Differential Exon Usage Analysis: DEXSeq
Last updated: 2022-05-23
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Knit directory: amnio-cell-free-RNA/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | de15fc8 | Jovana Maksimovic | 2021-09-25 | Build site. |
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Data import
readHTSeq <- function(files){
names(files) <- strsplit2(files, "_")[,6]
files <- files[grepl("CMV", names(files))]
tmp <- lapply(files, function(file){
read.delim(file, sep = "\t", row.names = 1, header = FALSE)
})
counts <- bind_cols(tmp)
colnames(counts) <- names(tmp)
rownames(counts) <- sub(":", ":E", rownames(counts))
counts
}
pe <- readHTSeq(list.files(here("data/star-genome-analysis/count-exons-pe"),
pattern="PE.txt$", full.names = TRUE))New names:
* V2 -> V2...1
* V2 -> V2...2
* V2 -> V2...3
* V2 -> V2...4
* V2 -> V2...5
* ...se <- readHTSeq(list.files(here("data/star-genome-analysis/count-exons-se"),
pattern="SE.txt$", full.names = TRUE))New names:
* V2 -> V2...1
* V2 -> V2...2
* V2 -> V2...3
* V2 -> V2...4
* V2 -> V2...5
* ...counts <- pe + se
counts <- counts[grepl("^ENSG", rownames(counts)),]
dim(counts)[1] 662662 28Load sample information.
| id | CMV_status | pair | sex | GA_at_amnio | indication |
|---|---|---|---|---|---|
| CMV2 | neg | M1 | F | 20 | no_us_ab |
| CMV1 | pos | M1 | F | 21 | no_us_ab |
| CMV4 | pos | M2 | M | 21 | no_us_ab |
| CMV3 | neg | M2 | M | 22 | no_us_ab |
| CMV19 | pos | NC2 | F | 18 | no_us_ab |
| CMV35 | neg | L5 | M | 21 | no_us_ab |
| CMV30 | pos | L1 | F | 21 | no_us_ab |
| CMV31 | neg | L1 | F | 21 | no_us_ab |
| CMV8 | neg | L2 | F | 23 | no_us_ab |
| CMV9 | pos | L2 | F | 23 | no_us_ab |
| CMV26 | pos | L3 | F | 22 | no_us_ab |
| CMV56 | neg | L3 | F | 21 | no_us_ab |
| CMV14 | neg | L4 | F | 21 | no_us_ab |
| CMV15 | pos | L4 | F | 22 | no_us_ab |
| CMV20 | pos | L5 | M | 21 | no_us_ab |
| CMV51 | neg | L6 | M | 22 | no_us_ab |
| CMV57 | pos | L6 | M | 21 | no_us_ab |
| CMV58 | pos | L7 | M | 20 | no_us_ab |
| CMV60 | neg | L7 | M | 20 | no_us_ab |
| CMV52 | pos | L8 | M | 22 | no_us_ab |
| CMV61 | neg | L8 | M | 22 | no_us_ab |
| CMV54 | neg | L9 | F | 21 | no_us_ab |
| CMV21 | neg | NC1 | F | 21 | no_us_ab |
Only retain paired samples with clinical information for downstream analysis.
int <- intersect(colnames(counts), targets$id)
targets <- targets[match(int, targets$id),]
counts <- counts[,match(int, colnames(counts))]
head(counts) %>% knitr::kable()| CMV30 | CMV31 | CMV8 | CMV9 | CMV26 | CMV14 | CMV15 | CMV20 | CMV21 | CMV1 | CMV2 | CMV3 | CMV4 | CMV19 | CMV35 | CMV51 | CMV52 | CMV54 | CMV56 | CMV57 | CMV58 | CMV60 | CMV61 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000000003.15:E001 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ENSG00000000003.15:E002 | 33 | 34 | 21 | 45 | 46 | 41 | 36 | 35 | 38 | 45 | 38 | 19 | 43 | 49 | 25 | 23 | 24 | 33 | 21 | 38 | 27 | 15 | 14 |
| ENSG00000000003.15:E003 | 7 | 13 | 3 | 6 | 9 | 11 | 14 | 6 | 6 | 13 | 12 | 5 | 9 | 10 | 5 | 3 | 4 | 7 | 3 | 9 | 4 | 7 | 2 |
| ENSG00000000003.15:E004 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ENSG00000000003.15:E005 | 6 | 10 | 3 | 7 | 6 | 6 | 10 | 3 | 6 | 3 | 10 | 5 | 7 | 6 | 5 | 4 | 4 | 8 | 3 | 4 | 1 | 4 | 1 |
| ENSG00000000003.15:E006 | 8 | 6 | 3 | 7 | 2 | 4 | 8 | 3 | 6 | 5 | 10 | 5 | 6 | 3 | 4 | 3 | 4 | 5 | 2 | 4 | 3 | 3 | 1 |
DEXSeq analysis
Create the sample table.
