Cecilia's Site
My class work for bimm143 at UC San Diego
Lab 14: RNA-Seq analysis mini-project
Cecilia Wang (A18625854)
Section 1: Differential Expression Analysis
library(DESeq2)
Load our data:
colData <- read.csv("GSE37704_metadata.csv", row.names=1)
countData <- read.csv("GSE37704_featurecounts.csv", row.names=1)
head(colData)
condition
SRR493366 control_sirna
SRR493367 control_sirna
SRR493368 control_sirna
SRR493369 hoxa1_kd
SRR493370 hoxa1_kd
SRR493371 hoxa1_kd
head(countData)
length SRR493366 SRR493367 SRR493368 SRR493369 SRR493370
ENSG00000186092 918 0 0 0 0 0
ENSG00000279928 718 0 0 0 0 0
ENSG00000279457 1982 23 28 29 29 28
ENSG00000278566 939 0 0 0 0 0
ENSG00000273547 939 0 0 0 0 0
ENSG00000187634 3214 124 123 205 207 212
SRR493371
ENSG00000186092 0
ENSG00000279928 0
ENSG00000279457 46
ENSG00000278566 0
ENSG00000273547 0
ENSG00000187634 258
- Complete the code below to remove the troublesome first column from countData
# Note we need to remove the odd first $length col
countData <- as.matrix(countData[,-1])
head(countData)
SRR493366 SRR493367 SRR493368 SRR493369 SRR493370 SRR493371
ENSG00000186092 0 0 0 0 0 0
ENSG00000279928 0 0 0 0 0 0
ENSG00000279457 23 28 29 29 28 46
ENSG00000278566 0 0 0 0 0 0
ENSG00000273547 0 0 0 0 0 0
ENSG00000187634 124 123 205 207 212 258
Q. Complete the code below to filter countData to exclude genes (i.e. rows) where we have 0 read count across all samples (i.e. columns). Tip: What will rowSums() of countData return and how could you use it in this context?
# Filter count data where you have 0 read count across all samples.
countData = countData[rowSums(countData) != 0, ]
head(countData)
SRR493366 SRR493367 SRR493368 SRR493369 SRR493370 SRR493371
ENSG00000279457 23 28 29 29 28 46
ENSG00000187634 124 123 205 207 212 258
ENSG00000188976 1637 1831 2383 1226 1326 1504
ENSG00000187961 120 153 180 236 255 357
ENSG00000187583 24 48 65 44 48 64
ENSG00000187642 4 9 16 14 16 16
Running DESeq2
dds = DESeqDataSetFromMatrix(countData=countData,
colData=colData,
design=~condition)
Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
design formula are characters, converting to factors
dds = DESeq(dds)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
dds
class: DESeqDataSet
dim: 15975 6
metadata(1): version
assays(4): counts mu H cooks
rownames(15975): ENSG00000279457 ENSG00000187634 ... ENSG00000276345
ENSG00000271254
rowData names(22): baseMean baseVar ... deviance maxCooks
colnames(6): SRR493366 SRR493367 ... SRR493370 SRR493371
colData names(2): condition sizeFactor
res = results(dds)
Q. Call the summary() function on your results to get a sense of how many genes are up or down-regulated at the default 0.1 p-value cutoff.
summary(res)
out of 15975 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4349, 27%
LFC < 0 (down) : 4396, 28%
outliers [1] : 0, 0%
low counts [2] : 1237, 7.7%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
Volcano Plot
library(ggplot2)
ggplot(res)+
aes(log2FoldChange,-log(padj))+
geom_point()+
labs(x="Log2 Fold-change",
y="-log Adjusted P-value",
title= "Volcano Plot")+
theme_minimal(base_size = 12)+
theme(
plot.title = element_text(face="bold",hjust=0.5),
axis.title= element_text(face="bold"),
legend.title= element_text(face="bold"),
panel.grid.minor= element_blank()
)
Warning: Removed 1237 rows containing missing values or values outside the scale range
(`geom_point()`).
Q. Improve this plot by completing the below code, which adds color, axis labels and cutoff lines:
# Make a color vector for all genes
mycols <- rep("gray", nrow(res))
# Color blue the genes with fold change above 2
mycols[res$log2FoldChange > 2 ] <- "blue"
mycols[res$log2FoldChange < -2 ] <- "blue"
# Color gray those with adjusted p-value more than 0.01
mycols[ res$padj > 0.01 ] <- "gray"
ggplot(res)+
aes(log2FoldChange,-log(padj))+
geom_point(colour=mycols)+
labs(x="Log2 Fold-change",
y="-log Adjusted P-value",
title= "Volcano Plot of Differential Gene Expression")+
theme_minimal(base_size = 12)+
theme(
plot.title = element_text(face="bold",hjust=0.5),
axis.title= element_text(face="bold"),
legend.title= element_text(face="bold"),
panel.grid.minor= element_blank()
)+
geom_vline(xintercept = c(-2,2)) +
geom_hline(yintercept = 0.05)
Warning: Removed 1237 rows containing missing values or values outside the scale range
(`geom_point()`).
Adding gene annotation
Q. Use the mapIDs() function multiple times to add SYMBOL, ENTREZID and GENENAME annotation to our results by completing the code below.
library("AnnotationDbi")
library("org.Hs.eg.db")
columns(org.Hs.eg.db)
[1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
[6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
[11] "GENETYPE" "GO" "GOALL" "IPI" "MAP"
[16] "OMIM" "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM"
[21] "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG"
[26] "UNIPROT"
res$symbol = mapIds(org.Hs.eg.db,
keys=row.names(res),
keytype="ENSEMBL",
column="SYMBOL",
multiVals="first")
'select()' returned 1:many mapping between keys and columns
res$entrez = mapIds(org.Hs.eg.db,
keys=row.names(res),
keytype="ENSEMBL",
column="ENTREZID",
multiVals="first")
'select()' returned 1:many mapping between keys and columns
res$name = mapIds(org.Hs.eg.db,
keys=row.names(res),
keytype="ENSEMBL",
column="GENENAME",
multiVals="first")
'select()' returned 1:many mapping between keys and columns
head(res, 10)
log2 fold change (MLE): 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>
ENSG00000279457 29.913579 0.1792571 0.3248216 0.551863 5.81042e-01
ENSG00000187634 183.229650 0.4264571 0.1402658 3.040350 2.36304e-03
ENSG00000188976 1651.188076 -0.6927205 0.0548465 -12.630158 1.43990e-36
ENSG00000187961 209.637938 0.7297556 0.1318599 5.534326 3.12428e-08
ENSG00000187583 47.255123 0.0405765 0.2718928 0.149237 8.81366e-01
ENSG00000187642 11.979750 0.5428105 0.5215598 1.040744 2.97994e-01
ENSG00000188290 108.922128 2.0570638 0.1969053 10.446970 1.51282e-25
ENSG00000187608 350.716868 0.2573837 0.1027266 2.505522 1.22271e-02
ENSG00000188157 9128.439422 0.3899088 0.0467163 8.346304 7.04321e-17
ENSG00000237330 0.158192 0.7859552 4.0804729 0.192614 8.47261e-01
padj symbol entrez name
<numeric> <character> <character> <character>
ENSG00000279457 6.86555e-01 NA NA NA
ENSG00000187634 5.15718e-03 SAMD11 148398 sterile alpha motif ..
ENSG00000188976 1.76549e-35 NOC2L 26155 NOC2 like nucleolar ..
ENSG00000187961 1.13413e-07 KLHL17 339451 kelch like family me..
ENSG00000187583 9.19031e-01 PLEKHN1 84069 pleckstrin homology ..
ENSG00000187642 4.03379e-01 PERM1 84808 PPARGC1 and ESRR ind..
ENSG00000188290 1.30538e-24 HES4 57801 hes family bHLH tran..
ENSG00000187608 2.37452e-02 ISG15 9636 ISG15 ubiquitin like..
ENSG00000188157 4.21963e-16 AGRN 375790 agrin
ENSG00000237330 NA RNF223 401934 ring finger protein ..
