erma: epigenomics road map adventures
August 2015
Contents
## Warning in fun(libname, pkgname): Package 'erma' is deprecated and will be removed from Bioconductor
## version 3.221 Introduction
The epigenomics road map describes locations of epigenetic marks in DNA from a variety of cell types. Of interest are locations of histone modifications, sites of DNA methylation, and regions of accessible chromatin.
This package presents a selection of elements of the road map including metadata and outputs of the ChromImpute procedure applied to ENCODE cell lines by Ernst and Kellis.
2 Metadata on the ChromImpute archive
2.1 Sample metadata
I have retrieved a Google Docs spreadsheet with comprehensive information. The mapmeta() function provides access to a local DataFrame image of the file as retrieved in mid April 2015. We provide a dynamic view of a selection of columns. Use the search box to filter records shown, for example .
## NOTE: input data had non-ASCII characters replaced by ' '.2.2 Metadata on the inferred states
The chromatin states and standard colorings
used are enumerated in states_25:
The emission parameters of the 25 state model are depicted in the supplementary Figure 33 of Ernst and Kellis:
3 Managing access to imputed chromatin states for a set of cell types
I have retrieved a modest number of roadmap bed files with ChromImpute
mnemonic labeling of chromatin by states. These can be
managed with an ErmaSet instance,
a trivial extension of GenomicFiles class.
The cellTypes method yields a character vector. The colData
component has full metadata on the cell lines available.
## NOTE: input data had non-ASCII characters replaced by ' '.## ErmaSet object with 0 ranges and 31 files:
## files: E002_25_imputed12marks_mnemonics.bed.gz, E003_25_imputed12marks_mnemonics.bed.gz, ..., E088_25_imputed12marks_mnemonics.bed.gz, E096_25_imputed12marks_mnemonics.bed.gz
## detail: use files(), rowRanges(), colData(), ...
## cellTypes() for type names; data(short_celltype) for abbr.## [1] "ES-WA7 Cells"
## [2] "H1 Cells"
## [3] "iPS DF 6.9 Cells"
## [4] "Primary B cells from peripheral blood"
## [5] "Primary T cells from cord blood"4 Enumerating states in the vicinity of a gene, across cell types
We form a GRanges representing 50kb upstream of IL33.
## 'select()' returned 1:many mapping between keys and columns## GRanges object with 1 range and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr9 6165786-6215785 +
## -------
## seqinfo: 1 sequence from hg19 genomeBind this to the ErmaSet instance.
## ErmaSet object with 1 ranges and 31 files:
## files: E002_25_imputed12marks_mnemonics.bed.gz, E003_25_imputed12marks_mnemonics.bed.gz, ..., E088_25_imputed12marks_mnemonics.bed.gz, E096_25_imputed12marks_mnemonics.bed.gz
## detail: use files(), rowRanges(), colData(), ...
## cellTypes() for type names; data(short_celltype) for abbr.Now query the files for cell-specific states in this interval.
library(BiocParallel)
register(MulticoreParam(workers=2))
suppressWarnings({
csstates = lapply(reduceByFile(ermaset, MAP=function(range, file) {
imp = import(file, which=range, genome=genome(range)[1])
seqlevels(imp) = seqlevels(range)
imp$rgb = erma:::rgbByState(imp$name)
imp
}), "[[", 1)
})
tys = cellTypes(ermaset) # need to label with cell types
csstates = lapply(1:length(csstates), function(x) {
csstates[[x]]$celltype = tys[x]
csstates[[x]]
})
csstates[1:2]## [[1]]
## GRanges object with 15 ranges and 3 metadata columns:
## seqnames ranges strand | name rgb celltype
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## [1] chr9 6161801-6166600 * | 25_Quies #FEFEFE ES-WA7 Cells
## [2] chr9 6166601-6166800 * | 17_EnhW2 #FEFE00 ES-WA7 Cells
## [3] chr9 6166801-6171200 * | 25_Quies #FEFEFE ES-WA7 Cells
## [4] chr9 6171201-6171800 * | 17_EnhW2 #FEFE00 ES-WA7 Cells
## [5] chr9 6171801-6172000 * | 16_EnhW1 #FEFE00 ES-WA7 Cells
## ... ... ... ... . ... ... ...
## [11] chr9 6183401-6197400 * | 25_Quies #FEFEFE ES-WA7 Cells
## [12] chr9 6197401-6197600 * | 19_DNase #FEFE66 ES-WA7 Cells
## [13] chr9 6197601-6208800 * | 25_Quies #FEFEFE ES-WA7 Cells
## [14] chr9 6208801-6211000 * | 21_Het #8990CF ES-WA7 Cells
## [15] chr9 6211001-6217800 * | 25_Quies #FEFEFE ES-WA7 Cells
## -------
## seqinfo: 1 sequence from hg19 genome
##
## [[2]]
## GRanges object with 14 ranges and 3 metadata columns:
## seqnames ranges strand | name rgb celltype
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## [1] chr9 6161801-6166600 * | 25_Quies #FEFEFE H1 Cells
## [2] chr9 6166601-6166800 * | 17_EnhW2 #FEFE00 H1 Cells
## [3] chr9 6166801-6171200 * | 25_Quies #FEFEFE H1 Cells
## [4] chr9 6171201-6173000 * | 17_EnhW2 #FEFE00 H1 Cells
## [5] chr9 6173001-6175400 * | 21_Het #8990CF H1 Cells
## ... ... ... ... . ... ... ...
## [10] chr9 6183401-6197400 * | 25_Quies #FEFEFE H1 Cells
## [11] chr9 6197401-6197600 * | 19_DNase #FEFE66 H1 Cells
## [12] chr9 6197601-6209000 * | 25_Quies #FEFEFE H1 Cells
## [13] chr9 6209001-6211000 * | 21_Het #8990CF H1 Cells
## [14] chr9 6211001-6218200 * | 25_Quies #FEFEFE H1 Cells
## -------
## seqinfo: 1 sequence from hg19 genomeThis sort of code underlies
the csProfile utility to visualize variation in state assignments
in promoter regions for various genes.
## 'select()' returned 1:many mapping between keys and columns## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
Set useShiny to TRUE to permit interactive selection of
region to visualize.