Effect of Mst1-TG in young and old mice
Mark Ziemann
2020-11-23
Source: https://github.com/markziemann/mst1tg_mito_rna-seq
Loading packages
# to be run in R4
suppressPackageStartupMessages({
library("tidyverse")
library("reshape2")
library("gplots")
library("mitch")
})Import profiling data
young <- read.csv("Mst1TG_young_deseq.tsv",sep="\t")
rownames(young) <- sapply((strsplit(rownames(young),"\\.")),"[[",1)
old <- read.csv("Mst1TG_old_deseq.tsv",sep="\t")
rownames(old) <- sapply((strsplit(rownames(old),"\\.")),"[[",1)
x <- list("young" = young , "old" = old)
# import these in again for heatmaps
young <- read.csv("Mst1TG_young_deseq.tsv",sep="\t")
old <- read.csv("Mst1TG_old_deseq.tsv",sep="\t")Gene sets
gsets <- gmt_import("../ref/ReactomePathways_mouse.gmt")
# gene accession to symbol mapping
gt <- read.csv("../ref/mart_export.txt",sep="\t")
gt <- gt[,c(1,4)]mitch
Lets begin with priority by significance.
yy <- mitch_import(x, DEtype="deseq2", geneTable=gt)## Note: Mean no. genes in input = 17755## Note: no. genes in output = 14877## Note: estimated proportion of input genes in output = 0.838res <- mitch_calc(yy, gsets, priority="significance")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(res$enrichment_result,20)## set
## 1175 The-citric-acid-(TCA)-cycle-and-respiratory-electron-transport
## 963 Respiratory-electron-transport,-ATP-synthesis-by-chemiosmotic-coupling,-and-heat-production-by-uncoupling-proteins.
## 962 Respiratory-electron-transport
## 601 Metabolism
## 635 Mitochondrial-translation
## 636 Mitochondrial-translation-elongation
## 638 Mitochondrial-translation-termination
## 637 Mitochondrial-translation-initiation
## 335 Extracellular-matrix-organization
## 193 Complex-I-biogenesis
## 1215 Translation
## 826 Protein-localization
## 633 Mitochondrial-protein-import
## 836 Pyruvate-metabolism-and-Citric-Acid-(TCA)-cycle
## 630 Mitochondrial-biogenesis
## 186 Collagen-biosynthesis-and-modifying-enzymes
## 208 Cristae-formation
## 629 Mitochondrial-Fatty-Acid-Beta-Oxidation
## 189 Collagen-formation
## 1023 Signal-Transduction
## setSize pMANOVA s.young s.old p.young p.old
## 1175 168 2.515849e-83 -0.8361385 -0.81430465 1.230669e-78 1.234084e-74
## 963 121 9.359276e-66 -0.8833095 -0.83745735 1.258076e-63 2.530445e-57
## 962 100 4.626085e-57 -0.9035826 -0.85861000 2.801421e-55 4.761159e-50
## 601 1615 2.726231e-49 -0.2001755 -0.22456349 9.652047e-40 1.236627e-49
## 635 94 6.954962e-44 -0.8253673 -0.75822286 1.108541e-43 4.243812e-37
## 636 88 2.901428e-41 -0.8273809 -0.75794817 3.378270e-41 8.105151e-35
## 638 88 4.505778e-41 -0.8254722 -0.75609175 5.147975e-41 1.179238e-34
## 637 88 1.348162e-40 -0.8214244 -0.74964501 1.253713e-40 4.304968e-34
## 335 228 1.350544e-38 0.4991275 0.45777569 1.410377e-38 1.080239e-32
## 193 54 1.621653e-33 -0.9302161 -0.90362325 2.281589e-32 1.245806e-30
## 1215 243 6.643594e-31 -0.4108691 -0.42384188 2.925991e-28 5.659389e-30
## 826 142 7.412507e-29 -0.5118837 -0.53720327 6.064025e-26 2.039938e-28
## 633 57 8.672171e-23 -0.7619007 -0.68645027 2.285529e-23 2.947540e-19
## 836 51 4.483657e-21 -0.7249612 -0.76031508 3.153090e-19 5.373685e-21
## 630 88 1.702298e-19 -0.5304265 -0.55687581 7.776007e-18 1.670651e-19
## 186 56 1.436322e-17 0.6369799 0.65537076 1.592143e-16 2.099903e-17
## 208 29 4.274261e-17 -0.9046104 -0.86083346 3.206166e-17 9.786321e-16
## 629 31 5.132212e-17 -0.8584391 -0.84985203 1.245379e-16 2.491625e-16
## 189 75 1.224413e-16 0.5455389 