Tutorial: RNA-seq differential expression & pathwayanalysis with Sailfish, DESeq2, GAGE, and Pathview

Stephen TurnerDecember 4, 2015

Contents

Background 1

Differential expression analysis 1

Pathway analysis 5

KEGG pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Gene Ontology (GO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Background

This tutorial1 shows an example of RNA-seq data analysis with DESeq2, followed by KEGG pathwayanalysis using GAGE. Using data from GSE37704, with processed data available on Figshare DOI:10.6084/m9.figshare.1601975. This dataset has six samples from GSE37704, where expression was quantifiedby either: (A) mapping to to GRCh38 using STAR then counting reads mapped to genes with featureCountsunder the union-intersection model, or (B) alignment-free quantification using Sailfish, summarized at thegene level using the GRCh38 GTF file. Both datasets are restricted to protein-coding genes only. Here I’lluse the Sailfish gene-level estimated counts.

Differential expression analysis

First, import the countdata and metadata directly from the web. Set up the DESeqDataSet, run the DESeq2pipeline.

# Note importing BioC pkgs after dplyr requires explicitly using dplyr::select()library(dplyr)library(DESeq2)

# Which data do you want to use? Let's use the sailfish counts.# browseURL("http://dx.doi.org/10.6084/m9.figshare.1601975")# countDataURL <- "http://files.figshare.com/2439061/GSE37704_featurecounts.csv"countDataURL <- "http://files.figshare.com/2600373/GSE37704_sailfish_genecounts.csv"

# Import countdatacountData <- read.csv(countDataURL, row.names=1) %>%

dplyr::select(-length) %>%as.matrix()

1See blog post here: http://www.gettinggeneticsdone.com/2015/12/tutorial-rna-seq-differential.html.

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# Filter data where you only have 0 or 1 read count across all samples.countData <- countData[rowSums(countData)>1, ]head(countData)

## SRR493366 SRR493367 SRR493368 SRR493369 SRR493370## ENSG00000198888 17528 23007 30241 24418 29152## ENSG00000198763 21264 26720 35550 28878 32416## ENSG00000198804 130975 151207 195514 178130 196727## ENSG00000198712 49769 61906 78608 66478 69758## ENSG00000228253 9304 11160 12830 12608 13041## ENSG00000198899 45401 51260 66851 63433 66123## SRR493371## ENSG00000198888 34416## ENSG00000198763 38422## ENSG00000198804 244670## ENSG00000198712 86808## ENSG00000228253 16063## ENSG00000198899 79215

# Import metadatacolData <- read.csv("http://files.figshare.com/2439060/GSE37704_metadata.csv", row.names=1)colData

## condition## SRR493366 control_sirna## SRR493367 control_sirna## SRR493368 control_sirna## SRR493369 hoxa1_kd## SRR493370 hoxa1_kd## SRR493371 hoxa1_kd

# Set up the DESeqDataSet Object and run the DESeq pipelinedds <- DESeqDataSetFromMatrix(countData=countData,

colData=colData,design=~condition)

dds <- DESeq(dds)dds

## class: DESeqDataSet## dim: 16755 6## metadata(0):## assays(3): counts mu cooks## rownames(16755): ENSG00000198888 ENSG00000198763 ...## ENSG00000267795 ENSG00000165795## rowRanges metadata column names(27): baseMean baseVar ... deviance## maxCooks## colnames(6): SRR493366 SRR493367 ... SRR493370 SRR493371## colData names(2): condition sizeFactor

Next, get results for the HoxA1 knockdown versus control siRNA, and reorder them by p-value. Call summaryon the results object to get a sense of how many genes are up or down-regulated at FDR<0.1.

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res <- results(dds, contrast=c("condition", "hoxa1_kd", "control_sirna"))res <- res[order(res$pvalue),]summary(res)

#### out of 16755 with nonzero total read count## adjusted p-value < 0.1## LFC > 0 (up) : 4193, 25%## LFC < 0 (down) : 4286, 26%## outliers [1] : 22, 0.13%## low counts [2] : 1299, 7.8%## (mean count < 1)## [1] see 'cooksCutoff' argument of ?results## [2] see 'independentFiltering' argument of ?results

Since we mapped and counted against the Ensembl annotation, our results only have information aboutEnsembl gene IDs. But, our pathway analysis downstream will use KEGG pathways, and genes in KEGGpathways are annotated with Entrez gene IDs. I wrote an R package for doing this offline the dplyr way(https://github.com/stephenturner/annotables), but the canonical Bioconductor way to do it is with theAnnotationDbi and organism annotation packages. Here we’re using the organism package (“org”) for Homosapiens (“Hs”), organized as an AnnotationDbi database package (“db”) using Entrez Gene IDs (“eg”) asprimary keys. To see what all the keys are, use the columns function.

