Contents

1 Abstract

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

2 Introduction

RNA-sequencing (RNA-seq) has become the primary technology used for gene expression profiling, with the genome-wide detection of differentially expressed genes between two or more conditions of interest one of the most commonly asked questions by researchers. The edgeR (Robinson, McCarthy, and Smyth 2010) and limma packages (Ritchie et al. 2015) available from the Bioconductor project (Huber et al. 2015) offer a well-developed suite of statistical methods for dealing with this question for RNA-seq data.

In this article, we describe an edgeR - limma workflow for analysing RNA-seq data that takes gene-level counts as its input, and moves through pre-processing and exploratory data analysis before obtaining lists of differentially expressed (DE) genes and gene signatures. This analysis is enhanced through the use of interactive graphics from the Glimma package (???), that allows for a more detailed exploration of the data at both the sample and gene-level than is possible using static R plots.

The experiment analysed in this workflow is from Sheridan et al. (2015) (Sheridan et al. 2015) and consists of three cell populations (basal, luminal progenitor (LP) and mature luminal (ML)) sorted from the mammary glands of female virgin mice, each profiled in triplicate. RNA samples were sequenced across three batches on an Illumina HiSeq 2000 to obtain 100 base-pair single-end reads. The analysis outlined in this article assumes that reads obtained from an RNA-seq experiment have been aligned to an appropriate reference genome and summarised into counts associated with gene-specific regions. In this instance, reads were aligned to the mouse reference genome (mm10) using the R based pipeline available in the Rsubread package (specifically the align function (Liao, Smyth, and Shi 2013) followed by featureCounts (Liao, Smyth, and Shi 2014) for gene-level summarisation based on the in-built mm10 RefSeq-based annotation).

Count data for these samples can be downloaded from the Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/geo/ using GEO Series accession number GSE63310. Further information on experimental design and sample preparation is also available from GEO under this accession number.

3 Set-up

library(limma)
library(Glimma)
library(edgeR)
library(Mus.musculus)

4 Data packaging

4.1 Reading in count-data

To get started with this analysis, download the file GSE63310_RAW.tar available online from https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE63310&format=file, and extract the relevant files from this archive. The code below will do this, or you can do this step manually and then move on.

url <- "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE63310&format=file"
utils::download.file(url, destfile="GSE63310_RAW.tar", mode="wb") 
utils::untar("GSE63310_RAW.tar", exdir = ".")
files <- c("GSM1545535_10_6_5_11.txt", "GSM1545536_9_6_5_11.txt", "GSM1545538_purep53.txt",
  "GSM1545539_JMS8-2.txt", "GSM1545540_JMS8-3.txt", "GSM1545541_JMS8-4.txt",
  "GSM1545542_JMS8-5.txt", "GSM1545544_JMS9-P7c.txt", "GSM1545545_JMS9-P8c.txt")
for(i in paste(files, ".gz", sep=""))
  R.utils::gunzip(i, overwrite=TRUE)

Each of these text files contains the raw gene-level counts for a given sample. Note that our analysis only includes the basal, LP and ML samples from this experiment (see associated file names below).

files <- c("GSM1545535_10_6_5_11.txt", "GSM1545536_9_6_5_11.txt", 
   "GSM1545538_purep53.txt", "GSM1545539_JMS8-2.txt", 
   "GSM1545540_JMS8-3.txt", "GSM1545541_JMS8-4.txt", 
   "GSM1545542_JMS8-5.txt", "GSM1545544_JMS9-P7c.txt", 
   "GSM1545545_JMS9-P8c.txt")
read.delim(files[1], nrow=5)
##    EntrezID GeneLength Count
## 1    497097       3634     1
## 2 100503874       3259     0
## 3 100038431       1634     0
## 4     19888       9747     0
## 5     20671       3130     1

Whilst each of the nine text files can be read into R separately and combined into a matrix of counts, edgeR offers a convenient way to do this in one step using the readDGE function. The resulting DGEList-object contains a matrix of counts with 27,179 rows associated with unique Entrez gene identifiers (IDs) and nine columns associated with the individual samples in the experiment.

x <- readDGE(files, columns=c(1,3))
class(x)
## [1] "DGEList"
## attr(,"package")
## [1] "edgeR"
dim(x)
## [1] 27179     9

If the counts from all samples were stored in a single file, the data can be read into R and then converted into a DGEList-object using the DGEList function.

