Contents

R version: R Under development (unstable) (2019-10-24 r77329)

Bioconductor version: 3.11

Package: 1.11.1

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 working with gene annotations (gene and transcript locations in the genome, as well as gene ID lookup). 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.

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 quantification input to DESeq2

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 un-normalized 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) can be 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), or peptide sequences (with quantitative mass spectrometry).

The values in the matrix should be counts or estimated 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.

2.1 Transcript quantification and tximport / tximeta

A previous version of this workflow (including the published version) demonstrated how to align reads to the genome and then count the number of reads that are consistent with gene models. We now recommend a faster, alternative pipeline to genome alignment and read counting. This workflow will demonstrate how to import transcript-level quantification data, aggregating to the gene-level with tximport or tximeta. Transcript quantification methods such as Salmon (Patro et al. 2017), kallisto (Bray et al. 2016), or RSEM (Li and Dewey 2011) perform mapping or alignment of reads to reference transcripts, outputting estimated counts per transcript as well as effective transcript lengths which summarize bias effects. After running one of these tools, the tximport (Soneson, Love, and Robinson 2015) or tximeta (Love et al. 2019) packages can be used to assemble estimated count and offset matrices for use with Bioconductor differential gene expression packages, as will be demonstrated below.

A tutorial on how to use the Salmon software for quantifying transcript abundance can be found here. We recommend using the --gcBias flag which estimates a correction factor for systematic biases commonly present in RNA-seq data (Love, Hogenesch, and Irizarry 2016; Patro et al. 2017), unless you are certain that your data do not contain such bias.

The advantages of using the transcript abundance quantifiers in conjunction with tximport/tximeta to produce gene-level count matrices and normalizing offsets, are: (1) this approach corrects for any potential changes in gene length across samples (e.g. from differential isoform usage) (Trapnell et al. 2013); (2) some of these methods are substantially faster and require less memory and disk usage compared to alignment-based methods; and (3) 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, 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.

tximeta (Love et al. 2019) extends tximport, offering the same functionality, plus the additional benefit of automatic addition of annotation metadata for commonly used transcriptomes (GENCODE, Ensembl, RefSeq for human and mouse). See the tximeta vignette package vignette for more details. tximeta produces a SummarizedExperiment that can be loaded easily into DESeq2 using the DESeqDataSet function, which will be demonstrated below. We will also discuss the various possible inputs into DESeq2, whether using tximport, tximeta, htseq (Anders, Pyl, and Huber 2015), or a pre-computed count matrix.

2.2 Quantifying with Salmon

As mentioned above, a short tutorial on how to use Salmon can be found here, so instead we will provide the code that was used to quantify the files used in this workflow. Salmon can be conveniently run on a cluster using the Snakemake workflow management system (Köster and Rahmann 2012).

The following Snakemake file was used to quantify the eight samples that were downloaded from the SRA (the SRR identifier is the run identifier, and there was only one run per sample for these eight samples).

DATASETS = ["SRR1039508",
            "SRR1039509",
            "SRR1039512",
            "SRR1039513",
            "SRR1039516",
            "SRR1039517",
            "SRR1039520",
            "SRR1039521"]

SALMON = "/path/to/salmon_0.14.1/bin/salmon"

rule all:
  input: expand("quants/{dataset}/quant.sf", dataset=DATASETS)

rule salmon_quant:
    input:
        r1 = "fastq/{sample}_1.fastq.gz",
        r2 = "fastq/{sample}_2.fastq.gz",
        index = "/path/to/gencode.v29_salmon_0.14.1"
    output:
        "quants/{sample}/quant.sf"
    params:
        dir = "quants/{sample}"
    shell:
        "{SALMON} quant -i {input.index} -l A -p 6 --validateMappings \
         --gcBias --numGibbsSamples 20 -o {params.dir} \
         -1 {input.r1} -2 {input.r2}"

The last line is the key one which runs Salmon. It says to quantify using a specific index, with automatic library type detection, using 6 threads, with the validate mappings setting (this is default in versions of Salmon \(\ge\) 0.99), with GC bias correction, and writing out 20 Gibbs samples (this is optional). The last three arguments specify the output directory and the two paired read files.

