In jokergoo/rGREAT: GREAT Analysis - Functional Enrichment on Genomic Regions

library(knitr)
knitr::opts_chunk$set(
    error = FALSE,
    tidy  = FALSE,
    message = FALSE,
    warning = FALSE,
    fig.align = "center")

library(rGREAT)
library(eulerr)

In this document, we will compare the enrichment results from online GREAT and local GREAT. The four datasets are all from UCSC table browser. Parameters are:

clade = Mammal
genome = Human
assembly = GRCh37/hg19
group = Regulation
track = ENCODE 3 TFBS
table: A549 JUN, A549 ELF1, H1-hESC RXRA, GM12878 MYB

And in the “Retrieve and display data” section:

output format = BED - browser extensible data

Then click the button “get output”.

We first read the files into GRanges objects:

read_bed = function(f) {
    df = read.table(f)
    df = df[df[, 1] %in% paste0("chr", c(1:22, "X", "Y")), ]
    GRanges(seqnames = df[, 1], ranges = IRanges(df[, 2] + 1, df[, 3]))
}
grl = list()
grl$A549_JUN = read_bed("data/tb_encTfChipPkENCFF708LCH_A549_JUN_hg19.bed")
grl$A549_ELF1 = read_bed("data/tb_encTfChipPkENCFF533NIV_A549_ELF1_hg19.bed")
grl$H1_hESC_RXRA = read_bed("data/tb_encTfChipPkENCFF369JAI_H1_hESC_RXRA_hg19.bed")
grl$GM12878_MYB = read_bed("data/tb_encTfChipPkENCFF215YWS_GM12878_MYB_hg19.bed")
sapply(grl, length)

A549_JUN (`r length(grl$A549_JUN)` input regions)

Apply both online and local GREAT analysis. Note online GREAT exclude gap regions, and in local GREAT, by default gap regions are removed as well.

gr = grl$A549_JUN
job = submitGreatJob(gr)
tbl = getEnrichmentTables(job)
tb1 = tbl[["GO Biological Process"]]

res = great(gr, "GO:BP", "hg19")
tb2 = getEnrichmentTable(res)

tb1 and tb2 contain the full table of all GO terms under test. First we take the common GO terms in the two result tables.

cn = intersect(tb1$ID, tb2$id)
length(cn)
rownames(tb1) = tb1$ID
rownames(tb2) = tb2$id
tb1 = tb1[cn, ]
tb2 = tb2[cn, ]

The significant GO terms from the two tables.

lt2 = list(online = tb1$ID[tb1$Binom_Adjp_BH < 0.001],
          local = tb2$id[tb2$p_adjust < 0.001])
plot(euler(lt2), quantities = TRUE, main = "A549_JUN")

Next we compare the observed region hits and fold enrichment in the two results.

par(mfrow = c(1, 2))
plot(tb1$Binom_Observed_Region_Hits, tb2$observed_region_hits, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Observed region hits")
plot(tb1$Binom_Fold_Enrichment, tb2$fold_enrichment, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Fold enrichment")

Next we compare the two significant GO term lists by clustering them into groups.

lt3 = list(online = data.frame(id = tb1$ID, p_adjust = tb1$Binom_Adjp_BH), 
           local = data.frame(id = tb2$id, p_adjust = tb2$p_adjust))
library(simplifyEnrichment)
se_opt$verbose = FALSE
simplifyGOFromMultipleLists(lt3, padj_cutoff = 0.001)

A549_ELF1 (`r length(grl$A549_ELF1)` input regions)

Apply both online and local GREAT analysis:

gr = grl$A549_ELF1
job = submitGreatJob(gr)
tbl = getEnrichmentTables(job)
tb1 = tbl[["GO Biological Process"]]

res = great(gr, "GO:BP", "hg19")
tb2 = getEnrichmentTable(res)

tb1 and tb2 contain the full table of all GO terms under test. First we take the common GO terms in the two result tables.

cn = intersect(tb1$ID, tb2$id)
length(cn)
rownames(tb1) = tb1$ID
rownames(tb2) = tb2$id
tb1 = tb1[cn, ]
tb2 = tb2[cn, ]

The significant GO terms from the two tables.

lt2 = list(online = tb1$ID[tb1$Binom_Adjp_BH < 0.001],
          local = tb2$id[tb2$p_adjust < 0.001])
plot(euler(lt2), quantities = TRUE, main = "A549_ELF1")

Next we compare the observed region hits and fold enrichment in the two results.

par(mfrow = c(1, 2))
plot(tb1$Binom_Observed_Region_Hits, tb2$observed_region_hits, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Observed region hits")
plot(tb1$Binom_Fold_Enrichment, tb2$fold_enrichment, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Fold enrichment")

Next we compare the two significant GO term lists by clustering them into groups.

lt3 = list(online = data.frame(id = tb1$ID, p_adjust = tb1$Binom_Adjp_BH), 
           local = data.frame(id = tb2$id, p_adjust = tb2$p_adjust))
library(simplifyEnrichment)
se_opt$verbose = FALSE
simplifyGOFromMultipleLists(lt3, padj_cutoff = 0.001)

H1_hESC_RXRA (`r length(grl$H1_hESC_RXRA)` input regions)

Apply both online and local GREAT analysis:

gr = grl$H1_hESC_RXRA
job = submitGreatJob(gr)
tbl = getEnrichmentTables(job)
tb1 = tbl[["GO Biological Process"]]

res = great(gr, "GO:BP", "hg19")
tb2 = getEnrichmentTable(res)

tb1 and tb2 contain the full table of all GO terms under test. First we take the common GO terms in the two result tables.

cn = intersect(tb1$ID, tb2$id)
length(cn)
rownames(tb1) = tb1$ID
rownames(tb2) = tb2$id
tb1 = tb1[cn, ]
tb2 = tb2[cn, ]

The significant GO terms from the two tables.

lt2 = list(online = tb1$ID[tb1$Binom_Adjp_BH < 0.001],
          local = tb2$id[tb2$p_adjust < 0.001])
plot(euler(lt2), quantities = TRUE, main = "H1_hESC_RXRA")

Next we compare the observed region hits and fold enrichment in the two results.

par(mfrow = c(1, 2))
plot(tb1$Binom_Observed_Region_Hits, tb2$observed_region_hits, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Observed region hits")
plot(tb1$Binom_Fold_Enrichment, tb2$fold_enrichment, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Fold enrichment")

Next we compare the two significant GO term lists by clustering them into groups.

lt3 = list(online = data.frame(id = tb1$ID, p_adjust = tb1$Binom_Adjp_BH), 
           local = data.frame(id = tb2$id, p_adjust = tb2$p_adjust))
library(simplifyEnrichment)
se_opt$verbose = FALSE
simplifyGOFromMultipleLists(lt3, padj_cutoff = 0.001)

GM12878_MYB (`r length(grl$GM12878_MYB)` input regions)

Apply both online and local GREAT analysis:

gr = grl$GM12878_MYB
job = submitGreatJob(gr)
tbl = getEnrichmentTables(job)
tb1 = tbl[["GO Biological Process"]]

res = great(gr, "GO:BP", "hg19")
tb2 = getEnrichmentTable(res)

tb1 and tb2 contain the full table of all GO terms under test. First we take the common GO terms in the two result tables.

cn = intersect(tb1$ID, tb2$id)
length(cn)
rownames(tb1) = tb1$ID
rownames(tb2) = tb2$id
tb1 = tb1[cn, ]
tb2 = tb2[cn, ]

The significant GO terms from the two tables.

lt2 = list(online = tb1$ID[tb1$Binom_Adjp_BH < 0.001],
          local = tb2$id[tb2$p_adjust < 0.001])
plot(euler(lt2), quantities = TRUE, main = "GM12878_MYB")

Next we compare the observed region hits and fold enrichment in the two results.

par(mfrow = c(1, 2))
plot(tb1$Binom_Observed_Region_Hits, tb2$observed_region_hits, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Observed region hits")
plot(tb1$Binom_Fold_Enrichment, tb2$fold_enrichment, pch = 16, col = "#00000010",
    xlab = "online GREAT", ylab = "local GREAT", main = "Fold enrichment")

Next we compare the two significant GO term lists by clustering them into groups.

lt3 = list(online = data.frame(id = tb1$ID, p_adjust = tb1$Binom_Adjp_BH), 
           local = data.frame(id = tb2$id, p_adjust = tb2$p_adjust))
library(simplifyEnrichment)
se_opt$verbose = FALSE
simplifyGOFromMultipleLists(lt3, padj_cutoff = 0.001)