4.2 Organising sample information

For downstream analysis, sample-level information related to the experimental design needs to be associated with the columns of the counts matrix. This should include experimental variables, both biological and technical, that could have an effect on expression levels. Examples include cell type (basal, LP and ML in this experiment), genotype (wild-type, knock-out), phenotype (disease status, sex, age), sample treatment (drug, control) and batch information (date experiment was performed if samples were collected and analysed at distinct time points) to name just a few.

Our DGEList-object contains a samples data frame that stores both cell type (or group) and batch (sequencing lane) information, each of which consists of three distinct levels. Note that within x$samples, library sizes are automatically calculated for each sample and normalisation factors are set to 1. For simplicity, we remove the GEO sample IDs (GSM*) from the column names of our DGEList-object x.

samplenames <- substring(colnames(x), 12, nchar(colnames(x)))
samplenames
## [1] "10_6_5_11" "9_6_5_11"  "purep53"   "JMS8-2"    "JMS8-3"    "JMS8-4"    "JMS8-5"   
## [8] "JMS9-P7c"  "JMS9-P8c"
colnames(x) <- samplenames
group <- as.factor(c("LP", "ML", "Basal", "Basal", "ML", "LP", 
                     "Basal", "ML", "LP"))
x$samples$group <- group
lane <- as.factor(rep(c("L004","L006","L008"), c(3,4,2)))
x$samples$lane <- lane
x$samples
##                              files group lib.size norm.factors lane
## 10_6_5_11 GSM1545535_10_6_5_11.txt    LP 32863052            1 L004
## 9_6_5_11   GSM1545536_9_6_5_11.txt    ML 35335491            1 L004
## purep53     GSM1545538_purep53.txt Basal 57160817            1 L004
## JMS8-2       GSM1545539_JMS8-2.txt Basal 51368625            1 L006
## JMS8-3       GSM1545540_JMS8-3.txt    ML 75795034            1 L006
## JMS8-4       GSM1545541_JMS8-4.txt    LP 60517657            1 L006
## JMS8-5       GSM1545542_JMS8-5.txt Basal 55086324            1 L006
## JMS9-P7c   GSM1545544_JMS9-P7c.txt    ML 21311068            1 L008
## JMS9-P8c   GSM1545545_JMS9-P8c.txt    LP 19958838            1 L008

4.3 Organising gene annotations

A second data frame named genes in the DGEList-object is used to store gene-level information associated with rows of the counts matrix. This information can be retrieved using organism specific packages such as Mus.musculus (Bioconductor Core Team 2016b) for mouse (or Homo.sapiens (Bioconductor Core Team 2016a) for human) or the biomaRt package (Durinck et al. 2005, 2009) which interfaces the Ensembl genome databases in order to perform gene annotation.

The type of information that can be retrieved includes gene symbols, gene names, chromosome names and locations, Entrez gene IDs, Refseq gene IDs and Ensembl gene IDs to name just a few. biomaRt primarily works off Ensembl gene IDs, whereas Mus.musculus packages information from various sources and allows users to choose between many different gene IDs as the key.

The Entrez gene IDs available in our dataset were annotated using the Mus.musculus package to retrieve associated gene symbols and chromosome information.

geneid <- rownames(x)
genes <- select(Mus.musculus, keys=geneid, columns=c("SYMBOL", "TXCHROM"), 
                keytype="ENTREZID")
head(genes)
##    ENTREZID  SYMBOL TXCHROM
## 1    497097    Xkr4    chr1
## 2 100503874 Gm19938    <NA>
## 3 100038431 Gm10568    <NA>
## 4     19888     Rp1    chr1
## 5     20671   Sox17    chr1
## 6     27395  Mrpl15    chr1