sampleTable <- data.frame(row.names = targets$id,
condition = targets$CMV_status,
pair = targets$pair)
head(sampleTable) %>% knitr::kable()| condition | pair | |
|---|---|---|
| CMV30 | pos | L1 |
| CMV31 | neg | L1 |
| CMV8 | neg | L2 |
| CMV9 | pos | L2 |
| CMV26 | pos | L3 |
| CMV14 | neg | L4 |
Setup the data. Compare exon usage between CMV negative and positive samples. Sample pairing is taken into account.
formulaFullModel = ~ sample + exon + pair:exon + condition:exon
formulaReducedModel = ~ sample + exon + pair:exon
out <- here("data/rds/DEXSeq-exclude-US-ab.rds")
if(!file.exists(out)){
splitted <- strsplit(rownames(counts), ":")
exons <- sapply(splitted, "[[", 2)
genesrle <- sapply(splitted, "[[", 1)
flatGff <- list.files(here("data/star-genome-analysis"),
pattern="DEXSeq.chr.gff$", full.names = TRUE)
aggregates <- read.delim(flatGff, stringsAsFactors = FALSE,
header = FALSE)
colnames(aggregates) <- c("chr", "source", "class", "start",
"end", "ex", "strand", "ex2", "attr")
aggregates$strand <- gsub("\\.", "*", aggregates$strand)
aggregates <- aggregates[which(aggregates$class == "exonic_part"), ]
aggregates$attr <- gsub("\"|=|;", "", aggregates$attr)
aggregates$gene_id <- sub(".*gene_id\\s(\\S+).*", "\\1",
aggregates$attr)
transcripts <- gsub(".*transcripts\\s(\\S+).*", "\\1",
aggregates$attr)
transcripts <- strsplit(transcripts, "\\+")
exonids <- gsub(".*exonic_part_number\\s(\\S+).*", "\\1",
aggregates$attr)
exoninfo <- GRanges(as.character(aggregates$chr),
IRanges(start = aggregates$start,
end = aggregates$end),
strand = aggregates$strand)
names(exoninfo) <- paste(aggregates$gene_id, exonids,
sep = ":E")
names(transcripts) <- rownames(exoninfo)
matching <- match(rownames(counts), names(exoninfo))
dxd <- DEXSeqDataSet(
as.matrix(counts),
sampleData = sampleTable,
design = ~ sample + exon + condition:exon,
featureID = exons,
groupID = genesrle,
exoninfo[matching],
transcripts[matching])
} else {
dxd <- readRDS(file = out)
}if(!file.exists(out)){
dxd = estimateSizeFactors( dxd )
} Estimate disperions. Plot.
if(!file.exists(out)){
BPPARAM = MulticoreParam(min(26, multicoreWorkers()))
dxd = estimateDispersions( dxd,
BPPARAM=BPPARAM,
formula = formulaFullModel )
}
plotDispEsts( dxd )
| Version | Author | Date |
|---|---|---|
| de15fc8 | Jovana Maksimovic | 2021-09-25 |
Test for differential exon usage.
if(!file.exists(out)){
dxd = testForDEU( dxd,
BPPARAM=BPPARAM,
reducedModel = formulaReducedModel,
fullModel = formulaFullModel )
}Estimate exon fold changes.
if(!file.exists(out)){
dxd = estimateExonFoldChanges(dxd, BPPARAM=BPPARAM )
saveRDS(dxd, file = out)
} Number of statistically significant differentially used exons.
dxr1 = DEXSeqResults( dxd )
table ( dxr1$padj < 0.1 )
FALSE TRUE
42301 14 Number of genes with statistically significant differential exon usage.
table ( tapply( dxr1$padj < 0.1, dxr1$groupID, any ) )
FALSE TRUE
66 13 MA plots of differential exons usage. Significant exons are shown in red.