0.54164212 3.060851e-16 4.962533e-16
## 1023 1718 3.251919e-14 0.1166064 0.09353717 3.255389e-15 2.628458e-10
## s.dist SD p.adjustMANOVA
## 1175 1.1671417 0.015438895 3.288214e-80
## 963 1.2171978 0.032422401 6.116287e-63
## 962 1.2464641 0.031800425 2.015431e-54
## 601 0.3008305 0.017244941 8.907961e-47
## 635 1.1207734 0.047478264 1.818027e-41
## 636 1.1220716 0.049096367 6.320277e-39
## 638 1.1194102 0.049059420 8.412931e-39
## 637 1.1120727 0.050755696 2.202560e-38
## 335 0.6772643 0.029240169 1.961289e-36
## 193 1.2968565 0.018803954 2.119500e-31
## 1215 0.5903011 0.009173139 7.893798e-29
## 826 0.7420325 0.017903671 8.073456e-27
## 633 1.0255275 0.053351514 8.718867e-21
## 836 1.0505464 0.024998980 4.185814e-19
## 630 0.7690663 0.018702498 1.483269e-17
## 186 0.9139224 0.013004325 1.173296e-15
## 208 1.2487411 0.030954971 3.286152e-15
## 629 1.2079595 0.006071986 3.726556e-15
## 189 0.7687580 0.002755433 8.422672e-15
## 1023 0.1494866 0.016312415 2.125129e-12unlink("Mst1TG_young_and_old_sig_mitch.html")
# top N gene heatmap
mitch_report(res, outfile = "Mst1TG_young_and_old_sig_mitch.html")## Dataset saved as " /tmp/Rtmp8aocg6/Mst1TG_young_and_old_sig_mitch.rds ".##
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## ordinary text without R code## output file: /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp8aocg6/mitch_report.html --email-obfuscation none --self-contained --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/Rtmp8aocg6/rmarkdown-str74174224659a.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/Rtmp8aocg6/mitch_report.html## [1] TRUE# heatmaps of top sets by significance
# colour scheme
colfunc <- colorRampPalette(c("blue", "white", "red"))
topsets <- head(res$enrichment_result,50)$set
mytopsets <- gsets[which(names( gsets ) %in% topsets)]
yo <- young[,7:ncol(young)]
colnames(yo) <- paste("y",colnames(yo))
ol <- old[,7:ncol(old)]
colnames(ol) <- paste("o",colnames(ol))
mx <- merge(yo,ol,by=0)
rownames(mx) <- mx$Row.names
mx$Row.names = NULL
i = 1
par(mar = c(10, 8, 8, 4) + 0.2)
for ( i in seq_along(mytopsets) ) {
g <- as.vector(unlist(mytopsets[i]))
symbol <- sapply((strsplit(rownames(mx)," ")),"[[",2)
z <- mx[which(symbol %in% g),]
SETNAME=names(mytopsets[i])
heatmap.2( as.matrix(z), col=colfunc(25),scale="row",
trace="none",margins = c(6,20), cexRow=.4, cexCol=.8)
mtext(SETNAME)
}
Now prioritise by effect size.
res <- mitch_calc(yy, gsets, priority = "effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.head(res$enrichment_result,20)## set
## 361 Formation-of-ATP-by-chemiosmotic-coupling
## 193 Complex-I-biogenesis
## 176 Citric-acid-cycle-(TCA-cycle)
## 208 Cristae-formation
## 962 Respiratory-electron-transport
## 963 Respiratory-electron-transport,-ATP-synthesis-by-chemiosmotic-coupling,-and-heat-production-by-uncoupling-proteins.
## 629 Mitochondrial-Fatty-Acid-Beta-Oxidation
## 110 Branched-chain-amino-acid-catabolism
## 1175 The-citric-acid-(TCA)-cycle-and-respiratory-electron-transport
## 1298 rRNA-processing-in-the-mitochondrion
## 1304 tRNA-processing-in-the-mitochondrion
## 636 Mitochondrial-translation-elongation
## 635 Mitochondrial-translation
## 638 Mitochondrial-translation-termination
## 637 Mitochondrial-translation-initiation
## 632 Mitochondrial-iron-sulfur-cluster-biogenesis
## 440 Glyoxylate-metabolism-and-glycine-degradation
## 836 Pyruvate-metabolism-and-Citric-Acid-(TCA)-cycle
## 187 Collagen-chain-trimerization
## 633 Mitochondrial-protein-import