Q. Finally for this section let’s reorder these results by adjusted p-value and save them to a CSV file in your current project directory.
write.csv(res,file ="deseq_results.csv")
Section 2. Pathway Analysis
KEGG Pathway
library(pathview)
library(gage)
library(gageData)
data(kegg.sets.hs)
data(sigmet.idx.hs)
# Focus on signaling and metabolic pathways only
kegg.sets.hs = kegg.sets.hs[sigmet.idx.hs]
# Examine the first 3 pathways
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" "1551"
[9] "1553" "1576" "1577" "1806" "1807" "1890" "221223" "2990"
[17] "3251" "3614" "3615" "3704" "51733" "54490" "54575" "54576"
[25] "54577" "54578" "54579" "54600" "54657" "54658" "54659" "54963"
[33] "574537" "64816" "7083" "7084" "7172" "7363" "7364" "7365"
[41] "7366" "7367" "7371" "7372" "7378" "7498" "79799" "83549"
[49] "8824" "8833" "9" "978"
$`hsa00230 Purine metabolism`
[1] "100" "10201" "10606" "10621" "10622" "10623" "107" "10714"
[9] "108" "10846" "109" "111" "11128" "11164" "112" "113"
[17] "114" "115" "122481" "122622" "124583" "132" "158" "159"
[25] "1633" "171568" "1716" "196883" "203" "204" "205" "221823"
[33] "2272" "22978" "23649" "246721" "25885" "2618" "26289" "270"
[41] "271" "27115" "272" "2766" "2977" "2982" "2983" "2984"
[49] "2986" "2987" "29922" "3000" "30833" "30834" "318" "3251"
[57] "353" "3614" "3615" "3704" "377841" "471" "4830" "4831"
[65] "4832" "4833" "4860" "4881" "4882" "4907" "50484" "50940"
[73] "51082" "51251" "51292" "5136" "5137" "5138" "5139" "5140"
[81] "5141" "5142" "5143" "5144" "5145" "5146" "5147" "5148"
[89] "5149" "5150" "5151" "5152" "5153" "5158" "5167" "5169"
[97] "51728" "5198" "5236" "5313" "5315" "53343" "54107" "5422"
[105] "5424" "5425" "5426" "5427" "5430" "5431" "5432" "5433"
[113] "5434" "5435" "5436" "5437" "5438" "5439" "5440" "5441"
[121] "5471" "548644" "55276" "5557" "5558" "55703" "55811" "55821"
[129] "5631" "5634" "56655" "56953" "56985" "57804" "58497" "6240"
[137] "6241" "64425" "646625" "654364" "661" "7498" "8382" "84172"
[145] "84265" "84284" "84618" "8622" "8654" "87178" "8833" "9060"
[153] "9061" "93034" "953" "9533" "954" "955" "956" "957"
[161] "9583" "9615"
foldchanges = res$log2FoldChange
names(foldchanges) = res$entrez
head(foldchanges)
<NA> 148398 26155 339451 84069 84808
0.17925708 0.42645712 -0.69272046 0.72975561 0.04057653 0.54281049
Run the gage pathway analysis:
# Get the results
keggres = gage(foldchanges, gsets=kegg.sets.hs)
attributes(keggres)
$names
[1] "greater" "less" "stats"
# Look at the first few down (less) pathways
head(keggres$less)
p.geomean stat.mean p.val
hsa04110 Cell cycle 8.995727e-06 -4.378644 8.995727e-06
hsa03030 DNA replication 9.424076e-05 -3.951803 9.424076e-05
hsa03013 RNA transport 1.246882e-03 -3.059466 1.246882e-03
hsa03440 Homologous recombination 3.066756e-03 -2.852899 3.066756e-03
hsa04114 Oocyte meiosis 3.784520e-03 -2.698128 3.784520e-03
hsa00010 Glycolysis / Gluconeogenesis 8.961413e-03 -2.405398 8.961413e-03
q.val set.size exp1
hsa04110 Cell cycle 0.001448312 121 8.995727e-06
hsa03030 DNA replication 0.007586381 36 9.424076e-05
hsa03013 RNA transport 0.066915974 144 1.246882e-03
hsa03440 Homologous recombination 0.121861535 28 3.066756e-03
hsa04114 Oocyte meiosis 0.121861535 102 3.784520e-03
hsa00010 Glycolysis / Gluconeogenesis 0.212222694 53 8.961413e-03
Pathway analysis for cell cycle (hsa04110)
pathview(gene.data=foldchanges, pathway.id="hsa04110")
'select()' returned 1:1 mapping between keys and columns
Info: Working in directory /Users/yushiwang/BIMM 143 R /Class14/Class 14
Info: Writing image file hsa04110.pathview.png
Now, let’s process our results a bit more to automagicaly pull out the top 5 upregulated pathways, We’ll use these KEGG pathway IDs for pathview plotting below.