library("AnnotationDbi")library("org.Hs.eg.db")columns(org.Hs.eg.db)

## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT"## [5] "ENSEMBLTRANS" "ENTREZID" "ENZYME" "EVIDENCE"## [9] "EVIDENCEALL" "GENENAME" "GO" "GOALL"## [13] "IPI" "MAP" "OMIM" "ONTOLOGY"## [17] "ONTOLOGYALL" "PATH" "PFAM" "PMID"## [21] "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"## [25] "UNIGENE" "UNIPROT"

Let’s use the mapIds function to add more columns to the results. The row.names of our results table hasthe Ensembl gene ID (our key), so we need to specify keytype=ENSEMBL. The column argument tells themapIds function which information we want, and the multiVals argument tells the function what to do ifthere are multiple possible values for a single input value. Here we ask to just give us back the first one thatoccurs in the database. Let’s get the Entrez IDs, gene symbols, and full gene names.2

res$symbol <- mapIds(org.Hs.eg.db,keys=row.names(res),column="SYMBOL",keytype="ENSEMBL",multiVals="first")

res$entrez <- mapIds(org.Hs.eg.db,keys=row.names(res),column="ENTREZID",

2Code from: Love, Michael I., et al. “RNA-Seq workflow: gene-level exploratory analysis and differential expression.”F1000Research 4 (2015).

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keytype="ENSEMBL",multiVals="first")

res$name <- mapIds(org.Hs.eg.db,keys=row.names(res),column="GENENAME",keytype="ENSEMBL",multiVals="first")

head(res, 10)

## log2 fold change (MAP): condition hoxa1_kd vs control_sirna## Wald test p-value: condition hoxa1_kd vs control_sirna## DataFrame with 10 rows and 9 columns## baseMean log2FoldChange lfcSE stat pvalue## <numeric> <numeric> <numeric> <numeric> <numeric>## ENSG00000148773 1885.344 -3.172502 0.07868572 -40.31865 0## ENSG00000138623 2939.936 -2.418238 0.05889229 -41.06205 0## ENSG00000104368 13601.963 2.016802 0.05249643 38.41789 0## ENSG00000124766 2692.200 2.379545 0.06193654 38.41908 0## ENSG00000122861 35889.413 2.224779 0.05258658 42.30697 0## ENSG00000116016 4558.157 -1.885339 0.04258766 -44.26961 0## ENSG00000164251 2404.103 3.325196 0.07021236 47.35912 0## ENSG00000125257 6187.386 1.943762 0.04259189 45.63692 0## ENSG00000104321 9334.555 3.186856 0.06227530 51.17367 0## ENSG00000183508 2110.345 3.190612 0.07488305 42.60794 0## padj symbol entrez## <numeric> <character> <character>## ENSG00000148773 0 MKI67 4288## ENSG00000138623 0 SEMA7A 8482## ENSG00000104368 0 PLAT 5327## ENSG00000124766 0 SOX4 6659## ENSG00000122861 0 PLAU 5328## ENSG00000116016 0 EPAS1 2034## ENSG00000164251 0 F2RL1 2150## ENSG00000125257 0 ABCC4 10257## ENSG00000104321 0 TRPA1 8989## ENSG00000183508 0 FAM46C 54855## name## <character>## ENSG00000148773 marker of proliferation Ki-67## ENSG00000138623 semaphorin 7A, GPI membrane anchor (John Milton Hagen blood group)## ENSG00000104368 plasminogen activator, tissue## ENSG00000124766 SRY (sex determining region Y)-box 4## ENSG00000122861 plasminogen activator, urokinase## ENSG00000116016 endothelial PAS domain protein 1## ENSG00000164251 coagulation factor II (thrombin) receptor-like 1## ENSG00000125257 ATP-binding cassette, sub-family C (CFTR/MRP), member 4## ENSG00000104321 transient receptor potential cation channel, subfamily A, member 1## ENSG00000183508 family with sequence similarity 46, member C

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Pathway analysis

We’re going to use the gage package (Generally Applicable Gene-set Enrichment for Pathway Analysis) forpathway analysis. See also the gage package workflow vignette for RNA-seq pathway analysis. Once we havea list of enriched pathways, we’re going to use the pathview package to draw pathway diagrams, shading themolecules in the pathway by their degree of up/down-regulation.