## setSize pMANOVA s.young s.old p.young p.old
## 361 16 1.292423e-10 -0.9329789 -0.9194031 1.017112e-10 1.887907e-10
## 193 54 1.621653e-33 -0.9302161 -0.9036232 2.281589e-32 1.245806e-30
## 176 22 2.511926e-13 -0.8942872 -0.8892751 3.711622e-13 5.015120e-13
## 208 29 4.274261e-17 -0.9046104 -0.8608335 3.206166e-17 9.786321e-16
## 962 100 4.626085e-57 -0.9035826 -0.8586100 2.801421e-55 4.761159e-50
## 963 121 9.359276e-66 -0.8833095 -0.8374574 1.258076e-63 2.530445e-57
## 629 31 5.132212e-17 -0.8584391 -0.8498520 1.245379e-16 2.491625e-16
## 110 21 2.430098e-11 -0.8328525 -0.8426674 3.838241e-11 2.259224e-11
## 1175 168 2.515849e-83 -0.8361385 -0.8143047 1.230669e-78 1.234084e-74
## 1298 22 1.564605e-11 -0.7831156 -0.8542333 2.002556e-10 3.928719e-12
## 1304 18 3.426684e-09 -0.7504019 -0.8415326 3.521252e-08 6.262141e-10
## 636 88 2.901428e-41 -0.8273809 -0.7579482 3.378270e-41 8.105151e-35
## 635 94 6.954962e-44 -0.8253673 -0.7582229 1.108541e-43 4.243812e-37
## 638 88 4.505778e-41 -0.8254722 -0.7560918 5.147975e-41 1.179238e-34
## 637 88 1.348162e-40 -0.8214244 -0.7496450 1.253713e-40 4.304968e-34
## 632 10 6.993237e-05 -0.7409968 -0.7750185 4.948957e-05 2.191278e-05
## 440 25 7.782677e-11 -0.7513547 -0.7477350 7.741915e-11 9.534951e-11
## 836 51 4.483657e-21 -0.7249612 -0.7603151 3.153090e-19 5.373685e-21
## 187 33 1.118654e-13 0.7367001 0.7402562 2.337306e-13 1.793245e-13
## 633 57 8.672171e-23 -0.7619007 -0.6864503 2.285529e-23 2.947540e-19
## s.dist SD p.adjustMANOVA
## 361 1.309867 0.009599542 4.826277e-09
## 193 1.296857 0.018803954 2.119500e-31
## 176 1.261174 0.003544080 1.492313e-11
## 208 1.248741 0.030954971 3.286152e-15
## 962 1.246464 0.031800425 2.015431e-54
## 963 1.217198 0.032422401 6.116287e-63
## 629 1.207960 0.006071986 3.726556e-15
## 110 1.184792 0.006940152 1.134335e-09
## 1175 1.167142 0.015438895 3.288214e-80
## 1298 1.158872 0.050287861 7.573845e-10
## 1304 1.127511 0.064439138 1.210453e-07
## 636 1.122072 0.049096367 6.320277e-39
## 635 1.120773 0.047478264 1.818027e-41
## 638 1.119410 0.049059420 8.412931e-39
## 637 1.112073 0.050755696 2.202560e-38
## 632 1.072255 0.024056946 9.451158e-04
## 440 1.060020 0.002559525 3.184758e-09
## 836 1.050546 0.024998980 4.185814e-19
## 187 1.044369 0.002514596 6.962290e-12
## 633 1.025527 0.053351514 8.718867e-21unlink("Mst1TG_young_and_old_eff_mitch.html")
mitch_report(res, outfile = "Mst1TG_young_and_old_eff_mitch.html")## Dataset saved as " /tmp/Rtmp8aocg6/Mst1TG_young_and_old_eff_mitch.rds ".##
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## ordinary text without R code## output file: /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp8aocg6/mitch_report.html --email-obfuscation none --self-contained --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/Rtmp8aocg6/rmarkdown-str741772d09b3d.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/Rtmp8aocg6/mitch_report.html## [1] TRUE# heatmaps of top sets by effect size
topsets <- head(res$enrichment_result,50)$set
mytopsets <- gsets[which(names( gsets ) %in% topsets)]
i=1
par(mar = c(10, 8, 8, 4) + 0.2)
for ( i in seq_along(mytopsets) ) {
g <- as.vector(unlist(mytopsets[i]))
symbol <- sapply((strsplit(rownames(mx)," ")),"[[",2)
z <- mx[which(symbol %in% g),]
SETNAME = names(mytopsets[i])
heatmap.2( as.matrix(z), col = colfunc(25),scale = "row",
trace="none",margins = c(6,20), cexRow = .4, cexCol = .8)
mtext(SETNAME)
}
Now prioritise by discordant effect size.
