Contents

R version: R version 3.6.0 (2019-04-26)

Bioconductor version: 3.9

Package: 1.8.0

1 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 importing and processing raw sequencing data and loading gene annotations. We will also use contributed packages for statistical analysis and visualization of sequencing data. 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 package 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.

1.1 Experimental data

The data used in this workflow is stored in the airway package that summarizes 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.

2 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 input data as obtained, e.g., from RNA-seq or another high-throughput sequencing experiment, in the form of a matrix of integer values. 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 DESeq2’s statistical model to hold, as only counts allow assessing the measurement precision correctly. It is important to never 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.

2.2 Aligning reads to a reference genome

The computational analysis of an RNA-seq experiment begins from the FASTQ files that contain the nucleotide sequence of each read and a quality score at each position. These reads must first be aligned to a reference genome or transcriptome, or the abundances and estimated counts per transcript can be estimated without alignment, as described above. In either case, it is important to know if the sequencing experiment was single-end or paired-end, as the alignment software will require the user to specify both FASTQ files for a paired-end experiment. The output of this alignment step is commonly stored in a file format called SAM/BAM.

A number of software programs exist to align reads to a reference genome, and we encourage you to check out some of the benchmarking papers that discuss the advantages and disadvantages of each software, which include accuracy, sensitivity in aligning reads over splice junctions, speed, memory required, usability, and many other features. Here, we used the STAR read aligner (Dobin et al. 2013) to align the reads for our current experiment to the Ensembl release 75 (Flicek et al. 2014) human reference genome. In the following code example, it is assumed that there is a file in the current directory called files with each line containing an identifier for each experiment, and we have all the FASTQ files in a subdirectory fastq. If you have downloaded the FASTQ files from the Sequence Read Archive, the identifiers would be SRA run IDs, e.g. SRR1039520. You should have two files for a paired-end experiment for each ID, fastq/SRR1039520_1.fastq1 and fastq/SRR1039520_2.fastq, which give the first and second read for the paired-end fragments. If you have performed a single-end experiment, you would only have one file per ID. We have also created a subdirectory, aligned, where STAR will output its alignment files.

for f in `cat files`; do STAR --genomeDir ../STAR/ENSEMBL.homo_sapiens.release-75 \
--readFilesIn fastq/$f\_1.fastq fastq/$f\_2.fastq \
--runThreadN 12 --outFileNamePrefix aligned/$f.; done

SAMtools (Li et al. 2009) was used to generate BAM files. The -@ flag can be used to allocate additional threads.

for f in `cat files`; do samtools view -bS aligned/$f.Aligned.out.sam \
-o aligned/$f.bam; done

The BAM files for a number of sequencing runs can then be used to generate count matrices, as described in the following section.

2.3 Locating alignment files

Besides the count matrix that we will use later, the airway package also contains eight files with a small subset of the total number of reads in the experiment. The reads were selected which aligned to a small region of chromosome 1. Here, for demonstration, we chose a subset of reads because the full alignment files are large (a few gigabytes each), and because it takes between 10-30 minutes to count the fragments for each sample. We will use these files to demonstrate how a count matrix can be constructed from BAM files. Afterwards, we will load the full count matrix corresponding to all samples and all data, which is already provided in the same package, and will continue the analysis with that full matrix.

We load the data package with the example data:

library("airway")

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 airway package.

indir <- system.file("extdata", package="airway", mustWork=TRUE)

In this directory, we find the eight BAM files (and some other files):

list.files(indir)
##  [1] "GSE52778_series_matrix.txt"        "Homo_sapiens.GRCh37.75_subset.gtf"
##  [3] "SRR1039508_subset.bam"             "SRR1039509_subset.bam"            
##  [5] "SRR1039512_subset.bam"             "SRR1039513_subset.bam"            
##  [7] "SRR1039516_subset.bam"             "SRR1039517_subset.bam"            
##  [9] "SRR1039520_subset.bam"             "SRR1039521_subset.bam"            
## [11] "SraRunInfo_SRP033351.csv"          "sample_table.csv"

Typically, we have a table with detailed information for each of our samples that links samples to the associated FASTQ and BAM files. For your own project, you might create such a comma-separated value (CSV) file using a text editor or spreadsheet software such as Excel.

