Using mitch package for Infinium methylation analysis of phyllodes methylation
The GMEA team
2024-03-10
Introduction
Here we are comparing non-malignant vs malignant phyllodes samples.
Data provided by Prof Ruth Pidsley’s team.
Source code: https://github.com/markziemann/phyllodes_pathways
Gene set information provided by Reactome, Gene Ontology, and transcription factor targets from MsigDB.
Requirements
Load packages.
Important: ensure that the mitch version used is 1.15.0 (patched), 1.15.1 or higher.
devtools::install_github("markziemann/mitch") #get devel mitch
suppressPackageStartupMessages({
library("limma")
library("eulerr")
library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")
library("HGNChelper")
library("tictoc")
library("mitch")
library("kableExtra")
library("beeswarm")
library("missMethyl")
library("gridExtra")
library("png")
})Load methylation data
The limma results are read in. Results from Meyer et al (PMID:38300122).
dm <- read.csv("Phyllodes_topTable.csv.gz",header=TRUE,row.names=1)
dim(dm)## [1] 722950 9head(dm)## logFC AveExpr t P.Value adj.P.Val B
## cg00000103 -0.15742152 -1.0974082 -0.3747647 0.71024055 0.9918339 -5.238335
## cg00000109 0.04093016 1.1093811 0.1202041 0.90507542 0.9977811 -5.264622
## cg00000155 0.11957601 2.4638979 0.5842297 0.56305076 0.9865123 -5.161199
## cg00000158 -0.28276800 2.3744478 -0.9616803 0.34322562 0.9815375 -4.940397
## cg00000165 -1.10536550 0.5984195 -2.2890880 0.02862825 0.9087206 -3.443783
## cg00000221 -0.20409047 0.8348021 -0.7099382 0.48274450 0.9833988 -5.099152
## meth_base meth_alt meth_delta
## cg00000103 0.3491598 0.3193451 0.029814658
## cg00000109 0.6703594 0.6737406 -0.003381181
## cg00000155 0.8387686 0.8492984 -0.010529792
## cg00000158 0.8365238 0.8110297 0.025494072
## cg00000165 0.6584762 0.4925376 0.165938636
## cg00000221 0.6448128 0.6137003 0.031112513Note that this object has probes with their own column, not simply as row names.
Load pathways
reactome <- gmt_import("ReactomePathways_2024-03-10.gmt")
gobp <- gmt_import("c5.go.v2023.2.Hs.symbols.gmt")
gobp <- gobp[grep("GOBP_",names(gobp))]
names(gobp) <- gsub("_"," ",gsub("GOBP_","",names(gobp)))
tft <- gmt_import("c3.tft.gtrd.v2023.2.Hs.symbols.gmt")
names(tft) <- gsub("_"," ",names(tft))Curate the annotation
Curate the table which matches probes to gene names.
It is important to update defunct gene symbols.
tic()
anno <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
myann <- data.frame(anno[,c("UCSC_RefGene_Name","UCSC_RefGene_Group","Islands_Name","Relation_to_Island")])
gp <- myann[,"UCSC_RefGene_Name",drop=FALSE]
gp2 <- strsplit(gp$UCSC_RefGene_Name,";")
names(gp2) <- rownames(gp)
gp2 <- lapply(gp2,unique)
gt <- stack(gp2)
colnames(gt) <- c("gene","probe")
gt$probe <- as.character(gt$probe)
dim(gt)## [1] 684970 2str(gt)## 'data.frame': 684970 obs. of 2 variables:
## $ gene : chr "YTHDF1" "EIF2S3" "PKN3" "CCDC57" ...
## $ probe: chr "cg18478105" "cg09835024" "cg14361672" "cg01763666" ...toc() #9.0s## 16.899 sec elapsedtic()
#new.hgnc.table <- getCurrentHumanMap()
new.hgnc.table <- readRDS("new.hgnc.table.rds")
fix <- checkGeneSymbols(gt$gene,map=new.hgnc.table)## Warning in checkGeneSymbols(gt$gene, map = new.hgnc.table): Human gene symbols