res <- mitch_calc(yy, gsets, priority = "SD")## Note: Prioritisation by SD after selecting sets with
## p.adjustMANOVA<=0.05.head(res$enrichment_result,20)## set
## 1171 Termination-of-O-glycan-biosynthesis
## 1111 Synaptic-adhesion-like-molecules
## 519 Interferon-alpha/beta-signaling
## 1174 The-canonical-retinoid-cycle-in-rods-(twilight-vision)
## 658 N-glycan-antennae-elongation-in-the-medial/trans-Golgi
## 166 Chemokine-receptors-bind-chemokines
## 730 O-linked-glycosylation-of-mucins
## 460 HS-GAG-biosynthesis
## 1266 WNT5A-dependent-internalization-of-FZD2,-FZD5-and-ROR2
## 461 HS-GAG-degradation
## 350 FOXO-mediated-transcription-of-oxidative-stress,-metabolic-and-neuronal-genes
## 1022 Sialic-acid-metabolism
## 285 Downregulation-of-SMAD2/3:SMAD4-transcriptional-activity
## 1126 Synthesis-of-active-ubiquitin:-roles-of-E1-and-E2-enzymes
## 1236 Triglyceride-catabolism
## 1242 Ubiquitin-Mediated-Degradation-of-Phosphorylated-Cdc25A
## 1290 p53-Independent-DNA-Damage-Response
## 1291 p53-Independent-G1/S-DNA-damage-checkpoint
## 86 Assembly-of-the-pre-replicative-complex
## 948 Regulation-of-ornithine-decarboxylase-(ODC)
## setSize pMANOVA s.young s.old p.young p.old
## 1171 10 1.115890e-03 0.39081187 -0.0052599717 0.0323685019 9.770257e-01
## 1111 15 1.732267e-03 0.28323689 -0.0347373615 0.0575633910 8.158480e-01
## 519 40 1.387142e-08 0.04785671 0.3600053919 0.6006283244 8.180409e-05
## 1174 12 4.087033e-04 0.24527413 0.5572149344 0.1412962416 8.309092e-04
## 658 20 2.410343e-04 0.35078414 0.0531197415 0.0066187051 6.809598e-01
## 166 20 2.501491e-03 0.29927980 0.0474321868 0.0205218841 7.135212e-01
## 730 32 1.254537e-05 0.38636746 0.1405776356 0.0001554919 1.688774e-01
## 460 23 1.159881e-04 0.43421476 0.1915455360 0.0003126898 1.118798e-01
## 1266 13 5.292945e-03 0.44633394 0.2064461373 0.0053319120 1.975302e-01
## 461 18 4.696817e-04 0.47066125 0.2343660034 0.0005463251 8.522719e-02
## 350 23 9.528103e-03 0.09551519 -0.1308914009 0.4279310937 2.773219e-01
## 1022 26 3.270409e-03 0.23833671 0.0168131646 0.0354594522 8.820684e-01
## 285 23 6.014951e-03 0.24809011 0.0279532376 0.0394778318 8.165394e-01
## 1126 29 3.254697e-03 -0.01472856 -0.2248485806 0.8908463045 3.615487e-02
## 1236 15 9.291510e-06 -0.50318037 -0.7077827121 0.0007405841 2.065018e-06
## 1242 48 2.720323e-05 -0.31450480 -0.1268151370 0.0001642006 1.286981e-01
## 1290 48 2.720323e-05 -0.31450480 -0.1268151370 0.0001642006 1.286981e-01
## 1291 48 2.720323e-05 -0.31450480 -0.1268151370 0.0001642006 1.286981e-01
## 86 64 6.669333e-05 -0.18632493 -0.0003354317 0.0099862346 9.962998e-01
## 948 46 3.967504e-05 -0.32015490 -0.1351077209 0.0001728409 1.130494e-01
## s.dist SD p.adjustMANOVA
## 1171 0.3908473 0.2800651 8.733342e-03
## 1111 0.2853591 0.2248417 1.271951e-02
## 519 0.3631723 0.2207225 4.316653e-07
## 1174 0.6088086 0.2205755 3.842987e-03
## 658 0.3547833 0.2104805 2.540579e-03
## 166 0.3030152 0.1780832 1.646570e-02
## 730 0.4111470 0.1737997 2.309409e-04
## 460 0.4745863 0.1715931 1.430155e-03
## 1266 0.4917662 0.1696263 3.158849e-02
## 461 0.5257846 0.1670860 4.292825e-03
## 350 0.1620361 0.1600936 4.845615e-02
## 1022 0.2389290 0.1566408 2.045179e-02
## 285 0.2496599 0.1556603 3.448045e-02
## 1126 0.2253305 0.1485773 2.045139e-02
## 1236 0.8684163 0.1446757 1.760001e-04
## 1242 0.3391096 0.1327166 4.389460e-04
## 1290 0.3391096 0.1327166 4.389460e-04
## 1291 0.3391096 0.1327166 4.389460e-04
## 86 0.1863252 0.1315144 9.273211e-04
## 948 0.3474957 0.1308481 6.092662e-04unlink("Mst1TG_young_and_old_dis_mitch.html")
mitch_report(res, outfile = "Mst1TG_young_and_old_dis_mitch.html")## Dataset saved as " /tmp/Rtmp8aocg6/Mst1TG_young_and_old_dis_mitch.rds ".##
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## ordinary text without R code## output file: /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp8aocg6/mitch_report.html --email-obfuscation none --self-contained --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/Rtmp8aocg6/rmarkdown-str74172130dcd4.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/Rtmp8aocg6/mitch_report.html## [1] TRUE# heatmaps of top sets by discordant size
topsets <- head(res$enrichment_result,50)$set
mytopsets <- gsets[which(names( gsets ) %in% topsets)]
i=1
par(mar = c(10, 8, 8, 4) + 0.2)
for ( i in seq_along(mytopsets) ) {
g <- as.vector(unlist(mytopsets[i]))
symbol <- sapply((strsplit(rownames(mx)," ")),"[[",2)
z <- mx[which(symbol %in% g),]
SETNAME = names(mytopsets[i])
heatmap.2( as.matrix(z), col = colfunc(25),scale = "row",
trace="none",margins = c(6,20), cexRow = .4, cexCol = .8)
mtext(SETNAME)
}
Custom gene set analysis
Run mitch with some hand-picked gene sets that are relevant to the Mst1 model.
