Introduction

suppressPackageStartupMessages({
    library("RhpcBLASctl")
    library("gplots")
    library("mitch")
    library("eulerr")
    library("limma")
    library("beeswarm")
    library("kableExtra")
})

RhpcBLASctl::blas_set_num_threads(1)

Import data

Firstly for H3K9ac.

tf <- gmt_import("ref/c3.tft.v2024.1.Hs.symbols.gmt")
names(tf) <- gsub("_"," ",names(tf))

tf2 <- gmt_import("ref/gene_set_library_crisp.gmt")
names(tf2) <- gsub("_"," ",names(tf2))

tf3 <- gmt_import("ref/human_tss_TFBS.1000bpUpstream.1000bpDownstream.500genes.gmt")
names(tf3) <- gsub("_"," ",names(tf3))

go <- gmt_import("ref/c5.go.v2024.1.Hs.symbols.gmt")
names(go) <- gsub("_"," ",names(go))

gt <- read.table("gt.tsv")

rna <- read.table("rna_deseq.tsv",sep="\t")
rna$gene <- rownames(rna)

k9a <- read.table("k9a_deseq.tsv",sep="\t")
k9a$gene <- paste(sapply(strsplit(rownames(k9a)," "),"[[",1),sapply(strsplit(rownames(k9a)," "),"[[",2))

k36a <- read.table("k36a_deseq.tsv",sep="\t")
k36a$gene <- paste(sapply(strsplit(rownames(k36a)," "),"[[",1),sapply(strsplit(rownames(k36a)," "),"[[",2))

l <- list("RNA"=rna,"H3K9ac"=k9a,"H3K36a"=k36a)

m <- mitch_import(x=l,DEtype="deseq2",geneTable=gt,geneIDcol="gene")
## Note: Mean no. genes in input = 67547.3333333333
## Note: no. genes in output = 16631
## Note: estimated proportion of input genes in output = 0.246
head(m)
##                  RNA     H3K9ac     H3K36a
## 5_8S_rRNA  2.8723535 -3.2507754 -3.6420447
## 7SK        0.1300210  1.0312425  0.5671120
## A1BG       0.2484592 -2.3043529 -2.2664671
## A2M        3.4477904  0.6768063  0.6358702
## A2M-AS1   -0.5669178  1.8634279 -1.6689451
## A4GALT     0.1378113  1.2597913 -0.7913393
gt2 <- gt

gt2[,1] <- sapply(strsplit(gt[,1],"\\."),"[[",1)

m2 <-m

rownames(m2) <- gt2[match(rownames(m),gt2[,2]),1]

HATs and HDACs

Let’s look at these HAT genes. https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-016-0225-2/tables/1

ENSG00000010282 HAT1 ENSG00000122390 NAA60 ENSG00000108773 KAT2A ENSG00000114166 KAT2B ENSG00000172977 KAT5 ENSG00000083168 KAT6A ENSG00000156650 KAT6B ENSG00000136504 KAT7 ENSG00000103510 KAT8 ENSG00000100393 EP300 ENSG00000005339 CREBBP ENSG00000147133 TAF1 ENSG00000125484 GTF3C4 ENSG00000084676 NCOA1 ENSG00000124151 NCOA3 ENSG00000140396 NCOA2 ENSG00000134852.15 CLOCK

Now HDACs.

ENSG00000116478.12 HDAC1 ENSG00000196591.12 HDAC2 ENSG00000171720.10 HDAC3 ENSG00000068024.18 HDAC4 ENSG00000108840.17 HDAC5 ENSG00000094631.22 HDAC6 ENSG00000061273.18 HDAC7 ENSG00000147099.21 HDAC8 ENSG00000048052.25 HDAC9 ENSG00000100429.18 HDAC10 ENSG00000163517.15 HDAC11 ENSG00000096717.12 SIRT1 ENSG00000068903.21 SIRT2 ENSG00000142082.15 SIRT3 ENSG00000089163.4 SIRT4 ENSG00000124523.17 SIRT5 ENSG00000077463.15 SIRT6 ENSG00000187531.14 SIRT7

