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In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling transcriptional activity in biological systems. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. Changes in expression can then be associated with differences in biology, providing avenues for further investigation into potential mechanisms of action.
This article provides a detailed workflow for analyzing an RNA-seq study from the raw reads through to differential expression and pathway analysis using Bioconductor packages (Huber et al. 2015). The article gives a complete analysis of RNA-seq data that were collected to study the effects of pregnancy and lactation on the luminal cell lineage in the mouse mammary gland (Fu et al. 2015). The pipeline uses the Rsubread package (Liao, Smyth, and Shi 2013) for mapping reads and assigning them to genes, and the edgeR package (Robinson, McCarthy, and Smyth 2010) for statistical analyses.
A published version of this workflow, including reviewer reports and comments is available at F1000Research.
RNA-seq analysis involves a number of steps, including read alignment, read summarization, differential expression and pathway analysis. Here we use the Subread aligner (Liao, Smyth, and Shi 2013) for mapping and featureCounts (Liao, Smyth, and Shi 2014) for assigning reads to genes. As well as being fast and efficient, these algorithms have the advantage of having native implementations as R functions in the Rsubread package. This means that the entire analysis can be conducted efficiently within the R environment.
The workflow uses edgeR’s quasi-likelihood pipeline (edgeR-quasi) for differential expression. This statistical methodology uses negative binomial generalized linear models (McCarthy, Chen, and Smyth 2012) but with F-tests instead of likelihood ratio tests (Lund et al. 2012). This method provides stricter error rate control than other negative binomial based pipelines, including the traditional edgeR pipelines (Robinson and Smyth 2008; Robinson and Smyth 2007; McCarthy, Chen, and Smyth 2012) or DESeq2 (Love, Huber, and Anders 2014). The edgeR-quasi pipeline is based on similar statistical methodology to that of the QuasiSeq package (Lund et al. 2012), which has performed well in third-party comparisons (Burden, Qureshi, and Wilson 2014). Compared to QuasiSeq, the edgeR functions offer speed improvements and some additional statistical refinements (Lun, Chen, and Smyth 2016). The RNA-seq pipelines of the limma package also offer excellent error rate control (Law et al. 2014; Ritchie et al. 2015). While the limma pipelines are recommended for large-scale datasets, because of their speed and flexibility, the edgeR-quasi pipeline gives better performance in low-count situations (Lun and Smyth 2014; Lun and Smyth 2015). For the data analyzed here, the edgeR-quasi, limma-voom and limma-trend pipelines are all equally suitable and give similar results.
The analysis approach illustrated in this article can be applied to any RNA-seq study that includes some replication, but it is especially appropriate for designed experiments with multiple treatment factors and with small numbers of biological replicates. The approach assumes that RNA samples have been extracted from cells of interest under two or more treatment conditions, that RNA-seq profiling has been applied to each RNA sample and that there are independent biological replicates for at least one of the treatment conditions. The Rsubread part of the workflow takes FASTQ files of raw sequence reads as input, while the edgeR part of the pipeline takes a matrix of genewise read counts as input.
This workflow demonstrates a complete bioinformatics analysis of an RNA-seq study that is available from the GEO repository as series GSE60450. The RNA-seq data were collected to study the lineage of luminal cells in the mouse mammary gland and in particular how the expression profiles of the members of the lineage change upon pregnancy and lactation (Fu et al. 2015). Specifically, the study examined the expression profiles of basal stem-cell enriched cells (B) and committed luminal cells (L) in the mammary glands of virgin, pregnant and lactating mice. There are therefore six groups of RNA samples, one for each combination of cell type and mouse status. Two biological replicates were collected for each group.
This study used an Illumina Hiseq sequencer to generate about 30 million 100bp single-end reads for each sample. Subread version 1.4.4 (http://subread.sourceforge.net) was used to align the reads to the mouse mm10 genome and featureCounts was used to assign reads to Entrez Genes using RefSeq gene annotation. The FASTQ files containing the raw sequence reads were deposited to the Sequence Read Archive (SRA) repository and the read counts were deposited to GEO.
This experimental design is summarized in the table below, where the
basal and luminal cell types are abbreviated with
respectively. The GEO and SRA identifiers for each RNA sample are also
library(RnaSeqGeneEdgeRQL) ## Loading required package: edgeR ## Loading required package: limma ## Loading required package: gplots ## ## Attaching package: 'gplots' ## The following object is masked from 'package:stats': ## ## lowess ## Loading required package: org.Mm.eg.db ## Loading required package: AnnotationDbi ## Loading required package: stats4 ## Loading required package: BiocGenerics ## Loading required package: parallel ## ## Attaching package: 'BiocGenerics' ## The following objects are masked from 'package:parallel': ## ## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, ## clusterExport, clusterMap, parApply, parCapply, parLapply, ## parLapplyLB, parRapply, parSapply, parSapplyLB ## The following object is masked from 'package:limma': ## ## plotMA ## The following objects are masked from 'package:stats': ## ## IQR, mad, sd, var, xtabs ## The following objects are masked from 'package:base': ## ## anyDuplicated, append, as.data.frame, cbind, colMeans, ## colnames, colSums, do.call, duplicated, eval, evalq, Filter, ## Find, get, grep, grepl, intersect, is.unsorted, lapply, ## lengths, Map, mapply, match, mget, order, paste, pmax, ## pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, ## rowMeans, rownames, rowSums, sapply, setdiff, sort, table, ## tapply, union, unique, unsplit, which, which.max, which.min ## Loading required package: Biobase ## Welcome to Bioconductor ## ## Vignettes contain introductory material; view with ## 'browseVignettes()'. To cite Bioconductor, see ## 'citation("Biobase")', and for packages 'citation("pkgname")'. ## Loading required package: IRanges ## Loading required package: S4Vectors ## ## Attaching package: 'S4Vectors' ## The following object is masked from 'package:gplots': ## ## space ## The following object is masked from 'package:base': ## ## expand.grid ## ## Loading required package: GO.db ## targetsFile <- system.file("extdata", "targets.txt", package="RnaSeqGeneEdgeRQL") targets <- read.delim(targetsFile, stringsAsFactors=FALSE) targets ## GEO SRA CellType Status ## MCL1.DG GSM1480297 SRR1552450 B virgin ## MCL1.DH GSM1480298 SRR1552451 B virgin ## MCL1.DI GSM1480299 SRR1552452 B pregnant ## MCL1.DJ GSM1480300 SRR1552453 B pregnant ## MCL1.DK GSM1480301 SRR1552454 B lactating ## MCL1.DL GSM1480302 SRR1552455 B lactating ## MCL1.LA GSM1480291 SRR1552444 L virgin ## MCL1.LB GSM1480292 SRR1552445 L virgin ## MCL1.LC GSM1480293 SRR1552446 L pregnant ## MCL1.LD GSM1480294 SRR1552447 L pregnant ## MCL1.LE GSM1480295 SRR1552448 L lactating ## MCL1.LF GSM1480296 SRR1552449 L lactating
The experiment can be viewed as a one-way layout with six groups. For later use, we combine the treatment factors into a single grouping factor:
group <- paste(targets$CellType, targets$Status, sep=".") group <- factor(group) table(group) ## group ## B.lactating B.pregnant B.virgin L.lactating L.pregnant L.virgin ## 2 2 2 2 2 2
Note. This study isolated carefully sorted cell populations obtained from genetically indentical mice under controlled laboratory conditions. As will be shown during the analysis below, the relatively small sample size (n = 2 in each group) here is justified by the low background variability and by the fact that the expression profiles of the different groups are distinctly different. Studies with more variability (for example on human subjects) or with smaller effect sizes may well require more biological replicates for reliable results.