As with any gene ID, Entrez gene IDs may not map one-to-one to the gene information of interest. It is important to check for duplicated gene IDs and to understand the source of duplication before resolving them. Our gene annotation contains 28 genes that map to multiple chromosomes (e.g. gene Gm1987 is associated with chr4 and chr4_JH584294_random and microRNA Mir5098 is associated with chr2, chr5, chr8, chr11 and chr17). To resolve duplicate gene IDs one could combine all chromosome information from the multi-mapped genes, such that gene Gm1987 would be is assigned to chr4 and chr4_JH584294_random, or select one of the chromosomes to represent the gene with duplicate annotation. For simplicity we do the latter, keeping only the first occurrence of each gene ID.

genes <- genes[!duplicated(genes$ENTREZID),]

In this example, the gene order is the same in both the annotation and the data object. If this is not the case due to missing and/or rearranged gene IDs, the match function can be used to order genes correctly. The data frame of gene annotations is then added to the data object and neatly packaged in a DGEList-object containing raw count data with associated sample information and gene annotations.

x$genes <- genes
x
## An object of class "DGEList"
## $samples
##                              files group lib.size norm.factors lane
## 10_6_5_11 GSM1545535_10_6_5_11.txt    LP 32863052            1 L004
## 9_6_5_11   GSM1545536_9_6_5_11.txt    ML 35335491            1 L004
## purep53     GSM1545538_purep53.txt Basal 57160817            1 L004
## JMS8-2       GSM1545539_JMS8-2.txt Basal 51368625            1 L006
## JMS8-3       GSM1545540_JMS8-3.txt    ML 75795034            1 L006
## JMS8-4       GSM1545541_JMS8-4.txt    LP 60517657            1 L006
## JMS8-5       GSM1545542_JMS8-5.txt Basal 55086324            1 L006
## JMS9-P7c   GSM1545544_JMS9-P7c.txt    ML 21311068            1 L008
## JMS9-P8c   GSM1545545_JMS9-P8c.txt    LP 19958838            1 L008
## 
## $counts
##            Samples
## Tags        10_6_5_11 9_6_5_11 purep53 JMS8-2 JMS8-3 JMS8-4 JMS8-5 JMS9-P7c JMS9-P8c
##   497097            1        2     342    526      3      3    535        2        0
##   100503874         0        0       5      6      0      0      5        0        0
##   100038431         0        0       0      0      0      0      1        0        0
##   19888             0        1       0      0     17      2      0        1        0
##   20671             1        1      76     40     33     14     98       18        8
## 27174 more rows ...
## 
## $genes
##    ENTREZID  SYMBOL TXCHROM
## 1    497097    Xkr4    chr1
## 2 100503874 Gm19938    <NA>
## 3 100038431 Gm10568    <NA>
## 4     19888     Rp1    chr1
## 5     20671   Sox17    chr1
## 27174 more rows ...

5 Data pre-processing

5.1 Transformations from the raw-scale

For differential expression and related analyses, gene expression is rarely considered at the level of raw counts since libraries sequenced at a greater depth will result in higher counts. Rather, it is common practice to transform raw counts onto a scale that accounts for such library size differences. Popular transformations include counts per million (CPM), log2-counts per million (log-CPM), reads per kilobase of transcript per million (RPKM), and fragments per kilobase of transcript per million (FPKM).

In our analyses, CPM and log-CPM transformations are used regularly although they do not account for gene length differences as RPKM and FPKM values do. Whilst RPKM and FPKM values can just as well be used, CPM and log-CPM values can be calculated using a counts matrix alone and will suffice for the type of comparisons we are interested in. Assuming that there are no differences in isoform usage between conditions, differential expression analyses look at gene expression changes between conditions rather than comparing expression across multiple genes or drawing conclusions on absolute levels of expression. In other words, gene lengths remain constant for comparisons of interest and any observed differences are a result of changes in condition rather than changes in gene length.

Here raw counts are converted to CPM and log-CPM values using the cpm function in edgeR. RPKM values are just as easily calculated as CPM values using the rpkm function in edgeR if gene lengths are available.

cpm <- cpm(x)
lcpm <- cpm(x, log=TRUE)

A CPM value of 1 for a gene equates to having 20 counts in the sample with the lowest sequencing depth (JMS0-P8c, library size approx. 20 million) or 76 counts in the sample with the greatest sequencing depth (JMS8-3, library size approx. 76 million).