# gene sets of interest
gsets <- gmt_import("../ref/pathways_of_interest_mouse.gmt")
# mitch pathways of interest
yy <- mitch_import(x, DEtype="deseq2", geneTable=gt)## Note: Mean no. genes in input = 17755## Note: no. genes in output = 14877## Note: estimated proportion of input genes in output = 0.838res <- mitch_calc(yy, gsets, priority="significance")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(res$enrichment_result,20)## set
## 24 The-citric-acid-(TCA)-cycle-and-respiratory-electron-transport
## 20 Respiratory-electron-transport
## 15 Mitochondrial-translation
## 13 Mitochondrial-protein-import
## 18 Pyruvate-metabolism-and-Citric-Acid-(TCA)-cycle
## 10 Mitochondrial-biogenesis
## 9 Mitochondrial-Fatty-Acid-Beta-Oxidation
## 5 Citric-acid-cycle-(TCA-cycle)
## 26 rRNA-processing-in-the-mitochondrion
## 2 Branched-chain-amino-acid-catabolism
## 14 Mitochondrial-tRNA-aminoacylation
## 25 Transcriptional-activation-of-mitochondrial-biogenesis
## 12 Mitochondrial-iron-sulfur-cluster-biogenesis
## 11 Mitochondrial-calcium-ion-transport
## 17 Oxygen-dependent-proline-hydroxylation-of-Hypoxia-inducible-Factor-Alpha
## 4 Cellular-response-to-hypoxia
## 22 Smooth-Muscle-Contraction
## 7 Glycolysis
## 21 Signaling-by-Hippo
## 16 Muscle-contraction
## setSize pMANOVA s.young s.old p.young p.old
## 24 167 1.252333e-82 -0.83525078 -0.81311829 5.140819e-78 5.461735e-74
## 20 100 4.626085e-57 -0.90358259 -0.85861000 2.801421e-55 4.761159e-50
## 15 94 6.954962e-44 -0.82536726 -0.75822286 1.108541e-43 4.243812e-37
## 13 57 8.672171e-23 -0.76190070 -0.68645027 2.285529e-23 2.947540e-19
## 18 51 4.483657e-21 -0.72496118 -0.76031508 3.153090e-19 5.373685e-21
## 10 88 1.702298e-19 -0.53042648 -0.55687581 7.776007e-18 1.670651e-19
## 9 31 5.132212e-17 -0.85843911 -0.84985203 1.245379e-16 2.491625e-16
## 5 22 2.511926e-13 -0.89428720 -0.88927511 3.711622e-13 5.015120e-13
## 26 22 1.564605e-11 -0.78311557 -0.85423335 2.002556e-10 3.928719e-12
## 2 19 5.851091e-11 -0.85859115 -0.87076252 9.044799e-11 4.893714e-11
## 14 19 3.662479e-08 -0.75631770 -0.71231518 1.135934e-08 7.591141e-08
## 25 51 3.864014e-08 -0.41798854 -0.46831084 2.427091e-07 7.229049e-09
## 12 10 6.993237e-05 -0.74099684 -0.77501850 4.948957e-05 2.191278e-05
## 11 21 1.528545e-04 -0.49488422 -0.50940457 8.639334e-05 5.318105e-05
## 17 60 4.598722e-04 -0.27615577 -0.16661267 2.172375e-04 2.569236e-02
## 4 68 8.973591e-04 -0.24792952 -0.14957121 4.101207e-04 3.305621e-02
## 22 31 9.211060e-03 0.03144107 0.21101807 7.619922e-01 4.206934e-02
## 7 63 1.433187e-02 -0.12364966 -0.20145251 8.984079e-02 5.713843e-03
## 21 20 1.540622e-02 0.37300263 0.30910682 3.883350e-03 1.672262e-02
## 16 148 2.975029e-02 -0.04379208 0.03414573 3.585713e-01 4.740819e-01
## s.dist SD p.adjustMANOVA
## 24 1.16567801 0.015650031 3.256067e-81
## 20 1.24646406 0.031800425 6.013911e-56
## 15 1.12077340 0.047478264 6.027634e-43
## 13 1.02552750 0.053351514 5.636911e-22
## 18 1.05054640 0.024998980 2.331501e-20
## 10 0.76906627 0.018702498 7.376623e-19
## 9 1.20795951 0.006071986 1.906250e-16
## 5 1.26117399 0.003544080 8.163761e-13
## 26 1.15887213 0.050287861 4.519969e-11
## 2 1.22286799 0.008606453 1.521284e-10
## 14 1.03894628 0.031114482 8.372031e-08
## 25 0.62771766 0.035583240 8.372031e-08
## 12 1.07225463 0.024056946 1.398647e-04
## 11 0.71021364 0.010267436 2.838726e-04
## 17 0.32252409 0.077458663 7.971118e-04
## 4 0.28955240 0.069549829 1.458209e-03
## 22 0.21334752 0.126980108 1.408750e-02
## 7 0.23637333 0.055014923 2.070159e-02
## 21 0.48443574 0.045181158 2.108219e-02
## 16 0.05553086 0.055110351 3.867538e-02unlink("Mst1TG_young_and_old_pathwayofinterest_mitch.html")
# top N gene heatmap
mitch_report(res, outfile = "Mst1TG_young_and_old_pathwayofinterest_mitch.html")## Dataset saved as " /tmp/Rtmp8aocg6/Mst1TG_young_and_old_pathwayofinterest_mitch.rds ".##