## should be all upper-case except for the 'orf' in open reading frames. The case
## of some letters was corrected.## Warning in checkGeneSymbols(gt$gene, map = new.hgnc.table): x contains
## non-approved gene symbolsfix2 <- fix[which(fix$x != fix$Suggested.Symbol),]
length(unique(fix2$x))## [1] 3253gt$gene <- fix$Suggested.Symbol
toc()## 237.559 sec elapsedhead(gt)## gene probe
## 1 YTHDF1 cg18478105
## 2 EIF2S3 cg09835024
## 3 PKN3 cg14361672
## 4 CCDC57 cg01763666
## 5 INF2 cg12950382
## 6 CDC16 cg02115394str(gt)## 'data.frame': 684970 obs. of 2 variables:
## $ gene : chr "YTHDF1" "EIF2S3" "PKN3" "CCDC57" ...
## $ probe: chr "cg18478105" "cg09835024" "cg14361672" "cg01763666" ...Mitch pipeline
The first part is to import the data into mitch.
m <- mitch_import(x=dm,DEtype="limma",geneTable=gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 722950## Note: no. genes in output = 23030## Warning in mitch_import(x = dm, DEtype = "limma", geneTable = gt): Warning: less than half of the input genes are also in the
## outputhead(m) %>%
kbl(caption = "Differential gene methylation scores used for pathway analysis") %>%
kable_paper("hover", full_width = F)| x | |
|---|---|
| A1BG | -0.2987185 |
| A1BG-AS1 | -0.4355672 |
| A1CF | -0.0075548 |
| A2M | 0.2520922 |
| A2M-AS1 | -0.8309598 |
| A2ML1 | 0.0785158 |
Now run the enrichment analysis.
Note that the results are not sorted by p-value, rather S.distance, an enrichment score. I think this works better for interpretation.
Reactome
Reactome is the first analysis.
The S distance is the enrichment score, which 0 means no chance and has a maximum of +1 and minimum of -1.
mres1 <- mitch_calc(x=m,genesets=reactome,minsetsize=5, priority="effect",cores=8)## Note: Enrichments with large effect sizes may not be
## statistically significant.mtable1 <- mres1$enrichment_result
up <- subset(mtable1,s.dist>0 & p.adjustANOVA<0.05)
dn <- subset(mtable1,s.dist<0 & p.adjustANOVA<0.05)
nrow(up)## [1] 10nrow(dn)## [1] 15head(up,10) %>%
kbl(caption = "Top significant pathways with higher methylation") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 573 | Fatty acids | 15 | 0.0000013 | 0.7223086 | 0.0005098 |
| 499 | Eicosanoids | 12 | 0.0000525 | 0.6741101 | 0.0070416 |
| 528 | Expression and translocation of olfactory receptors | 334 | 0.0000000 | 0.4984898 | 0.0000000 |
| 1124 | Olfactory Signaling Pathway | 342 | 0.0000000 | 0.4857232 | 0.0000000 |
| 159 | Beta defensins | 27 | 0.0000203 | 0.4736564 | 0.0048811 |
| 405 | Defensins | 35 | 0.0000057 | 0.4428876 | 0.0019255 |
| 118 | Antimicrobial peptides | 79 | 0.0000000 | 0.4209601 | 0.0000000 |
| 1585 | Sensory Perception | 558 | 0.0000000 | 0.2868942 | 0.0000000 |
| 332 | Cytochrome P450 - arranged by substrate type | 65 | 0.0002817 | 0.2604050 | 0.0288458 |
| 770 | Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell | 128 | 0.0000304 | 0.2134045 | 0.0050871 |
head(dn,10) %>%
kbl(caption = "Top significant pathways with lower methylation") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 1047 | NR1H3 & NR1H2 regulate gene expression linked to cholesterol transport and efflux | 44 | 0.0003545 | -0.3111598 | 0.0339462 |
| 1046 | NR1H2 and NR1H3-mediated signaling | 54 | 0.0001371 | -0.2999281 | 0.0162158 |
| 247 | Cell junction organization | 117 | 0.0002357 | -0.1967636 | 0.0263317 |
| 363 | Death Receptor Signaling | 154 | 0.0001134 | -0.1801684 | 0.0142503 |
| 195 | CDC42 GTPase cycle | 153 | 0.0004602 | -0.1640361 | 0.0402335 |
| 273 | Chromatin modifying enzymes | 229 | 0.0005931 | -0.1316908 | 0.0477082 |
| 274 | Chromatin organization | 229 | 0.0005931 | -0.1316908 | 0.0477082 |
| 1667 | Signaling by Nuclear Receptors | 267 | 0.0003746 | -0.1264188 | 0.0342378 |
| 945 | Metabolism of carbohydrates | 298 | 0.0002869 | -0.1220823 | 0.0288458 |
| 1299 | RHO GTPase cycle | 444 | 0.0000422 | -0.1132904 | 0.0060593 |
Now make a barplot of these top findings.