Readers wishing to reproduce the analysis presented in this article can either download the matrix of read counts from GEO or recreate the read count matrix from the raw sequence counts. We will present first the analysis using the downloaded matrix of counts. At the end of this article we will present the R commands needed to recreate this matrix.
The following commands download the genewise read counts for the GEO
series GSE60450. The zipped tab-delimited text file
GSE60450_Lactation-GenewiseCounts.txt.gz will be downloaded to the
working R directory:
FileURL <- paste( "http://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE60450", "format=file", "file=GSE60450_Lactation-GenewiseCounts.txt.gz", sep="&") download.file(FileURL, "GSE60450_Lactation-GenewiseCounts.txt.gz")
The counts can then be read into a data.frame in R:
GenewiseCounts <- read.delim("GSE60450_Lactation-GenewiseCounts.txt.gz", row.names="EntrezGeneID") colnames(GenewiseCounts) <- substring(colnames(GenewiseCounts),1,7) dim(GenewiseCounts) ##  27179 13 head(GenewiseCounts) ## Length MCL1.DG MCL1.DH MCL1.DI MCL1.DJ MCL1.DK MCL1.DL MCL1.LA ## 497097 3634 438 300 65 237 354 287 0 ## 100503874 3259 1 0 1 1 0 4 0 ## 100038431 1634 0 0 0 0 0 0 0 ## 19888 9747 1 1 0 0 0 0 10 ## 20671 3130 106 182 82 105 43 82 16 ## 27395 4203 309 234 337 300 290 270 560 ## MCL1.LB MCL1.LC MCL1.LD MCL1.LE MCL1.LF ## 497097 0 0 0 0 0 ## 100503874 0 0 0 0 0 ## 100038431 0 0 0 0 0 ## 19888 3 10 2 0 0 ## 20671 25 18 8 3 10 ## 27395 464 489 328 307 342
The row names of
GenewiseCounts are the Entrez Gene Identifiers. The
first column contains the length of each gene, being the total number of
bases in exons and UTRs for that gene. The remaining 12 columns contain
read counts and correspond to rows of
The edgeR package stores data in a simple list-based data object
DGEList. This object is easy to use as it can be manipulated
like an ordinary list in R, and it can also be subsetted like a matrix.
The main components of a
DGEList object are a matrix of read counts,
sample information in the
data.frame format and optional gene
annotation. We enter the counts into a
DGEList object using the
DGEList in edgeR:
library(edgeR) y <- DGEList(GenewiseCounts[,-1], group=group, genes=GenewiseCounts[,1,drop=FALSE]) options(digits=3) y$samples ## group lib.size norm.factors ## MCL1.DG B.virgin 23227641 1 ## MCL1.DH B.virgin 21777891 1 ## MCL1.DI B.pregnant 24100765 1 ## MCL1.DJ B.pregnant 22665371 1 ## MCL1.DK B.lactating 21529331 1 ## MCL1.DL B.lactating 20015386 1 ## MCL1.LA L.virgin 20392113 1 ## MCL1.LB L.virgin 21708152 1 ## MCL1.LC L.pregnant 22241607 1 ## MCL1.LD L.pregnant 21988240 1 ## MCL1.LE L.lactating 24723827 1 ## MCL1.LF L.lactating 24657293 1
The Entrez Gene Ids link to gene information in the NCBI database. The
org.Mm.eg.db package can be used to complement the gene annotation
information. Here, a column of gene symbols is added to
library(org.Mm.eg.db) y$genes$Symbol <- mapIds(org.Mm.eg.db, rownames(y), keytype="ENTREZID", column="SYMBOL") head(y$genes) ## Length Symbol ## 497097 3634 Xkr4 ## 100503874 3259 Gm19938 ## 100038431 1634 Gm10568 ## 19888 9747 Rp1 ## 20671 3130 Sox17 ## 27395 4203 Mrpl15
Entrez Ids that no longer have official gene symbols are dropped from
the analysis. The whole
DGEList object, including annotation as well
as counts, can be subsetted by rows as if it was a matrix:
y <- y[!is.na(y$genes$Symbol), ] dim(y) ##  26327 12
Genes that have very low counts across all the libraries should be removed prior to downstream analysis. This is justified on both biological and statistical grounds. From biological point of view, a gene must be expressed at some minimal level before it is likely to be translated into a protein or to be considered biologically important. From a statistical point of view, genes with consistently low counts are very unlikely be assessed as significantly DE because low counts do not provide enough statistical evidence for a reliable judgement to be made. Such genes can therefore be removed from the analysis without any loss of information.
As a rule of thumb, we require that a gene have a count of at least 10–15 in at least some libraries before it is considered to be expressed in the study. We could explicitly select for genes that have at least a couple of counts of 10 or more, but it is slightly better to base the filtering on count-per-million (CPM) values so as to avoid favoring genes that are expressed in larger libraries over those expressed in smaller libraries. For the current analysis, we keep genes that have CPM values above 0.5 in at least two libraries:
keep <- rowSums(cpm(y) > 0.5) >= 2 table(keep) ## keep ## FALSE TRUE ## 10686 15641
Here the cutoff of 0.5 for the CPM has been chosen because it is roughly equal to 10/L where L is the minimum library size in millions. The library sizes here are 20–25 million. We used a round value of 0.5 just for simplicity; the exact value is not important because the downstream differential expression analysis is not sensitive to the small changes in this parameter. The requirement of ≥2 libraries is because each group contains two replicates. This ensures that a gene will be retained if it is expressed in both the libraries belonging to any of the six groups.
The above filtering rule attempts to keep the maximum number of
interesting genes in the analysis, but other sensible filtering criteria
are also possible. For example
keep <- rowSums(y\$counts) > 50 is a
very simple criterion that would keep genes with a total read count of
more than 50. This would give similar downstream results for this
dataset to the filtering actually used. Whatever the filtering rule, it
should be independent of the information in the targets file. It should
not make any reference to which RNA libraries belong to which group,
because doing so would bias the subsequent differential expression
DGEList object is subsetted to retain only the non-filtered genes:
y <- y[keep, , keep.lib.sizes=FALSE]
keep.lib.sizes=FALSE causes the library sizes to be
recomputed after the filtering. This is generally recommended, although
the effect on the downstream analysis is usually small.
Normalization by trimmed mean of M values (TMM) (Robinson and Oshlack
2010) is performed by using the
calcNormFactors function, which
DGEList argument with only the
norm.factors changed. It
calculates a set of normalization factors, one for each sample, to
eliminate composition biases between libraries. The product of these
factors and the library sizes defines the effective library size, which
replaces the original library size in all downstream analyses.
y <- calcNormFactors(y) y$samples ## group lib.size norm.factors ## MCL1.DG B.virgin 23137472 1.235 ## MCL1.DH B.virgin 21687755 1.213 ## MCL1.DI B.pregnant 23974787 1.125 ## MCL1.DJ B.pregnant 22545375 1.069 ## MCL1.DK B.lactating 21420532 1.036 ## MCL1.DL B.lactating 19916685 1.087 ## MCL1.LA L.virgin 20273585 1.370 ## MCL1.LB L.virgin 21568458 1.368 ## MCL1.LC L.pregnant 22117517 1.006 ## MCL1.LD L.pregnant 21877287 0.924 ## MCL1.LE L.lactating 24657903 0.529 ## MCL1.LF L.lactating 24600304 0.535
The normalization factors of all the libraries multiply to unity. A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size. As a result, the effective library size will be scaled down for that sample. Here we see that the luminal-lactating samples have low normalization factors. This is a sign that these samples contain a number of very highly upregulated genes.