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## ordinary text without R code## output file: /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp8aocg6/mitch_report.html --email-obfuscation none --self-contained --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/Rtmp8aocg6/rmarkdown-str7417210a4a39.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/Rtmp8aocg6/mitch_report.html## [1] TRUE# heatmaps of pathways of interest
# colour scheme
colfunc <- colorRampPalette(c("blue", "white", "red"))
topsets <- head(res$enrichment_result,50)$set
mytopsets <- gsets[which(names( gsets ) %in% topsets)]
yo <- young[,7:ncol(young)]
colnames(yo) <- paste("y",colnames(yo))
ol <- old[,7:ncol(old)]
colnames(ol) <- paste("o",colnames(ol))
mx <- merge(yo,ol,by=0)
rownames(mx) <- mx$Row.names
mx$Row.names = NULL
i = 1
par(mar = c(10, 8, 8, 4) + 0.2)
for ( i in seq_along(mytopsets) ) {
g <- as.vector(unlist(mytopsets[i]))
symbol <- sapply((strsplit(rownames(mx)," ")),"[[",2)
z <- mx[which(symbol %in% g),]
SETNAME=names(mytopsets[i])
heatmap.2( as.matrix(z), col=colfunc(25),scale="row",
trace="none",margins = c(6,20), cexRow=.4, cexCol=.8)
mtext(SETNAME)
}
Mitochondrial complexes
mt <- gmt_import("../ref/mt_complexes_mouse.gmt")
# have a look and check the gene names are correct
mt## $`Complex-1`
## [1] "1700029P11Rik" "mt-Nd1" "mt-Nd2" "mt-Nd3"
## [5] "mt-Nd4" "mt-Nd4l" "mt-Nd5" "mt-Nd6"
## [9] "Ndufa1" "Ndufa10" "Ndufa11" "Ndufa12"
## [13] "Ndufa13" "Ndufa2" "Ndufa3" "Ndufa5"
## [17] "Ndufa6" "Ndufa7" "Ndufa8" "Ndufa9"
## [21] "Ndufab1" "Ndufb10" "Ndufb1-ps" "Ndufb2"
## [25] "Ndufb3" "Ndufb4" "Ndufb5" "Ndufb6"
## [29] "Ndufb7" "Ndufb8" "Ndufb9" "Ndufc1"
## [33] "Ndufc2" "Ndufs1" "Ndufs2" "Ndufs3"
## [37] "Ndufs4" "Ndufs5" "Ndufs6" "Ndufs7"
## [41] "Ndufs8" "Ndufv1" "Ndufv2" "Ndufv3"
##
## $`Complex-1-Assembly-Factors`
## [1] "Acad9" "Ecsit" "Foxred1" "Ndufaf1" "Ndufaf2" "Ndufaf4"
## [7] "Ndufaf5" "Ndufaf6" "Ndufaf7" "Ndufaf8" "Nubpl" "Timmdc1"
## [13] "Tmem126b"
##
## $`Complex-2`
## [1] "Sdha" "Sdhb" "Sdhc" "Sdhd"
##
## $`Complex-2-Assembly-Factors`
## [1] "Sdhaf1" "Sdhaf2" "Sdhaf3" "Sdhaf4"
##
## $`Complex-3`
## [1] "Cyc1" "mt-Cytb" "Uqcr10" "Uqcr11" "Uqcrb" "Uqcrc1" "Uqcrc2"
## [8] "Uqcrfs1" "Uqcrq"
##
## $`Complex-3-Assembly-Factors`
## [1] "Bcs1l" "Lyrm7" "Ptcd2" "Ttc19" "Uqcc1" "Uqcc2" "Uqcc3"
##
## $`Complex-4`
## [1] "Cox5a" "Cox5b" "Cox6c" "Cox7b" "Cox7c" "mt-Co1" "mt-Co2" "mt-Co3"
##
## $`Complex-4-Assembly-Factors`
## [1] "Apopt1" "Cep89" "Coa3" "Coa4" "Coa5" "Coa6" "Coa7"
## [8] "Cox10" "Cox11" "Cox14" "Cox16" "Cox18" "Cox19" "Cox20"
## [15] "Fastkd2" "Gm21987" "Lrpprc" "Ndufa4" "Oxa1l" "Pet100" "Pet117"
## [22] "Sco1" "Sco2" "Surf1" "Taco1"
##
## $`Complex-5`
## [1] "Atp5a1" "Atp5b" "Atp5c1" "Atp5d" "Atp5e" "Atp5g3" "Atp5h"
## [8] "Atp5j" "Atp5j2" "Atp5k" "Atp5l" "Atp5o" "Atp5pb" "mt-Atp6"
## [15] "mt-Atp8"
##
## $`Complex-5-Assembly-Factors`
## [1] "Atpaf1" "Atpaf2" "Tmem70"
##
## attr(,"originfile")
## [1] "../ref/mt_complexes_mouse.gmt"# mitch pathways of interest
res <- mitch_calc(yy, mt, priority="effect",minsetsize=3)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(res$enrichment_result,20)## set setSize pMANOVA s.young s.old