We load such a CSV file with read.csv:

csvfile <- file.path(indir, "sample_table.csv")
sampleTable <- read.csv(csvfile, row.names = 1)
sampleTable
##            SampleName    cell   dex albut        Run avgLength Experiment
## SRR1039508 GSM1275862  N61311 untrt untrt SRR1039508       126  SRX384345
## SRR1039509 GSM1275863  N61311   trt untrt SRR1039509       126  SRX384346
## SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512       126  SRX384349
## SRR1039513 GSM1275867 N052611   trt untrt SRR1039513        87  SRX384350
## SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516       120  SRX384353
## SRR1039517 GSM1275871 N080611   trt untrt SRR1039517       126  SRX384354
## SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520       101  SRX384357
## SRR1039521 GSM1275875 N061011   trt untrt SRR1039521        98  SRX384358
##               Sample    BioSample
## SRR1039508 SRS508568 SAMN02422669
## SRR1039509 SRS508567 SAMN02422675
## SRR1039512 SRS508571 SAMN02422678
## SRR1039513 SRS508572 SAMN02422670
## SRR1039516 SRS508575 SAMN02422682
## SRR1039517 SRS508576 SAMN02422673
## SRR1039520 SRS508579 SAMN02422683
## SRR1039521 SRS508580 SAMN02422677

Once the reads have been aligned, there are a number of tools that can be used to count the number of reads/fragments that can be assigned to genomic features for each sample. These often take as input SAM/BAM alignment files and a file specifying the genomic features, e.g. a GFF3 or GTF file specifying the gene models.

2.4 DESeq2 import functions

The following tools can be used generate count matrices: summarizeOverlaps (Lawrence et al. 2013), featureCounts (Liao, Smyth, and Shi 2014), tximport (Soneson, Love, and Robinson 2015), htseq-count (Anders, Pyl, and Huber 2015).

function package framework output DESeq2 input function
summarizeOverlaps GenomicAlignments R/Bioconductor SummarizedExperiment DESeqDataSet
featureCounts Rsubread R/Bioconductor matrix DESeqDataSetFromMatrix
tximport tximport R/Bioconductor list of matrices DESeqDataSetFromTximport
htseq-count HTSeq Python files DESeqDataSetFromHTSeq

We now proceed using summarizeOverlaps. Using the Run column in the sample table, we construct the full paths to the files we want to perform the counting operation on:

filenames <- file.path(indir, paste0(sampleTable$Run, "_subset.bam"))
file.exists(filenames)
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

We indicate in Bioconductor that these files are BAM files using the BamFileList function from the Rsamtools package that provides an R interface to BAM files. Here we also specify details about how the BAM files should be treated, e.g., only process 2 million reads at a time. See ?BamFileList for more information.

library("Rsamtools")
bamfiles <- BamFileList(filenames, yieldSize=2000000)

Note: make sure that the chromosome names of the genomic features in the annotation you use are consistent with the chromosome names of the reference used for read alignment. Otherwise, the scripts might fail to count any reads to features due to the mismatching names. For example, a common mistake is when the alignment files contain chromosome names in the style of 1 and the gene annotation in the style of chr1, or the other way around. See the seqlevelsStyle function in the GenomeInfoDb package for solutions. We can check the chromosome names (here called “seqnames”) in the alignment files like so:

seqinfo(bamfiles[1])
## Seqinfo object with 84 sequences from an unspecified genome:
##   seqnames   seqlengths isCircular genome
##   1           249250621       <NA>   <NA>
##   10          135534747       <NA>   <NA>
##   11          135006516       <NA>   <NA>
##   12          133851895       <NA>   <NA>
##   13          115169878       <NA>   <NA>
##   ...               ...        ...    ...
##   GL000210.1      27682       <NA>   <NA>
##   GL000231.1      27386       <NA>   <NA>
##   GL000229.1      19913       <NA>   <NA>
##   GL000226.1      15008       <NA>   <NA>
##   GL000207.1       4262       <NA>   <NA>

2.5 Defining gene models

Next, we need to read in the gene model that will be used for counting reads/fragments. We will read the gene model from an Ensembl GTF file (Flicek et al. 2014), using makeTxDbFromGFF from the GenomicFeatures package. GTF files can be downloaded from Ensembl’s FTP site or other gene model repositories. A TxDb object is a database that can be used to generate a variety of range-based objects, such as exons, transcripts, and genes. We want to make a list of exons grouped by gene for counting read/fragments.