KEGG pathways

The gageData package has pre-compiled databases mapping genes to KEGG pathways and GO terms forcommon organisms. kegg.sets.hs is a named list of 229 elements. Each element is a character vector ofmember gene Entrez IDs for a single KEGG pathway. (See also go.sets.hs). sigmet.idx.hs is an indexof numbers of sinaling and metabolic pathways in kegg.set.gs. In other words, KEGG pathway includeother types of pathway definitions, like “Global Map” and “Human Diseases”, which may be undesirable inpathway analysis. Therefore, kegg.sets.hs[sigmet.idx.hs] gives you the “cleaner” gene sets of sinalingand metabolic pathways only.

library(pathview)library(gage)library(gageData)data(kegg.sets.hs)data(sigmet.idx.hs)kegg.sets.hs <- kegg.sets.hs[sigmet.idx.hs]head(kegg.sets.hs, 3)

## $`hsa00232 Caffeine metabolism`## [1] "10" "1544" "1548" "1549" "1553" "7498" "9"#### $`hsa00983 Drug metabolism - other enzymes`## [1] "10" "1066" "10720" "10941" "151531" "1548" "1549"## [8] "1551" "1553" "1576" "1577" "1806" "1807" "1890"## [15] "221223" "2990" "3251" "3614" "3615" "3704" "51733"## [22] "54490" "54575" "54576" "54577" "54578" "54579" "54600"## [29] "54657" "54658" "54659" "54963" "574537" "64816" "7083"## [36] "7084" "7172" "7363" "7364" "7365" "7366" "7367"## [43] "7371" "7372" "7378" "7498" "79799" "83549" "8824"## [50] "8833" "9" "978"#### $`hsa00230 Purine metabolism`## [1] "100" "10201" "10606" "10621" "10622" "10623" "107"## [8] "10714" "108" "10846" "109" "111" "11128" "11164"## [15] "112" "113" "114" "115" "122481" "122622" "124583"## [22] "132" "158" "159" "1633" "171568" "1716" "196883"## [29] "203" "204" "205" "221823" "2272" "22978" "23649"## [36] "246721" "25885" "2618" "26289" "270" "271" "27115"## [43] "272" "2766" "2977" "2982" "2983" "2984" "2986"## [50] "2987" "29922" "3000" "30833" "30834" "318" "3251"## [57] "353" "3614" "3615" "3704" "377841" "471" "4830"## [64] "4831" "4832" "4833" "4860" "4881" "4882" "4907"## [71] "50484" "50940" "51082" "51251" "51292" "5136" "5137"## [78] "5138" "5139" "5140" "5141" "5142" "5143" "5144"## [85] "5145" "5146" "5147" "5148" "5149" "5150" "5151"

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## [92] "5152" "5153" "5158" "5167" "5169" "51728" "5198"## [99] "5236" "5313" "5315" "53343" "54107" "5422" "5424"## [106] "5425" "5426" "5427" "5430" "5431" "5432" "5433"## [113] "5434" "5435" "5436" "5437" "5438" "5439" "5440"## [120] "5441" "5471" "548644" "55276" "5557" "5558" "55703"## [127] "55811" "55821" "5631" "5634" "56655" "56953" "56985"## [134] "57804" "58497" "6240" "6241" "64425" "646625" "654364"## [141] "661" "7498" "8382" "84172" "84265" "84284" "84618"## [148] "8622" "8654" "87178" "8833" "9060" "9061" "93034"## [155] "953" "9533" "954" "955" "956" "957" "9583"## [162] "9615"

The gage() function requires a named vector of fold changes, where the names of the values are the Entrezgene IDs.

foldchanges <- res$log2FoldChangenames(foldchanges) <- res$entrezhead(foldchanges)

## 4288 8482 5327 6659 5328 2034## -3.172502 -2.418238 2.016802 2.379545 2.224779 -1.885339

Now, let’s run the pathway analysis. See help on the gage function with ?gage. Specifically, you might wantto try changing the value of same.dir. This value determins whether to test for changes in a gene set towarda single direction (all genes up or down regulated) or changes towards both directions simultaneously (anygenes in the pathway dysregulated).

For experimentally derived gene sets, GO term groups, etc, coregulation is commonly the case,hence same.dir = TRUE (default); In KEGG, BioCarta pathways, genes frequently are notcoregulated, hence it could be informative to let same.dir = FALSE. Although same.dir = TRUEcould also be interesting for pathways.

Here, we’re using same.dir = TRUE, which will give us separate lists for pathways that are upregulatedversus pathways that are downregulated. Let’s look at the first few results from each.