Note. In general, we find TMM normalization to be satisfactory for almost all well-designed mRNA gene expression experiments. Single-cell RNA-seq is an exception, for which specialized normalization methods are needed (Lun, Bach, and Marioni 2016). Another, less common, type of study requiring special treatment is that with global differential expression, with more than half of the genome differentially expressed between experimental conditions in the same direction (D. Wu et al. 2013). Global differential expression should generally be avoided in well designed experiments. When it can’t be avoided, then some normalization reference such as spike-ins needs to be built into the experiment for reliable normalization to be done (Risso et al. 2014).
The RNA samples can be clustered in two dimensions using multi-dimensional scaling (MDS) plots. This is both an analysis step and a quality control step to explore the overall differences between the expression profiles of the different samples. Here we decorate the MDS plot to indicate the cell groups:
pch <- c(0,1,2,15,16,17) colors <- rep(c("darkgreen", "red", "blue"), 2) plotMDS(y, col=colors[group], pch=pch[group]) legend("topleft", legend=levels(group), pch=pch, col=colors, ncol=2)
In the MDS plot, the distance between each pair of samples can be interpreted as the leading log-fold change between the samples for the genes that best distinguish that pair of samples. By default, leading fold-change is defined as the root-mean-square of the largest 500 log2-fold changes between that pair of samples. The figure above shows that replicate samples from the same group cluster together while samples from different groups are well separated. In other words, differences between groups are much larger than those within groups, meaning that there are likely to be statistically significant differences between the groups. The distance between basal cells on the left and luminal cells on the right is about six units on the x-axis, corresponding to a leading fold change of about 64-fold between the two cell types. The differences between the virgin, pregnant and lactating expression profiles appear to be magnified in luminal cells compared to basal.
The expression profiles of individual samples can be explored more closely with mean-difference (MD) plots. An MD plot visualizes the library size-adjusted log-fold change between two libraries (the difference) against the average log-expression across those libraries (the mean). The following command produces an MD plot that compares sample 1 to an artificial reference library constructed from the average of all the other samples:
plotMD(y, column=1) abline(h=0, col="red", lty=2, lwd=2)
The bulk of the genes are centered around the line of zero log-fold change. The diagonal lines in the lower left of the plot correspond to genes with counts of 0, 1, 2 and so on in the first sample.
It is good practice to make MD plots for all the samples as a quality check. We now look at one of the luminal-lactating samples that were observed have low normalization factors:
plotMD(y, column=11) abline(h=0, col="red", lty=2, lwd=2)
For this sample, the log-ratios show noticeable positive skew, with a number of very highly upregulated genes. In particular, there are a number of points in the upper right of the plot, corresponding to genes that are both highly expressed and highly up-regulated in this sample compared to others. These genes explain why the normalization factor for this sample is well below one. By contrast, the log-ratios for sample 1 were somewhat negatively skewed, corresponding to a normalization factor above one.
Linear modeling and differential expression analysis in edgeR requires a design matrix to be specified. The design matrix records which treatment conditions were applied to each samples, and it also defines how the experimental effects are parametrized in the linear models. The experimental design for this study can be viewed as a one-way layout and the design matrix can be constructed in a simple and intuitive way by:
design <- model.matrix(~0+group) colnames(design) <- levels(group) design ## B.lactating B.pregnant B.virgin L.lactating L.pregnant L.virgin ## 1 0 0 1 0 0 0 ## 2 0 0 1 0 0 0 ## 3 0 1 0 0 0 0 ## 4 0 1 0 0 0 0 ## 5 1 0 0 0 0 0 ## 6 1 0 0 0 0 0 ## 7 0 0 0 0 0 1 ## 8 0 0 0 0 0 1 ## 9 0 0 0 0 1 0 ## 10 0 0 0 0 1 0 ## 11 0 0 0 1 0 0 ## 12 0 0 0 1 0 0 ## attr(,"assign") ##  1 1 1 1 1 1 ## attr(,"contrasts") ## attr(,"contrasts")$group ##  "contr.treatment"
This design matrix simply links each group to the samples that belong to it. Each row of the design matrix corresponds to a sample whereas each column represents a coefficient corresponding to one of the six groups.
edgeR uses the negative binomial (NB) distribution to model the read
counts for each gene in each sample. The dispersion parameter of the NB
distribution accounts for variability between biological replicates
(McCarthy, Chen, and Smyth 2012). edgeR estimates an empirical Bayes
moderated dispersion for each individual gene. It also estimates a
common dispersion, which is a global dispersion estimate averaged over
all genes, and a trended dispersion where the dispersion of a gene is
predicted from its abundance. Dispersion estimates are most easily
obtained from the
y <- estimateDisp(y, design, robust=TRUE)
This returns a
DGEList object with additional components
added to hold the estimated dispersions. Here
robust=TRUE has been
used to protect the empirical Bayes estimates against the possibility of
outlier genes with exceptionally large or small individual dispersions
(Phipson et al. 2016).
The dispersion estimates can be visualized with
The vertical axis of the
plotBCV plot shows square-root dispersion,
also known as biological coefficient of variation (BCV) (McCarthy,
Chen, and Smyth 2012). For RNA-seq studies, the NB dispersions tend to
be higher for genes with very low counts. The dispersion trend tends to
decrease smoothly with abundance and to asymptotic to a constant value
for genes with larger counts. From our past experience, the asymptotic
value for the BCV tends to be in range from 0.05 to 0.2 for genetically
identical mice or cell lines, whereas somewhat larger values (>0.3)
are observed for human subjects.
The NB model can be extended with quasi-likelihood (QL) methods to account for gene-specific variability from both biological and technical sources (Lund et al. 2012; Lun, Chen, and Smyth 2016). Under the QL framework, the NB dispersion trend is used to describe the overall biological variability across all genes, and gene-specific variability above and below the overall level is picked up by the QL dispersion. In the QL approach, the individual (tagwise) NB dispersions are not used.
The estimation of QL dispersions is performed using the
fit <- glmQLFit(y, design, robust=TRUE) head(fit$coefficients) ## B.lactating B.pregnant B.virgin L.lactating L.pregnant L.virgin ## 497097 -11.13 -12.01 -11.22 -19.0 -19.03 -19.0 ## 20671 -12.76 -12.51 -12.15 -14.5 -14.30 -14.1 ## 27395 -11.27 -11.29 -11.53 -10.6 -10.86 -10.9 ## 18777 -10.15 -10.21 -10.76 -10.1 -10.38 -10.4 ## 21399 -9.89 -9.73 -9.78 -10.2 -9.97 -10.0 ## 58175 -16.15 -14.85 -15.98 -13.3 -12.29 -12.0
This returns a
DGEGLM object with the estimated values of the GLM
coefficients for each gene. It also contains a number of empirical Bayes
(EB) statistics including the QL dispersion trend, the squeezed QL
dispersion estimates and the prior degrees of freedom (df). The QL
dispersions can be visualized by
The QL functions moderate the genewise QL dispersion estimates in the same way that the limma package moderates variances (Smyth 2004). The raw QL dispersion estimates are squeezed towards a global trend, and this moderation reduces the uncertainty of the estimates and improves testing power. The extent of the squeezing is governed by the value of the prior df estimated from the data. Large prior df estimates indicate that the QL dispersions are less variable between genes, meaning that strong EB moderation should be performed. Smaller prior df estimates indicate that the true unknown dispersions are highly variable, so weaker moderation towards the trend is appropriate.
summary(fit$df.prior) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 3.00 6.77 6.77 6.63 6.77 6.77
glmQLFit is usually recommended (Phipson et
al. 2016). This allows gene-specific prior df estimates, with lower
values for outlier genes and higher values for the main body of genes.