There are other options for constructing a TxDb. For the known genes track from the UCSC Genome Browser (Kent et al. 2002), one can use the pre-built Transcript DataBase: TxDb.Hsapiens.UCSC.hg19.knownGene. If the annotation file is accessible from AnnotationHub (as is the case for the Ensembl genes), a pre-scanned GTF file can be imported using makeTxDbFromGRanges.

Here we will demonstrate loading from a GTF file:

library("GenomicFeatures")

We indicate that none of our sequences (chromosomes) are circular using a 0-length character vector.

gtffile <- file.path(indir,"Homo_sapiens.GRCh37.75_subset.gtf")
txdb <- makeTxDbFromGFF(gtffile, format = "gtf", circ_seqs = character())
txdb
## TxDb object:
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Data source: /home/biocbuild/bbs-3.9-bioc/R/library/airway/extdata/Homo_sapiens.GRCh37.75_subset.gtf
## # Organism: NA
## # Taxonomy ID: NA
## # miRBase build ID: NA
## # Genome: NA
## # transcript_nrow: 65
## # exon_nrow: 279
## # cds_nrow: 158
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2019-05-03 12:06:18 -0400 (Fri, 03 May 2019)
## # GenomicFeatures version at creation time: 1.36.0
## # RSQLite version at creation time: 2.1.1
## # DBSCHEMAVERSION: 1.2

The following line produces a GRangesList of all the exons grouped by gene (Lawrence et al. 2013). Each element of the list is a GRanges object of the exons for a gene.

ebg <- exonsBy(txdb, by="gene")
ebg
## GRangesList object of length 20:
## $ENSG00000009724 
## GRanges object with 18 ranges and 2 metadata columns:
##        seqnames            ranges strand |   exon_id       exon_name
##           <Rle>         <IRanges>  <Rle> | <integer>     <character>
##    [1]        1 11086580-11087705      - |        98 ENSE00000818830
##    [2]        1 11090233-11090307      - |        99 ENSE00000472123
##    [3]        1 11090805-11090939      - |       100 ENSE00000743084
##    [4]        1 11094885-11094963      - |       101 ENSE00000743085
##    [5]        1 11097750-11097868      - |       102 ENSE00003482788
##    ...      ...               ...    ... .       ...             ...
##   [14]        1 11106948-11107176      - |       111 ENSE00003467404
##   [15]        1 11106948-11107176      - |       112 ENSE00003489217
##   [16]        1 11107260-11107280      - |       113 ENSE00001833377
##   [17]        1 11107260-11107284      - |       114 ENSE00001472289
##   [18]        1 11107260-11107290      - |       115 ENSE00001881401
## 
## ...
## <19 more elements>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths

2.6 Read counting step

After these preparations, the actual counting is easy. The function summarizeOverlaps from the GenomicAlignments package will do this. This produces a SummarizedExperiment object that contains a variety of information about the experiment, and will be described in more detail below.

Note: If it is desired to perform counting using multiple cores, one can use the register and MulticoreParam or SnowParam functions from the BiocParallel package before the counting call below. Expect that the summarizeOverlaps call will take at least 30 minutes per file for a human RNA-seq file with 30 million aligned reads. By sending the files to separate cores, one can speed up the entire counting process.

library("GenomicAlignments")
library("BiocParallel")

Here we specify to use one core, not multiple cores. We could have also skipped this line and the counting step would run in serial.

register(SerialParam())

The following call creates the SummarizedExperiment object with counts:

se <- summarizeOverlaps(features=ebg, reads=bamfiles,
                        mode="Union",
                        singleEnd=FALSE,
                        ignore.strand=TRUE,
                        fragments=TRUE )

We specify a number of arguments besides the features and the reads. The mode argument describes what kind of read overlaps will be counted. These modes are shown in Figure 1 of the Counting reads with summarizeOverlaps vignette for the GenomicAlignments package. Note that fragments will be counted only once to each gene, even if they overlap multiple exons of a gene which may themselves be overlapping. Setting singleEnd to FALSE indicates that the experiment produced paired-end reads, and we want to count a pair of reads (a fragment) only once toward the count for a gene. The fragments argument can be used when singleEnd=FALSE to specify if unpaired reads should be counted (yes if fragments=TRUE).