DEXSeq::plotMA( dxr1, cex=0.8 )
| Version | Author | Date |
|---|---|---|
| de15fc8 | Jovana Maksimovic | 2021-09-25 |
Annotate results with gene symbols. Display most statistically significant differentially used exons.
deg <- read.csv(here("output/star-fc-ruv-all.csv"))
dxr1[which(dxr1$padj < 0.1),] %>% data.frame %>%
dplyr::arrange(groupID, padj) %>%
mutate(symbol = NA, .before = groupID) %>%
mutate(deg = NA, .after = symbol)-> topDex
for(i in 1:nrow(topDex)){
keys <- gsub("\\.[0-9]*", "",
strsplit(topDex$groupID[i], "+",
fixed=TRUE)[[1]])
symbol <- select(org.Hs.eg.db, keys = keys,
columns = c("ENTREZID", "SYMBOL"),
keytype = "ENSEMBL")$SYMBOL
topDex$symbol[i] <- paste(symbol, collapse = "+")
topDex$deg[i] <- any(keys %in% deg$Ensembl)
}'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columns
'select()' returned 1:1 mapping between keys and columnstopDex %>% knitr::kable()| symbol | deg | groupID | featureID | exonBaseMean | dispersion | stat | pvalue | padj | neg | pos | log2fold_pos_neg | genomicData.seqnames | genomicData.start | genomicData.end | genomicData.width | genomicData.strand | countData.CMV30 | countData.CMV31 | countData.CMV8 | countData.CMV9 | countData.CMV26 | countData.CMV14 | countData.CMV15 | countData.CMV20 | countData.CMV21 | countData.CMV1 | countData.CMV2 | countData.CMV3 | countData.CMV4 | countData.CMV19 | countData.CMV35 | countData.CMV51 | countData.CMV52 | countData.CMV54 | countData.CMV56 | countData.CMV57 | countData.CMV58 | countData.CMV60 | countData.CMV61 | transcripts | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000007202.15:E041 | KIAA0100 | FALSE | ENSG00000007202.15 | E041 | 20.36674 | 0.0010632 | 17.34702 | 3.11e-05 | 0.0967841 | 1.3933713 | 1.246101 | -0.5139657 | chr17 | 28634671 | 28635609 | 939 | - | 18 | 28 | 25 | 24 | 13 | 27 | 32 | 15 | 19 | 16 | 20 | 27 | 28 | 20 | 24 | 38 | 7 | 25 | 21 | 17 | 16 | 16 | 14 | ENST0000…. |
| ENSG00000135272.11:E017 | MDFIC | FALSE | ENSG00000135272.11 | E017 | 223.77382 | 0.0001602 | 30.17716 | 0.00e+00 | 0.0016686 | 2.3396646 | 2.375240 | 0.1186993 | chr7 | 115015962 | 115019202 | 3241 | + | 311 | 381 | 155 | 180 | 369 | 215 | 306 | 302 | 318 | 270 | 289 | 188 | 256 | 99 | 249 | 213 | 152 | 222 | 232 | 207 | 334 | 171 | 106 | ENST0000…. |
| ENSG00000136872.20:E020 | ALDOB | TRUE | ENSG00000136872.20 | E020 | 30.36784 | 0.0004139 | 18.88255 | 1.39e-05 | 0.0616827 | 1.5478150 | 1.395801 | -0.5227151 | chr9 | 101430776 | 101430897 | 122 | - | 27 | 39 | 31 | 41 | 23 | 18 | 15 | 32 | 24 | 17 | 22 | 25 | 63 | 142 | 29 | 24 | 8 | 23 | 16 | 57 | 21 | 16 | 5 | ENST0000…. |
| ENSG00000136872.20:E023 | ALDOB | TRUE | ENSG00000136872.20 | E023 | 20.44411 | 0.0008302 | 18.79209 | 1.46e-05 | 0.0616827 | 1.4207578 | 1.212531 | -0.7271615 | chr9 | 101435709 | 101435762 | 54 | - | 20 | 31 | 25 | 26 | 15 | 11 | 12 | 24 | 18 | 15 | 15 | 21 | 40 | 79 | 19 | 20 | 5 | 15 | 11 | 36 | 14 | 10 | 5 | ENST0000…. |