The above Snakemake file requires that an index be created at /path/to/gencode.vVV_salmon_X.Y.Z, where VV and X,Y,Z should help specify the release of the reference transcripts and of Salmon. For human and mouse reference transcripts, we recommend to use GENCODE (Frankish et al. 2018).

The Salmon index can be created easily with the following command:

salmon index -t transcripts.fa.gz -i name_of_index

If the transcripts are downloaded from GENCODE, it is recommended to use something similar to the following command (which simply helps to strip the extra information from the transcript names):

salmon index --gencode -t gencode.v29.transcripts.fa.gz \
  -i gencode.v29_salmon_X.Y.Z

The above Snakemake file can then be used to execute Snakemake in various ways, including submitting multiple jobs to a compute cluster or in the cloud. The above Snakemake file was executed on a cluster with SLURM scheduling, with the following line in a separate job submitted to the cluster:

snakemake -j 4 --latency-wait 30 --cluster "sbatch -N 1 -n 6"

2.3 Reading in data with tximeta

Later in the workflow, we will load an object that contains the quantification data at the gene-level for all eight samples. However, the airway package also contains two quantification directories output by Salmon, in order to demonstrate reading this data into R/Bioconductor. In order to make the data package smaller, the quant.sf files in the quantification directories have been gzipped, so below where you see quant.sf.gz, you would probably use quant.sf on your own machine.

After we demonstrate importing with tximeta, 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 data object.

We first 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.

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

In this directory, we find a number of files, including eight BAM files that were used in the previous version of this workflow demonstrating alignment and counting. We will focus on the two directories that are in the quants directory, which contain the output from Salmon on two files.

list.files(dir)
##  [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"          "quants"                           
## [13] "sample_table.csv"
list.files(file.path(dir, "quants"))
## [1] "SRR1039508" "SRR1039509"

Typically, we have a table with detailed information for each of our samples that links samples to the associated FASTQ and Salmon directories. 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(dir, "sample_table.csv")
coldata <- read.csv(csvfile, row.names=1, stringsAsFactors=FALSE)
coldata
##            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

To demonstrate loading Salmon quantifiation data into R, we will just work with the two samples that are provided in the airway package. We create a column called names and a column called files:

coldata <- coldata[1:2,]
coldata$names <- coldata$Run
coldata$files <- file.path(dir, "quants", coldata$names, "quant.sf.gz")
file.exists(coldata$files)
## [1] TRUE TRUE

Now we load the tximeta package and run its main function:

library("tximeta")
se <- tximeta(coldata)
## importing quantifications
## reading in files with read_tsv
## 1 2 
## found matching transcriptome:
## [ GENCODE - Homo sapiens - release 29 ]
## building TxDb with 'GenomicFeatures' package
## Import genomic features from the file as a GRanges object ... OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK
## generating transcript ranges
## fetching genome info for GENCODE

If the reference transcriptome checksum was recognized by tximeta (details on this in the tximeta vignette), and if we have a working internet connection, tximeta will locate and download the relevant annotation data from various sources. A few details: the annotation data is only downloaded and parsed once, subsequently it will used locally cached versions of the metadata as needed (if you load data a second time that was quantified against the same reference transcripts). Also, the very first time that one uses tximeta, it will ask you to approve the default cache location (following the paradigm of the cache location used by other R and Bioconductor packages). You can change this location at any point later.

We will discuss what is the structure of the se object in the next section, but we can first just consider the dimensions. Note that tximeta imports data at the transcript level.

dim(se)
## [1] 205870      2
head(rownames(se))
## [1] "ENST00000456328.2" "ENST00000450305.2" "ENST00000488147.1"
## [4] "ENST00000619216.1" "ENST00000473358.1" "ENST00000469289.1"

As this workflow is concerned with gene-level analysis, we will now summarize the transcript-level quantifications to the gene level (which internally makes use of the methods in tximport (Soneson, Love, and Robinson 2015)). The correct transcript-to-gene mapping table is automatically created based on the metadata stored within the se object.