In order to produce correct counts, it is important to know if the RNA-seq experiment was strand-specific or not. This experiment was not strand-specific so we set ignore.strand to TRUE. However, certain strand-specific protocols could have the reads align only to the opposite strand of the genes. The user must check if the experiment was strand-specific and if so, whether the reads should align to the forward or reverse strand of the genes. For various counting/quantifying tools, one specifies counting on the forward or reverse strand in different ways, although this task is currently easiest with htseq-count, featureCounts, or the transcript abundance quantifiers mentioned previously. It is always a good idea to check the column sums of the count matrix (see below) to make sure these totals match the expected of the number of reads or fragments aligning to genes. Additionally, one can visually check the read alignments using a genome visualization tool.

2.7 SummarizedExperiment

The component parts of a SummarizedExperiment object. The assay (pink block) contains the matrix of counts, the rowRanges (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 fragment counts for each gene and sample, which is stored in assay. It is also possible to store multiple matrices, accessed with assays. The rowRanges for our object is the GRangesList we used for counting (one GRanges of exons for each row of the count matrix). The component parts of the SummarizedExperiment are accessed with an R function of the same name: assay (or assays), rowRanges and colData.

This example code above actually only counted a small subset of fragments from the original experiment. Nevertheless, we can still investigate the resulting SummarizedExperiment by looking at the counts in the assay slot, the phenotypic data about the samples in colData slot (in this case an empty DataFrame), and the data about the genes in the rowRanges slot.

se
## class: RangedSummarizedExperiment 
## dim: 20 8 
## metadata(0):
## assays(1): counts
## rownames(20): ENSG00000009724 ENSG00000116649 ... ENSG00000271794
##   ENSG00000271895
## rowData names(0):
## colnames(8): SRR1039508_subset.bam SRR1039509_subset.bam ...
##   SRR1039520_subset.bam SRR1039521_subset.bam
## colData names(0):
dim(se)
## [1] 20  8
assayNames(se)
## [1] "counts"
head(assay(se), 3)
##                 SRR1039508_subset.bam SRR1039509_subset.bam
## ENSG00000009724                    38                    28
## ENSG00000116649                  1004                  1255
## ENSG00000120942                   218                   256
##                 SRR1039512_subset.bam SRR1039513_subset.bam
## ENSG00000009724                    66                    24
## ENSG00000116649                  1122                  1313
## ENSG00000120942                   233                   252
##                 SRR1039516_subset.bam SRR1039517_subset.bam
## ENSG00000009724                    42                    41
## ENSG00000116649                  1100                  1879
## ENSG00000120942                   269                   465
##                 SRR1039520_subset.bam SRR1039521_subset.bam
## ENSG00000009724                    47                    36
## ENSG00000116649                   745                  1536
## ENSG00000120942                   207                   400
colSums(assay(se))
## SRR1039508_subset.bam SRR1039509_subset.bam SRR1039512_subset.bam 
##                  6478                  6501                  7699 
## SRR1039513_subset.bam SRR1039516_subset.bam SRR1039517_subset.bam 
##                  6801                  8009                 10849 
## SRR1039520_subset.bam SRR1039521_subset.bam 
##                  5254                  9168

