hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of space/seqnames (i.e. chromosomes) and return associated annotation per space/seqname and rows from the query matching space/seqnames and rows from the subject (i.e. genes or cpg islands).
This package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. The package is also equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame.
The work horse functions performing most of the calculations are from GenomicRanges package which comes from the Bioconductor repository. Most of the functions in the hiAnnotator package are wrapper around following functions: nearest(), and findOverlaps().
Below are few simple steps to get you started.
First load this package and the parallel backend of choice. See loading parallel backend section at the bottom of the page for more choices.
library(hiAnnotator)
## Loading required package: GenomicRanges
## Loading required package: stats4
## Loading required package: BiocGenerics
## Loading required package: parallel
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, basename, cbind, colMeans, colSums, colnames,
## dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
## intersect, is.unsorted, lapply, lengths, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
## rowMeans, rowSums, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which, which.max, which.min
## Loading required package: S4Vectors
##
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:base':
##
## expand.grid
## Loading required package: IRanges
## Loading required package: GenomeInfoDb
The package comes with example dataframes: sites and genes. In the rest of this tutorial we will use sites as query and genes as subject. Using the makeGRanges() function supplied with the package, one can easily go from a dataframe to a GRanges object without too much hassle.
data(sites)
## sites object doesn't have a start & stop column to denote genomic range, hence soloStart parameter must be TRUE or a nasty error will be thrown!
alldata.rd <- makeGRanges(sites, soloStart = TRUE)
data(genes)
## adding freeze populates SeqInfo slot of GRanges object.
genes.rd <- makeGRanges(genes, freeze = "hg18")
## Warning in valid.GenomicRanges.seqinfo(x, suggest.trim = TRUE): GRanges object contains 1 out-of-bound range located on sequence
## chr6_cox_hap1. Note that ranges located on a sequence whose length
## is unknown (NA) or on a circular sequence are not considered
## out-of-bound (use seqlengths() and isCircular() to get the lengths
## and circularity flags of the underlying sequences). You can use
## trim() to trim these ranges. See ?`trim,GenomicRanges-method` for
## more information.
The package also comes with wrapper functions to download annotation tracks off of UCSC genome browser using rtracklayer package.
refflat <- getUCSCtable("refFlat", "RefSeq Genes")
genes <- makeGRanges(refflat)
With the data loaded and formatted, next series of functions highlight various ways they can be annotated. One thing to keep in mind is that, only the intersect of spaces/chromosomes/seqnames between query & subject will be annotated, rest will be ignored and will have NAs in the output.
Given a query object, the function retrieves the nearest feature and its properties from a subject and then appends them as new columns within the query object. When used in genomic context, the function can be used to retrieve a nearest gene 5' or 3' end relative to a genomic position of interest. By default, nearest distance to either boundary is calculated unless specifically defined using the side parameter.
