Gene set testing
Gene set testing
Here, we will explore software for testing differential expression in a set of genes. These tests differ from the gene-by-gene tests we saw previously. Again, the gene set testing software we will use lives in the limma package.
We download an experiment from the GEO website, using the getGEO function from the GEOquery package:
library(GEOquery)
g <- getGEO("GSE34313")
e <- g[[1]]
This dataset is hosted by GEO at the following link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34313
The experiment is described in the paper by Masuno 2011.
Briefly, the investigators applied a glucocorticoid hormone to cultured human airway smooth muscle. The glucocorticoid hormone is used to treat asthma, as it reduces the inflammation response, however it has many other effects throughout the different tissues of the body.
The groups are defined in the characteristics_ch1.2 variable:
e$condition <- e$characteristics_ch1.2
levels(e$condition) <- c("dex24","dex4","control")
table(e$condition)
##
## dex24 dex4 control
## 3 3 4
By examining boxplots, we can guess that the data has already been normalized somehow, and on the GEO site the investigators report that they normalized using Agilent software.
We will subset to the control samples and the samples treated with dexamethasone (the hormone) after 4 hours.
boxplot(exprs(e), range=0)
names(fData(e))
## [1] "ID" "SPOT_ID" "CONTROL_TYPE"
## [4] "REFSEQ" "GB_ACC" "GENE"
## [7] "GENE_SYMBOL" "GENE_NAME" "UNIGENE_ID"
## [10] "ENSEMBL_ID" "TIGR_ID" "ACCESSION_STRING"
## [13] "CHROMOSOMAL_LOCATION" "CYTOBAND" "DESCRIPTION"
## [16] "GO_ID" "SEQUENCE"
lvls <- c("control", "dex4")
es <- e[,e$condition %in% lvls]
es$condition <- factor(es$condition, levels=lvls)
The following lines run the linear model in limma. We note that the top genes are common immune-response genes (CSF2, LIF, CCL2, IL6). Also present is FKBP5, a gene which regulates and is regulated by the protein which receives the glucocorticoid hormone.
library(limma)
design <- model.matrix(~ es$condition)
fit <- lmFit(es, design=design)
fit <- eBayes(fit)
tt <- topTable(fit, coef=2, genelist=fData(es)$GENE_SYMBOL)
tt
## ID logFC AveExpr t P.Value
## A_23_P133408 CSF2 -4.724965 8.187709 -69.36145 2.381749e-12
## A_24_P122137 LIF -6.689927 11.052517 -67.23929 3.048075e-12
## A_32_P5276 ARHGEF26 3.411468 7.373888 54.47705 1.619429e-11
## A_23_P89431 CCL2 -3.618725 13.762759 -54.13669 1.701965e-11
## A_23_P42257 IER3 -4.808854 13.417263 -48.57810 4.018095e-11
## A_23_P71037 IL6 -4.568355 11.595039 -45.78704 6.422142e-11
## A_24_P20327 KLF15 4.013070 6.824730 43.90736 8.951793e-11
## A_24_P38081 FKBP5 3.567059 9.310028 43.77025 9.176326e-11
## A_23_P213944 HBEGF -2.902190 10.269416 -43.72761 9.247448e-11
## A_24_P250922 PTGS2 -3.518360 10.476972 -42.52588 1.153122e-10
## adj.P.Val B
## A_23_P133408 6.248554e-08 15.93230
## A_24_P122137 6.248554e-08 15.84703
## A_32_P5276 1.744514e-07 15.16827
## A_23_P89431 1.744514e-07 15.14523
## A_23_P42257 3.294838e-07 14.72004
## A_23_P71037 4.212726e-07 14.46609
## A_24_P20327 4.212726e-07 14.27685
## A_24_P38081 4.212726e-07 14.26242
## A_23_P213944 4.212726e-07 14.25791
## A_24_P250922 4.727798e-07 14.12733
We will use the ROAST method for gene set testing. We can test a single gene set by looking up the genes which contain a certain GO ID, and providing this to the roast function. We will show how to get such lists of genes associated with a GO ID in the next chunk.
The roast function performs an advanced statistical technique, rotation of residuals, in order to generate a sense of the null distribution for the test statistic. The test statistics in this case is the summary of the scores from each gene. The tests are self-contained because only the summary for a single set is used, whereas other gene set tests might compare a set to all the other genes in the dataset, e.g., a competitive gene set test.
The result here tells us that the immune response genes are significantly down-regulated, and additionally, mixed up and down.