The rowRanges, when printed, only shows the first GRanges, and tells us there are 19 more elements:

rowRanges(se)
## GRangesList object of length 20:
## $ENSG00000009724 
## GRanges object with 18 ranges and 2 metadata columns:
##        seqnames            ranges strand |   exon_id       exon_name
##           <Rle>         <IRanges>  <Rle> | <integer>     <character>
##    [1]        1 11086580-11087705      - |        98 ENSE00000818830
##    [2]        1 11090233-11090307      - |        99 ENSE00000472123
##    [3]        1 11090805-11090939      - |       100 ENSE00000743084
##    [4]        1 11094885-11094963      - |       101 ENSE00000743085
##    [5]        1 11097750-11097868      - |       102 ENSE00003482788
##    ...      ...               ...    ... .       ...             ...
##   [14]        1 11106948-11107176      - |       111 ENSE00003467404
##   [15]        1 11106948-11107176      - |       112 ENSE00003489217
##   [16]        1 11107260-11107280      - |       113 ENSE00001833377
##   [17]        1 11107260-11107284      - |       114 ENSE00001472289
##   [18]        1 11107260-11107290      - |       115 ENSE00001881401
## 
## ...
## <19 more elements>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths

The rowRanges also contains metadata about the construction of the gene model in the metadata slot. Here we use a helpful R function, str, to display the metadata compactly:

str(metadata(rowRanges(se)))
## List of 1
##  $ genomeInfo:List of 15
##   ..$ Db type                                 : chr "TxDb"
##   ..$ Supporting package                      : chr "GenomicFeatures"
##   ..$ Data source                             : chr "/home/biocbuild/bbs-3.9-bioc/R/library/airway/extdata/Homo_sapiens.GRCh37.75_subset.gtf"
##   ..$ Organism                                : chr NA
##   ..$ Taxonomy ID                             : chr NA
##   ..$ miRBase build ID                        : chr NA
##   ..$ Genome                                  : chr NA
##   ..$ transcript_nrow                         : chr "65"
##   ..$ exon_nrow                               : chr "279"
##   ..$ cds_nrow                                : chr "158"
##   ..$ Db created by                           : chr "GenomicFeatures package from Bioconductor"
##   ..$ Creation time                           : chr "2019-05-03 12:06:18 -0400 (Fri, 03 May 2019)"
##   ..$ GenomicFeatures version at creation time: chr "1.36.0"
##   ..$ RSQLite version at creation time        : chr "2.1.1"
##   ..$ DBSCHEMAVERSION                         : chr "1.2"

The colData:

colData(se)
## DataFrame with 8 rows and 0 columns

The colData slot, so far empty, should contain all the metadata. Because we used a column of sampleTable to produce the bamfiles vector, we know the columns of se are in the same order as the rows of sampleTable. We can assign the sampleTable as the colData of the summarized experiment, by converting it into a DataFrame and using the assignment function:

colData(se) <- DataFrame(sampleTable)
colData(se)
## DataFrame with 8 rows and 9 columns
##            SampleName     cell      dex    albut        Run avgLength
##              <factor> <factor> <factor> <factor>   <factor> <integer>
## SRR1039508 GSM1275862   N61311    untrt    untrt SRR1039508       126
## SRR1039509 GSM1275863   N61311      trt    untrt SRR1039509       126
## SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126
## SRR1039513 GSM1275867  N052611      trt    untrt SRR1039513        87
## SRR1039516 GSM1275870  N080611    untrt    untrt SRR1039516       120
## SRR1039517 GSM1275871  N080611      trt    untrt SRR1039517       126
## SRR1039520 GSM1275874  N061011    untrt    untrt SRR1039520       101
## SRR1039521 GSM1275875  N061011      trt    untrt SRR1039521        98
##            Experiment    Sample    BioSample
##              <factor>  <factor>     <factor>
## SRR1039508  SRX384345 SRS508568 SAMN02422669
## SRR1039509  SRX384346 SRS508567 SAMN02422675
## SRR1039512  SRX384349 SRS508571 SAMN02422678
## SRR1039513  SRX384350 SRS508572 SAMN02422670
## SRR1039516  SRX384353 SRS508575 SAMN02422682
## SRR1039517  SRX384354 SRS508576 SAMN02422673
## SRR1039520  SRX384357 SRS508579 SAMN02422683
## SRR1039521  SRX384358 SRS508580 SAMN02422677

2.8 Branching point

At this point, we have counted the fragments which overlap the genes in the gene model we specified. 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). The SummarizedExperiment object is all we need to start our analysis. In the following section we will show how to use it to create the data object used by DESeq2.