hats <- c("ENSG00000010282.13 HAT1",
  "ENSG00000122390.19 NAA60",
  "ENSG00000108773.11 KAT2A",
  "ENSG000001141663.8 KAT2B",
  "ENSG00000172977.13 KAT5",
  "ENSG00000083168.11 KAT6A",
  "ENSG00000156650.14 KAT6B",
  "ENSG00000136504.15 KAT7",
  "ENSG00000103510.20 KAT8",
  "ENSG00000100393.16 EP300",
  "ENSG00000005339.15 CREBBP",
  "ENSG00000147133.18 TAF1",
  "ENSG00000125484.12 GTF3C4",
  "ENSG00000084676.16 NCOA1",
  "ENSG00000124151.19 NCOA3",
  "ENSG00000140396.13 NCOA2",
  "ENSG00000134852.15 CLOCK")

hdacs <- c("ENSG00000116478.12 HDAC1",
  "ENSG00000196591.12 HDAC2",
  "ENSG00000171720.10 HDAC3",
  "ENSG00000068024.18 HDAC4",
  "ENSG00000108840.17 HDAC5",
  "ENSG00000094631.22 HDAC6",
  "ENSG00000061273.18 HDAC7",
  "ENSG00000147099.21 HDAC8",
  "ENSG00000048052.25 HDAC9",
  "ENSG00000100429.18 HDAC10",
  "ENSG00000163517.15 HDAC11",
  "ENSG00000096717.12 SIRT1",
  "ENSG00000068903.21 SIRT2",
  "ENSG00000142082.15 SIRT3",
  "ENSG00000089163.4 SIRT4",
  "ENSG00000124523.17 SIRT5",
  "ENSG00000077463.15 SIRT6",
  "ENSG00000187531.14 SIRT7")

hat_rna <- rna[rownames(rna) %in% hats,1:6]

hat_rna <- hat_rna[order(hat_rna$log2FoldChange),]

hat_rna |>
  kbl(caption = "RNA expression of HAT genes after exercise") |>
  kable_paper("hover", full_width = F)
RNA expression of HAT genes after exercise
baseMean log2FoldChange lfcSE stat pvalue padj
ENSG00000108773.11 KAT2A 823.7213 -0.3904717 0.0927294 -4.2108727 0.0000254 0.0014766
ENSG00000156650.14 KAT6B 722.2880 -0.0717389 0.1025425 -0.6996012 0.4841764 0.8007768
ENSG00000136504.15 KAT7 1220.3262 -0.0697401 0.0757823 -0.9202688 0.3574323 0.7168281
ENSG00000172977.13 KAT5 220.8768 -0.0503774 0.1493393 -0.3373355 0.7358640 0.9174250
ENSG00000122390.19 NAA60 406.7988 -0.0185196 0.1270129 -0.1458090 0.8840722 0.9648047
ENSG00000103510.20 KAT8 570.3480 -0.0094622 0.1250996 -0.0756377 0.9397074 0.9808612
ENSG00000084676.16 NCOA1 868.3112 0.2277903 0.0969115 2.3504993 0.0187482 0.1609802
ENSG00000140396.13 NCOA2 320.7790 0.2350403 0.1219461 1.9274120 0.0539283 0.2979807
ENSG00000125484.12 GTF3C4 248.6127 0.2404250 0.1499079 1.6038184 0.1087541 0.4293916
ENSG00000134852.15 CLOCK 388.0292 0.2413520 0.1192960 2.0231357 0.0430592 0.2598946
ENSG00000005339.15 CREBBP 1002.6364 0.2425909 0.0900621 2.6935951 0.0070686 0.0916149
ENSG00000083168.11 KAT6A 562.2708 0.2586337 0.1076801 2.4018704 0.0163115 0.1471017
ENSG00000124151.19 NCOA3 864.1477 0.2705133 0.0923327 2.9297674 0.0033922 0.0560482
ENSG00000100393.16 EP300 747.8770 0.2721092 0.1034069 2.6314409 0.0085024 0.1005081
ENSG00000147133.18 TAF1 376.8089 0.2883571 0.1334746 2.1603896 0.0307425 0.2147070 
hdac_rna <- rna[rownames(rna) %in% hdacs,1:6]

hdac_rna <- hdac_rna[order(hdac_rna$log2FoldChange),]

