RNAseq in Bioconductor exercises (part 4)
Rockefeller University, Bioinformatics Resource Centre
https://rockefelleruniversity.github.io/RU_RNAseq/
In todays session we will work with some of the RNAseq data of adult mouse tissues from Bing Ren’s lab, Liver and Heart.
More information on liver data can be found here
More information on heart data can be found here
Differential expression results for Heart minus Liver can be found in the data directory:
DGE - data/Heart_minus_liver.csv
Counts - data/gC_TissueFull.RData
Exercises
1. goseq cellular component analysis
Identify GO term cellular component groups enriched in genes significantly upregulated in Liver with goseq. What are the top 5 terms?
HINT: Use “GO:CC” as the test category for goseq
## Can't find mm10/knownGene length data in genLenDataBase...## Found the annotation package, TxDb.Mmusculus.UCSC.mm10.knownGene## Trying to get the gene lengths from it.
## Fetching GO annotations...## For 289 genes, we could not find any categories. These genes will be excluded.## To force their use, please run with use_genes_without_cat=TRUE (see documentation).## This was the default behavior for version 1.15.1 and earlier.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columns## category over_represented_pvalue under_represented_pvalue numDEInCat
## 259 GO:0005783 1.262910e-38 1 480
## 168 GO:0005615 1.441967e-31 1 329
## 147 GO:0005576 3.411939e-26 1 420
## 255 GO:0005777 2.214583e-19 1 71
## 1041 GO:0042579 2.214583e-19 1 71
## numInCat term ontology
## 259 1622 endoplasmic reticulum CC
## 168 1047 extracellular space CC
## 147 1536 extracellular region CC
## 255 140 peroxisome CC
## 1041 140 microbody CC2. ClusterProfiler for KEGG enrichment
Plot the -log10 pvalue for top 5 terms as point plot.
HINT: The “gseKEGG” is the clusterPorfiler function for looking at KEGG terms
## preparing geneSet collections...## GSEA analysis...## leading edge analysis...## done...## ID Description setSize enrichmentScore
## mmu00190 mmu00190 Oxidative phosphorylation 111 0.7303096
## mmu00830 mmu00830 Retinol metabolism 81 -0.7868858
## mmu04260 mmu04260 Cardiac muscle contraction 78 0.7853715
## mmu05415 mmu05415 Diabetic cardiomyopathy 180 0.7332201
## mmu00140 mmu00140 Steroid hormone biosynthesis 71 -0.7974849
## mmu04610 mmu04610 Complement and coagulation cascades 86 -0.7545846
## NES pvalue p.adjust qvalues rank
## mmu00190 2.753080 1.000000e-19 8.225000e-18 4.763158e-18 2500
## mmu00830 -2.763184 1.000000e-19 8.225000e-18 4.763158e-18 2570
## mmu04260 2.783327 1.000000e-19 8.225000e-18 4.763158e-18 1875
## mmu05415 2.917856 1.000000e-19 8.225000e-18 4.763158e-18 2319
## mmu00140 -2.728415 1.149096e-18 7.561054e-17 4.378662e-17 1346
## mmu04610 -2.665677 3.676729e-18 2.016073e-16 1.167523e-16 1204
## leading_edge
## mmu00190 tags=69%, list=16%, signal=59%
## mmu00830 tags=79%, list=16%, signal=67%
## mmu04260 tags=58%, list=12%, signal=51%
## mmu05415 tags=63%, list=15%, signal=54%
## mmu00140 tags=66%, list=8%, signal=61%
## mmu04610 tags=56%, list=8%, signal=52%
## core_enrichment
## mmu00190 12865/12862/12858/17992/12869/28080/228033/11947/11946/22273/68342/12859/67264/66576/12857/11951/66445/69875/226646/11949/67680/12856/12866/17995/68198/11958/57423/68202/78330/407785/66108/68349/70383/75406/17993/66916/66046/84682/17991/67273/66377/66218/110323/76252/68197/104130/230075/11950/66152/54405/225887/227197/66091/407790/66142/72900/66043/66416/67126/71679/66414/68375/67003/66945/66694/11957/595136/66925/13063/70316/66052/67184/66495/73834/74776/66594/11984
## mmu00830 72082/241452/394432/546726/216453/337924/100041375/20148/28200/13098/11529/404195/98711/26358/13082/72094/666168/71773/63857/13095/394434/56388/100727/22236/22238/433247/100040843/54150/13112/394433/112417/79235/100559/231396/107141/103142/67442/11532/17252/11761/13086/216454/13099/13097/394436/94284/27400/19683/13118/26876/11522/13119/11668/13113/243085/13096/13117/226143/13087/13085/277753/13077/226105/71724
## mmu04260 22003/17888/11464/12865/20191/140781/21924/11938/98660/12288/20541/17906/21956/76757/21954/17897/12293/12862/12373/12296/15464/12858/12869/11932/22273/12859/326618/66576/12857/66445/22004/11931/12866/84682/17896/110323/65973/66152/66142/53313/12292/12295/67003/66694/11928
## mmu05415 11739/18821/12865/20191/11938/12491/12895/20528/27273/21954/12862/11421/12858/14936/210789/18798/17992/12869/28080/228033/17390/11947/21809/11946/22273/68342/14584/18127/12859/67264/66576/12842/12843/22334/12857/11951/108058/66445/69875/226646/26414/11949/67680/12866/17995/23797/68198/68202/12322/78330/407785/66108/68349/18597/75406/17993/29857/66916/66046/84682/17991/67273/66377/66218/12825/13057/110323/19046/22333/68197/104130/230075/11950/18710/66152/105675/21808/54405/225887/227197/66091/407790/55951/66142/53313/72900/66043/13033/66416/21813/19045/67126/22335/71679/66414/68263/68375/67003/66945/66694/18604/11957/595136/11651/66925/21803/70316/66052/67184/66495/70456/12325/18752
## mmu00140 15494/72094/13074/380997/71773/545123/13095/394434/56388/100727/22236/22238/433247/56448/13112/394433/112417/13106/100559/231396/13101/107141/223706/15490/15493/71754/56348/13099/13097/394436/94284/27400/13113/12846/243085/78925/13096/13123/208665/226143/15483/15486/13077/226105/13122/76279/13105
## mmu04610 14058/16415/20702/110382/99571/14161/56373/19123/20701/14069/12258/17174/12263/625018/12266/14068/50909/50702/109821/545366/50908/110135/14060/16644/15160/385643/12274/20700/20704/14061/12269/17175/18815/12268/20703/14067/11905/17194/14071/230558/12279/58992/12630/15139/69379/14962/18816/223703. Dotplot
Show dotplot of the clusterProfiler results. Show the top 20 terms.
4. Enrichment Map
Show emap of the clusterProfiler results. Show the top 20 terms.
5. Heatmap of Leading Edge Genes
Draw a heatmap of the genes driving enrichment in the top 3 KEGG terms. Use rlog counts, scale across row using a Z-score and include the kidney data as a reference point.
HINT - The gene IDs are in the core_enrichment column of the clusterProfiler result
## renaming the first element in assays to 'counts'## Warning in DESeqDataSet(geneCounts, design = ~Tissue): some variables in design
## formula are characters, converting to factors## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing