(resTop20) with the filter parameter to the genes
function. Filter expressions have to be written in the form of a formula
(i.e. starting with ~) and support any logical R expression and any database
column/field in the EnsDb database (use supportedFilters(edb) to list all
supported fields). Also, by specifying the respective field names with the
columns parameter we extract only the gene name (equivalent with the HGNC
symbol for most genes), description the gene biotype and the NCBI Entrezgene ID
from the database.
anns <- genes(edb, filter = ~ gene_id == rownames(resTop20),
columns = c("gene_name", "description", "gene_biotype",
"entrezid"), return.type = "DataFrame")Note that the order of the genes in the returned data.frame is not the
same as in resTop20. We thus below re-order the annotations to match the
order of genes in the top table and subsequently join the two data frames.
resTop20 <- cbind(anns[match(rownames(resTop20), anns$gene_id), ],
resTop20)Be aware that mappings between Ensembl and NCBI identifiers is not necessarily a
1:1 mapping. In our case we got for some Ensembl gene IDs more than one NCBI
Entrezgene ID. The column "entrezid" in our result table is thus a list with
eventually more than one Entrezgene ID in one row. Exporting such a table could
be problematic and we collapse therefore Entrezgene IDs in this columns into a
single character string, with multiple IDs separated by a semicolon (";").
resTop20$entrezid <- sapply(resTop20$entrezid, function(z) {
if (any(is.na(z))) z
else paste(z, collapse = ";")
})The 20 most significantly regulated genes in the present analysis are listed below.
| gene_name | description | gene_biotype | entrezid | gene_id | baseMean | log2FoldChange | lfcSE | stat | pvalue | padj |
|---|---|---|---|---|---|---|---|---|---|---|
| MAOA | monoamine oxidase A [Source:HGNC Symbol;Acc:HGNC:6833] | protein_coding | 4128 | ENSG00000189221 | 2422.4722 | 3.422182 | 0.1343714 | 25.46809 | 0 | 0 |
| DUSP1 | dual specificity phosphatase 1 [Source:HGNC Symbol;Acc:HGNC:3064] | protein_coding | 1843 | ENSG00000120129 | 3468.9003 | 2.967590 | 0.1202484 | 24.67883 | 0 | 0 |
| SAMHD1 | SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1 [Source:HGNC Symbol;Acc:HGNC:15925] | protein_coding | 25939 | ENSG00000101347 | 14201.8878 | 3.764005 | 0.1579738 | 23.82676 | 0 | 0 |
| GPX3 | glutathione peroxidase 3 [Source:HGNC Symbol;Acc:HGNC:4555] | protein_coding | 2878 | ENSG00000211445 | 12652.1625 | 3.767896 | 0.1652127 | 22.80634 | 0 | 0 |
| SERPINA3 | serpin family A member 3 [Source:HGNC Symbol;Acc:HGNC:16] | protein_coding | 12 | ENSG00000196136 | 2745.2239 | 3.240508 | 0.1437457 | 22.54335 | 0 | 0 |
| NEXN | nexilin F-actin binding protein [Source:HGNC Symbol;Acc:HGNC:29557] | protein_coding | 91624 | ENSG00000162614 | 5674.3163 | 1.991769 | 0.0896421 | 22.21912 | 0 | 0 |
| MT2A | metallothionein 2A [Source:HGNC Symbol;Acc:HGNC:7406] | protein_coding | 4502 | ENSG00000125148 | 3720.1844 | 2.226467 | 0.1054555 | 21.11287 | 0 | 0 |
| ZBTB16 | zinc finger and BTB domain containing 16 [Source:HGNC Symbol;Acc:HGNC:12930] | protein_coding | 7704 | ENSG00000109906 | 415.9916 | 6.325861 | 0.3032907 | 20.85742 | 0 | 0 |
| SPARCL1 | SPARC like 1 [Source:HGNC Symbol;Acc:HGNC:11220] | protein_coding | 8404 | ENSG00000152583 | 966.0441 | 4.417228 | 0.2194653 | 20.12723 | 0 | 0 |
| ADAMTS1 | ADAM metallopeptidase with thrombospondin type 1 motif 1 [Source:HGNC Symbol;Acc:HGNC:217] | protein_coding | 9510 | ENSG00000154734 | 29966.0657 | 2.233413 | 0.1117150 | 19.99205 | 0 | 0 |
| SORT1 | sortilin 1 [Source:HGNC Symbol;Acc:HGNC:11186] | protein_coding | 6272 | ENSG00000134243 | 5594.1519 | 2.187524 | 0.1111422 | 19.68220 | 0 | 0 |
| STEAP2 | STEAP2 metalloreductase [Source:HGNC Symbol;Acc:HGNC:17885] | protein_coding | 261729 | ENSG00000157214 | 3051.7626 | 2.001168 | 0.1024791 | 19.52758 | 0 | 0 |