3 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 rowRanges 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. It is built on top of the SummarizedExperiment class, and it is easy to convert SummarizedExperiment objects into DESeqDataSet objects, which we show below. One of the two main differences is that the assay slot is instead accessed using the counts accessor function, and the DESeqDataSet class enforces that the values in this matrix are non-negative integers.

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 see each of the columns just using the $ directly on the SummarizedExperiment or DESeqDataSet:

se$cell
## [1] N61311  N61311  N052611 N052611 N080611 N080611 N061011 N061011
## Levels: N052611 N061011 N080611 N61311
se$dex
## [1] untrt trt   untrt trt   untrt trt   untrt trt  
## Levels: 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:

library("magrittr")
se$dex %<>% relevel("untrt")
se$dex
## [1] untrt trt   untrt trt   untrt trt   untrt trt  
## Levels: untrt trt

%<>% is the compound assignment pipe-operator from the magrittr package, the above line of code is a concise way of saying

se$dex <- relevel(se$dex, "untrt")

For running DESeq2 models, you can use R’s formula notation to express any fixed-effects experimental design. Note that DESeq2 uses 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. We will show how to use an interaction term to test for condition-specific changes over time in a time course example below.

In the following sections, we will demonstrate the construction of the DESeqDataSet from two starting points:

For a full example of using the HTSeq Python package for read counting, please see the pasilla vignette. For an example of generating the DESeqDataSet from files produced by htseq-count, please see the DESeq2 vignette.

3.1 Starting from SummarizedExperiment

We now use R’s data command to load a prepared SummarizedExperiment that was generated from the publicly available sequencing data files associated with Himes et al. (2014), described above. The steps we used to produce this object were equivalent to those you worked through in the previous sections, except that we used all the reads and all the genes. For more details on the exact steps used to create this object, type vignette("airway") into your R session.

data("airway")
se <- airway

Again, we want to specify that untrt is the reference level for the dex variable:

se$dex %<>% relevel("untrt")
se$dex
## [1] untrt trt   untrt trt   untrt trt   untrt trt  
## Levels: untrt trt

We can quickly check the millions of fragments that uniquely aligned to the genes (the second argument of round tells how many decimal points to keep).

round( colSums(assay(se)) / 1e6, 1 )
## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 
##       20.6       18.8       25.3       15.2       24.4       30.8       19.1 
## SRR1039521 
##       21.2

Supposing we have constructed a SummarizedExperiment using one of the methods described in the previous section, we now need to make sure that the object contains all the necessary information about the samples, i.e., a table with metadata on the count matrix’s columns stored in the colData slot:

colData(se)
## DataFrame with 8 rows and 9 columns
##            SampleName     cell      dex    albut        Run avgLength
##              <factor> <factor> <factor> <factor>   <factor> <integer>
## SRR1039508 GSM1275862   N61311    untrt    untrt SRR1039508       126
## SRR1039509 GSM1275863   N61311      trt    untrt SRR1039509       126
## SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126
## SRR1039513 GSM1275867  N052611      trt    untrt SRR1039513        87
## SRR1039516 GSM1275870  N080611    untrt    untrt SRR1039516       120
## SRR1039517 GSM1275871  N080611      trt    untrt SRR1039517       126
## SRR1039520 GSM1275874  N061011    untrt    untrt SRR1039520       101
## SRR1039521 GSM1275875  N061011      trt    untrt SRR1039521        98
##            Experiment    Sample    BioSample
##              <factor>  <factor>     <factor>
## SRR1039508  SRX384345 SRS508568 SAMN02422669
## SRR1039509  SRX384346 SRS508567 SAMN02422675
## SRR1039512  SRX384349 SRS508571 SAMN02422678
## SRR1039513  SRX384350 SRS508572 SAMN02422670
## SRR1039516  SRX384353 SRS508575 SAMN02422682
## SRR1039517  SRX384354 SRS508576 SAMN02422673
## SRR1039520  SRX384357 SRS508579 SAMN02422683
## SRR1039521  SRX384358 SRS508580 SAMN02422677

Here we see that this object already contains an informative colData slot – because we have already prepared it for you, as described in the airway vignette. However, when you work with your own data, you will have to add the pertinent sample / phenotypic information for the experiment at this stage. We highly recommend keeping this information in a comma-separated value (CSV) or tab-separated value (TSV) file, which can be exported from an Excel spreadsheet, and the assign this to the colData slot, making sure that the rows correspond to the columns of the SummarizedExperiment. We made sure of this correspondence earlier by specifying the BAM files using a column of the sample table.