This reduces the chance of getting false positives from genes with
extremely high or low raw dispersions, while at the same time increasing
statistical power to detect differential expression for the main body of
The next step is to test for differential expression between the
experimental groups. One of the most interesting comparisons is that
between the basal pregnant and lactating groups. The contrast
corresponding to any specified comparison can be constructed
conveniently using the
B.LvsP <- makeContrasts(B.lactating-B.pregnant, levels=design)
In subsequent results, a positive log2-fold-change (logFC) will indicate a gene up-regulated in lactating mice relative to pregnant, whereas a negative logFC will indicate a gene more highly expressed in pregnant mice.
We will use QL F-tests instead of the more usual likelihood ratio tests (LRT) as they give stricter error rate control by accounting for the uncertainty in dispersion estimation:
res <- glmQLFTest(fit, contrast=B.LvsP)
The top DE genes can be viewed with
topTags(res) ## Coefficient: 1*B.lactating -1*B.pregnant ## Length Symbol logFC logCPM F PValue FDR ## 12992 765 Csn1s2b 6.08 10.19 421 4.77e-11 7.47e-07 ## 211577 2006 Mrgprf 5.15 2.75 343 1.32e-10 7.94e-07 ## 226101 7094 Myof 2.32 6.45 322 1.97e-10 7.94e-07 ## 381290 8292 Atp2b4 2.14 6.15 320 2.03e-10 7.94e-07 ## 140474 11281 Muc4 -7.17 6.06 308 2.61e-10 8.16e-07 ## 231830 3346 Micall2 -2.25 5.19 282 4.43e-10 1.15e-06 ## 24117 2242 Wif1 -1.82 6.77 261 7.10e-10 1.59e-06 ## 12740 1812 Cldn4 -5.32 9.87 298 9.07e-10 1.73e-06 ## 21953 667 Tnni2 5.75 3.86 313 9.93e-10 1.73e-06 ## 231991 2873 Creb5 2.57 4.87 241 1.17e-09 1.83e-06
In order to control the false discovery rate (FDR), multiple testing correction is performed using the Benjamini-Hochberg method. The top DE gene Csn1s2b has a large positive logFC, showing that it is far more highly expressed in the basal cells of lactating than pregnant mice. This gene is indeed known to be a major source of protein in milk.
The total number of DE genes identified at an FDR of 5% can be shown
decideTestsDGE. There are in fact more than 5000 DE genes in this
is.de <- decideTestsDGE(res) summary(is.de) ## [,1] ## -1 2758 ## 0 10396 ## 1 2487
The magnitude of the differential expression changes can be visualized with a fitted model MD plot:
plotMD(res, status=is.de, values=c(1,-1), col=c("red","blue"), legend="topright")
logFC for each gene is plotted against the average abundance in
logCPM in the table above. Genes that are
significantly DE are highlighted.
glmQLFTest identifies differential expression based on statistical
significance regardless of how small the difference might be. For some
purposes we might be interested only in genes with reasonably large
expression changes. The above analysis found more than 5000 DE genes
between the basal pregnant and lactating groups. With such a large
number of DE genes, it makes sense to narrow down the list to genes that
are more biologically meaningful.
A commonly used approach is to apply FDR and logFC cutoffs
simultaneously. However this tends to favor lowly expressed genes, and
also fails to control the FDR correctly. A better and more rigorous
approach is to modify the statistical test so as to detect expression
changes greater than a specified threshold. In edgeR, this can be done
glmTreat function. This function is analogous to the TREAT
method for microarrays (McCarthy and Smyth 2009) but is adapted to the
NB framework. Here we test whether the differential expression fold
changes are significantly greater than 1.5, that is, whether the logFCs
are significantly greater than log2(1.5):
tr <- glmTreat(fit, contrast=B.LvsP, lfc=log2(1.5)) topTags(tr) ## Coefficient: 1*B.lactating -1*B.pregnant ## Length Symbol logFC unshrunk.logFC logCPM PValue FDR ## 12992 765 Csn1s2b 6.08 6.09 10.19 6.25e-11 9.78e-07 ## 211577 2006 Mrgprf 5.15 5.15 2.75 1.89e-10 1.48e-06 ## 140474 11281 Muc4 -7.17 -7.34 6.06 4.21e-10 2.20e-06 ## 226101 7094 Myof 2.32 2.32 6.45 9.85e-10 2.91e-06 ## 12740 1812 Cldn4 -5.32 -5.32 9.87 1.20e-09 2.91e-06 ## 21953 667 Tnni2 5.75 5.76 3.86 1.25e-09 2.91e-06 ## 381290 8292 Atp2b4 2.14 2.14 6.15 1.30e-09 2.91e-06 ## 231830 3346 Micall2 -2.25 -2.25 5.19 2.43e-09 4.75e-06 ## 231991 2873 Creb5 2.57 2.57 4.87 4.33e-09 7.52e-06 ## 16012 1289 Igfbp6 2.87 2.87 3.68 5.42e-09 8.48e-06
Note that the argument is an abbreviation for ``log-fold-change”. About 1100 genes are detected as DE with a FC significantly above 1.5 at an FDR cut-off of 5%.
is.de <- decideTestsDGE(tr) summary(is.de) ## [,1] ## -1 681 ## 0 14595 ## 1 365
The p-values from
glmTreat are larger than those from
and the number of significantly DE genes is fewer, because it is testing
an interval null hypothesis and requires stronger evidence for
differential expression than does a conventional test. It provides
greater specificity for identifying the most important genes with large
The test results can be visualized in an MD plot:
plotMD(tr, status=is.de, values=c(1,-1), col=c("red","blue"), legend="topright")
The method evaluates variability as well as the magnitude of change of expression values and therefore is not equivalent to a simple fold change cutoff. Nevertheless, all the statistically significant expression changes have logFC greater than 0.8 and almost all (97%) are greater than 0.9. These values compare to the threshold value of log2(1.5)=0.58. In general, an estimated logFC must exceed the TREAT threshold by a number of standard errors for it to be called significant. In other words, the whole confidence interval for the logFC must clear the threshold rather than just the estimated value itself. It is better to interpret the threshold as the FC below which we are definitely not interested in the gene rather than the FC above which we are interested in the gene.
The value of the FC threshold can be varied depending on the dataset. In
the presence of a huge number of DE genes, a relatively large FC
threshold may be appropriate to narrow down the search to genes of
interest. In the absence of DE genes, on the other hand, a small or even
no FC threshold shall be used. If the threshold level is set to zero,
glmTreat becomes equivalent to
glmQLFTest in the workflow shown
In general, using
glmTreat to reduce the number of DE genes is better
than simply reducing the FDR cutoff, because
genes with larger changes that are likely to be more biologically
glmTreat can also be used with edgeR pipelines other
than quasi-likelihood, although we don’t demonstrate that here.