| CCDC69 | coiled-coil domain containing 69 [Source:HGNC Symbol;Acc:HGNC:24487] | protein_coding | 26112 | ENSG00000198624 | 2043.6957 | 2.860487 | 0.1526328 | 18.74097 | 0 | 0 |
| KCTD12 | potassium channel tetramerization domain containing 12 [Source:HGNC Symbol;Acc:HGNC:14678] | protein_coding | 115207 | ENSG00000178695 | 2712.3559 | -2.533603 | 0.1371684 | -18.47075 | 0 | 0 |
| FGD4 | FYVE, RhoGEF and PH domain containing 4 [Source:HGNC Symbol;Acc:HGNC:19125] | protein_coding | 121512 | ENSG00000139132 | 1187.2946 | 2.048211 | 0.1152607 | 17.77025 | 0 | 0 |
| NNMT | nicotinamide N-methyltransferase [Source:HGNC Symbol;Acc:HGNC:7861] | protein_coding | 4837 | ENSG00000166741 | 7590.6769 | 2.241321 | 0.1278966 | 17.52448 | 0 | 0 |
| PER1 | period circadian regulator 1 [Source:HGNC Symbol;Acc:HGNC:8845] | protein_coding | 5187 | ENSG00000179094 | 741.0586 | 2.892767 | 0.1662671 | 17.39831 | 0 | 0 |
| TRNP1 | TMF1-regulated nuclear protein 1 [Source:HGNC Symbol;Acc:HGNC:34348] | protein_coding | 388610 | ENSG00000253368 | 1060.5358 | 2.015573 | 0.1164440 | 17.30937 | 0 | 0 |
| CACNB2 | calcium voltage-gated channel auxiliary subunit beta 2 [Source:HGNC Symbol;Acc:HGNC:1402] | protein_coding | 783 | ENSG00000165995 | 502.9501 | 3.025600 | 0.1755551 | 17.23448 | 0 | 0 |
| KLF15 | Kruppel like factor 15 [Source:HGNC Symbol;Acc:HGNC:14536] | protein_coding | 28999 | ENSG00000163884 | 576.5858 | 4.417895 | 0.2568764 | 17.19853 | 0 | 0 |
6.5.1 Using ensembldb to query for genes encoding a specific protein domain
For some experiments it might be interesting to search for genes, or rather transcripts, that encode a protein with a certain protein domain. For the present data set we could for example search for genes with proteins encoding the ligand binding domain of nuclear hormone receptors (such as the glucocorticoid receptor, the gene activated by treatment with the synthetic glucocorticoid dexamethasone and hence being mainly responsible for the transcriptional changes observed in the present dataset). Below we thus query the database for all such genes using the protein domain ID PF00104 from Pfam. The filter used in the query below will return all genes with a transcript annotated to the specific protein domain restricting to genes that are detected in the present analysis.
hormoneR <- genes(edb,
filter = ~ protein_domain_id == "PF00104" &
gene_id == rownames(res),
return.type = "data.frame")
rownames(hormoneR) <- hormoneR$gene_id
nrow(hormoneR)## [1] 46In total 46 genes were identified. Next we identify nuclear hormone receptors which are significantly differentially expressed at a 5% FDR in the present data set.
resHormoneR <- res[hormoneR$gene_id, ]
resHormoneR <- resHormoneR[which(resHormoneR$padj < 0.05), ]
resHormoneR <- cbind(hormoneR[rownames(resHormoneR), ],
resHormoneR)The table below lists the significantly differentially expressed hormone receptors.
knitr::kable(resHormoneR[order(resHormoneR$pvalue),
c("gene_name", "padj", "log2FoldChange")])| gene_name | padj | log2FoldChange | |
|---|---|---|---|
| ENSG00000119508 | NR4A3 | 0.0000000 | 2.0347679 |
| ENSG00000113580 | NR3C1 | 0.0000000 | -0.9459376 |
| ENSG00000123358 | NR4A1 | 0.0000000 | 1.3837656 |
| ENSG00000132170 | PPARG | 0.0000000 | 1.0746543 |
| ENSG00000175745 | NR2F1 | 0.0000278 | -0.5088171 |
| ENSG00000185551 | NR2F2 | 0.0001163 | 0.7837373 |
| ENSG00000131408 | NR1H2 | 0.0005496 | 0.3629349 |
| ENSG00000174738 | NR1D2 | 0.0020364 | -0.6147602 |
| ENSG00000151623 | NR3C2 | 0.0022242 | -1.1918428 |
| ENSG00000091831 | ESR1 | 0.0326543 | -1.1631755 |
Among the regulated hormone receptors are NR4A3, encoding a member of the steroid-thyroid hormone-retinoid receptor superfamily (4-fold upregulated), but also the glucocorticoid receptor (NR3C1) itself, which is known to down-regulate in certain cell types its own gene as part of a negative feedback loop.