Once we have our fully annotated SummarizedExperiment object, we can construct a DESeqDataSet object from it that will then form the starting point of the analysis. We add an appropriate design for the analysis:

library("DESeq2")
dds <- DESeqDataSet(se, design = ~ cell + dex)

3.2 Starting from count matrices

In this section, we will show how to build an DESeqDataSet supposing we only have a count matrix and a table of sample information.

Note: if you have prepared a SummarizedExperiment you should skip this section. While the previous section would be used to construct a DESeqDataSet from a SummarizedExperiment, here we first extract the individual object (count matrix and sample info) from the SummarizedExperiment in order to build it back up into a new object – only for demonstration purposes. In practice, the count matrix would either be read in from a file or perhaps generated by an R function like featureCounts from the Rsubread package (Liao, Smyth, and Shi 2014).

The information in a SummarizedExperiment object can be accessed with accessor functions. For example, to see the actual data, i.e., here, the fragment counts, we use the assay function. (The head function restricts the output to the first few lines.)

countdata <- assay(se)
head(countdata, 3)
##                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003        679        448        873        408       1138
## ENSG00000000005          0          0          0          0          0
## ENSG00000000419        467        515        621        365        587
##                 SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003       1047        770        572
## ENSG00000000005          0          0          0
## ENSG00000000419        799        417        508

In this count matrix, each row represents an Ensembl gene, each column a sequenced RNA library, and the values give the raw numbers of fragments that were uniquely assigned to the respective gene in each library. We also have information on each of the samples (the columns of the count matrix). If you’ve counted reads with some other software, it is very important to check that the columns of the count matrix correspond to the rows of the sample information table.

coldata <- colData(se)

We now have all the ingredients to prepare our data object in a form that is suitable for analysis, namely:

  • countdata: a table with the fragment counts
  • coldata: a table with information about the samples

To now construct the DESeqDataSet object from the matrix of counts and the sample information table, we use:

ddsMat <- DESeqDataSetFromMatrix(countData = countdata,
                                  colData = coldata,
                                  design = ~ cell + dex)

We will continue with the object generated from the SummarizedExperiment section.

4 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.

4.1 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 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] 64102
dds <- dds[ rowSums(counts(dds)) > 1, ]
nrow(dds)
## [1] 29391

4.2 The variance stabilizing transformation and the rlog

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)

And for logarithm-transformed counts:

log.cts.one <- log2(cts + 1)
meanSdPlot(log.cts.one, ranks = FALSE)

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: the variance stabilizing transformation (VST) for negative binomial data with a dispersion-mean trend (Anders and Huber 2010), implemented in the vst function, and the regularized-logarithm transformation or rlog (Love, Huber, and Anders 2014).

For genes with high counts, both the VST and the rlog will give similar result to the ordinary log2 transformation of normalized counts. For genes with lower counts, however, the values are shrunken towards a middle value. The VST or rlog-transformed data then become approximately homoskedastic (more flat trend in the meanSdPlot), 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? The VST is much faster to compute and is less sensitive to high count outliers than the rlog. The rlog tends to work well on small datasets (n < 30), potentially outperforming the VST when there is a wide range of sequencing depth across samples (an order of magnitude difference). We therefore recommend the VST for medium-to-large datasets (n > 30). You can perform both transformations and compare the meanSdPlot or PCA plots generated, as described below.