The log-CPM values will be used for exploratory plots. When log=TRUE, the cpm function adds an offset to the CPM values before converting to the log2-scale. By default, the offset is 2/L where 2 is the “prior count” and L is the average library size in millions, so the log-CPM values are related to the CPM values by log2(CPM + 2/L). This calculation ensures that any two read counts with identical CPM values will also have identical log-CPM values. The prior count avoids taking the logarithm of zero, and also reduces spurious variability for genes with very low counts by shrinking all the inter-sample log-fold-changes towards zero, something that is helpful for exploratory plotting. For this dataset, the average library size is about 45.5 million, so L approx. 45.5 and the minimum log-CPM value for each sample becomes log2(2/45.5) = -4.51. In other words, a counr of zero for this data maps to a log-CPM value of -4.51 after adding the prior count or offset:

L <- mean(x$samples$lib.size) * 1e-6
M <- median(x$samples$lib.size) * 1e-6
c(L, M)
## [1] 45.5 51.4
summary(lcpm)
##    10_6_5_11        9_6_5_11        purep53          JMS8-2          JMS8-3     
##  Min.   :-4.51   Min.   :-4.51   Min.   :-4.51   Min.   :-4.51   Min.   :-4.51  
##  1st Qu.:-4.51   1st Qu.:-4.51   1st Qu.:-4.51   1st Qu.:-4.51   1st Qu.:-4.51  
##  Median :-0.68   Median :-0.36   Median :-0.10   Median :-0.09   Median :-0.43  
##  Mean   : 0.17   Mean   : 0.33   Mean   : 0.44   Mean   : 0.41   Mean   : 0.32  
##  3rd Qu.: 4.29   3rd Qu.: 4.56   3rd Qu.: 4.60   3rd Qu.: 4.55   3rd Qu.: 4.58  
##  Max.   :14.76   Max.   :13.50   Max.   :12.96   Max.   :12.85   Max.   :12.96  
##      JMS8-4          JMS8-5         JMS9-P7c        JMS9-P8c    
##  Min.   :-4.51   Min.   :-4.51   Min.   :-4.51   Min.   :-4.51  
##  1st Qu.:-4.51   1st Qu.:-4.51   1st Qu.:-4.51   1st Qu.:-4.51  
##  Median :-0.41   Median :-0.07   Median :-0.17   Median :-0.33  
##  Mean   : 0.25   Mean   : 0.40   Mean   : 0.37   Mean   : 0.27  
##  3rd Qu.: 4.32   3rd Qu.: 4.43   3rd Qu.: 4.60   3rd Qu.: 4.44  
##  Max.   :14.85   Max.   :13.19   Max.   :12.94   Max.   :14.01

Log-CPM values are also used in downstream linear modeling via limma’s voom function, although voom recomputes its own log-CPM values internally with a smaller prior count.

5.2 Removing genes that are lowly expressed

All datasets will include a mix of genes that are expressed and those that are not expressed. Whilst it is of interest to examine genes that are expressed in one condition but not in another, some genes are unexpressed throughout all samples. In fact, 19% of genes in this dataset have zero counts across all nine samples.

table(rowSums(x$counts==0)==9)
## 
## FALSE  TRUE 
## 22026  5153

Plotting the distribution log-CPM values shows that a sizeable proportion of genes within each sample are either unexpressed or lowly-expressed with log-CPM values that are small or negative (Figure 1A).

Genes that do not have a worthwhile number of reads in any sample should be filtered out of the downstream analyses. There are several reasons for this. From a biological point of view, genes that not expressed at a biologically meaningful level in any condition are not of interest and are therefore best ignored. From a statistical point of view, removing low count genes allows the mean-variance relationship in the data to be estimated with greater reliability and also reduces the number of statistical tests that need to be carried out in downstream analyses looking at differential expression.