top <- rbind(up[1:10,],dn[1:10,])
top <- top[order(top$s.dist),]
barnames <- gsub("_"," ",top$set)
cols <- as.character(((sign(top$s.dist)+1)/2)+1)
cols <- gsub("1","blue",gsub("2","red",cols))
par(mar = c(5.1, 29.1, 4.1, 2.1))
barplot(abs(top$s.dist),horiz=TRUE,las=1,names.arg=barnames, cex.names=0.8,
cex.axis=0.8,col=cols, xlab="Enrichment score",main="Reactomes")
grid()
par( mar = c(5.1, 4.1, 4.1, 2.1) )GO Biological Process
GOBP is a larger annotation set.
It looks like defensins are increased in methylation, while some lipid processes are reduced.
mres2 <- mitch_calc(x=m,genesets=gobp,minsetsize=5, priority="effect",cores=8)## Note: Enrichments with large effect sizes may not be
## statistically significant.mtable2 <- mres2$enrichment_result
up <- subset(mtable2,s.dist>0 & p.adjustANOVA<0.05)
dn <- subset(mtable2,s.dist<0 & p.adjustANOVA<0.05)
nrow(up)## [1] 22nrow(dn)## [1] 87head(up,10) %>%
kbl(caption = "Top significant GO BPs with higher methylation") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 1165 | DISRUPTION OF PLASMA MEMBRANE INTEGRITY IN ANOTHER ORGANISM | 6 | 0.0001008 | 0.9165943 | 0.0137146 |
| 1164 | DISRUPTION OF CELLULAR ANATOMICAL STRUCTURE IN ANOTHER ORGANISM | 10 | 0.0000037 | 0.8452997 | 0.0010075 |
| 4594 | POSITIVE REGULATION OF PEPTIDYL SERINE PHOSPHORYLATION OF STAT PROTEIN | 14 | 0.0001611 | 0.5823402 | 0.0180179 |
| 6974 | SERINE PHOSPHORYLATION OF STAT PROTEIN | 18 | 0.0000980 | 0.5302644 | 0.0135837 |
| 6956 | SENSORY PERCEPTION OF SMELL | 360 | 0.0000000 | 0.4645606 | 0.0000000 |
| 6952 | SENSORY PERCEPTION OF CHEMICAL STIMULUS | 435 | 0.0000000 | 0.4051028 | 0.0000000 |
| 1104 | DETECTION OF STIMULUS INVOLVED IN SENSORY PERCEPTION | 451 | 0.0000000 | 0.3994080 | 0.0000000 |
| 1039 | DEFENSE RESPONSE TO FUNGUS | 53 | 0.0000031 | 0.3702792 | 0.0009435 |
| 6182 | REGULATION OF PRESYNAPTIC MEMBRANE POTENTIAL | 29 | 0.0005621 | 0.3700064 | 0.0412113 |
| 1103 | DETECTION OF STIMULUS | 574 | 0.0000000 | 0.3025920 | 0.0000000 |
head(dn,10) %>%
kbl(caption = "Top significant GO BPs with lower methylation") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 5024 | PROTEIN LIPID COMPLEX ASSEMBLY | 30 | 0.0001258 | -0.4044174 | 0.0158901 |
| 3895 | PHOSPHATIDYLCHOLINE BIOSYNTHETIC PROCESS | 29 | 0.0001765 | -0.4023108 | 0.0188955 |
| 6141 | REGULATION OF PHOSPHOLIPID METABOLIC PROCESS | 34 | 0.0006470 | -0.3379744 | 0.0461074 |
| 1866 | HISTONE METHYLATION | 38 | 0.0004448 | -0.3291694 | 0.0385442 |
| 3202 | NEGATIVE REGULATION OF MUSCLE CELL APOPTOTIC PROCESS | 50 | 0.0002914 | -0.2960957 | 0.0277772 |
| 2539 | MIRNA MEDIATED GENE SILENCING BY MRNA DESTABILIZATION | 64 | 0.0000593 | -0.2901783 | 0.0098236 |
| 1551 | FIBROBLAST MIGRATION | 53 | 0.0005164 | -0.2756514 | 0.0394998 |
| 6467 | REGULATION OF TUMOR NECROSIS FACTOR MEDIATED SIGNALING PATHWAY | 58 | 0.0004917 | -0.2645289 | 0.0390603 |
| 4008 | POSITIVE REGULATION OF ACTIN FILAMENT BUNDLE ASSEMBLY | 61 | 0.0005818 | -0.2546090 | 0.0422470 |
| 3897 | PHOSPHATIDYLCHOLINE METABOLIC PROCESS | 76 | 0.0001901 | -0.2475317 | 0.0198552 |
These results are more interesting than the reactome ones.