nearestGenes <- getNearestFeature(alldata.rd, genes.rd, "NearestGene")
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:GenomicRanges':
##
## intersect, setdiff, union
## The following object is masked from 'package:GenomeInfoDb':
##
## intersect
## The following objects are masked from 'package:IRanges':
##
## collapse, desc, intersect, setdiff, slice, union
## The following objects are masked from 'package:S4Vectors':
##
## first, intersect, rename, setdiff, setequal, union
## The following objects are masked from 'package:BiocGenerics':
##
## combine, intersect, setdiff, union
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
head(nearestGenes)
## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus NearestGeneDist NearestGene
## <character> <character> <character> <integer> <character>
## [1] chr7 - MLV -891 INTS1
## [2] chr7 - MLV 794 PSMG3
## [3] chr7 + HIV -60916 PHF14
## [4] chr7 + MLV -49477 TMEM106B
## [5] chr7 - MLV -73263 SCIN
## [6] chr7 + MLV 29449 ARL4A
## NearestGeneOrt
## <character>
## [1] -
## [2] -
## [3] +
## [4] +
## [5] +
## [6] +
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
# nearestGenes <- getNearestFeature(alldata.rd,genes.rd,"NearestGene", parallel=TRUE)
## get nearest 5' genes
nearestGenes <- getNearestFeature(alldata.rd, genes.rd, "NearestGene", side = "5p")
head(nearestGenes)
## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus X5pNearestGeneDist
## <character> <character> <character> <integer>
## [1] chr7 - MLV -891
## [2] chr7 - MLV 3455
## [3] chr7 + HIV 68851
## [4] chr7 + MLV -49477
## [5] chr7 - MLV -73263
## [6] chr7 + MLV 33047
## X5pNearestGene X5pNearestGeneOrt
## <character> <character>
## [1] INTS1 -
## [2] PSMG3 -
## [3] PHF14 +
## [4] TMEM106B +
## [5] SCIN +
## [6] ARL4A +
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
## get nearest 3' genes
nearestGenes <- getNearestFeature(alldata.rd, genes.rd, "NearestGene", side = "3p")
head(nearestGenes)
## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus X3pNearestGeneDist
## <character> <character> <character> <integer>
## [1] chr7 - MLV -34997
## [2] chr7 - MLV 794
## [3] chr7 + HIV -60916
## [4] chr7 + MLV -75519
## [5] chr7 - MLV -156289
## [6] chr7 + MLV 29449
## X3pNearestGene X3pNearestGeneOrt
## <character> <character>
## [1] INTS1 -
## [2] PSMG3 -
## [3] PHF14 +
## [4] TMEM106B +
## [5] SCIN +
## [6] ARL4A +
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
## get midpoint of genes
nearestGenes <- getNearestFeature(alldata.rd, genes.rd, "NearestGene", side = "midpoint")
head(nearestGenes)
## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus midpointNearestGeneDist
## <character> <character> <character> <integer>
## [1] chr7 - MLV -17944
## [2] chr7 - MLV 2124
## [3] chr7 + HIV 3968
## [4] chr7 + MLV -62498
## [5] chr7 - MLV -114776
## [6] chr7 + MLV 31248
## midpointNearestGene midpointNearestGeneOrt
## <character> <character>
## [1] INTS1 -
## [2] PSMG3 -
## [3] PHF14 +
## [4] TMEM106B +
## [5] SCIN +
## [6] ARL4A +
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
### get two nearest upstream and downstream genes relative the query
nearestTwoGenes <- get2NearestFeature(alldata.rd, genes.rd, "NearestGene")
## u = upstream, d = downstream
## thinking concept: u2.....u1.....intSite(+).....d1.....d2
## thinking concept: d2.....d1.....intSite(-).....u1.....u2
## u1
## u2
## d1
## d2
head(nearestTwoGenes)
## GRanges object with 6 ranges and 17 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus
## <character> <character> <character>
## [1] chr7 - MLV
## [2] chr7 - MLV
## [3] chr7 + HIV
## [4] chr7 + MLV
## [5] chr7 - MLV
## [6] chr7 + MLV
## Either.NearestGene.upStream1.Dist Either.NearestGene.upStream1
## <integer> <character>
## [1] -891 INTS1
## [2] 794 PSMG3
## [3] -60916 PHF14
## [4] -49477 TMEM106B
## [5] -73263 SCIN
## [6] 29449 ARL4A
## Either.NearestGene.upStream1.Ort Either.NearestGene.upStream2.Dist
## <character> <integer>
## [1] - 36961
## [2] - 249253
## [3] + 1406919
## [4] + -49477
## [5] + -92208
## [6] + 29449
## Either.NearestGene.upStream2 Either.NearestGene.upStream2.Ort
## <character> <character>
## [1] TMEM184A -
## [2] MAD1L1 -
## [3] PER4 +
## [4] TMEM106B +
## [5] SCIN +
## [6] ARL4A +