gse <- summarizeToGene(se)
## loading existing TxDb created: 2019-12-10 16:10:51
## Loading required package: GenomicFeatures
## Loading required package: AnnotationDbi
## obtaining transcript-to-gene mapping from TxDb
## generating gene ranges
## summarizing abundance
## summarizing counts
## summarizing length

Now we can check that the dimensions are reduced and the row IDs are now gene IDs:

dim(gse)
## [1] 58294     2
head(rownames(gse))
## [1] "ENSG00000000003.14" "ENSG00000000005.5"  "ENSG00000000419.12"
## [4] "ENSG00000000457.13" "ENSG00000000460.16" "ENSG00000000938.12"

2.4 DESeq2 import functions

While the above section described use of Salmon and tximeta, there are many possible inputs to DESeq2, each of which have their own dedicated import functions. The following tools can be used generate or compile count data for use with DESeq2: tximport (Soneson, Love, and Robinson 2015), tximeta (Love et al. 2019), htseq-count (Anders, Pyl, and Huber 2015), featureCounts (Liao, Smyth, and Shi 2014), summarizeOverlaps (Lawrence et al. 2013).

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

We will next describe the class of object created by tximeta which was saved above as se and gse, and how to create a DESeqDataSet object from this for use with DESeq2 (the other functions above also create a DESeqDataSet).

2.5 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, tximeta has created an object gse with three matrices: “counts” - the estimated fragment counts for each gene and sample, “abundance” - the estimated transcript abundances in TPM, and “length” - the effective gene lengths which include changes in length due to biases as well as due to transcript usage. The names of the assays can be examined with assayNames, and the assays themselves are stored as assays (a list of matrices). The first matrix in the list can be pulled out via assay. The rowRanges for our object is the GRanges of the genes (from the left-most position of all the transcripts to the right-most position of all the transcripts). The component parts of the SummarizedExperiment are accessed with an R function of the same name: assay (or assays), rowRanges and colData.

We now will load the full count matrix corresponding to all samples and all data, which is provided in the airway package, and will continue the analysis with the full data object. We can investigate this SummarizedExperiment object by looking at the matrices in the assays slot, the phenotypic data about the samples in colData slot, and the data about the genes in the rowRanges slot.

data(gse)
gse
## class: RangedSummarizedExperiment 
## dim: 58294 8 
## metadata(6): tximetaInfo quantInfo ... txomeInfo txdbInfo
## assays(3): counts abundance length
## rownames(58294): ENSG00000000003.14 ENSG00000000005.5 ...
##   ENSG00000285993.1 ENSG00000285994.1
## rowData names(1): gene_id
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(3): names donor condition

The counts are the first matrix, so we can examine them with just assay:

assayNames(gse)
## [1] "counts"    "abundance" "length"
head(assay(gse), 3)
##                    SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003.14    708.164    467.962    900.992    424.368   1188.295
## ENSG00000000005.5       0.000      0.000      0.000      0.000      0.000
## ENSG00000000419.12    455.000    510.000    604.000    352.000    583.000
##                    SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003.14   1090.668    805.929    599.337
## ENSG00000000005.5       0.000      0.000      0.000
## ENSG00000000419.12    773.999    409.999    499.000
colSums(assay(gse))
## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 
##   21100805   19298584   26145537   15688246   25268618   31891456   19683767 
## SRR1039521 
##   21813903

The rowRanges, when printed, shows the ranges for the first five and last five genes:

rowRanges(gse)
## GRanges object with 58294 ranges and 1 metadata column:
##                      seqnames              ranges strand |            gene_id
##                         <Rle>           <IRanges>  <Rle> |        <character>
##   ENSG00000000003.14     chrX 100627109-100639991      - | ENSG00000000003.14
##    ENSG00000000005.5     chrX 100584802-100599885      + |  ENSG00000000005.5
##   ENSG00000000419.12    chr20   50934867-50958555      - | ENSG00000000419.12
##   ENSG00000000457.13     chr1 169849631-169894267      - | ENSG00000000457.13
##   ENSG00000000460.16     chr1 169662007-169854080      + | ENSG00000000460.16
##                  ...      ...                 ...    ... .                ...
##    ENSG00000285990.1    chr14   19244904-19269380      - |  ENSG00000285990.1
##    ENSG00000285991.1     chr6 149817937-149896011      - |  ENSG00000285991.1
##    ENSG00000285992.1     chr8   47129262-47132628      + |  ENSG00000285992.1
##    ENSG00000285993.1    chr18   46409197-46410645      - |  ENSG00000285993.1
##    ENSG00000285994.1    chr10   12563151-12567351      + |  ENSG00000285994.1
##   -------
##   seqinfo: 25 sequences (1 circular) from hg38 genome