## 3 Complex-2 4 1.630452e-03 -0.9933773 -0.9735090
## 7 Complex-4 8 7.489554e-06 -0.9618838 -0.9204889
## 1 Complex-1 43 5.682735e-28 -0.9484747 -0.9254259
## 5 Complex-3 9 2.035360e-06 -0.9607360 -0.9060473
## 9 Complex-5 15 4.962915e-10 -0.9358364 -0.9197416
## 10 Complex-5-Assembly-Factors 3 1.395221e-02 -0.9142575 -0.9381023
## 2 Complex-1-Assembly-Factors 13 2.965384e-07 -0.8456363 -0.8233419
## 4 Complex-2-Assembly-Factors 4 1.885567e-02 -0.8053184 -0.7200968
## 6 Complex-3-Assembly-Factors 7 2.810338e-03 -0.7364204 -0.6735325
## 8 Complex-4-Assembly-Factors 23 7.371441e-07 -0.6348575 -0.5616581
## p.young p.old s.dist SD p.adjustMANOVA
## 3 5.793699e-04 7.455891e-04 1.3908696 0.01404895 2.329217e-03
## 7 2.448833e-06 6.500947e-06 1.3313603 0.02927058 1.248259e-05
## 1 4.414790e-27 7.427705e-26 1.3251481 0.01629801 5.682735e-27
## 5 5.966715e-07 2.502900e-06 1.3205814 0.03867073 4.070720e-06
## 9 3.424155e-10 6.823345e-10 1.3121411 0.01138070 2.481457e-09
## 10 6.095260e-03 4.890470e-03 1.3099247 0.01686078 1.550245e-02
## 2 1.286773e-07 2.727380e-07 1.1802511 0.01576455 9.884612e-07
## 4 5.279602e-03 1.262398e-02 1.0803134 0.06026073 1.885567e-02
## 6 7.401164e-04 2.028537e-03 0.9979785 0.04446845 3.512922e-03
## 8 1.353405e-07 3.113497e-06 0.8476461 0.05175981 1.842860e-06unlink("Mst1TG_young_and_old_mitocomplex_mitch.html")
# top N gene heatmap
mitch_report(res, outfile = "Mst1TG_young_and_old_mitocomplex_mitch.html")## Dataset saved as " /tmp/Rtmp8aocg6/Mst1TG_young_and_old_mitocomplex_mitch.rds ".##
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## ordinary text without R code## output file: /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp8aocg6/mitch_report.html --email-obfuscation none --self-contained --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/Rtmp8aocg6/rmarkdown-str74172298d707.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/Rtmp8aocg6/mitch_report.html## [1] TRUEmytopsets <- head(res$enrichment_result,50)$set
i = 1
par(mar = c(10, 8, 8, 4) + 0.2)
for ( i in seq_along(mt) ) {
g <- as.vector(unlist(mt[i]))
symbol <- sapply((strsplit(rownames(mx)," ")),"[[",2)
z <- mx[which(symbol %in% g),]
SETNAME=names(mt)[i]
heatmap.2( as.matrix(z), col=colfunc(25),scale="row",
trace="none",margins = c(6,20), cexRow=.8, cexCol=.8)
mtext(SETNAME)
}
# now 1D mitch for young and old to create beeswarm charts
young <- yy[,1,drop=FALSE]
res <- mitch_calc(young, mt, priority="effect",minsetsize=3)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(res$enrichment_result,20)## set setSize pANOVA s.dist p.adjustANOVA
## 3 Complex-2 4 5.793699e-04 -0.9933773 8.276713e-04
## 7 Complex-4 8 2.448833e-06 -0.9618838 4.081389e-06
## 5 Complex-3 9 5.966715e-07 -0.9607360 1.193343e-06
## 1 Complex-1 43 4.414790e-27 -0.9484747 4.414790e-26
## 9 Complex-5 15 3.424155e-10 -0.9358364 1.712077e-09
## 10 Complex-5-Assembly-Factors 3 6.095260e-03 -0.9142575 6.095260e-03
## 2 Complex-1-Assembly-Factors 13 1.286773e-07 -0.8456363 3.383513e-07
## 4 Complex-2-Assembly-Factors 4 5.279602e-03 -0.8053184 5.866225e-03
## 6 Complex-3-Assembly-Factors 7 7.401164e-04 -0.7364204 9.251456e-04
## 8 Complex-4-Assembly-Factors 23 1.353405e-07 -0.6348575 3.383513e-07unlink("Mst1TG_young_mitocomplex_mitch.html")
mitch_report(res, outfile = "Mst1TG_young_mitocomplex_mitch.html")## Dataset saved as " /tmp/Rtmp8aocg6/Mst1TG_young_mitocomplex_mitch.rds ".