# Immune response
idx <- grep("GO:0006955", fData(es)$GO_ID)
length(idx)
## [1] 504
r1 <- roast(es, idx, design)
# ?roast
r1
## Active.Prop P.Value
## Down 0.16269841 0.01550775
## Up 0.09325397 0.98499250
## UpOrDown 0.16269841 0.03100000
## Mixed 0.25595238 0.00800000
Testing multiple gene sets
We can also use the mroast function to perform multiple roast tests. First we need to create a list, which contains the indices of genes in the ExpressionSet for each of a number of gene sets. We will use the org.Hs.eg.db package to gather the gene set information.
# biocLite("org.Hs.eg.db")
library(org.Hs.eg.db)
## Loading required package: AnnotationDbi
## Loading required package: stats4
## Loading required package: GenomeInfoDb
## Loading required package: S4Vectors
## Loading required package: IRanges
##
## Attaching package: 'AnnotationDbi'
##
## The following object is masked from 'package:GenomeInfoDb':
##
## species
##
## Loading required package: DBI
org.Hs.egGO2EG
## GO2EG map for Human (object of class "Go3AnnDbBimap")
go2eg <- as.list(org.Hs.egGO2EG)
head(go2eg)
## $$`GO:0000002`
## TAS TAS ISS IMP NAS IMP IEA IMP
## "291" "1890" "4205" "4358" "9361" "10000" "80119" "92667"
##
## $$`GO:0000003`
## IEP
## "8510"
##
## $$`GO:0000012`
## IDA IDA IEA IMP IDA IDA
## "3981" "7141" "7515" "23411" "54840" "55775"
## IMP IMP IEA
## "55775" "200558" "100133315"
##
## $$`GO:0000018`
## TAS TAS TAS IMP IMP IEP
## "3575" "3836" "3838" "9984" "10189" "56916"
##
## $$`GO:0000019`
## TAS IDA
## "4361" "10111"
##
## $$`GO:0000022`
## TAS TAS
## "9055" "9493"
The following code unlists the list, then gets matches for each Entrez gene ID to the index in the ExpressionSet. Finally, we rebuild the list.
govector <- unlist(go2eg)
golengths <- sapply(go2eg, length)
head(fData(es)$GENE)
## [1] "400451" "10239" "9899" "348093" "57099" "146050"
idxvector <- match(govector, fData(es)$GENE)
table(is.na(idxvector))
##
## FALSE TRUE
## 221613 7296
idx <- split(idxvector, rep(names(go2eg), golengths))
go2eg[[1]]
## TAS TAS ISS IMP NAS IMP IEA IMP
## "291" "1890" "4205" "4358" "9361" "10000" "80119" "92667"
fData(es)$GENE[idx[[1]]]
## [1] "291" "1890" "4205" "4358" "9361" "10000" "80119" "92667"
We need to clean this list such that there are no NA values. We also clean it to remove gene sets which have less than 10 genes.
idxclean <- lapply(idx, function(x) x[!is.na(x)])
idxlengths <- sapply(idxclean, length)
idxsub <- idxclean[idxlengths > 10]
length(idxsub)
## [1] 2906
The following line of code runs the multiple ROAST test. This can take about 3 minutes.
r2 <- mroast(es, idxsub, design)
head(r2)
## NGenes PropDown PropUp Direction PValue FDR
## GO:0005125 169 0.2662722 0.04733728 Down 0.001 0.009435065
## GO:0008083 167 0.2874251 0.07784431 Down 0.001 0.009435065
## GO:0043433 61 0.2786885 0.13114754 Down 0.001 0.009435065
## GO:0007623 57 0.2105263 0.12280702 Down 0.001 0.009435065
## GO:0006959 52 0.2500000 0.09615385 Down 0.001 0.009435065
## GO:0051781 49 0.3265306 0.08163265 Down 0.001 0.009435065
## PValue.Mixed FDR.Mixed
## GO:0005125 0.001 0.00256261
## GO:0008083 0.001 0.00256261
## GO:0043433 0.001 0.00256261
## GO:0007623 0.001 0.00256261
## GO:0006959 0.001 0.00256261
## GO:0051781 0.001 0.00256261
r2 <- r2[order(r2$PValue.Mixed),]
We can use the GO.db annotation package to extract the GO terms for the top results, by the mixed test.