Heatmaps are a popular way to display differential expression results
for publication purposes. To create a heatmap, we first convert the read
counts into log2-counts-per-million (logCPM) values. This can be done
logCPM <- cpm(y, prior.count=2, log=TRUE) rownames(logCPM) <- y$genes$Symbol colnames(logCPM) <- paste(y$samples$group, 1:2, sep="-")
The introduction of
prior.count is to avoid undefined values and to
reduce the variability of the logCPM values for genes with low counts.
Larger values for
prior.count shrink the logFCs for low count genes
We will create a heatmap to visualize the top 30 DE genes according to
the TREAT test between
B.pregnant. The advantage of
a heatmap is that it can display the expression pattern of the genes
across all the samples. Visualization of the results is aided by
clustering together genes that have correlated expression patterns.
First we select the logCPM values for the 30 top genes:
o <- order(tr$table$PValue) logCPM <- logCPM[o[1:30],]
Then we scale each row (each gene) to have mean zero and standard deviation one:
logCPM <- t(scale(t(logCPM)))
This scaling is commonly done for heatmaps and ensures that the heatmap
displays relative changes for each gene. A heat map can then be produced
heatmap.2 function in the gplots package:
library(gplots) col.pan <- colorpanel(100, "blue", "white", "red") heatmap.2(logCPM, col=col.pan, Rowv=TRUE, scale="none", trace="none", dendrogram="both", cexRow=1, cexCol=1.4, density.info="none", margin=c(10,9), lhei=c(2,10), lwid=c(2,6))
heatmap.2 clusters genes and samples based on Euclidean distance
between the expression values. Because we have pre-standardized the rows
of the logCPM matrix, the Euclidean distance between each pair of genes
is proportional to (1 − r)2, where r is the Pearson
correlation coefficient between the two genes. This shows that the
heatmap will cluster together genes that have positively correlated
logCPM values, because large positive correlations correspond to small
The positioning of the samples in the heatmap is dependent on how the
genes in the display have been chosen. Here we are displaying those
genes that are most DE between
B.pregnant, so those
two cell populations are well separated on the plot. As expected, the
two replicate samples from each group are clustered together.
The differential expression analysis comparing two groups can be easily extended to comparisons between three or more groups. This is done by creating a matrix of independent contrasts. In this manner, users can perform a one-way analysis of deviance (ANODEV) for each gene (McCullagh and Nelder 1989).
Suppose we want to compare the three groups in the luminal population, i.e., virgin, pregnant and lactating. An appropriate contrast matrix can be created as shown below, to make pairwise comparisons between all three groups:
con <- makeContrasts( L.PvsL = L.pregnant - L.lactating, L.VvsL = L.virgin - L.lactating, L.VvsP = L.virgin - L.pregnant, levels=design)
The QL F-test is then applied to identify genes that are DE between the
three groups. This combines the three pairwise comparisons into a single
F-statistic and p-value. The top set of significant genes can be
res <- glmQLFTest(fit, contrast=con) topTags(res) ## Coefficient: LR test of 2 contrasts ## Length Symbol logFC.L.PvsL logFC.L.VvsL logCPM F PValue ## 19242 2021 Ptn -1.54 7.26 7.97 2389 3.70e-17 ## 13645 4757 Egf -5.36 -7.22 3.67 1123 4.49e-15 ## 52150 4089 Kcnk6 -2.42 -7.00 5.91 1020 8.29e-15 ## 15439 1345 Hp 1.08 5.42 4.93 991 9.92e-15 ## 12992 765 Csn1s2b -8.55 -11.36 10.19 1050 1.00e-14 ## 14183 5346 Fgfr2 -1.15 3.95 7.38 952 1.28e-14 ## 20856 1793 Stc2 -1.81 3.20 6.11 920 1.59e-14 ## 11941 7050 Atp2b2 -7.37 -10.56 6.60 1134 1.78e-14 ## 13358 1678 Slc25a1 -4.13 -4.91 7.50 887 2.01e-14 ## 17068 691 Ly6d 3.42 9.24 4.69 887 2.01e-14 ## FDR ## 19242 5.79e-13 ## 13645 3.14e-11 ## 52150 3.14e-11 ## 15439 3.14e-11 ## 12992 3.14e-11 ## 14183 3.15e-11 ## 20856 3.15e-11 ## 11941 3.15e-11 ## 13358 3.15e-11 ## 17068 3.15e-11
Note that the three contrasts of pairwise comparisons are linearly
dependent. Constructing the contrast matrix with any two of the
contrasts would be sufficient for an ANODEV test. If the contrast matrix
contains all three possible pairwise comparisons, then only the log-fold
changes of the first two contrasts are shown in the output of
The flexibility of the GLM framework makes it possible to specify arbitrary contrasts for differential expression tests. Suppose we are interested in testing whether the change in expression between lactating and pregnant mice is the same for basal cells as it is for luminal cells. In statistical terminology, this is the interaction effect between mouse status and cell type. The contrast corresponding to this testing hypothesis can be made as follows.
con <- makeContrasts( (L.lactating-L.pregnant)-(B.lactating-B.pregnant), levels=design)
Then the QL F-test is conducted to identify genes that are DE under this
contrast. The top set of DE genes are viewed with
res <- glmQLFTest(fit, contrast=con) topTags(res) ## Coefficient: -1*B.lactating 1*B.pregnant 1*L.lactating -1*L.pregnant ## Length Symbol logFC logCPM F PValue FDR ## 19041 6277 Ppl -4.62 6.96 525 9.52e-12 1.49e-07 ## 231991 2873 Creb5 -5.61 4.87 438 2.91e-11 2.14e-07 ## 20512 4206 Slc1a3 5.03 3.65 415 4.10e-11 2.14e-07 ## 217294 1952 BC006965 -3.88 4.68 372 8.09e-11 2.88e-07 ## 14598 2022 Ggt1 3.17 6.38 357 1.03e-10 2.88e-07 ## 13358 1678 Slc25a1 3.47 7.50 353 1.10e-10 2.88e-07 ## 192166 4558 Sardh 2.92 5.11 341 1.37e-10 3.05e-07 ## 19659 2628 Rbp1 -4.40 6.83 337 1.63e-10 3.18e-07 ## 67547 3707 Slc39a8 6.19 5.07 376 1.83e-10 3.18e-07 ## 14063 2768 F2rl1 -3.92 5.61 301 2.94e-10 4.23e-07
We now consider the problem of interpreting the differential expression results in terms of higher order biological processes or molecular pathways. One of the most common used resources is gene ontology (GO) databases, which annotate genes according to a dictionary of annotation terms. A simple and often effective way to interpret the list of DE genes is to count the number of DE genes that are annotated with each possible GO term. GO terms that occur frequently in the list of DE genes are said to be over-represented or enriched.