hdac_rna |>
  kbl(caption = "RNA expression of HDAC genes after exercise") |>
  kable_paper("hover", full_width = F)
RNA expression of HDAC genes after exercise
baseMean log2FoldChange lfcSE stat pvalue padj
ENSG00000100429.18 HDAC10 118.93227 -0.5738910 0.2395790 -2.3954149 0.0166016 0.1489558
ENSG00000077463.15 SIRT6 48.24593 -0.4012873 0.3278881 -1.2238544 0.2210072 0.5906585
ENSG00000061273.18 HDAC7 289.17215 -0.1088144 0.1278717 -0.8509654 0.3947886 0.7441069
ENSG00000187531.14 SIRT7 58.08990 -0.0799818 0.2735825 -0.2923499 0.7700191 0.9270378
ENSG00000171720.10 HDAC3 345.26553 -0.0507674 0.1344191 -0.3776800 0.7056683 0.9056275
ENSG00000094631.22 HDAC6 467.85028 -0.0274934 0.1113145 -0.2469888 0.8049169 0.9394714
ENSG00000068903.21 SIRT2 2510.46934 -0.0264005 0.0706421 -0.3737217 0.7086114 0.9064394
ENSG00000142082.15 SIRT3 356.76481 -0.0192083 0.1195850 -0.1606248 0.8723890 0.9615212
ENSG00000116478.12 HDAC1 156.58809 0.0162711 0.1923753 0.0845798 0.9325955 0.9784341
ENSG00000124523.17 SIRT5 581.25061 0.0506139 0.0967254 0.5232744 0.6007833 0.8562811
ENSG00000196591.12 HDAC2 512.92419 0.0637936 0.1108116 0.5756942 0.5648219 0.8445889
ENSG00000163517.15 HDAC11 362.27215 0.0697718 0.1378074 0.5062995 0.6126464 0.8608961
ENSG00000068024.18 HDAC4 796.56869 0.1522836 0.0894729 1.7020081 0.0887539 0.3901151
ENSG00000147099.21 HDAC8 151.26882 0.1607133 0.1814863 0.8855396 0.3758657 0.7316116
ENSG00000089163.4 SIRT4 51.44017 0.1752674 0.3048703 0.5748918 0.5653644 0.8447942
ENSG00000048052.25 HDAC9 380.48088 0.1843314 0.1220415 1.5103989 0.1309417 0.4702410
ENSG00000108840.17 HDAC5 929.64390 0.2006982 0.0902478 2.2238557 0.0261582 0.1946451
ENSG00000096717.12 SIRT1 216.46509 0.5519410 0.1484932 3.7169443 0.0002016 0.0069943

Analyse data

GO

mgo <- mitch_calc(m,go,minsetsize=5,cores=8,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
#mitch_report(mgo,"multi_go_report.html",overwrite=TRUE)
mitch_plots(mgo,"multi_go_plots.pdf")
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## ℹ The deprecated feature was likely used in the mitch package.
##   Please report the issue at <https://github.com/markziemann/mitch>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
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## png 
##   2
mgores <- mgo$enrichment_result
mgores <- mgores[1:50,c(1,4:6)]
rownames(mgores) <- mgores[,1] ; mgores[,1]=NULL
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 25)
heatmap.2(as.matrix(mgores),trace="none",scale="none",col=my_palette)

pdf("integrate_heat_go.pdf")
heatmap.2(as.matrix(mgores),trace="none",scale="none",col=my_palette,mar=c(5,27),cexCol=0.8,cexRow=0.6)
dev.off()
## png 
##   2

TF

mtf <- mitch_calc(m,tf,minsetsize=50,cores=8,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
#mitch_report(mtf,"multi_tf_report.html",overwrite=TRUE)
mitch_plots(mtf,"multi_tf_plots.pdf")
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## png 
##   2
mtfres <- mtf$enrichment_result
mtfres <- mtfres[1:50,c(1,4:6)]
rownames(mtfres) <- mtfres[,1] ; mtfres[,1]=NULL
heatmap.2(as.matrix(mtfres),trace="none",scale="none",col=my_palette)

pdf("integrate_heat_tf.pdf")
heatmap.2(as.matrix(mtfres),trace="none",scale="none",col=my_palette,mar=c(5,27),cexCol=0.8,cexRow=0.6)
dev.off()
## png 
##   2
mtfres2 <- mtf$enrichment_result