6.6 Performing differential expression testing with edgeR
Next we will show how to perform differential expression analysis with edgeR.
Recall that we have a DGEList dge, containing three objects:
names(dge)## [1] "counts" "samples" "genes" "offset"We first define a design matrix, using the same formula syntax as for DESeq2 above.
design <- model.matrix(~ cell + dex, dge$samples)Then, we calculate normalization factors and estimate the dispersion for each gene. Note that we need to specify the design in the dispersion calculation.
dge <- calcNormFactors(dge)
dge <- estimateDisp(dge, design)Finally, we fit the generalized linear model and perform the test. In the
glmQLFTest function, we indicate which coefficient (which column in the design
matrix) that we would like to test for. It is possible to test more general
contrasts as well, and the user guide contains many examples on how to do this.
The topTags function extracts the top-ranked genes. You can indicate the
adjusted p-value cutoff, and/or the number of genes to keep.
fit <- glmQLFit(dge, design)
qlf <- glmQLFTest(fit, coef = ncol(design))
tt <- topTags(qlf, n = nrow(dge), p.value = 0.1)
tt10 <- topTags(qlf) # just the top 10 by default
tt10## Coefficient: dextrt
## gene.id logFC logCPM F PValue
## ENSG00000109906 ENSG00000109906 6.295485 4.109371 1194.3763 3.296559e-37
## ENSG00000101347 ENSG00000101347 3.748587 9.298121 1111.9553 1.911432e-36
## ENSG00000211445 ENSG00000211445 3.750816 9.113950 1046.1195 8.531279e-36
## ENSG00000189221 ENSG00000189221 3.403750 6.722805 933.9829 1.358415e-34
## ENSG00000152583 ENSG00000152583 4.390957 5.494222 910.3392 2.534658e-34
## ENSG00000196136 ENSG00000196136 3.225391 6.914366 818.5211 3.335556e-33
## ENSG00000120129 ENSG00000120129 2.951575 7.260226 790.8825 7.644427e-33
## ENSG00000096060 ENSG00000096060 3.996190 6.859061 756.9908 2.195437e-32
## ENSG00000163884 ENSG00000163884 4.418589 4.650387 748.9891 2.834664e-32
## ENSG00000127954 ENSG00000127954 4.857722 4.270540 729.4205 5.353765e-32
## FDR
## ENSG00000109906 1.079557e-32
## ENSG00000101347 3.129778e-32
## ENSG00000211445 9.312744e-32
## ENSG00000189221 1.112134e-30
## ENSG00000152583 1.660100e-30
## ENSG00000196136 1.820547e-29
## ENSG00000120129 3.576281e-29
## ENSG00000096060 8.987020e-29
## ENSG00000163884 1.031440e-28
## ENSG00000127954 1.753251e-28We can compare to see how the results from the two software overlap:
tt.all <- topTags(qlf, n = nrow(dge), sort.by = "none")
table(DESeq2 = res$padj < 0.1, edgeR = tt.all$table$FDR < 0.1)## edgeR
## DESeq2 FALSE TRUE
## FALSE 11099 876
## TRUE 194 4071We can also compare the two lists by the ranks:
common <- !is.na(res$padj)
plot(rank(res$padj[common]),
rank(tt.all$table$FDR[common]), cex = .1,
xlab = "DESeq2", ylab = "edgeR")
Figure 14: Comparison of genes ranked by DESeq2 and edgeR
Also with edgeR we can test for significance relative to a fold-change
threshold, using the function glmTreat instead of glmLRT. Below we set the
log fold-change threshold to 1 (i.e., fold change threshold equal to 2), as for
DESeq2 above. We also compare the results to those obtained with DESeq2.
treatres <- glmTreat(fit, coef = ncol(design), lfc = 1)
tt.treat <- topTags(treatres, n = nrow(dge), sort.by = "none")
table(DESeq2 = resLFC1$padj < 0.1, edgeR = tt.treat$table$FDR < 0.1)## edgeR
## DESeq2 FALSE TRUE
## FALSE 18940 270
## TRUE 0 2056.7 Citing packages used in research
If you use the results from an R analysis package in published
research, you can find the proper citation for the software by typing
citation("pkgName"), where you would substitute the name of the
package for pkgName. Citing methods papers helps to support and
reward the individuals who put time into open source software for
genomic data analysis.