In edgeR, GO analyses can be conveniently conducted using the
function. Here were apply
goana to the output of the TREAT analysis
B.pregant. The top most significantly
enriched GO terms can be viewed with
go <- goana(tr, species="Mm") topGO(go, n=15) ## Term Ont N Up Down P.Up ## GO:0007059 chromosome segregation BP 261 1 56 0.998 ## GO:0000280 nuclear division BP 507 10 79 0.792 ## GO:1903047 mitotic cell cycle process BP 665 8 92 0.993 ## GO:0007067 mitotic nuclear division BP 407 5 68 0.972 ## GO:0022402 cell cycle process BP 949 15 111 0.975 ## GO:0000278 mitotic cell cycle BP 747 8 96 0.998 ## GO:0048285 organelle fission BP 553 12 79 0.697 ## GO:0051301 cell division BP 496 3 72 1.000 ## GO:0000070 mitotic sister chromatid segregation BP 125 0 35 1.000 ## GO:0098813 nuclear chromosome segregation BP 203 1 44 0.993 ## GO:0000776 kinetochore CC 123 1 34 0.952 ## GO:0007049 cell cycle BP 1317 17 129 0.999 ## GO:0000819 sister chromatid segregation BP 148 0 37 1.000 ## GO:0000775 chromosome, centromeric region CC 176 1 40 0.987 ## GO:0098687 chromosomal region CC 299 5 49 0.856 ## P.Down ## GO:0007059 3.22e-23 ## GO:0000280 7.62e-23 ## GO:1903047 9.70e-23 ## GO:0007067 1.83e-21 ## GO:0022402 2.30e-21 ## GO:0000278 2.31e-21 ## GO:0048285 1.93e-20 ## GO:0051301 4.41e-19 ## GO:0000070 7.25e-19 ## GO:0098813 1.20e-18 ## GO:0000776 3.60e-18 ## GO:0007049 4.23e-18 ## GO:0000819 4.66e-18 ## GO:0000775 7.83e-18 ## GO:0098687 2.16e-15
goana function automatically extracts DE genes from the
object, and conducts overlap tests for the up- and down-regulated DE
genes separately. By default, an FDR cutoff of 5% is used when
extracting DE genes, but this can be varied. The row names of the output
are the universal identifiers of the GO terms and the
gives the human-readable names of the terms. The
Ont column shows the
ontology domain that each GO term belongs to. The three domains are:
biological process (BP), cellular component (CC) and molecular function
N column represents the total number of genes annotated with
each GO term. The
Down columns indicate the number of genes
within the GO term that are significantly up- and down-regulated in this
differential expression comparison, respectively. The
P.Down columns contain the p-values for over-representation of the GO
term in the up- and down-regulated genes, respectively. Note that the
p-values are not adjusted for multiple testing—we would usually ignore
GO terms with p-values greater than about 10−5.
By default the output table from
topGO is sorted by the minimum of
P.Down. Other options are available. For example,
topGO(go, sort="up") lists the top GO terms that are over-represented
in the up-regulated genes. The domain of the enriched GO terms can also
be specified by users. For example,
topGO(go, ontology="BP") restricts
to the top GO terms belonging to the biological process domain while
topGO(go, ontology="MF") restricts to molecular function terms.
goana function uses the NCBI RefSeq annotation and requires the
use of Entrez Gene Identifiers.
Another popular annotation database is the Kyoto Encyclopedia of Genes
and Genomes (KEGG). Much smaller than GO, this is a curated database of
molecular pathways and disease signatures. A KEGG analysis can be done
exactly as for GO, but using the
keg <- kegga(tr, species="Mm") topKEGG(keg, n=15, truncate=34) ## Pathway N Up Down P.Up ## path:mmu03008 Ribosome biogenesis in eukaryot... 76 0 19 1.000000 ## path:mmu05150 Staphylococcus aureus infection 30 0 10 1.000000 ## path:mmu04110 Cell cycle 120 0 20 1.000000 ## path:mmu00100 Steroid biosynthesis 18 5 0 0.000046 ## path:mmu00240 Pyrimidine metabolism 96 0 15 1.000000 ## path:mmu04970 Salivary secretion 63 8 7 0.000103 ## path:mmu00900 Terpenoid backbone biosynthesis 21 5 0 0.000103 ## path:mmu04972 Pancreatic secretion 66 8 4 0.000144 ## path:mmu04610 Complement and coagulation casc... 47 2 9 0.306293 ## path:mmu00230 Purine metabolism 151 2 17 0.878509 ## path:mmu04510 Focal adhesion 187 12 8 0.001459 ## path:mmu04261 Adrenergic signaling in cardiom... 120 9 5 0.002012 ## path:mmu05144 Malaria 38 2 7 0.226798 ## path:mmu04640 Hematopoietic cell lineage 60 2 9 0.417769 ## path:mmu04911 Insulin secretion 60 6 5 0.002754 ## P.Down ## path:mmu03008 3.10e-09 ## path:mmu05150 1.05e-06 ## path:mmu04110 1.58e-06 ## path:mmu00100 1.00e+00 ## path:mmu00240 7.17e-05 ## path:mmu04970 3.70e-02 ## path:mmu00900 1.00e+00 ## path:mmu04972 4.24e-01 ## path:mmu04610 4.44e-04 ## path:mmu00230 1.34e-03 ## path:mmu04510 7.29e-01 ## path:mmu04261 7.27e-01 ## path:mmu05144 2.43e-03 ## path:mmu04640 2.72e-03 ## path:mmu04911 1.81e-01
The output from
topKEGG is the same as from
topGO except that row
names become KEGG pathway IDs,
Pathway and there is no
Ont column. Both the GO and KEGG analyses show that the cell cycle
pathway is strongly down-regulated upon lactation in mammary stem cells.
By default, the
kegga function automatically reads the latest KEGG
annotation from the Internet each time it is run. The KEGG database uses
Entrez Gene Ids, and the
kegga function assumes these are available as
the row names of
The GO and KEGG analyses shown above are relatively simple analyses that rely on a list of DE genes. The list of DE genes is overlapped with the various GO and KEGG annotation terms. The results will depend on the significance threshold that is used to assess differential expression.
If the aim is to test for particular gene expression signatures or
particular pathways, a more nuanced approach is to conduct a
fry gene set test (Wu et al. 2010). These functions test whether a set
of genes is DE, assessing the whole set of genes as a whole. Gene set
tests consider all the genes in the specified set and do not depend on
any pre-emptive significance cutoff. The set of genes can be chosen to
be representative of any pathway or phenotype of interest.
roast gives p-values using random rotations of the residual space. In
the edgeR context,
fry is generally recommended over
gives an accurate analytic approximation to the results that
would give, with default settings, if an extremely large number of
rotations was used.
Here, suppose we are interested in three GO terms related to cytokinesis. Each GO term is used to define a set of genes annotated with that term. The names of these terms are shown below:
library(GO.db) cyt.go <- c("GO:0032465", "GO:0000281", "GO:0000920") term <- select(GO.db, keys=cyt.go, columns="TERM") term ## GOID TERM ## 1 GO:0032465 regulation of cytokinesis ## 2 GO:0000281 mitotic cytokinesis ## 3 GO:0000920 cell separation after cytokinesis
The first step is to extract the genes associated with each GO term from the GO database. This produces a list of three components, one for each GO term. Each component is a vector of Entrez Gene IDs for that GO term:
Rkeys(org.Mm.egGO2ALLEGS) <- cyt.go cyt.go.genes <- as.list(org.Mm.egGO2ALLEGS)
Suppose the comparison of interest is between the virgin and lactating
groups in the basal population. We can use
fry to test whether the
cytokinesis GO terms are DE for this comparison:
B.VvsL <- makeContrasts(B.virgin-B.lactating, levels=design) fry(y, index=cyt.go.genes, design=design, contrast=B.VvsL) ## NGenes Direction PValue FDR PValue.Mixed FDR.Mixed ## GO:0032465 49 Up 0.000549 0.00165 3.82e-06 7.03e-06 ## GO:0000920 16 Down 0.001856 0.00207 4.69e-06 7.03e-06 ## GO:0000281 37 Up 0.002067 0.00207 1.20e-05 1.20e-05
Each row of the output corresponds to a gene set. The
provides the number of genes in each set. The
indicates the net direction of change. The
PValue column gives the
two-sided p-value for testing whether the set is DE as a whole, either
up or down. The
PValue.Mixed column gives a p-value for testing
whether genes in the set tend to be DE, without regard to direction. The
PValue column is appropriate when genes in the set are expected to be
co-regulated, all or most changing expression in the same direction. The
PValue.Mixed column is appropriate when genes in the set are not
necessarily co-regulated or may be regulated in different directions for
the contrast in question. FDRs are calculated from the corresponding
p-values across all sets.