## Either.NearestGene.downStream1.Dist Either.NearestGene.downStream1
## <integer> <character>
## [1] -45800 MICALL2
## [2] 794 PSMG3
## [3] 68851 PHF14
## [4] -49477 TMEM106B
## [5] 260050 TMEM106B
## [6] 29449 ARL4A
## Either.NearestGene.downStream1.Ort
## <character>
## [1] -
## [2] -
## [3] +
## [4] +
## [5] +
## [6] +
## Either.NearestGene.downStream2.Dist Either.NearestGene.downStream2
## <integer> <character>
## [1] -345054 ZFAND2A
## [2] 794 PSMG3
## [3] -1168481 TMEM106B
## [4] -408832 SCIN
## [5] 260050 TMEM106B
## [6] 29449 ARL4A
## Either.NearestGene.downStream2.Ort
## <character>
## [1] -
## [2] -
## [3] +
## [4] +
## [5] +
## [6] +
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
Given a query object and window size(s), the function finds all the rows in subject which are <= window size/2 distance away. If weights are assigned to each positions in the subject, then tallied counts are multiplied accordingly. If annotation object is large, spanning greater than 100 million rows, then getFeatureCountsBig() is used which uses midpoint and drops any weights column if specified to get the job done. The time complexity of this function can be found in ?findOverlaps.
geneCounts <- getFeatureCounts(alldata.rd, genes.rd, "NumOfGene")
head(geneCounts)
## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus NumOfGene.1Kb NumOfGene.10Kb
## <character> <character> <character> <integer> <integer>
## [1] chr7 - MLV 0 1
## [2] chr7 - MLV 0 5
## [3] chr7 + HIV 2 2
## [4] chr7 + MLV 0 0
## [5] chr7 - MLV 0 0
## [6] chr7 + MLV 0 0
## NumOfGene.1Mb
## <integer>
## [1] 24
## [2] 22
## [3] 4
## [4] 6
## [5] 8
## [6] 8
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
# geneCounts <- getFeatureCounts(alldata.rd, genes.rd, "NumOfGene", parallel=TRUE)
If dealing with really large set of input objects, the function can break up the data using the chunkSize parameter. This is handy when trying to annotated ChipSeq data on an average laptop/machine. There is also getFeatureCountsBig() function which uses an alternative method to get the counts using findInterval.
geneCounts <- getFeatureCounts(alldata.rd, genes.rd, "NumOfGene",
doInChunks = TRUE, chunkSize = 100)
head(geneCounts)
geneCounts <- getFeatureCountsBig(alldata.rd, genes.rd, "NumOfGene")
head(geneCounts)
When used in genomic context, the function annotates genomic positions of interest with information like if they were in a gene or cpg island or whatever annotation that was supplied in the subject.
## Shows which feature(s) a position was found in.
InGenes <- getSitesInFeature(alldata.rd, genes.rd, "InGene")
head(InGenes)
## GRanges object with 6 ranges and 7 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus InGene InGeneOrt
## <character> <character> <character> <character> <character>
## [1] chr7 - MLV FALSE <NA>
## [2] chr7 - MLV FALSE <NA>
## [3] chr7 + HIV PHF14 +
## [4] chr7 + MLV FALSE <NA>
## [5] chr7 - MLV FALSE <NA>
## [6] chr7 + MLV FALSE <NA>
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
## Simply shows TRUE/FALSE
InGenes <- getSitesInFeature(alldata.rd, genes.rd, "InGene", asBool = TRUE)
head(InGenes)
## GRanges object with 6 ranges and 6 metadata columns:
## seqnames ranges strand | Sequence Position
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr7 1511435 - | Burgess-MLV-HeLa-nj09f06 1511435
## [2] chr7 1572700 - | Burgess-MLV-HeLa-nj12h11 1572700
## [3] chr7 11048891 + | Burgess-HIV-HeLa-01a12 11048891
## [4] chr7 12167895 + | Burgess-MLV-HeLa-nj08f04 12167895
## [5] chr7 12503464 - | Burgess-MLV-HeLa-nj15c02 12503464
## [6] chr7 12726532 + | Burgess-MLV-HeLa-nj19e04 12726532
## Chr Ort virus InGene
## <character> <character> <character> <logical>
## [1] chr7 - MLV FALSE
## [2] chr7 - MLV FALSE
## [3] chr7 + HIV TRUE
## [4] chr7 + MLV FALSE
## [5] chr7 - MLV FALSE
## [6] chr7 + MLV FALSE
## -------
## seqinfo: 27 sequences from an unspecified genome; no seqlengths
# InGenes <- getSitesInFeature(alldata.rd, genes.rd, "InGene", asBool=TRUE, parallel=TRUE)
This is a wrapper function which calls one of the functions shown above depending on annotType parameter: within, nearest, twoNearest, counts, countsBig. You can also pass any function to call on the resulting object for any post processing steps.