| ENSG00000139726.11:E015 | DENR | FALSE | ENSG00000139726.11 | E015 | 50.66492 | 0.0002702 | 17.50242 | 2.87e-05 | 0.0967841 | 1.6814271 | 1.758811 | 0.2620617 | chr12 | 122770071 | 122771064 | 994 | + | 70 | 73 | 44 | 45 | 68 | 45 | 86 | 67 | 76 | 78 | 77 | 42 | 83 | 40 | 45 | 48 | 35 | 38 | 48 | 41 | 56 | 20 | 32 | ENST0000…. |
| ENSG00000140988.16+ENSG00000206811.1+ENSG00000255513.1+ENSG00000207405.1:E027 | RPS2+SNORA10+NA+SNORA64 | FALSE | ENSG00000140988.16+ENSG00000206811.1+ENSG00000255513.1+ENSG00000207405.1 | E027 | 25.73094 | 0.0005985 | 21.75290 | 3.10e-06 | 0.0262456 | 1.4801025 | 1.333642 | -0.5064787 | chr16 | 1964357 | 1964365 | 9 | - | 23 | 36 | 25 | 26 | 26 | 33 | 23 | 18 | 18 | 24 | 39 | 23 | 25 | 26 | 50 | 45 | 14 | 25 | 37 | 18 | 15 | 15 | 28 | ENST0000…. |
| ENSG00000166801.15:E027 | FAM111A | FALSE | ENSG00000166801.15 | E027 | 57.11587 | 0.0002819 | 17.29391 | 3.20e-05 | 0.0967841 | 1.7298976 | 1.808517 | 0.2656931 | chr11 | 59152219 | 59155034 | 2816 | + | 61 | 70 | 47 | 49 | 69 | 76 | 129 | 70 | 68 | 81 | 68 | 35 | 61 | 53 | 47 | 60 | 45 | 56 | 65 | 74 | 71 | 30 | 27 | ENST0000…. |
| ENSG00000174992.8:E004 | ZG16 | FALSE | ENSG00000174992.8 | E004 | 14.33009 | 0.0010231 | 18.32582 | 1.86e-05 | 0.0716148 | 1.2520622 | 1.131955 | -0.4264955 | chr16 | 29780104 | 29782973 | 2870 | + | 13 | 7 | 11 | 27 | 60 | 13 | 18 | 15 | 15 | 14 | 19 | 13 | 12 | 10 | 12 | 18 | 4 | 14 | 16 | 18 | 17 | 11 | 2 | ENST0000…. |
| ENSG00000182795.13:E001 | C1orf116 | FALSE | ENSG00000182795.13 | E001 | 177.13545 | 0.0007915 | 20.34286 | 6.50e-06 | 0.0456537 | 2.2307619 | 2.274289 | 0.1454078 | chr1 | 207018522 | 207020694 | 2173 | - | 216 | 240 | 160 | 193 | 244 | 156 | 265 | 286 | 252 | 218 | 197 | 162 | 176 | 130 | 188 | 177 | 124 | 165 | 155 | 194 | 241 | 94 | 107 | ENST0000…. |
| ENSG00000197249.14:E016 | SERPINA1 | TRUE | ENSG00000197249.14 | E016 | 95.85830 | 0.0001960 | 19.95172 | 7.90e-06 | 0.0480108 | 2.0286795 | 1.926626 | -0.3426304 | chr14 | 94383003 | 94383170 | 168 | - | 120 | 93 | 38 | 162 | 112 | 116 | 55 | 68 | 99 | 60 | 86 | 51 | 189 | 243 | 97 | 131 | 24 | 128 | 74 | 164 | 110 | 69 | 19 | ENST0000…. |
| ENSG00000204520.14+ENSG00000288587.1+ENSG00000206337.12:E040 | MICA+NA+HCP5 | TRUE | ENSG00000204520.14+ENSG00000288587.1+ENSG00000206337.12 | E040 | 12.77231 | 0.2154214 | 21.90977 | 2.90e-06 | 0.0262456 | 0.5466140 | 1.248350 | 2.7293487 | chr6 | 31465889 | 31472408 | 6520 | + | 4 | 0 | 1 | 66 | 15 | 12 | 11 | 17 | 0 | 26 | 1 | 0 | 31 | 44 | 2 | 2 | 14 | 0 | 1 | 30 | 25 | 1 | 1 | ENST0000…. |