The results of a gene set test can be viewed in a barcode plot produced
barcodeplot function. Suppose visualization is performed for
the gene set defined by the GO term
res <- glmQLFTest(fit, contrast=B.VvsL) index <- rownames(fit) %in% cyt.go.genes[] barcodeplot(res$table$logFC, index=index, labels=c("B.virgin","B.lactating"), main=cyt.go)
![Barcode plot showing enrichment of the GO term
In the plot, all genes are ranked from left to right by decreasing
log-fold change for the contrast and the genes within the gene set are
represented by vertical bars, forming the barcode-like pattern. The
curve (or worm) above the barcode shows the relative local enrichment
of the bars in each part of the plot. The dotted horizontal line
indicates neutral enrichment; the worm above the dotted line shows
enrichment while the worm below the dotted line shows depletion. In this
particular barcode plot the worm shows enrichment on the left for
positive logFCs, and depletion on the right for negative logFCs. The
conclusion is that genes associated with this GO term tend to be
up-regulated in the basal cells of virgin mice compared to lactating
mice, confirming the result of the
fry test above.
Finally we demonstrate a gene set enrichment style analysis using the Molecular Signatures Database (MSigDB) (Subramanian et al. 2005). We will use the C2 collection of the MSigDB, which is a collection of nearly 5000 curated gene sets, each representing the molecular signature of a particular biological process or phenotype. The MSigDB itself is purely human, but the Walter and Eliza Hall Institute (WEHI) maintains a mouse version of the database. We load the mouse version of the C2 collection from the WEHI website:
This will load
Mm.c2, which is a list of gene sets, each a vector of
Entrez Ids. This can be converted to a list of index numbers:
idx <- ids2indices(Mm.c2,id=rownames(y))
First we compare virgin stem cells to virgin luminal cells:
BvsL.v <- makeContrasts(B.virgin - L.virgin, levels=design) cam <- camera(y, idx, design, contrast=BvsL.v, inter.gene.cor=0.01) options(digits=2) head(cam,14) ## NGenes Direction ## LIM_MAMMARY_STEM_CELL_UP 782 Up ## LIM_MAMMARY_LUMINAL_MATURE_DN 169 Up ## SASAI_RESISTANCE_TO_NEOPLASTIC_TRANSFROMATION 80 Up ## LIM_MAMMARY_STEM_CELL_DN 664 Down ## FARMER_BREAST_CANCER_CLUSTER_4 74 Up ## NABA_BASEMENT_MEMBRANES 52 Up ## HAEGERSTRAND_RESPONSE_TO_IMATINIB 35 Up ## ROZANOV_MMP14_TARGETS_SUBSET 83 Up ## REACTOME_COLLAGEN_FORMATION 70 Up ## REACTOME_NCAM1_INTERACTIONS 71 Up ## OXFORD_RALB_TARGETS_UP 27 Up ## ANASTASSIOU_CANCER_MESENCHYMAL_TRANSITION_SIGNATURE 148 Up ## PAPASPYRIDONOS_UNSTABLE_ATEROSCLEROTIC_PLAQUE_DN 68 Up ## LIM_MAMMARY_LUMINAL_PROGENITOR_UP 92 Down ## PValue FDR ## LIM_MAMMARY_STEM_CELL_UP 2.0e-43 9.5e-40 ## LIM_MAMMARY_LUMINAL_MATURE_DN 3.8e-25 9.0e-22 ## SASAI_RESISTANCE_TO_NEOPLASTIC_TRANSFROMATION 4.1e-20 6.5e-17 ## LIM_MAMMARY_STEM_CELL_DN 2.3e-19 2.7e-16 ## FARMER_BREAST_CANCER_CLUSTER_4 2.9e-19 2.8e-16 ## NABA_BASEMENT_MEMBRANES 1.6e-17 1.2e-14 ## HAEGERSTRAND_RESPONSE_TO_IMATINIB 4.0e-17 2.7e-14 ## ROZANOV_MMP14_TARGETS_SUBSET 5.2e-16 3.1e-13 ## REACTOME_COLLAGEN_FORMATION 1.3e-15 6.8e-13 ## REACTOME_NCAM1_INTERACTIONS 2.2e-15 1.0e-12 ## OXFORD_RALB_TARGETS_UP 4.6e-15 2.0e-12 ## ANASTASSIOU_CANCER_MESENCHYMAL_TRANSITION_SIGNATURE 7.1e-15 2.8e-12 ## PAPASPYRIDONOS_UNSTABLE_ATEROSCLEROTIC_PLAQUE_DN 1.3e-14 4.9e-12 ## LIM_MAMMARY_LUMINAL_PROGENITOR_UP 2.0e-14 6.9e-12
With a large gene set collection, setting
inter.gene.cor = 0.01 gives
a good compromise between biological interpretability and FDR control.
As expected, the mammary stem cell and mammary luminal cell signatures
from Lim et al (Lim et al. 2010) are top-ranked, and in the expected
We can visualize the top signature, combining the up and down mammary stem cell signatures to make a bi-directional signature set:
res <- glmQLFTest(fit, contrast=BvsL.v) barcodeplot(res$table$logFC, index=idx[["LIM_MAMMARY_STEM_CELL_UP"]], index2=idx[["LIM_MAMMARY_STEM_CELL_DN"]], labels=c("B.virgin","L.virgin"), main="LIM_MAMMARY_STEM_CELL", alpha=1)
This workflow depends on various packages from version 3.3 of the Bioconductor project, running on R version 3.3.0 or higher. The complete list of the packages used for this workflow are shown below:
sessionInfo() ## R version 3.4.0 alpha (2017-04-04 r72482) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 16.04.1 LTS ## ## Matrix products: default ## BLAS: /usr/local/lib/R/lib/libRblas.so ## LAPACK: /usr/local/lib/R/lib/libRlapack.so ## ## locale: ##  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ##  LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 ##  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 ##  LC_PAPER=en_US.UTF-8 LC_NAME=C ##  LC_ADDRESS=C LC_TELEPHONE=C ##  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ##  parallel stats4 stats graphics grDevices utils datasets ##  methods base ## ## other attached packages: ##  RnaSeqGeneEdgeRQL_0.99.18 GO.db_3.4.1 ##  org.Mm.eg.db_3.4.1 AnnotationDbi_1.37.4 ##  IRanges_2.9.19 S4Vectors_0.13.15 ##  Biobase_2.35.1 BiocGenerics_0.21.3 ##  gplots_3.0.1 edgeR_3.17.5 ##  limma_3.31.20 BiocStyle_2.3.31 ##  rmarkdown_1.4 knitr_1.15.1 ## ## loaded via a namespace (and not attached): ##  Rcpp_0.12.10 magrittr_1.5 splines_3.4.0 ##  statmod_1.4.29 lattice_0.20-35 highr_0.6 ##  stringr_1.2.0 caTools_1.17.1 tools_3.4.0 ##  grid_3.4.0 KernSmooth_2.23-15 DBI_0.6-1 ##  htmltools_0.3.5 gtools_3.5.0 yaml_2.1.14 ##  rprojroot_1.2 digest_0.6.12 bitops_1.0-6 ##  memoise_1.0.0 RSQLite_1.1-2 evaluate_0.10 ##  gdata_2.17.0 stringi_1.1.5 compiler_3.4.0 ##  backports_1.0.5 locfit_1.5-9.1
We now revisit the question of recreating the matrix of read counts from
the raw sequence reads. Unlike the above workflow, which works for any
version of R, read alignment requires Unix or Mac OS and, in practice, a
high performance Unix server is recommended. Read alignment and read
counting require only one Bioconductor package, Rsubread. However the
fastq-dump utility from the SRA Toolkit is also required to convert
from SRA to FASTQ format. This can be downloaded from the NCBI website
(http://www.ncbi.nlm.nih.gov/Traces/sra/?view=software) and installed
on any Unix system.