# Get the resultskeggres <- gage(foldchanges, gsets=kegg.sets.hs, same.dir=TRUE)

# Look at both up (greater), down (less), and statatistics.lapply(keggres, head)

## $greater## p.geomean stat.mean p.val## hsa04142 Lysosome 0.0002630657 3.517890 0.0002630657## hsa04640 Hematopoietic cell lineage 0.0017919390 2.976432 0.0017919390## hsa04630 Jak-STAT signaling pathway 0.0048980977 2.604390 0.0048980977## hsa00140 Steroid hormone biosynthesis 0.0051115493 2.636206 0.0051115493## hsa04062 Chemokine signaling pathway 0.0125582961 2.250765 0.0125582961## hsa00511 Other glycan degradation 0.0223819919 2.104311 0.0223819919## q.val set.size exp1## hsa04142 Lysosome 0.04261664 116 0.0002630657## hsa04640 Hematopoietic cell lineage 0.14514706 61 0.0017919390

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## hsa04630 Jak-STAT signaling pathway 0.20701775 119 0.0048980977## hsa00140 Steroid hormone biosynthesis 0.20701775 39 0.0051115493## hsa04062 Chemokine signaling pathway 0.40688879 156 0.0125582961## hsa00511 Other glycan degradation 0.49956506 15 0.0223819919#### $less## p.geomean stat.mean p.val## hsa04110 Cell cycle 2.165725e-06 -4.722301 2.165725e-06## hsa03030 DNA replication 3.807440e-06 -4.835336 3.807440e-06## hsa04114 Oocyte meiosis 1.109869e-04 -3.767561 1.109869e-04## hsa03013 RNA transport 1.181787e-03 -3.071947 1.181787e-03## hsa03440 Homologous recombination 1.197124e-03 -3.190747 1.197124e-03## hsa00240 Pyrimidine metabolism 1.570318e-03 -2.992059 1.570318e-03## q.val set.size exp1## hsa04110 Cell cycle 0.0003084027 121 2.165725e-06## hsa03030 DNA replication 0.0003084027 36 3.807440e-06## hsa04114 Oocyte meiosis 0.0059932916 101 1.109869e-04## hsa03013 RNA transport 0.0387868193 145 1.181787e-03## hsa03440 Homologous recombination 0.0387868193 28 1.197124e-03## hsa00240 Pyrimidine metabolism 0.0423985796 96 1.570318e-03#### $stats## stat.mean exp1## hsa04142 Lysosome 3.517890 3.517890## hsa04640 Hematopoietic cell lineage 2.976432 2.976432## hsa04630 Jak-STAT signaling pathway 2.604390 2.604390## hsa00140 Steroid hormone biosynthesis 2.636206 2.636206## hsa04062 Chemokine signaling pathway 2.250765 2.250765## hsa00511 Other glycan degradation 2.104311 2.104311

Now, let’s process the results to pull out the top 5 upregulated pathways, then further process that just toget the IDs. We’ll use these KEGG pathway IDs downstream for plotting.

# Get the pathwayskeggrespathways <- data.frame(id=rownames(keggres$greater), keggres$greater) %>%

tbl_df() %>%filter(row_number()<=5) %>%.$id %>%as.character()

keggrespathways

## [1] "hsa04142 Lysosome"## [2] "hsa04640 Hematopoietic cell lineage"## [3] "hsa04630 Jak-STAT signaling pathway"## [4] "hsa00140 Steroid hormone biosynthesis"## [5] "hsa04062 Chemokine signaling pathway"

# Get the IDs.keggresids <- substr(keggrespathways, start=1, stop=8)keggresids

## [1] "hsa04142" "hsa04640" "hsa04630" "hsa00140" "hsa04062"

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Finally, the pathview() function in the pathview package makes the plots. Let’s write a function so we canloop through and draw plots for the top 5 pathways we created above.

# Define plotting function for applying laterplot_pathway <- function(pid) pathview(gene.data=foldchanges, pathway.id=pid, species="hsa", new.signature=FALSE)

# plot multiple pathways (plots saved to disk and returns a throwaway list object)tmp <- sapply(keggresids, function(pid) pathview(gene.data=foldchanges, pathway.id=pid, species="hsa"))

Here are the plots:

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Gene Ontology (GO)

We can also do a similar procedure with gene ontology. Similar to above, go.sets.hs has all GO terms.go.subs.hs is a named list containing indexes for the BP, CC, and MF ontologies. Let’s only do BiologicalProcess.

data(go.sets.hs)data(go.subs.hs)gobpsets <- go.sets.hs[go.subs.hs$BP]

gobpres <- gage(foldchanges, gsets=gobpsets, same.dir=TRUE)

lapply(gobpres, head)