The filterByExpr function in the edgeR package provides an automatic way to filter genes, while keeping as many genes as possible with worthwhile counts.

keep.exprs <- filterByExpr(x, group=group)
x <- x[keep.exprs,, keep.lib.sizes=FALSE]
dim(x)
## [1] 16624     9

By default, the function keeps genes with about 10 read counts or more in a minimum number of samples, where the number of samples is chosen according to the minimum group sample size. The actual filtering uses CPM values rather than counts in order to avoid giving preference to samples with large library sizes. For this dataset, the median library size is about 51 million and 10/51 approx. 0.2, so the filterByExpr function keeps genes that have a CPM of 0.2 or more in at least three samples. A biologically interesting gene should be expressed in at least three samples because all the cell type groups have three replicates. The cutoffs used depend on the sequencing depth and on the experimental design. If the library sizes had been larger then a lower CPM cutoff would have been chosen, because larger library sizes provide better resolution to explore more genes at lower expression levels. Alternatively, smaller library sizes decrease our ability to explore marginal genes and hence would have led to a higher CPM cutoff.

Using this criterion, the number of genes is reduced to 16,624, about 60% of the number that we started with (panel B of the next figure). Note that subsetting the entire DGEList-object removes both the counts and the associated gene information for the filtered genes. The filtered DGEList-object keeps the gene information and the counts for the retained genes correctly associated.

Code to produce the figure is given below.

lcpm.cutoff <- log2(10/M + 2/L)
library(RColorBrewer)
nsamples <- ncol(x)
col <- brewer.pal(nsamples, "Paired")
par(mfrow=c(1,2))
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")
title(main="A. Raw data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col=col, bty="n")
lcpm <- cpm(x, log=TRUE)
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")
title(main="B. Filtered data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col=col, bty="n")
The density of log-CPM values for raw pre-filtered data (A) and post-filtered data (B) are shown for each sample. Dotted vertical lines mark the log-CPM threshold (equivalent to a CPM value of about 0.2) used in the filtering step.

Figure 1: The density of log-CPM values for raw pre-filtered data (A) and post-filtered data (B) are shown for each sample
Dotted vertical lines mark the log-CPM threshold (equivalent to a CPM value of about 0.2) used in the filtering step.

5.3 Normalising gene expression distributions

During the sample preparation or sequencing process, external factors that are not of biological interest can affect the expression of individual samples. For example, samples processed in the first batch of an experiment can have higher expression overall when compared to samples processed in a second batch. It is assumed that all samples should have a similar range and distribution of expression values. Normalisation is required to ensure that the expression distributions of each sample are similar across the entire experiment.

Any plot showing the per sample expression distributions, such as a density or boxplot, is useful in determining whether any samples are dissimilar to others. Distributions of log-CPM values are similar throughout all samples within this dataset (panel B of the figure above).

Nonetheless, normalisation by the method of trimmed mean of M-values (TMM) (Robinson and Oshlack 2010) is performed using the calcNormFactors function in edgeR. The normalisation factors calculated here are used as a scaling factor for the library sizes. When working with DGEList-objects, these normalisation factors are automatically stored in x$samples$norm.factors. For this dataset the effect of TMM-normalisation is mild, as evident in the magnitude of the scaling factors, which are all relatively close to 1.

x <- calcNormFactors(x, method = "TMM")
x$samples$norm.factors
## [1] 0.894 1.025 1.046 1.046 1.016 0.922 0.996 1.086 0.984

To give a better visual representation of the effects of normalisation, the data was duplicated then adjusted so that the counts of the first sample are reduced to 5% of their original values, and in the second sample they are inflated to be 5-times larger.

x2 <- x
x2$samples$norm.factors <- 1
x2$counts[,1] <- ceiling(x2$counts[,1]*0.05)
x2$counts[,2] <- x2$counts[,2]*5

The figure below shows the expression distribution of samples for unnormalised and normalised data, where distributions are noticeably different pre-normalisation and are similar post-normalisation. Here the first sample has a small TMM scaling factor of 0.06, whereas the second sample has a large scaling factor of 6.08 – neither values are close to 1.

par(mfrow=c(1,2))
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="A. Example: Unnormalised data",ylab="Log-cpm")
x2 <- calcNormFactors(x2)  
x2$samples$norm.factors
## [1] 0.0577 6.0829 1.2202 1.1648 1.1966 1.0466 1.1505 1.2543 1.1090
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")
Example data: Boxplots of log-CPM values showing expression distributions for unnormalised data (A) and normalised data (B) for each sample in the modified dataset where the counts in samples 1 and 2 have been scaled to 5% and 500% of their original values respectively.