The rowRanges also contains metadata about the sequences (chromosomes in our case) in the seqinfo slot:

seqinfo(rowRanges(gse))
## Seqinfo object with 25 sequences (1 circular) from hg38 genome:
##   seqnames seqlengths isCircular genome
##   chr1      248956422      FALSE   hg38
##   chr2      242193529      FALSE   hg38
##   chr3      198295559      FALSE   hg38
##   chr4      190214555      FALSE   hg38
##   chr5      181538259      FALSE   hg38
##   ...             ...        ...    ...
##   chr21      46709983      FALSE   hg38
##   chr22      50818468      FALSE   hg38
##   chrX      156040895      FALSE   hg38
##   chrY       57227415      FALSE   hg38
##   chrM          16569       TRUE   hg38

The colData for the SummarizedExperiment reflects the data.frame that was provided to the tximeta function for importing the quantification data. Here we can see that there are columns indicating sample names, as well as the donor ID, and the treatment condition (treated with dexamethasone or untreated).

colData(gse)
## DataFrame with 8 rows and 3 columns
##                 names    donor     condition
##              <factor> <factor>      <factor>
## SRR1039508 SRR1039508   N61311     Untreated
## SRR1039509 SRR1039509   N61311 Dexamethasone
## SRR1039512 SRR1039512  N052611     Untreated
## SRR1039513 SRR1039513  N052611 Dexamethasone
## SRR1039516 SRR1039516  N080611     Untreated
## SRR1039517 SRR1039517  N080611 Dexamethasone
## SRR1039520 SRR1039520  N061011     Untreated
## SRR1039521 SRR1039521  N061011 Dexamethasone

2.6 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.

First, let’s examine the columns of the colData of gse. We can see each of the columns just using the $ directly on the SummarizedExperiment or DESeqDataSet.

gse$donor
## [1] N61311  N61311  N052611 N052611 N080611 N080611 N061011 N061011
## Levels: N052611 N061011 N080611 N61311
gse$condition
## [1] Untreated     Dexamethasone Untreated     Dexamethasone Untreated    
## [6] Dexamethasone Untreated     Dexamethasone
## Levels: Untreated Dexamethasone

We can rename our variables if we want. Let’s use cell to denote the donor cell line, and dex to denote the treatment condition.

gse$cell <- gse$donor
gse$dex <- gse$condition

We can also change the names of the levels. It is critical when one renames levels to not change the order. Here we will rename "Untreated" as "untrt" and "Dexamethasone" as "trt":

levels(gse$dex)
## [1] "Untreated"     "Dexamethasone"
# when renaming levels, the order must be preserved!
levels(gse$dex) <- c("untrt", "trt")

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).

Note: it is prefered in R that the first level of a factor be the reference level (e.g. control, or untreated samples). In this case, when the colData table was assembled the untreated samples were already set as the reference, but if this were not the case we could use relevel as shown below. While levels(...) <- above was simply for renaming the character strings associated with levels, relevel is a very different function, which decides how the variables will be coded, and how contrasts will be computed. For a two-group comparison, the use of relevel to change the reference level would flip the sign of a coefficient associated with a contrast between the two groups.

library("magrittr")
gse$dex %<>% relevel("untrt")
gse$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:

gse$dex <- relevel(gse$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

Again, we can quickly check the millions of fragments that could be mapped by Salmon to the genes (the second argument of round tells how many decimal points to keep).