# herenow
mtfres2[grep("KAT",mtfres2$set),]
##                    set setSize      pMANOVA         s.RNA  s.H3K9ac
## 115  KAT5 TARGET GENES     785 1.155227e-46  0.0004751144 0.2919508
## 114 KAT2A TARGET GENES    1027 2.892501e-52 -0.0129038394 0.2453379
##         s.H3K36a     p.RNA     p.H3K9ac     p.H3K36a   s.dist        SD
## 115 -0.007635595 0.9820458 9.838851e-44 0.7176019585 0.292051 0.1706731
## 114 -0.068044558 0.4878479 6.339968e-40 0.0002532813 0.254926 0.1673011
##     p.adjustMANOVA
## 115   3.874137e-45
## 114   1.293361e-50
mtfres2[grep("P300",mtfres2$set),]
##         set setSize      pMANOVA     s.RNA  s.H3K9ac      s.H3K36a        p.RNA
## 741 P300 01     212 1.001472e-05 0.1595517 0.1286981 -0.0009244927 6.369861e-05
##        p.H3K9ac  p.H3K36a    s.dist         SD p.adjustMANOVA
## 741 0.001259058 0.9815195 0.2049898 0.08515336   1.967328e-05
mtfres2[grep("NCOA",mtfres2$set),]
##                    set setSize      pMANOVA        s.RNA  s.H3K9ac     s.H3K36a
## 143 NCOA6 TARGET GENES     292 8.894576e-08  0.170319858 0.1124467 -0.007369124
## 141 NCOA2 TARGET GENES     399 7.066386e-07 -0.009021445 0.1470418  0.108168400
## 142 NCOA4 TARGET GENES     897 8.560975e-22  0.015866342 0.1800194 -0.026209280
##            p.RNA     p.H3K9ac     p.H3K36a    s.dist         SD p.adjustMANOVA
## 143 5.791040e-07 9.700749e-04 0.8288576875 0.2042240 0.09062607   2.352678e-07
## 141 7.578301e-01 4.970681e-07 0.0002175671 0.1827651 0.08124072   1.654697e-06
## 142 4.234166e-01 9.566731e-20 0.1860468823 0.1826080 0.10897009   8.119955e-21
mtfres2[grep("HDAC",mtfres2$set),]
##                   set setSize      pMANOVA        s.RNA   s.H3K9ac    s.H3K36a
## 86 HDAC4 TARGET GENES     563 1.991463e-07  0.002455594 0.11982810 -0.04006363
## 87 HDAC8 TARGET GENES     137 2.470271e-01 -0.034163275 0.07342285 -0.03296222
##        p.RNA     p.H3K9ac  p.H3K36a     s.dist         SD p.adjustMANOVA
## 86 0.9209888 1.286048e-06 0.1055881 0.12637206 0.08281461   5.054009e-07
## 87 0.4903884 1.382591e-01 0.5057667 0.08743313 0.06177108   2.580182e-01
mtfres2[grep("SIRT",mtfres2$set),]
##  [1] set            setSize        pMANOVA        s.RNA          s.H3K9ac      
##  [6] s.H3K36a       p.RNA          p.H3K9ac       p.H3K36a       s.dist        
## [11] SD             p.adjustMANOVA
## <0 rows> (or 0-length row.names)

TF2 - encode collection

mtf2 <- mitch_calc(m,tf2,minsetsize=50,cores=8,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
#mitch_report(mtf2,"multi_tf2_report.html",overwrite=TRUE)
mtf2res <- mtf2$enrichment_result