doAnnotation(annotType = "within", alldata.rd, genes.rd, "InGene")
doAnnotation(annotType = "counts", alldata.rd, genes.rd, "NumOfGene")
doAnnotation(annotType = "countsBig", alldata.rd, genes.rd, "ChipSeqCounts")
doAnnotation(annotType = "nearest", alldata.rd, genes.rd, "NearestGene")
doAnnotation(annotType = "twoNearest", alldata.rd, genes.rd, "TwoNearestGenes")
geneCheck <- function(x, wanted) { x$isWantedGene <- x$InGene %in% wanted;
return(x) }
doAnnotation(annotType = "within", alldata.rd, genes.rd, "InGene",
postProcessFun = geneCheck,
postProcessFunArgs = list("wanted" = c("FOXJ3", "SEPT9", "RPTOR")) )
hiAnnotator comes with a handy plotting function plotdisFeature which summarizes and plots the distribution of newly annotated data. Function can be used to easily visualize things like distribution of integration sites around gene TSS, density of genes within various window sizes, etc.
res <- doAnnotation(annotType = "within", alldata.rd, genes.rd, "InGene", asBool = TRUE)
plotdisFeature(res, "virus", "InGene")
## performing boolean summary
res <- doAnnotation(annotType = "nearest", alldata.rd, genes.rd, "NearestGene", side = '5p')
plotdisFeature(res, "virus", "X5pNearestGeneDist")
data(sites.ctrl)
sites$type <- "expr"
sites <- rbind(sites,sites.ctrl)
alldata.rd <- makeGRanges(sites, soloStart = TRUE)
res <- doAnnotation(annotType = "within", alldata.rd, genes.rd, "InGene", asBool = TRUE)
plotdisFeature(res, "virus", "InGene")
## performing boolean summary
plotdisFeature(res, "virus", "InGene", typeRatio = TRUE)
## performing boolean summary
1) Load one of the following libraries depending on machine/OS: doMC, doSMP, doSNOW, doMPI
2) Register the parallel backend using registerDoXXXX() function depending on the library. See the examples below:
## Example 1: library(doSMP)
w <- startWorkers(2)
registerDoSMP(w)
getNearestFeature(..., parallel = TRUE)
## Example 2: library(doMC)
registerDoMC(2)
getNearestFeature(..., parallel = TRUE)
## Example 3: library(doSNOW)
cl <- makeCluster(2, type = "SOCK")
registerDoSNOW(cl)
getNearestFeature(..., parallel = TRUE)
## Example 4: library(doParallel)
cl <- makeCluster(2)
registerDoParallel(cl)
getNearestFeature(..., parallel = TRUE)
3) Few backends launch worker processes in the background, so be sure to close them. Read the documentation of respective do* package to get more information. Few examples are shown below.
For doSMP library, use stopWorkers(w)
For doSNOW & doParallel library, use stopCluster(cl)