| ENSG00000234745.11+ENSG00000204525.16:E033 | HLA-B+HLA-C | TRUE | ENSG00000234745.11+ENSG00000204525.16 | E033 | 17.44191 | 0.0012901 | 27.28947 | 2.00e-07 | 0.0024707 | 0.9935214 | 1.280585 | 1.0303547 | chr6 | 31353875 | 31354296 | 422 | - | 9 | 6 | 9 | 76 | 16 | 6 | 22 | 21 | 4 | 18 | 2 | 11 | 32 | 66 | 4 | 11 | 7 | 6 | 2 | 47 | 29 | 2 | 6 | ENST0000…. |
| ENSG00000259529.2+ENSG00000213928.9+ENSG00000092098.17:E087 | NA+IRF9+RNF31 | FALSE | ENSG00000259529.2+ENSG00000213928.9+ENSG00000092098.17 | E087 | 13.45485 | 0.0009100 | 27.75840 | 1.00e-07 | 0.0024707 | 0.9986611 | 1.254060 | 0.9182167 | chr14 | 24162144 | 24162280 | 137 | + | 13 | 9 | 5 | 23 | 14 | 6 | 34 | 19 | 11 | 25 | 7 | 10 | 25 | 17 | 9 | 8 | 12 | 10 | 8 | 22 | 26 | 8 | 7 | ENST0000…. |
| ENSG00000283840.1+ENSG00000100811.14:E021 | MIR6764+YY1 | FALSE | ENSG00000283840.1+ENSG00000100811.14 | E021 | 26.67056 | 0.0004510 | 19.03412 | 1.28e-05 | 0.0616827 | 1.3870768 | 1.509828 | 0.4228808 | chr14 | 100278143 | 100278470 | 328 | + | 34 | 36 | 24 | 22 | 35 | 26 | 58 | 50 | 44 | 36 | 30 | 18 | 34 | 20 | 23 | 14 | 22 | 24 | 23 | 26 | 38 | 19 | 9 | ENST0000…. |
Plot genes with statistically significant differential exon usage. Exclude exons belonging to multiple genes.
keep <- !grepl("+",topDex$groupID, fixed = TRUE)
dexGenes <- unique(topDex$groupID[keep])
dexSymbols <- unique(topDex$symbol[keep])
par(oma = c(1,1,2,1))
for(i in 1:length(dexGenes)){
plotDEXSeq( dxr1, dexGenes[i],
legend=TRUE, cex.axis=1, cex=1, lwd=2,
displayTranscripts = TRUE, splicing = TRUE,
expression = TRUE, norCounts = TRUE)
title(main = dexSymbols[i], outer = TRUE, cex.main = 2)
}
| Version | Author | Date |
|---|---|---|
| de15fc8 | Jovana Maksimovic | 2021-09-25 |
| Version | Author | Date |
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| de15fc8 | Jovana Maksimovic | 2021-09-25 |
| Version | Author | Date |
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| de15fc8 | Jovana Maksimovic | 2021-09-25 |
| Version | Author | Date |
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| de15fc8 | Jovana Maksimovic | 2021-09-25 |
| Version | Author | Date |
|---|---|---|
| de15fc8 | Jovana Maksimovic | 2021-09-25 |
| Version | Author | Date |
|---|---|---|
| de15fc8 | Jovana Maksimovic | 2021-09-25 |
| Version | Author | Date |
|---|---|---|
| de15fc8 | Jovana Maksimovic | 2021-09-25 |
| Version | Author | Date |
|---|---|---|
| de15fc8 | Jovana Maksimovic | 2021-09-25 |
topDex[keep,] %>%
arrange(pvalue) %>%
knitr::kable()| symbol | deg | groupID | featureID | exonBaseMean | dispersion | stat | pvalue | padj | neg | pos | log2fold_pos_neg | genomicData.seqnames | genomicData.start | genomicData.end | genomicData.width | genomicData.strand | countData.CMV30 | countData.CMV31 | countData.CMV8 | countData.CMV9 | countData.CMV26 | countData.CMV14 | countData.CMV15 | countData.CMV20 | countData.CMV21 | countData.CMV1 | countData.CMV2 | countData.CMV3 | countData.CMV4 | countData.CMV19 | countData.CMV35 | countData.CMV51 | countData.CMV52 | countData.CMV54 | countData.CMV56 | countData.CMV57 | countData.CMV58 | countData.CMV60 | countData.CMV61 | transcripts | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000135272.11:E017 | MDFIC | FALSE | ENSG00000135272.11 | E017 | 223.77382 | 0.0001602 | 30.17716 | 0.00e+00 | 0.0016686 | 2.339665 | 2.375240 | 0.1186993 | chr7 | 115015962 | 115019202 | 3241 | + | 311 | 381 | 155 | 180 | 369 | 215 | 306 | 302 | 318 | 270 | 289 | 188 | 256 | 99 | 249 | 213 | 152 | 222 | 232 | 207 | 334 | 171 | 106 | ENST0000…. |
| ENSG00000182795.13:E001 | C1orf116 | FALSE | ENSG00000182795.13 | E001 | 177.13545 | 0.0007915 | 20.34286 | 6.50e-06 | 0.0456537 | 2.230762 | 2.274289 | 0.1454078 | chr1 | 207018522 | 207020694 | 2173 | - | 216 | 240 | 160 | 193 | 244 | 156 | 265 | 286 | 252 | 218 | 197 | 162 | 176 | 130 | 188 | 177 | 124 | 165 | 155 | 194 | 241 | 94 | 107 | ENST0000…. |
| ENSG00000197249.14:E016 | SERPINA1 | TRUE | ENSG00000197249.14 | E016 | 95.85830 | 0.0001960 | 19.95172 | 7.90e-06 | 0.0480108 | 2.028679 | 1.926626 | -0.3426304 | chr14 | 94383003 | 94383170 | 168 | - | 120 | 93 | 38 | 162 | 112 | 116 | 55 | 68 | 99 | 60 | 86 | 51 | 189 | 243 | 97 | 131 | 24 | 128 | 74 | 164 | 110 | 69 | 19 | ENST0000…. |
| ENSG00000136872.20:E020 | ALDOB | TRUE | ENSG00000136872.20 | E020 | 30.36784 | 0.0004139 | 18.88255 | 1.39e-05 | 0.0616827 | 1.547815 | 1.395801 | -0.5227151 | chr9 | 101430776 | 101430897 | 122 | - | 27 | 39 | 31 | 41 | 23 | 18 | 15 | 32 | 24 | 17 | 22 | 25 | 63 | 142 | 29 | 24 | 8 | 23 | 16 | 57 | 21 | 16 | 5 | ENST0000…. |
| ENSG00000136872.20:E023 | ALDOB | TRUE | ENSG00000136872.20 | E023 | 20.44411 | 0.0008302 | 18.79209 | 1.46e-05 | 0.0616827 | 1.420758 | 1.212531 | -0.7271615 | chr9 | 101435709 | 101435762 | 54 | - | 20 | 31 | 25 | 26 | 15 | 11 | 12 | 24 | 18 | 15 | 15 | 21 | 40 | 79 | 19 | 20 | 5 | 15 | 11 | 36 | 14 | 10 | 5 | ENST0000…. |
| ENSG00000174992.8:E004 | ZG16 | FALSE | ENSG00000174992.8 | E004 | 14.33009 | 0.0010231 | 18.32582 | 1.86e-05 | 0.0716148 | 1.252062 | 1.131955 | -0.4264955 | chr16 | 29780104 | 29782973 | 2870 | + | 13 | 7 | 11 | 27 | 60 | 13 | 18 | 15 | 15 | 14 | 19 | 13 | 12 | 10 | 12 | 18 | 4 | 14 | 16 | 18 | 17 | 11 | 2 | ENST0000…. |
| ENSG00000139726.11:E015 | DENR | FALSE | ENSG00000139726.11 | E015 | 50.66492 | 0.0002702 | 17.50242 | 2.87e-05 | 0.0967841 | 1.681427 | 1.758811 | 0.2620617 | chr12 | 122770071 | 122771064 | 994 | + | 70 | 73 | 44 | 45 | 68 | 45 | 86 | 67 | 76 | 78 | 77 | 42 | 83 | 40 | 45 | 48 | 35 | 38 | 48 | 41 | 56 | 20 | 32 | ENST0000…. |
| ENSG00000007202.15:E041 | KIAA0100 | FALSE | ENSG00000007202.15 | E041 | 20.36674 | 0.0010632 | 17.34702 | 3.11e-05 | 0.0967841 | 1.393371 | 1.246101 | -0.5139657 | chr17 | 28634671 | 28635609 | 939 | - | 18 | 28 | 25 | 24 | 13 | 27 | 32 | 15 | 19 | 16 | 20 | 27 | 28 | 20 | 24 | 38 | 7 | 25 | 21 | 17 | 16 | 16 | 14 | ENST0000…. |