Now make a barplot of these top findings.
top <- rbind(up[1:10,],dn[1:10,])
top <- top[order(top$s.dist),]
barnames <- gsub("_"," ",top$set)
cols <- as.character(((sign(top$s.dist)+1)/2)+1)
cols <- gsub("1","blue",gsub("2","red",cols))
par(mar = c(5.1, 29.1, 4.1, 2.1))
barplot(abs(top$s.dist),horiz=TRUE,las=1,names.arg=barnames, cex.names=0.8,
cex.axis=0.8,col=cols, xlab="Enrichment score",main="GO Biological Processes")
grid()
par( mar = c(5.1, 4.1, 4.1, 2.1) )Transcription factor targets
Might give some clues about potential mechanisms.
RBMX and NEUROD are interesting, but they are relatively small sets, however NFKBIA is more interesting.
mres3 <- mitch_calc(x=m,genesets=tft,minsetsize=5, priority="effect",cores=8)## Note: Enrichments with large effect sizes may not be
## statistically significant.mtable3 <- mres3$enrichment_result
up <- subset(mtable3,s.dist>0 & p.adjustANOVA<0.05)
dn <- subset(mtable3,s.dist<0 & p.adjustANOVA<0.05)
nrow(up)## [1] 12nrow(dn)## [1] 119head(up,10) %>%
kbl(caption = "Top significant transcription factor target sets with higher methylation") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 245 | RBMX TARGET GENES | 5 | 0.0074898 | 0.6905451 | 0.0303397 |
| 260 | SIGMAR1 TARGET GENES | 13 | 0.0123605 | 0.4006971 | 0.0464946 |
| 13 | ASXL2 TARGET GENES | 44 | 0.0001762 | 0.3267562 | 0.0011966 |
| 309 | TRIP13 TARGET GENES | 89 | 0.0044128 | 0.1745675 | 0.0187642 |
| 46 | CHAMP1 TARGET GENES | 185 | 0.0002944 | 0.1542766 | 0.0017993 |
| 131 | HSD17B8 TARGET GENES | 607 | 0.0000008 | 0.1174574 | 0.0000097 |
| 404 | ZNF486 TARGET GENES | 187 | 0.0062200 | 0.1159807 | 0.0257761 |
| 282 | SRSF9 TARGET GENES | 709 | 0.0000055 | 0.1001168 | 0.0000556 |
| 117 | HMGA1 TARGET GENES | 840 | 0.0000011 | 0.0990078 | 0.0000133 |
| 236 | PSMB5 TARGET GENES | 289 | 0.0122206 | 0.0856362 | 0.0463245 |
head(dn,10) %>%
kbl(caption = "Top significant transcription factor target sets with lower methylation") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 187 | NEUROD2 TARGET GENES | 5 | 0.0023905 | -0.7841650 | 0.0110279 |
| 422 | ZNF544 TARGET GENES | 6 | 0.0033997 | -0.6904534 | 0.0147119 |
| 180 | MYF6 TARGET GENES | 13 | 0.0124732 | -0.4001825 | 0.0465602 |
| 352 | ZNF16 TARGET GENES | 73 | 0.0000290 | -0.2829286 | 0.0002536 |
| 264 | SIX4 TARGET GENES | 41 | 0.0021885 | -0.2764493 | 0.0105957 |
| 191 | NFKBIA TARGET GENES | 1109 | 0.0000000 | -0.2698367 | 0.0000000 |
| 17 | ATOH8 TARGET GENES | 44 | 0.0023254 | -0.2652899 | 0.0108299 |
| 161 | MCM2 TARGET GENES | 81 | 0.0000444 | -0.2623891 | 0.0003555 |
| 401 | ZNF436 TARGET GENES | 465 | 0.0000000 | -0.2525475 | 0.0000000 |
| 234 | PRMT5 TARGET GENES | 84 | 0.0000904 | -0.2470542 | 0.0006694 |
Now make a barplot of these top findings.