## $greater## p.geomean## GO:0007156 homophilic cell adhesion 3.914568e-05## GO:0008285 negative regulation of cell proliferation 2.907332e-04## GO:0016339 calcium-dependent cell-cell adhesion 4.218753e-04## GO:0016337 cell-cell adhesion 6.170551e-04## GO:0048729 tissue morphogenesis 6.581460e-04## GO:1901617 organic hydroxy compound biosynthetic process 8.876161e-04## stat.mean## GO:0007156 homophilic cell adhesion 4.017207## GO:0008285 negative regulation of cell proliferation 3.453345## GO:0016339 calcium-dependent cell-cell adhesion 3.543891## GO:0016337 cell-cell adhesion 3.244296## GO:0048729 tissue morphogenesis 3.223979## GO:1901617 organic hydroxy compound biosynthetic process 3.157421## p.val## GO:0007156 homophilic cell adhesion 3.914568e-05## GO:0008285 negative regulation of cell proliferation 2.907332e-04## GO:0016339 calcium-dependent cell-cell adhesion 4.218753e-04## GO:0016337 cell-cell adhesion 6.170551e-04## GO:0048729 tissue morphogenesis 6.581460e-04## GO:1901617 organic hydroxy compound biosynthetic process 8.876161e-04## q.val## GO:0007156 homophilic cell adhesion 0.1613977## GO:0008285 negative regulation of cell proliferation 0.4720349## GO:0016339 calcium-dependent cell-cell adhesion 0.4720349## GO:0016337 cell-cell adhesion 0.4720349## GO:0048729 tissue morphogenesis 0.4720349## GO:1901617 organic hydroxy compound biosynthetic process 0.4720349## set.size## GO:0007156 homophilic cell adhesion 124## GO:0008285 negative regulation of cell proliferation 458## GO:0016339 calcium-dependent cell-cell adhesion 27## GO:0016337 cell-cell adhesion 355## GO:0048729 tissue morphogenesis 429## GO:1901617 organic hydroxy compound biosynthetic process 141## exp1## GO:0007156 homophilic cell adhesion 3.914568e-05## GO:0008285 negative regulation of cell proliferation 2.907332e-04## GO:0016339 calcium-dependent cell-cell adhesion 4.218753e-04

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## GO:0016337 cell-cell adhesion 6.170551e-04## GO:0048729 tissue morphogenesis 6.581460e-04## GO:1901617 organic hydroxy compound biosynthetic process 8.876161e-04#### $less## p.geomean stat.mean## GO:0048285 organelle fission 4.411540e-18 -8.850004## GO:0000280 nuclear division 7.459684e-18 -8.805564## GO:0007067 mitosis 7.459684e-18 -8.805564## GO:0000087 M phase of mitotic cell cycle 2.286444e-17 -8.655644## GO:0007059 chromosome segregation 1.872901e-13 -7.686883## GO:0051301 cell division 5.841375e-12 -6.887763## p.val q.val## GO:0048285 organelle fission 4.411540e-18 1.025209e-14## GO:0000280 nuclear division 7.459684e-18 1.025209e-14## GO:0007067 mitosis 7.459684e-18 1.025209e-14## GO:0000087 M phase of mitotic cell cycle 2.286444e-17 2.356752e-14## GO:0007059 chromosome segregation 1.872901e-13 1.544394e-10## GO:0051301 cell division 5.841375e-12 4.013998e-09## set.size exp1## GO:0048285 organelle fission 376 4.411540e-18## GO:0000280 nuclear division 352 7.459684e-18## GO:0007067 mitosis 352 7.459684e-18## GO:0000087 M phase of mitotic cell cycle 362 2.286444e-17## GO:0007059 chromosome segregation 141 1.872901e-13## GO:0051301 cell division 462 5.841375e-12#### $stats## stat.mean## GO:0007156 homophilic cell adhesion 4.017207## GO:0008285 negative regulation of cell proliferation 3.453345## GO:0016339 calcium-dependent cell-cell adhesion 3.543891## GO:0016337 cell-cell adhesion 3.244296## GO:0048729 tissue morphogenesis 3.223979## GO:1901617 organic hydroxy compound biosynthetic process 3.157421## exp1## GO:0007156 homophilic cell adhesion 4.017207## GO:0008285 negative regulation of cell proliferation 3.453345## GO:0016339 calcium-dependent cell-cell adhesion 3.543891## GO:0016337 cell-cell adhesion 3.244296## GO:0048729 tissue morphogenesis 3.223979## GO:1901617 organic hydroxy compound biosynthetic process 3.157421

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