## Focus on top 5 upregulated pathways here for demo purposes only
keggrespathways <- rownames(keggres$greater)[1:5]
# Extract the 8 character long IDs part of each string
keggresids = substr(keggrespathways, start=1, stop=8)
keggresids
[1] "hsa04640" "hsa04630" "hsa00140" "hsa04142" "hsa04330"
pathview(gene.data=foldchanges, pathway.id=keggresids, species="hsa")
Section 3. Gene Ontology (GO)
data(go.sets.hs)
data(go.subs.hs)
# Focus on Biological Process subset of GO
gobpsets = go.sets.hs[go.subs.hs$BP]
gobpres = gage(foldchanges, gsets=gobpsets)
lapply(gobpres, head)
$greater
p.geomean stat.mean p.val
GO:0007156 homophilic cell adhesion 8.519724e-05 3.824205 8.519724e-05
GO:0002009 morphogenesis of an epithelium 1.396681e-04 3.653886 1.396681e-04
GO:0048729 tissue morphogenesis 1.432451e-04 3.643242 1.432451e-04
GO:0007610 behavior 1.925222e-04 3.565432 1.925222e-04
GO:0060562 epithelial tube morphogenesis 5.932837e-04 3.261376 5.932837e-04
GO:0035295 tube development 5.953254e-04 3.253665 5.953254e-04
q.val set.size exp1
GO:0007156 homophilic cell adhesion 0.1951953 113 8.519724e-05
GO:0002009 morphogenesis of an epithelium 0.1951953 339 1.396681e-04
GO:0048729 tissue morphogenesis 0.1951953 424 1.432451e-04
GO:0007610 behavior 0.1967577 426 1.925222e-04
GO:0060562 epithelial tube morphogenesis 0.3565320 257 5.932837e-04
GO:0035295 tube development 0.3565320 391 5.953254e-04
$less
p.geomean stat.mean p.val
GO:0048285 organelle fission 1.536227e-15 -8.063910 1.536227e-15
GO:0000280 nuclear division 4.286961e-15 -7.939217 4.286961e-15
GO:0007067 mitosis 4.286961e-15 -7.939217 4.286961e-15
GO:0000087 M phase of mitotic cell cycle 1.169934e-14 -7.797496 1.169934e-14
GO:0007059 chromosome segregation 2.028624e-11 -6.878340 2.028624e-11
GO:0000236 mitotic prometaphase 1.729553e-10 -6.695966 1.729553e-10
q.val set.size exp1
GO:0048285 organelle fission 5.841698e-12 376 1.536227e-15
GO:0000280 nuclear division 5.841698e-12 352 4.286961e-15
GO:0007067 mitosis 5.841698e-12 352 4.286961e-15
GO:0000087 M phase of mitotic cell cycle 1.195672e-11 362 1.169934e-14
GO:0007059 chromosome segregation 1.658603e-08 142 2.028624e-11
GO:0000236 mitotic prometaphase 1.178402e-07 84 1.729553e-10
$stats
stat.mean exp1
GO:0007156 homophilic cell adhesion 3.824205 3.824205
GO:0002009 morphogenesis of an epithelium 3.653886 3.653886
GO:0048729 tissue morphogenesis 3.643242 3.643242
GO:0007610 behavior 3.565432 3.565432
GO:0060562 epithelial tube morphogenesis 3.261376 3.261376
GO:0035295 tube development 3.253665 3.253665
Section 4. Reactome Analysis
online software available (https://reactome.org/)
sig_genes <- res[res$padj <= 0.05 & !is.na(res$padj), "symbol"]
print(paste("Total number of significant genes:", length(sig_genes)))
[1] "Total number of significant genes: 8147"
write.table(sig_genes, file="significant_genes.txt", row.names=FALSE, col.names=FALSE, quote=FALSE)
Q: What pathway has the most significant “Entities p-value”? Do the most significant pathways listed match your previous KEGG results? What factors could cause differences between the two methods?
Cell cycle with entities p-value of 2.2E-5. Yes, it matched with previous KEGG results.