top <- rbind(up[1:10,],dn[1:10,])
top <- top[order(top$s.dist),]
barnames <- gsub("_"," ",top$set)
cols <- as.character(((sign(top$s.dist)+1)/2)+1)
cols <- gsub("1","blue",gsub("2","red",cols))
par(mar = c(5.1, 29.1, 4.1, 2.1))
barplot(abs(top$s.dist),horiz=TRUE,las=1,names.arg=barnames, cex.names=0.8,
cex.axis=0.8,col=cols, xlab="Enrichment score",main="Transcription factor target sets")
grid()
par( mar = c(5.1, 4.1, 4.1, 2.1) )Output reports and charts
Make a html report and some charts.
mitch_report(res=mres1,outfile="reactome_mitchreport.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/Rtmp9nQ3Ri/reactome_mitchreport.rds ".##
##
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## 34/34## output file: /mnt/mitch.knit.md## /usr/local/bin/pandoc +RTS -K512m -RTS /mnt/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp9nQ3Ri/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/Rtmp9nQ3Ri/rmarkdown-str5d17ce740ad.html##
## Output created: /tmp/Rtmp9nQ3Ri/mitch_report.html## [1] TRUEmitch_plots(res=mres1,outfile="reactome_mitchcharts.pdf")## png
## 2mitch_report(res=mres2,outfile="gobp_mitchreport.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/Rtmp9nQ3Ri/gobp_mitchreport.rds ".##
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## 34/34## output file: /mnt/mitch.knit.md## /usr/local/bin/pandoc +RTS -K512m -RTS /mnt/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp9nQ3Ri/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/Rtmp9nQ3Ri/rmarkdown-str5d1543430e8.html##
## Output created: /tmp/Rtmp9nQ3Ri/mitch_report.html## [1] TRUEmitch_plots(res=mres2,outfile="gobp_mitchcharts.pdf")## png
## 2mitch_report(res=mres3,outfile="tft_mitchreport.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/Rtmp9nQ3Ri/tft_mitchreport.rds ".##
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## 34/34## output file: /mnt/mitch.knit.md## /usr/local/bin/pandoc +RTS -K512m -RTS /mnt/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp9nQ3Ri/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/Rtmp9nQ3Ri/rmarkdown-str5d1158b7ea3.html##
## Output created: /tmp/Rtmp9nQ3Ri/mitch_report.html## [1] TRUEmitch_plots(res=mres3,outfile="tft_mitchcharts.pdf")## png
## 2Session information
sessionInfo()## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pkgload_1.3.3
## [2] GGally_2.2.0
## [3] ggplot2_3.4.4
## [4] reshape2_1.4.4
## [5] gplots_3.1.3
## [6] gtools_3.9.5
## [7] tibble_3.2.1
## [8] dplyr_1.1.4
## [9] echarts4r_0.4.5
## [10] png_0.1-8
## [11] gridExtra_2.3
## [12] missMethyl_1.36.0
## [13] beeswarm_0.4.0
## [14] kableExtra_1.3.4
## [15] tictoc_1.2
## [16] HGNChelper_0.8.1
## [17] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [18] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [19] minfi_1.48.0
## [20] bumphunter_1.44.0
## [21] locfit_1.5-9.8
## [22] iterators_1.0.14
## [23] foreach_1.5.2