# biocLite("GO.db")
library(GO.db)
##
columns(GO.db)
## [1] "GOID" "TERM" "ONTOLOGY" "DEFINITION"
keytypes(GO.db)
## [1] "GOID" "TERM" "ONTOLOGY" "DEFINITION"
GOTERM[[rownames(r2)[1]]]
## GOID: GO:0005125
## Term: cytokine activity
## Ontology: MF
## Definition: Functions to control the survival, growth,
## differentiation and effector function of tissues and cells.
## Synonym: autocrine activity
## Synonym: paracrine activity
r2tab <- select(GO.db, keys=rownames(r2)[1:10],
columns=c("GOID","TERM","DEFINITION"),
keytype="GOID")
r2tab[,1:2]
## GOID
## 1 GO:0005125
## 2 GO:0008083
## 3 GO:0043433
## 4 GO:0007623
## 5 GO:0006959
## 6 GO:0051781
## 7 GO:0048661
## 8 GO:0030593
## 9 GO:0032755
## 10 GO:0005912
## TERM
## 1 cytokine activity
## 2 growth factor activity
## 3 negative regulation of sequence-specific DNA binding transcription factor activity
## 4 circadian rhythm
## 5 humoral immune response
## 6 positive regulation of cell division
## 7 positive regulation of smooth muscle cell proliferation
## 8 neutrophil chemotaxis
## 9 positive regulation of interleukin-6 production
## 10 adherens junction
We can also look for the top results using the standard p-value and in the up direction.
r2 <- r2[order(r2$PValue),]
r2tab <- select(GO.db, keys=rownames(r2)[r2$Direction == "Up"][1:10],
columns=c("GOID","TERM","DEFINITION"),
keytype="GOID")
r2tab[,1:2]
## GOID
## 1 GO:0005912
## 2 GO:0030032
## 3 GO:0017147
## 4 GO:0048008
## 5 GO:0003950
## 6 GO:0046847
## 7 GO:0042813
## 8 GO:0010942
## 9 GO:0008631
## 10 GO:0045926
## TERM
## 1 adherens junction
## 2 lamellipodium assembly
## 3 Wnt-protein binding
## 4 platelet-derived growth factor receptor signaling pathway
## 5 NAD+ ADP-ribosyltransferase activity
## 6 filopodium assembly
## 7 Wnt-activated receptor activity
## 8 positive regulation of cell death
## 9 intrinsic apoptotic signaling pathway in response to oxidative stress
## 10 negative regulation of growth
Again but for the down direction.
r2tab <- select(GO.db, keys=rownames(r2)[r2$Direction == "Down"][1:5],
columns=c("GOID","TERM","DEFINITION"),
keytype="GOID")
r2tab[,1:2]
## GOID
## 1 GO:0005125
## 2 GO:0008083
## 3 GO:0043433
## 4 GO:0007623
## 5 GO:0006959
## TERM
## 1 cytokine activity
## 2 growth factor activity
## 3 negative regulation of sequence-specific DNA binding transcription factor activity
## 4 circadian rhythm
## 5 humoral immune response
Footnotes
Methods within the limma package
Wu D, Lim E, Vaillant F, Asselin-Labat ML, Visvader JE, Smyth GK. “ROAST: rotation gene set tests for complex microarray experiments”. Bioinformatics. 2010. http://www.ncbi.nlm.nih.gov/pubmed/20610611
Di Wu and Gordon K. Smyth, “Camera: a competitive gene set test accounting for inter-gene correlation” Nucleic Acids Research, 2012. http://nar.oxfordjournals.org/content/40/17/e133
GSEA
Subramanian A1, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP, “Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles” Proc Natl Acad Sci U S A. 2005. http://www.ncbi.nlm.nih.gov/pubmed/16199517
Correlation within gene sets
William T. Barry, Andrew B. Nobel, and Fred A. Wright, “A statistical framework for testing functional categories in microarray data” Ann. Appl. Stat, 2008. http://projecteuclid.org/euclid.aoas/1206367822
William Barry has a package safe in Bioconductor for gene set testing with resampling.
http://www.bioconductor.org/packages/release/bioc/html/safe.html
Daniel M Gatti, William T Barry, Andrew B Nobel, Ivan Rusyn and Fred A Wright, “Heading Down the Wrong Pathway: on the Influence of Correlation within Gene Sets”, BMC Genomics, 2010. http://www.biomedcentral.com/1471-2164/11/574#B24
Gene sets and power
The following article points out an issue with gene set testing: the power to detect differential expression for an individual gene depends on the number of NGS reads which align to that gene, which depends on the transcript length among other factors.
Alicia Oshlack* and Matthew J Wakefield, “Transcript length bias in RNA-seq data confounds systems biology”, Biology Direct, 2009. http://www.biologydirect.com/content/4/1/14
The dataset used in this lab
Masuno K, Haldar SM, Jeyaraj D, Mailloux CM, Huang X, Panettieri RA Jr, Jain MK, Gerber AN., “Expression profiling identifies Klf15 as a glucocorticoid target that regulates airway hyperresponsiveness”. Am J Respir Cell Mol Biol. 2011. http://www.ncbi.nlm.nih.gov/pubmed/21257922