Figure 2: Example data: Boxplots of log-CPM values showing expression distributions for unnormalised data (A) and normalised data (B) for each sample in the modified dataset where the counts in samples 1 and 2 have been scaled to 5% and 500% of their original values respectively

5.4 Unsupervised clustering of samples

In our opinion, one of the most important exploratory plots to examine for gene expression analyses is the multi-dimensional scaling (MDS) plot, or similar. The plot shows similarities and dissimilarities between samples in an unsupervised manner so that one can have an idea of the extent to which differential expression can be detected before carrying out formal tests. Ideally, samples would cluster well within the primary condition of interest, and any sample straying far from its group could be identified and followed up for sources of error or extra variation. If present, technical replicates should lie very close to one another.

Such a plot can be made in limma using the plotMDS function. The first dimension represents the leading-fold-change that best separates samples and explains the largest proportion of variation in the data, with subsequent dimensions having a smaller effect and being orthogonal to the ones before it. When experimental design involves multiple factors, it is recommended that each factor is examined over several dimensions. If samples cluster by a given factor in any of these dimensions, it suggests that the factor contributes to expression differences and is worth including in the linear modelling. On the other hand, factors that show little or no effect may be left out of downstream analysis.

In this dataset, samples can be seen to cluster well within experimental groups over dimension 1 and 2, and then separate by sequencing lane (sample batch) over dimension 3 (shown in the plot below). Keeping in mind that the first dimension explains the largest proportion of variation in the data, notice that the range of values over the dimensions become smaller as we move to higher dimensions.

Whilst all samples cluster by groups, the largest transcriptional difference is observed between basal and LP, and basal and ML over dimension 1. For this reason, it is expected that pairwise comparisons between cell populations will result in a greater number of DE genes for comparisons involving basal samples, and relatively small numbers of DE genes when comparing ML to LP. Datasets where samples do not cluster by experimental group may show little or no evidence of differential expression in the downstream analysis.

To create the MDS plots, we assign different colours to the factors of interest. Dimensions 1 and 2 are examined using the color grouping defined by cell types.

Dimensions 3 and 4 are examined using the colour grouping defined by sequencing lanes (batch).

lcpm <- cpm(x, log=TRUE)
par(mfrow=c(1,2))
col.group <- group
levels(col.group) <-  brewer.pal(nlevels(col.group), "Set1")
col.group <- as.character(col.group)
col.lane <- lane
levels(col.lane) <-  brewer.pal(nlevels(col.lane), "Set2")
col.lane <- as.character(col.lane)
plotMDS(lcpm, labels=group, col=col.group)
title(main="A. Sample groups")
plotMDS(lcpm, labels=lane, col=col.lane, dim=c(3,4))
title(main="B. Sequencing lanes")
MDS plots of log-CPM values over dimensions 1 and 2 with samples coloured and labeled by sample groups (A) and over dimensions 3 and 4 with samples coloured and labeled by sequencing lane (B). Distances on the plot correspond to the leading fold-change, which is the average (root-mean-square) log2-fold-change for the 500 genes most divergent between each pair of samples by default.

Figure 3: MDS plots of log-CPM values over dimensions 1 and 2 with samples coloured and labeled by sample groups (A) and over dimensions 3 and 4 with samples coloured and labeled by sequencing lane (B)
Distances on the plot correspond to the leading fold-change, which is the average (root-mean-square) log2-fold-change for the 500 genes most divergent between each pair of samples by default.