## [24] Biostrings_2.70.1
## [25] XVector_0.42.0
## [26] SummarizedExperiment_1.32.0
## [27] Biobase_2.62.0
## [28] MatrixGenerics_1.14.0
## [29] matrixStats_1.2.0
## [30] GenomicRanges_1.54.1
## [31] GenomeInfoDb_1.38.2
## [32] IRanges_2.36.0
## [33] S4Vectors_0.40.2
## [34] BiocGenerics_0.48.1
## [35] eulerr_7.0.0
## [36] limma_3.58.1
## [37] mitch_1.15.2
##
## loaded via a namespace (and not attached):
## [1] splines_4.3.2 later_1.3.2
## [3] BiocIO_1.12.0 bitops_1.0-7
## [5] filelock_1.0.3 preprocessCore_1.64.0
## [7] XML_3.99-0.16 lifecycle_1.0.4
## [9] lattice_0.22-5 MASS_7.3-60
## [11] base64_2.0.1 scrime_1.3.5
## [13] magrittr_2.0.3 sass_0.4.8
## [15] rmarkdown_2.25 jquerylib_0.1.4
## [17] yaml_2.3.8 httpuv_1.6.13
## [19] askpass_1.2.0 doRNG_1.8.6
## [21] DBI_1.1.3 RColorBrewer_1.1-3
## [23] abind_1.4-5 zlibbioc_1.48.0
## [25] quadprog_1.5-8 rvest_1.0.3
## [27] purrr_1.0.2 RCurl_1.98-1.13
## [29] rappdirs_0.3.3 GenomeInfoDbData_1.2.11
## [31] genefilter_1.84.0 annotate_1.80.0
## [33] svglite_2.1.3 DelayedMatrixStats_1.24.0
## [35] codetools_0.2-19 DelayedArray_0.28.0
## [37] xml2_1.3.6 tidyselect_1.2.0
## [39] beanplot_1.3.1 BiocFileCache_2.10.1
## [41] illuminaio_0.44.0 webshot_0.5.5
## [43] GenomicAlignments_1.38.0 jsonlite_1.8.8
## [45] multtest_2.58.0 ellipsis_0.3.2
## [47] survival_3.5-7 systemfonts_1.0.5
## [49] tools_4.3.2 progress_1.2.3
## [51] Rcpp_1.0.11 glue_1.6.2
## [53] SparseArray_1.2.2 xfun_0.41
## [55] HDF5Array_1.30.0 withr_2.5.2
## [57] fastmap_1.1.1 rhdf5filters_1.14.1
## [59] fansi_1.0.6 openssl_2.1.1
## [61] caTools_1.18.2 digest_0.6.33
## [63] R6_2.5.1 mime_0.12
## [65] colorspace_2.1-0 biomaRt_2.58.0
## [67] RSQLite_2.3.4 utf8_1.2.4
## [69] tidyr_1.3.0 generics_0.1.3
## [71] data.table_1.14.10 rtracklayer_1.62.0
## [73] prettyunits_1.2.0 httr_1.4.7
## [75] htmlwidgets_1.6.4 S4Arrays_1.2.0
## [77] ggstats_0.5.1 pkgconfig_2.0.3
## [79] gtable_0.3.4 blob_1.2.4
## [81] siggenes_1.76.0 htmltools_0.5.7
## [83] scales_1.3.0 knitr_1.45
## [85] rstudioapi_0.15.0 tzdb_0.4.0
## [87] rjson_0.2.21 nlme_3.1-163
## [89] curl_5.2.0 org.Hs.eg.db_3.18.0
## [91] cachem_1.0.8 rhdf5_2.46.1
## [93] stringr_1.5.1 KernSmooth_2.23-22
## [95] AnnotationDbi_1.64.1 restfulr_0.0.15
## [97] GEOquery_2.70.0 pillar_1.9.0
## [99] grid_4.3.2 reshape_0.8.9
## [101] vctrs_0.6.5 promises_1.2.1
## [103] dbplyr_2.4.0 xtable_1.8-4
## [105] evaluate_0.23 readr_2.1.4
## [107] GenomicFeatures_1.54.1 cli_3.6.2
## [109] compiler_4.3.2 Rsamtools_2.18.0
## [111] rlang_1.1.2 crayon_1.5.2
## [113] rngtools_1.5.2 nor1mix_1.3-2
## [115] mclust_6.0.1 plyr_1.8.9
## [117] stringi_1.8.3 viridisLite_0.4.2
## [119] BiocParallel_1.36.0 munsell_0.5.0
## [121] Matrix_1.6-1.1 hms_1.1.3
## [123] sparseMatrixStats_1.14.0 bit64_4.0.5
## [125] Rhdf5lib_1.24.1 KEGGREST_1.42.0
## [127] statmod_1.5.0 shiny_1.8.0
## [129] highr_0.10 memoise_2.0.1
## [131] bslib_0.6.1 bit_4.0.5