The first task is to download the raw sequence files, which are stored
in SRA format on the SRA repository. The SRA files need to be unpacked
into FASTQ format using the
fastq-dump utility. The following R code
makes a system call to
fastq-dump to download each SRA file and
convert it to FASTQ format:
for (sra in targets$SRA) system(paste("fastq-dump", sra))
fastq-dump utility automatically downloads the specified SRA data
set from the internet. The above code will produce 12 FASTQ files, in
the current working directory, with file names given by the following
all.fastq <- paste0(targets$SRA, ".fastq")
Sequencers typically store base-calling quality scores for each read in
the FASTQ files. Rsubread’s
qualityScores function can be used to
extract these scores from any particular file:
QS <- qualityScores("SRR1552444.fastq")
The boxplot function provides a compact way to view the quality scores by position across all reads:
boxplot(QS, ylab="Quality score", xlab="Base position", main="SRR1552444.fastq", cex=0.25, col="orange")
The vertical axis shows the Phred quality score, equal to −10log10(p) where p is the probability of an erroneous call. The maximum possible value is 40, and all values above 10 correspond to extremely small error probabilities. The horizontal axis shows position within a read. The file contains 100bp single-end reads, so the scale is from 1 to 100. The plot displays a compact boxplot at each base position. As is very commonly observed, the quality scores are best in the middle of the reads and decrease slightly towards the start and end of the reads. However the quality remains generally good even near the ends of the reads: the scores would need to be very much lower than this before they would cause problems for the alignment. Similar plots can be made for each of the FASTQ files.
Before the sequence reads can be aligned, we need to build an index for the GRCm38/mm10 (Dec 2011) build of the mouse genome. Most laboratories that use Rsubread regularly will already have an index file prepared, as this is a once-off operation for each genome release. If you are using Rsubread for mouse for the first time, then the latest mouse genome build can be downloaded from the NCBI location ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA_000001635.6_GRCm38.p4/GCA_000001635.6_GRCm38.p4_genomic.fna.gz.
(Note that this link is for patch 4 of mm10, which is valid at the time of writing in May 2016. The link will change as new patches are released periodically.) An index can then be built by:
library(Rsubread) buildindex(basename = "mm10", reference = "GCA_000001635.6_GRCm38.p4_genomic.fna.gz")
The sequence reads can now be aligned to the mouse genome using the
all.bam <- sub(".fastq", ".bam", all.fastq) align(index="mm10", readfile1=all.fastq, input_format="FASTQ", output_file=all.bam)
This produces a set of BAM files containing the read alignments for each
RNA library. The mapping proportions can be summarized by the
> propmapped(all.bam) Samples NumTotal NumMapped PropMapped 1 SRR1552450.bam 30109290 26577308 0.883 2 SRR1552451.bam 28322351 24794251 0.875 3 SRR1552452.bam 31688348 27937620 0.882 4 SRR1552453.bam 29614284 26074034 0.880 5 SRR1552454.bam 27225012 24381742 0.896 6 SRR1552455.bam 25433157 22813815 0.897 7 SRR1552444.bam 27919481 23927833 0.857 8 SRR1552445.bam 29731031 25487822 0.857 9 SRR1552446.bam 29879070 25500318 0.853 10 SRR1552447.bam 29245388 25187577 0.861 11 SRR1552448.bam 31425424 27326500 0.870 12 SRR1552449.bam 31276061 27204156 0.870
Ideally, the proportion of mapped reads should be above 80%. By default, only reads with unique mapping locations are reported by Rsubread as being successfully mapped. Restricting to uniquely mapped reads is recommended, as it avoids spurious signal from non-uniquely mapped reads derived from, e.g., repeat regions.
The read counts for each gene can be quantified using the
featureCounts function in Rsubread. Conveniently, the Rsubread
package includes inbuilt NCBI RefSeq annotation of the mouse and human
featureCounts generates a matrix of read counts for each gene
in each sample:
fc <- featureCounts(all.bam, annot.inbuilt="mm10")
The output is a simple list, containing the matrix of counts (
a data frame of gene characteristics (
annotation), a vector of file
targets) and summary mapping statistics (
> names(fc)  "counts" "annotation" "targets" "stat"
The row names of
fc$counts are the Entrez gene identifiers for each
gene. The column names are the output file names from
align, which we
simplify here for brevity:
> colnames(fc$counts) <- rownames(targets)
The first six rows of the counts matrix are shown below.
> head(fc$counts) MCL1.DG MCL1.DH MCL1.DI MCL1.DJ MCL1.DK MCL1.DL MCL1.LA MCL1.LB MCL1.LC 497097 438 299 65 237 354 287 0 0 0 100503874 1 0 1 1 0 4 0 0 0 100038431 0 0 0 0 0 0 0 0 0 19888 1 1 0 0 0 0 10 3 10 20671 106 181 82 104 43 83 16 25 18 27395 309 232 339 290 291 270 558 468 488 MCL1.LD MCL1.LE MCL1.LF 497097 0 0 0 100503874 0 0 0 100038431 0 0 0 19888 2 0 0 20671 8 3 10 27395 332 312 344
DGEList object can be assembled by:
y <- DGEList(fc$counts, group=group) y$genes <- fc$annotation[, "Length", drop=FALSE]
Except for the targets file
targets.txt, all data analyzed in the
workflow is read automatically from public websites as part of the code.
All software used is publicly available as part of Bioconductor 3.3,
except for the
fastq-dump utility, which can be downloaded from NCBI
website as described in the text. The article includes the complete code
necessary to reproduce the analyses shown.
The LaTeX version of this article was generated automatically by running
knitr::knit on an Rnw file of R commands. It is planned to make the
code and data available as an executable Bioconductor workflow at
http://www.bioconductor.org/help/workflows. In the meantime, the files
are available from http://bioinf.wehi.edu.au/edgeR/F1000Research2016/.
All authors developed and tested the code workflow. All authors wrote the article.
No competing interests were disclosed.
This work was supported by the National Health and Medical Research Council (Fellowship 1058892 and Program 1054618 to G.K.S, Independent Research Institutes Infrastructure Support to the Walter and Eliza Hall Institute) and by a Victorian State Government Operational Infrastructure Support Grant.
The authors thank Wei Shi and Yang Liao for advice with Rsubread and Yifang Hu for creating the mouse version of the MSigDB. We also wish to acknowledge the early developers of the quasi-likelihood testing approach. Davis McCarthy authored the first versions of the quasi-likelihood functions in edgeR in February 2011 and Steve Lund and Dan Nettleton worked independently on QuasiSeq around the same time. We thank Steve Lund and Dan Nettleton for a valuable and enjoyable collaboration that lead to Lund et al (Lund et al. 2012).
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