| ENSG00000166801.15:E027 | FAM111A | FALSE | ENSG00000166801.15 | E027 | 57.11587 | 0.0002819 | 17.29391 | 3.20e-05 | 0.0967841 | 1.729898 | 1.808517 | 0.2656931 | chr11 | 59152219 | 59155034 | 2816 | + | 61 | 70 | 47 | 49 | 69 | 76 | 129 | 70 | 68 | 81 | 68 | 35 | 61 | 53 | 47 | 60 | 45 | 56 | 65 | 74 | 71 | 30 | 27 | ENST0000…. |
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /config/binaries/R/4.0.2/lib64/R/lib/libRblas.so
LAPACK: /config/binaries/R/4.0.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] org.Hs.eg.db_3.12.0 limma_3.46.0
[3] DEXSeq_1.36.0 RColorBrewer_1.1-2
[5] AnnotationDbi_1.52.0 DESeq2_1.30.1
[7] SummarizedExperiment_1.20.0 GenomicRanges_1.42.0
[9] GenomeInfoDb_1.26.7 IRanges_2.24.1
[11] S4Vectors_0.28.1 MatrixGenerics_1.2.1
[13] matrixStats_0.59.0 BiocParallel_1.24.1
[15] patchwork_1.1.1 NMF_0.23.0
[17] Biobase_2.50.0 BiocGenerics_0.36.1
[19] cluster_2.1.0 rngtools_1.5
[21] pkgmaker_0.32.2 registry_0.5-1
[23] forcats_0.5.1 stringr_1.4.0
[25] dplyr_1.0.4 purrr_0.3.4
[27] readr_1.4.0 tidyr_1.1.2
[29] tibble_3.1.2 ggplot2_3.3.5
[31] tidyverse_1.3.0 here_1.0.1
[33] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 BiocFileCache_1.14.0
[4] plyr_1.8.6 splines_4.0.2 gridBase_0.4-7
[7] digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1
[10] fansi_0.5.0 magrittr_2.0.1 memoise_2.0.0.9000
[13] doParallel_1.0.16 Biostrings_2.58.0 annotate_1.68.0
[16] modelr_0.1.8 askpass_1.1 prettyunits_1.1.1
[19] colorspace_2.0-2 blob_1.2.1 rvest_0.3.6
[22] rappdirs_0.3.3 haven_2.3.1 xfun_0.23
[25] crayon_1.4.1 RCurl_1.98-1.3 jsonlite_1.7.2
[28] genefilter_1.72.1 survival_3.2-7 iterators_1.0.13
[31] glue_1.4.2 gtable_0.3.0 zlibbioc_1.36.0
[34] XVector_0.30.0 DelayedArray_0.16.3 scales_1.1.1
[37] DBI_1.1.1 Rcpp_1.0.6 xtable_1.8-4
[40] progress_1.2.2 bit_4.0.4 httr_1.4.2
[43] ellipsis_0.3.2 pkgconfig_2.0.3 XML_3.99-0.5
[46] dbplyr_2.1.0 locfit_1.5-9.4 utf8_1.2.1
[49] tidyselect_1.1.0 rlang_0.4.11 reshape2_1.4.4
[52] later_1.1.0.1 munsell_0.5.0 cellranger_1.1.0
[55] tools_4.0.2 cachem_1.0.4 cli_3.0.0
[58] generics_0.1.0 RSQLite_2.2.5 broom_0.7.4
[61] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
[64] knitr_1.31 bit64_4.0.5 fs_1.5.0
[67] whisker_0.4 xml2_1.3.2 biomaRt_2.46.3
[70] compiler_4.0.2 rstudioapi_0.13 curl_4.3
[73] reprex_1.0.0 statmod_1.4.35 geneplotter_1.68.0
[76] stringi_1.5.3 highr_0.8 lattice_0.20-41
[79] Matrix_1.3-2 vctrs_0.3.8 pillar_1.6.1
[82] lifecycle_1.0.0 bitops_1.0-7 httpuv_1.5.5
[85] R6_2.5.0 hwriter_1.3.2 promises_1.2.0.1
[88] codetools_0.2-18 assertthat_0.2.1 openssl_1.4.3
[91] rprojroot_2.0.2 withr_2.4.2 Rsamtools_2.6.0
[94] GenomeInfoDbData_1.2.4 hms_1.0.0 grid_4.0.2
[97] rmarkdown_2.6 git2r_0.28.0 lubridate_1.7.9.2