Alternatively, the Glimma package offers the convenience of an interactive MDS plot where multiple dimensions can be explored. The glMDSPlot function generates an html page (that is opened in a browser if launch=TRUE) with an MDS plot in the left panel and a barplot showing the proportion of variation explained by each dimension in the right panel. Clicking on the bars of the bar plot changes the pair of dimensions plotted in the MDS plot, and hovering over the individual points reveals the sample label. The colour scheme can be changed as well to highlight cell population or sequencing lane (batch). An interactive MDS plot of this dataset can be found at http://bioinf.wehi.edu.au/folders/limmaWorkflow/glimma-plots/MDS-Plot.html.

glMDSPlot(lcpm, labels=paste(group, lane, sep="_"), 
          groups=x$samples[,c(2,5)], launch=FALSE)

Link to Interactive MDS plot

6 Differential expression analysis

6.1 Creating a design matrix and contrasts

In this study, it is of interest to see which genes are expressed at different levels between the three cell populations profiled. In our analysis, linear models are fitted to the data with the assumption that the underlying data is normally distributed. To get started, a design matrix is set up with both the cell population and sequencing lane (batch) information.

design <- model.matrix(~0+group+lane)
colnames(design) <- gsub("group", "", colnames(design))
design
##   Basal LP ML laneL006 laneL008
## 1     0  1  0        0        0
## 2     0  0  1        0        0
## 3     1  0  0        0        0
## 4     1  0  0        1        0
## 5     0  0  1        1        0
## 6     0  1  0        1        0
## 7     1  0  0        1        0
## 8     0  0  1        0        1
## 9     0  1  0        0        1
## attr(,"assign")
## [1] 1 1 1 2 2
## attr(,"contrasts")
## attr(,"contrasts")$group
## [1] "contr.treatment"
## 
## attr(,"contrasts")$lane
## [1] "contr.treatment"

For a given experiment, there are usually several equivalent ways to set up an appropriate design matrix. For example, ~0+group+lane removes the intercept from the first factor, group, but an intercept remains in the second factor lane. Alternatively, ~group+lane could be used to keep the intercepts in both group and lane. Understanding how to interpret the coefficients estimated in a given model is key here. We choose the first model for our analysis, as setting up model contrasts is more straight forward in the absence of an intercept for group. Contrasts for pairwise comparisons between cell populations are set up in limma using the makeContrasts function.

contr.matrix <- makeContrasts(
   BasalvsLP = Basal-LP, 
   BasalvsML = Basal - ML, 
   LPvsML = LP - ML, 
   levels = colnames(design))
contr.matrix
##           Contrasts
## Levels     BasalvsLP BasalvsML LPvsML
##   Basal            1         1      0
##   LP              -1         0      1
##   ML               0        -1     -1
##   laneL006         0         0      0
##   laneL008         0         0      0

A key strength of limma’s linear modelling approach, is the ability accommodate arbitrary experimental complexity. Simple designs, such as the one in this workflow, with cell type and batch, through to more complicated factorial designs and models with interaction terms can be handled relatively easily. Where experimental or technical effects can be modelled using a random effect, another possibility in limma is to estimate correlations using duplicateCorrelation by specifying a block argument for both this function and in the lmFit linear modelling step.

6.2 Removing heteroscedascity from count data

It has been shown that for RNA-seq count data, the variance is not independent of the mean (Law et al. 2014) – this is true of raw counts or when transformed to log-CPM values. Methods that model counts using a Negative Binomial distribution assume a quadratic mean-variance relationship. In limma, linear modelling is carried out on the log-CPM values which are assumed to be normally distributed and the mean-variance relationship is accommodated using precision weights calculated by the voom function.

When operating on a DGEList-object, voom converts raw counts to log-CPM values by automatically extracting library sizes and normalisation factors from x itself. Additional normalisation to log-CPM values can be specified within voom using the normalize.method argument.

The mean-variance relationship of log-CPM values for this dataset is shown in the left-hand panel of the next figure. Typically, the voom-plot shows a decreasing trend between the means and variances resulting from a combination of technical variation in the sequencing experiment and biological variation amongst the replicate samples from different cell populations. Experiments with high biological variation usually result in flatter trends, where variance values plateau at high expression values. Experiments with low biological variation tend to result in sharp decreasing trends.