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 vst and rlog return a DESeqTransform object which is based on the SummarizedExperiment class. The transformed values are no longer counts, and are stored in the assay slot. The colData that was attached to dds is still accessible:

vsd <- vst(dds, blind = FALSE)
head(assay(vsd), 3)
##                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003   9.742074   9.430420   9.867627   9.645845  10.183143
## ENSG00000000419   9.333669   9.581707   9.486145   9.523093   9.427283
## ENSG00000000457   8.765274   8.698449   8.651473   8.732426   8.592787
##                 SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003   9.880416  10.010366   9.639782
## ENSG00000000419   9.574860   9.325999   9.509246
## ENSG00000000457   8.702674   8.761945   8.724101
colData(vsd)
## DataFrame with 8 rows and 10 columns
##            SampleName     cell      dex    albut        Run avgLength
##              <factor> <factor> <factor> <factor>   <factor> <integer>
## SRR1039508 GSM1275862   N61311    untrt    untrt SRR1039508       126
## SRR1039509 GSM1275863   N61311      trt    untrt SRR1039509       126
## SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126
## SRR1039513 GSM1275867  N052611      trt    untrt SRR1039513        87
## SRR1039516 GSM1275870  N080611    untrt    untrt SRR1039516       120
## SRR1039517 GSM1275871  N080611      trt    untrt SRR1039517       126
## SRR1039520 GSM1275874  N061011    untrt    untrt SRR1039520       101
## SRR1039521 GSM1275875  N061011      trt    untrt SRR1039521        98
##            Experiment    Sample    BioSample        sizeFactor
##              <factor>  <factor>     <factor>         <numeric>
## SRR1039508  SRX384345 SRS508568 SAMN02422669  1.02364764926706
## SRR1039509  SRX384346 SRS508567 SAMN02422675 0.896166721793923
## SRR1039512  SRX384349 SRS508571 SAMN02422678  1.17948612081678
## SRR1039513  SRX384350 SRS508572 SAMN02422670 0.670053829048202
## SRR1039516  SRX384353 SRS508575 SAMN02422682  1.17767135405022
## SRR1039517  SRX384354 SRS508576 SAMN02422673  1.39903646915342
## SRR1039520  SRX384357 SRS508579 SAMN02422683 0.920778683328161
## SRR1039521  SRX384358 SRS508580 SAMN02422677 0.944514089340919

Again, for the rlog:

rld <- rlog(dds, blind = FALSE)
head(assay(rld), 3)
##                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003   9.385681   9.052599   9.516877   9.285335   9.839093
## ENSG00000000419   8.869611   9.138274   9.036117   9.075296   8.972125
## ENSG00000000457   7.961373   7.881385   7.824075   7.921493   7.751690
##                 SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003   9.530313   9.663260   9.277695
## ENSG00000000419   9.131828   8.861529   9.060906
## ENSG00000000457   7.886441   7.957126   7.912125

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 (after adding 1, to avoid taking the log of zero), and then using the VST and rlog-transformed values. For the log2 approach, we need to first estimate size factors to account for sequencing depth, and then specify normalized=TRUE. Sequencing depth correction is done automatically for the vst and rlog.

library("dplyr")
library("ggplot2")

dds <- estimateSizeFactors(dds)

df <- bind_rows(
  as_data_frame(log2(counts(dds, normalized=TRUE)[, 1:2]+1)) %>%
         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)  

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.

4.3 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   44.08478                                                       
## SRR1039512   36.82952   51.50926                                            
## SRR1039513   59.23174   43.11327   47.21641                                 
## SRR1039516   41.08424   54.79377   40.18856   59.72262                      
## SRR1039517   59.08409   47.46042   54.13979   46.36635   44.74738           
## SRR1039520   37.27578   54.00938   34.56797   55.44564   42.81024   58.22766
## SRR1039521   59.26716   42.84573   54.13120   35.24746   60.64298   47.80456
##            SRR1039520
## SRR1039509           
## SRR1039512           
## SRR1039513           
## SRR1039516           
## SRR1039517           
## SRR1039520           
## SRR1039521   48.24754

We 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)

Heatmap of sample-to-sample distances using the rlog-transformed 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$dd )
rownames(samplePoisDistMatrix) <- paste( dds$dex, dds$cell, sep=" - " )
colnames(samplePoisDistMatrix) <- NULL
pheatmap(samplePoisDistMatrix,
         clustering_distance_rows = poisd$dd,
         clustering_distance_cols = poisd$dd,
         col = colors)

Heatmap of sample-to-sample distances using the Poisson Distance.

4.4 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"))