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## Output created: /tmp/Rtmp8aocg6/mitch_report.html## [1] TRUEold <- yy[,2,drop=FALSE]
res <- mitch_calc(old, mt, priority="effect",minsetsize=3)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(res$enrichment_result,20)## set setSize pANOVA s.dist p.adjustANOVA
## 3 Complex-2 4 7.455891e-04 -0.9735090 1.065127e-03
## 10 Complex-5-Assembly-Factors 3 4.890470e-03 -0.9381023 5.433856e-03
## 1 Complex-1 43 7.427705e-26 -0.9254259 7.427705e-25
## 7 Complex-4 8 6.500947e-06 -0.9204889 1.083491e-05
## 9 Complex-5 15 6.823345e-10 -0.9197416 3.411672e-09
## 5 Complex-3 9 2.502900e-06 -0.9060473 6.226995e-06
## 2 Complex-1-Assembly-Factors 13 2.727380e-07 -0.8233419 9.091267e-07
## 4 Complex-2-Assembly-Factors 4 1.262398e-02 -0.7200968 1.262398e-02
## 6 Complex-3-Assembly-Factors 7 2.028537e-03 -0.6735325 2.535671e-03
## 8 Complex-4-Assembly-Factors 23 3.113497e-06 -0.5616581 6.226995e-06unlink("Mst1TG_old_mitocomplex_mitch.html")
mitch_report(res, outfile = "Mst1TG_old_mitocomplex_mitch.html")## Dataset saved as " /tmp/Rtmp8aocg6/Mst1TG_old_mitocomplex_mitch.rds ".
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## ordinary text without R code## output file: /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /mnt/bfx6/bfx/du_xiao/young_mst1tg/integrate/mitch.utf8.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp8aocg6/mitch_report.html --email-obfuscation none --self-contained --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable 'theme:bootstrap' --include-in-header /tmp/Rtmp8aocg6/rmarkdown-str741742e8809e.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'##
## Output created: /tmp/Rtmp8aocg6/mitch_report.html
## [1] TRUESystem information
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] pkgload_1.1.0 GGally_2.0.0 beeswarm_0.2.3 gtools_3.8.2
## [5] echarts4r_0.3.3 mitch_1.2.2 gplots_3.1.0 reshape2_1.4.4
## [9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
## [13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
## [17] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.2 jsonlite_1.7.1 modelr_0.1.8 shiny_1.5.0
## [5] assertthat_0.2.1 highr_0.8 cellranger_1.1.0 yaml_2.2.1
## [9] pillar_1.4.6 backports_1.2.0 glue_1.4.2 digest_0.6.27
## [13] RColorBrewer_1.1-2 promises_1.1.1 rvest_0.3.6 colorspace_2.0-0
## [17] htmltools_0.5.0 httpuv_1.5.4 plyr_1.8.6 pkgconfig_2.0.3
## [21] broom_0.7.2 haven_2.3.1 xtable_1.8-4 scales_1.1.1
## [25] later_1.1.0.1 farver_2.0.3 generics_0.1.0 ellipsis_0.3.1
## [29] withr_2.3.0 cli_2.1.0 magrittr_1.5 crayon_1.3.4
## [33] readxl_1.3.1 mime_0.9 evaluate_0.14 ps_1.4.0
## [37] fs_1.5.0 fansi_0.4.1 MASS_7.3-53 xml2_1.3.2
## [41] tools_4.0.3 hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
## [45] reprex_0.3.0 compiler_4.0.3 caTools_1.18.0 rlang_0.4.8
## [49] grid_4.0.3 rstudioapi_0.12 htmlwidgets_1.5.2 labeling_0.4.2
## [53] bitops_1.0-6 rmarkdown_2.5 testthat_3.0.0 gtable_0.3.0
## [57] DBI_1.1.0 reshape_0.8.8 R6_2.5.0 gridExtra_2.3
## [61] lubridate_1.7.9 knitr_1.30 fastmap_1.0.1 rprojroot_1.3-2
## [65] desc_1.2.0 KernSmooth_2.23-18 stringi_1.5.3 parallel_4.0.3
## [69] Rcpp_1.0.5 vctrs_0.3.4 dbplyr_2.0.0 tidyselect_1.1.0
## [73] xfun_0.19