7 Plotting results
7.1 Counts plot with DESeq2
A quick way to visualize the counts for a particular gene is to use the plotCounts function that takes as arguments the DESeqDataSet, a gene name, and the group over which to plot the counts (figure below).
topGene <- rownames(res)[which.min(res$padj)]
plotCounts(dds, gene = topGene, intgroup = c("dex"))
Figure 15: Normalized counts for a single gene over treatment group
We can also make custom plots using the ggplot function from the ggplot2 package (figures below).
library("ggbeeswarm")
geneCounts <- plotCounts(dds, gene = topGene, intgroup = c("dex","cell"),
returnData = TRUE)
ggplot(geneCounts, aes(x = dex, y = count, color = cell)) +
scale_y_log10() + geom_beeswarm(cex = 3)
Figure 16: Normalized counts using ggbeeswarm
ggplot(geneCounts, aes(x = dex, y = count, color = cell, group = cell)) +
scale_y_log10() + geom_point(size = 3) + geom_line()
Figure 17: Normalized counts with lines connecting cell lines
Note that the DESeq test actually takes into account the cell line effect, so this figure more closely depicts the difference being tested.
7.2 MA plot with DESeq2
An MA plot (Dudoit et al. 2002) provides a useful overview for the distribution of the estimated coefficients in the model, e.g. the comparisons of interest, across all genes. On the y-axis, the “M” stands for “minus” – subtraction of log values is equivalent to the log of the ratio – and on the x-axis, the “A” stands for “average”. You may hear this plot also referred to as a mean-difference plot, or a Bland-Altman plot.
Before making the MA plot, we use the lfcShrink function to shrink the log2 fold changes for the comparison of dex treated vs untreated samples. This typically takes about 20 seconds on a laptop.
library("apeglm")
resultsNames(dds)## [1] "Intercept" "cell_N061011_vs_N052611"
## [3] "cell_N080611_vs_N052611" "cell_N61311_vs_N052611"
## [5] "dex_trt_vs_untrt"res <- lfcShrink(dds, coef="dex_trt_vs_untrt", type="apeglm")
DESeq2::plotMA(res, xlim=c(5,1e6), ylim=c(-5,5))
Figure 18: An MA plot of changes induced by treatment
The log2 fold change for a particular comparison is plotted on the y-axis and the average of the counts normalized by size factor is shown on the x-axis. Each gene is represented with a dot. Genes with an adjusted p value below a threshold (here 0.1, the default) are shown in red.
The DESeq2 package uses a Bayesian procedure to moderate (or “shrink”) log2 fold changes from genes with very low counts and highly variable counts, as can be seen by the narrowing of the vertical spread of points on the left side of the MA plot. As shown above, the lfcShrink function performs this operation. For a detailed explanation of the rationale of moderated fold changes, please see the DESeq2 paper (Love, Huber, and Anders 2014).
If we had not used statistical moderation to shrink the noisy log2 fold changes, we would have instead seen the following plot:
res.noshr <- results(dds)
DESeq2::plotMA(res.noshr, xlim=c(5,1e6), ylim=c(-5,5))
Figure 19: MA plot without shrinkage
We can label individual points on the MA plot as well. Here we use the
with R function to plot a circle and text for a selected row of the
results object. Within the with function, only the baseMean and
log2FoldChange values for the selected rows of res are used.
DESeq2::plotMA(res, xlim=c(5,1e6), ylim=c(-5,5))
topGene <- rownames(res)[which.min(res$padj)]
with(res[topGene, ], {
points(baseMean, log2FoldChange, col = "dodgerblue", cex = 2, lwd = 2)
text(baseMean, log2FoldChange, topGene, pos = 2, col = "dodgerblue")
})
Figure 20: Example of labeling an individual gene on an MA plot
Another useful diagnostic plot is the histogram of the p values (figure below). This plot is best formed by excluding genes with very small counts, which otherwise generate spikes in the histogram.