grep("HAT",mtf2res$set)
## integer(0)
grep("KAT",mtf2res$set)
## [1]  3 73
mtf2res[grep("HAT",mtf2res$set),]
##  [1] set            setSize        pMANOVA        s.RNA          s.H3K9ac      
##  [6] s.H3K36a       p.RNA          p.H3K9ac       p.H3K36a       s.dist        
## [11] SD             p.adjustMANOVA
## <0 rows> (or 0-length row.names)
mtf2res[grep("KAT",mtf2res$set),]
##      set setSize       pMANOVA      s.RNA  s.H3K9ac    s.H3K36a        p.RNA
## 73 KAT2A    6960  0.000000e+00 0.07842198 0.3235060 -0.03817609 5.132702e-18
## 74 KAT2B    3515 8.527081e-158 0.03592463 0.2704592 -0.02375761 1.051461e-03
##         p.H3K9ac     p.H3K36a    s.dist        SD p.adjustMANOVA
## 73 2.190984e-289 2.582993e-05 0.3350575 0.1846055    0.00000e+00
## 74 8.986791e-137 3.027037e-02 0.2738671 0.1555271   1.42118e-157
mtf2res[grep("P300",mtf2res$set),]
##      set setSize       pMANOVA     s.RNA  s.H3K9ac    s.H3K36a        p.RNA
## 38 EP300   12012 1.473154e-149 0.1486537 0.1595782 -0.09049005 2.431157e-50
##        p.H3K9ac     p.H3K36a    s.dist        SD p.adjustMANOVA
## 38 8.546591e-58 1.265374e-19 0.2361177 0.1413289  2.322541e-149
mtf2res[grep("TAF",mtf2res$set),]
##      set setSize       pMANOVA     s.RNA  s.H3K9ac    s.H3K36a        p.RNA
## 146 TAF7    6380 3.893409e-224 0.1084530 0.2706651  0.02101035 3.741766e-32
## 145 TAF1   10958 4.860739e-192 0.1375338 0.2000398 -0.07500803 2.434431e-48
##          p.H3K9ac     p.H3K36a    s.dist        SD p.adjustMANOVA
## 146 5.345724e-195 2.248501e-02 0.2923407 0.1266797  1.116961e-223
## 145 6.441998e-101 1.866774e-15 0.2540820 0.1441829  9.557632e-192
mtf2res[grep("HDAC",mtf2res$set),]
##      set setSize       pMANOVA      s.RNA  s.H3K9ac    s.H3K36a        p.RNA
## 60 HDAC1    9758 2.865593e-181 0.09502638 0.2061892 -0.06426685 1.206218e-25
## 61 HDAC2   11553 1.246079e-155 0.13446864 0.1536425 -0.10553370 9.364130e-44
## 62 HDAC6    8611 1.020856e-132 0.09317849 0.1715126 -0.05109154 2.092927e-25
##         p.H3K9ac     p.H3K36a    s.dist        SD p.adjustMANOVA
## 60 1.289485e-115 1.510441e-12 0.2359538 0.1359399  5.278724e-181
## 61  1.123379e-56 1.518160e-27 0.2298373 0.1444190  2.037979e-155
## 62  1.457274e-82 1.164455e-08 0.2017651 0.1129179  1.501259e-132
mtf2res[grep("SIRT",mtf2res$set),]
##       set setSize       pMANOVA      s.RNA  s.H3K9ac     s.H3K36a        p.RNA
## 126 SIRT6    6038 4.042698e-163 0.07447519 0.2317089 -0.008070164 1.178034e-15
##          p.H3K9ac p.H3K36a    s.dist        SD p.adjustMANOVA
## 126 2.771896e-139 0.386059 0.2435173 0.1218128  6.868662e-163

TF3 - MZ encode collection

mtf3 <- mitch_calc(m2,tf3,minsetsize=50,cores=8,priority="effect")
## Note: Enrichments with large effect sizes may not be 
##             statistically significant.
#mitch_report(mtf3,"multi_tf3_report.html",overwrite=TRUE)

Session information

sessionInfo()
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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       
## 
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] kableExtra_1.4.0    beeswarm_0.4.0      limma_3.60.6       
## [4] eulerr_7.0.2        mitch_1.19.3        gplots_3.2.0       
## [7] RhpcBLASctl_0.23-42
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6       xfun_0.49          bslib_0.8.0        ggplot2_3.5.1     
##  [5] htmlwidgets_1.6.4  caTools_1.18.3     GGally_2.2.1       vctrs_0.6.5       
##  [9] tools_4.4.2        bitops_1.0-9       generics_0.1.3     parallel_4.4.2    
## [13] tibble_3.2.1       fansi_1.0.6        pkgconfig_2.0.3    KernSmooth_2.23-24
## [17] RColorBrewer_1.1-3 lifecycle_1.0.4    farver_2.1.2       compiler_4.4.2    
## [21] stringr_1.5.1      statmod_1.5.0      munsell_0.5.1      httpuv_1.6.15     
## [25] htmltools_0.5.8.1  sass_0.4.9         yaml_2.3.10        crayon_1.5.3      
## [29] later_1.4.0        pillar_1.9.0       jquerylib_0.1.4    tidyr_1.3.1       
## [33] MASS_7.3-64        cachem_1.1.0       mime_0.12          ggstats_0.7.0     
## [37] gtools_3.9.5       tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
## [41] dplyr_1.1.4        reshape2_1.4.4     purrr_1.0.2        labeling_0.4.3    
## [45] fastmap_1.2.0      grid_4.4.2         colorspace_2.1-1   cli_3.6.3         
## [49] magrittr_2.0.3     utf8_1.2.4         withr_3.0.2        scales_1.3.0      
## [53] promises_1.3.1     rmarkdown_2.29     gridExtra_2.3      shiny_1.9.1       
## [57] evaluate_1.0.1     knitr_1.49         viridisLite_0.4.2  rlang_1.1.4       
## [61] Rcpp_1.0.13-1      xtable_1.8-4       glue_1.8.0         echarts4r_0.4.5   
## [65] xml2_1.3.6         svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
## [69] R6_2.5.1           plyr_1.8.9         systemfonts_1.1.0