round( colSums(assay(gse)) / 1e6, 1 )
## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 
##       21.1       19.3       26.1       15.7       25.3       31.9       19.7 
## SRR1039521 
##       21.8

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(gse, 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 <- round(assays(gse)[["counts"]])
head(countdata, 3)
##                    SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003.14        708        468        901        424       1188
## ENSG00000000005.5           0          0          0          0          0
## ENSG00000000419.12        455        510        604        352        583
##                    SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003.14       1091        806        599
## ENSG00000000005.5           0          0          0
## ENSG00000000419.12        774        410        499

In this count matrix, each row represents a gene, each column a sequenced RNA library, and the values give the estimated counts of fragments that were probabilistically assigned to the respective gene in each library by Salmon. We also have information on each of the samples (the columns of the count matrix). If you’ve imported the count data in some other way, for example loading a pre-computed count matrix, it is very important to check manually that the columns of the count matrix correspond to the rows of the sample information table.

coldata <- colData(gse)

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] 58294
keep <- rowSums(counts(dds)) > 1
dds <- dds[keep,]
nrow(dds)
## [1] 31604

For some datasets, it may make sense to perform additional filtering. For example, one can specify that at least 3 samples have a count of 10 or higher. One recommendation for the number of samples would be set to the smallest group size. Such a rule could be specified by creating a logic vector and subsetting the dds as above. Here is an example of another rule we could have used (here not used for filtering):

# at least 3 samples with a count of 10 or higher
keep <- rowSums(counts(dds) >= 10) >= 3

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
## ENSG00000000003.14  10.105781   9.852029  10.169726   9.991545  10.424865
## ENSG00000000419.12   9.692244   9.923647   9.801921   9.798653   9.763455
## ENSG00000000457.13   9.449592   9.312186   9.362754   9.459168   9.281415
##                    SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003.14  10.194490  10.315814  10.002177
## ENSG00000000419.12   9.874703   9.683211   9.845507
## ENSG00000000457.13   9.395937   9.477971   9.477027
colData(vsd)
## DataFrame with 8 rows and 5 columns
##                 names    donor     condition     cell      dex
##              <factor> <factor>      <factor> <factor> <factor>
## SRR1039508 SRR1039508   N61311     Untreated   N61311    untrt
## SRR1039509 SRR1039509   N61311 Dexamethasone   N61311      trt
## SRR1039512 SRR1039512  N052611     Untreated  N052611    untrt
## SRR1039513 SRR1039513  N052611 Dexamethasone  N052611      trt
## SRR1039516 SRR1039516  N080611     Untreated  N080611    untrt
## SRR1039517 SRR1039517  N080611 Dexamethasone  N080611      trt
## SRR1039520 SRR1039520  N061011     Untreated  N061011    untrt
## SRR1039521 SRR1039521  N061011 Dexamethasone  N061011      trt

Again, for the rlog:

rld <- rlog(dds, blind = FALSE)
head(assay(rld), 3)
##                    SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003.14   9.482613   9.172197   9.558383   9.346001   9.851349
## ENSG00000000419.12   8.860186   9.150196   9.000042   8.995902   8.951327
## ENSG00000000457.13   8.354790   8.166700   8.236582   8.366693   8.121781
##                    SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003.14   9.587602   9.727248   9.357876
## ENSG00000000419.12   9.091075   8.848782   9.054384
## ENSG00000000457.13   8.282307   8.392384   8.391023

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   39.42362                                                       
## SRR1039512   32.37620   44.93748                                            
## SRR1039513   51.09677   37.18799   41.79886                                 
## SRR1039516   35.59642   47.54671   34.83458   52.05265                      
## SRR1039517   51.26314   41.58572   46.89609   40.67315   39.74268           
## SRR1039520   32.38578   46.96000   30.35980   48.08669   37.07106   50.38349
## SRR1039521   51.49108   37.57383   47.17283   31.45899   52.62276   41.35941
##            SRR1039520
## SRR1039509           
## SRR1039512           
## SRR1039513           
## SRR1039516           
## SRR1039517           
## SRR1039520           
## SRR1039521   43.01502

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 variance stabilizing 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"))