hist(res$pvalue[res$baseMean > 1], breaks = 0:20/20,
col = "grey50", border = "white")
Figure 21: Histogram of p values for genes with mean normalized count larger than 1
7.3 Counts plot with edgeR
As we made a counts plot previously, we can also make this with the top gene from the edgeR analysis:
cpms <- cpm(dge)
topGene <- as.character(tt10$table$gene.id[1])
ord <- order(dge$samples$dex)
col <- as.integer(dge$samples$dex[ord])
par(mar=c(10,4,4,2))
barplot(cpms[topGene,ord],
col=c("skyblue","orange")[col],
names.arg=paste(dge$sample$dex[ord],"--",dge$sample$cell[ord]),
las=2, ylab="counts per million (CPM)")
Figure 22: CPM per sample for the top gene from the edgeR analysis
7.4 MA / Smear plot with edgeR
In edgeR, the MA plot is obtained via the plotSmear function.
plotSmear(qlf, de.tags = tt$table$gene.id)
Figure 23: The edgeR package includes the plotSmear function for generating MA plots
7.5 Gene clustering
In the sample distance heatmap made previously, the dendrogram at the side shows us a hierarchical clustering of the samples. Such a clustering can also be performed for the genes. Since the clustering is only relevant for genes that actually carry a signal, one usually would only cluster a subset of the most highly variable genes. Here, for demonstration, let us select the 20 genes with the highest variance across samples. We will work with the VST data:
library("genefilter")
topVarGenes <- head(order(rowVars(assay(vsd)), decreasing = TRUE), 20)The heatmap becomes more interesting if we do not look at absolute expression strength but rather at the amount by which each gene deviates in a specific sample from the gene’s average across all samples. Hence, we center each genes’ values across samples, and plot a heatmap (figure below). We provide a data.frame that instructs the pheatmap function how to label the columns.
mat <- assay(vsd)[ topVarGenes, ]
mat <- mat - rowMeans(mat)
anno <- as.data.frame(colData(vsd)[, c("cell","dex")])
pheatmap(mat, annotation_col = anno)
Figure 24: Heatmap of relative VST values across samples
Treatment status and cell line information are shown with colored bars at the top of the heatmap. Blocks of genes that covary across patients. Note that a set of genes at the top of the heatmap are separating the N061011 cell line from the others. In the center of the heatmap, we see a set of genes for which the dexamethasone treated samples have higher gene expression.
7.6 Exporting results
You can easily save the results table in a CSV file that you can then share or load with a spreadsheet program such as Excel. The call to as.data.frame is necessary to convert the DataFrame object (IRanges package) to a data.frame object that can be processed by write.csv. Here, we take just the top 100 genes for demonstration.
resOrderedDF <- as.data.frame(res[order(res$pvalue), ])[1:100, ]
write.csv(resOrderedDF, file = "results.csv")A more sophisticated way for exporting results the Bioconductor package ReportingTools (Huntley et al. 2013). ReportingTools will automatically generate dynamic HTML documents, including links to external databases using gene identifiers and boxplots summarizing the normalized counts across groups. See the ReportingTools vignettes for full details. The simplest version of creating a dynamic ReportingTools report is performed with the following code:
library("ReportingTools")
htmlRep <- HTMLReport(shortName = "report", title = "My report",
reportDirectory = "./report")
publish(resOrderedDF %>% tibble::rownames_to_column("gene"), htmlRep)
url <- finish(htmlRep)
browseURL(url)7.7 Exploring results interactively
The iSEE package enables interactive exploration of any data stored in a SummarizedExperiment container. Since the DESeqDataSet class directly extends SummarizedExperiment, it can also be used as input to iSEE. To open an iSEE instance, we simply type
library("iSEE")
if (interactive()) {
iSEE(dds)
}We can also add additional information, such as the results of the statistical test, to the rowData of the object before providing it to iSEE.
stopifnot(all(rownames(dds) == rownames(res)))
res$neglog10pvalue <- -log10(res$pvalue)
rowData(dds)$res <- res
if (interactive()) {
iSEE(dds)
}
9 Session information
As the last part of this document, we call the function sessionInfo, which reports the version numbers of R and all the packages used in this session. It is good practice to always keep such a record of this as it will help to track down what has happened in case an R script ceases to work or gives different results because the functions have been changed in a newer version of one of your packages. By including it at the bottom of a script, your reports will become more reproducible.
The session information should also always be included in any emails to the Bioconductor support site along with all code used in the analysis.
devtools::session_info()## ─ Session info ────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.0 (2019-04-26)
## os macOS High Sierra 10.13.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/Zurich
## date 2019-07-09
##
## ─ Packages ────────────────────────────────────────────────────────────────
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## [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/libraryReferences
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