Moreover, the voom-plot provides a visual check on the level of filtering performed upstream. If filtering of lowly-expressed genes is insufficient, a drop in variance levels can be observed at the low end of the expression scale due to very small counts. If this is observed, one should return to the earlier filtering step and increase the expression threshold applied to the dataset.

Where sample-level variation is evident from earlier inspections of the MDS plot, the voomWithQualityWeights function can be used to simultaneously incorporate sample-level weights together with the abundance dependent weights estimated by voom (Liu et al. 2015). For an example of this approach, see Liu et al. (2016) (Liu et al. 2016).

par(mfrow=c(1,2))
v <- voom(x, design, plot=TRUE)
v
## An object of class "EList"
## $genes
##   ENTREZID SYMBOL TXCHROM
## 1   497097   Xkr4    chr1
## 5    20671  Sox17    chr1
## 6    27395 Mrpl15    chr1
## 7    18777 Lypla1    chr1
## 9    21399  Tcea1    chr1
## 16619 more rows ...
## 
## $targets
##                              files group lib.size norm.factors lane
## 10_6_5_11 GSM1545535_10_6_5_11.txt    LP 29387429        0.894 L004
## 9_6_5_11   GSM1545536_9_6_5_11.txt    ML 36212498        1.025 L004
## purep53     GSM1545538_purep53.txt Basal 59771061        1.046 L004
## JMS8-2       GSM1545539_JMS8-2.txt Basal 53711278        1.046 L006
## JMS8-3       GSM1545540_JMS8-3.txt    ML 77015912        1.016 L006
## JMS8-4       GSM1545541_JMS8-4.txt    LP 55769890        0.922 L006
## JMS8-5       GSM1545542_JMS8-5.txt Basal 54863512        0.996 L006
## JMS9-P7c   GSM1545544_JMS9-P7c.txt    ML 23139691        1.086 L008
## JMS9-P8c   GSM1545545_JMS9-P8c.txt    LP 19634459        0.984 L008
## 
## $E
##         Samples
## Tags     10_6_5_11 9_6_5_11 purep53 JMS8-2 JMS8-3 JMS8-4 JMS8-5 JMS9-P7c JMS9-P8c
##   497097     -4.29    -3.86   2.519  3.293  -4.46  -3.99  3.287   -3.210    -5.30
##   20671      -4.29    -4.59   0.356 -0.407  -1.20  -1.94  0.844   -0.323    -1.21
##   27395       3.88     4.41   4.517  4.562   4.34   3.79  3.899    4.340     4.12
##   18777       4.71     5.57   5.396  5.162   5.65   5.08  5.060    5.751     5.14
##   21399       4.79     4.75   5.370  5.122   4.87   4.94  5.138    5.031     4.98
## 16619 more rows ...
## 
## $weights
##       [,1]  [,2] [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9]
## [1,]  1.08  1.33 19.8 20.27  1.99  1.40 20.49  1.11  1.08
## [2,]  1.17  1.46  4.8  8.66  3.61  2.63  8.76  3.21  2.54
## [3,] 20.22 25.57 30.4 28.53 31.35 25.74 28.72 21.20 16.66
## [4,] 26.95 32.51 33.6 33.23 34.23 32.35 33.33 30.35 24.26
## [5,] 26.61 28.50 33.6 33.21 33.57 32.00 33.31 25.17 23.57
## 16619 more rows ...
## 
## $design
##   Basal LP ML laneL006 laneL008
## 1     0  1  0        0        0
## 2     0  0  1        0        0
## 3     1  0  0        0        0
## 4     1  0  0        1        0
## 5     0  0  1        1        0
## 6     0  1  0        1        0
## 7     1  0  0        1        0
## 8     0  0  1        0        1
## 9     0  1  0        0        1
## attr(,"assign")
## [1] 1 1 1 2 2
## attr(,"contrasts")
## attr(,"contrasts")$group
## [1] "contr.treatment"
## 
## attr(,"contrasts")$lane
## [1] "contr.treatment"
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend")