%\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{Using hiAnnotator}
Introduction
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.
Quick hiAnnotator tutorial.
Step 1: Loading the tools
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
##
## Attaching package: 'BiocGenerics'
## 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, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
## tapply, union, unique, unsplit, which.max, which.min
## Loading required package: S4Vectors
##
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:utils':
##
## findMatches
## The following objects are masked from 'package:base':
##
## I, expand.grid, unname
## Loading required package: IRanges
## Loading required package: GenomeInfoDb
Step 2: Loading & formatting the data
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, freeze = "hg18")
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)
Step 3: Annotating the data
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.
getNearestFeature: find the nearest neighbor
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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus NearestGeneDist NearestGene NearestGeneOrt
## <character> <character> <integer> <character> <character>
## [1] + HIV -6205 LOC643837 +
## [2] - HIV 9435 CDK11B -
## [3] - HIV -4551 C1orf93 +
## [4] + MLV -15422 MEGF6 -
## [5] - MLV -25884 MEGF6 -
## [6] + MLV -94855 ERRFI1 -
## -------
## seqinfo: 27 sequences from an unspecified genome
# 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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus X5pNearestGeneDist X5pNearestGene
## <character> <character> <integer> <character>
## [1] + HIV 20472 LOC643837
## [2] - HIV 9435 CDK11B
## [3] - HIV -4551 C1orf93
## [4] + MLV 48781 ARHGEF16
## [5] - MLV 46965 MIR551A
## [6] + MLV -94855 ERRFI1
## X5pNearestGeneOrt
## <character>
## [1] +
## [2] -
## [3] +
## [4] +
## [5] -
## [6] -
## -------
## seqinfo: 27 sequences from an unspecified genome
## 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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus X3pNearestGeneDist X3pNearestGene
## <character> <character> <integer> <character>
## [1] + HIV -6205 LOC643837
## [2] - HIV -12171 CDK11A
## [3] - HIV 8383 MMEL1
## [4] + MLV -15422 MEGF6
## [5] - MLV -25884 MEGF6
## [6] + MLV -109469 ERRFI1
## X3pNearestGeneOrt
## <character>
## [1] +
## [2] -
## [3] -
## [4] -
## [5] -
## [6] -
## -------
## seqinfo: 27 sequences from an unspecified genome
## 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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus midpointNearestGeneDist midpointNearestGene
## <character> <character> <integer> <character>
## [1] + HIV 7134 LOC643837
## [2] - HIV -1360 CDK11A
## [3] - HIV -6878 C1orf93
## [4] + MLV 35516 ARHGEF16
## [5] - MLV 35893 MEGF6
## [6] + MLV -102162 ERRFI1
## midpointNearestGeneOrt
## <character>
## [1] +
## [2] -
## [3] +
## [4] +
## [5] -
## [6] -
## -------
## seqinfo: 27 sequences from an unspecified genome
### 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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus Either.NearestGene.upStream1.Dist
## <character> <character> <integer>
## [1] + HIV -6205
## [2] - HIV 9435
## [3] - HIV -4551
## [4] + MLV -15422
## [5] - MLV -25884
## [6] + MLV -94855
## Either.NearestGene.upStream1 Either.NearestGene.upStream1.Ort
## <character> <character>
## [1] LOC643837 +
## [2] CDK11B -
## [3] C1orf93 +
## [4] MEGF6 -
## [5] MEGF6 -
## [6] ERRFI1 -
## Either.NearestGene.upStream2.Dist Either.NearestGene.upStream2
## <integer> <character>
## [1] 414938 OR4F3
## [2] 9435 CDK11B
## [3] -424348 ACTRT2
## [4] -435638 FLJ42875
## [5] 46872 MIR551A
## [6] -180361 TNFRSF9
## Either.NearestGene.upStream2.Ort Either.NearestGene.downStream1.Dist
## <character> <integer>
## [1] + -77585
## [2] - 9435
## [3] + 76733
## [4] - 57334
## [5] - -446100
## [6] - 231215
## Either.NearestGene.downStream1 Either.NearestGene.downStream1.Ort
## <character> <character>
## [1] SAMD11 +
## [2] CDK11B -
## [3] PLCH2 +
## [4] MIR551A -
## [5] FLJ42875 -
## [6] RERE -
## Either.NearestGene.downStream2.Dist Either.NearestGene.downStream2
## <integer> <character>
## [1] -112431 KLHL17
## [2] 9435 CDK11B
## [3] 176816 RER1
## [4] 127404 WDR8
## [5] -446100 FLJ42875
## [6] 231215 RERE
## Either.NearestGene.downStream2.Ort
## <character>
## [1] +
## [2] -
## [3] +
## [4] -
## [5] -
## [6] -
## -------
## seqinfo: 27 sequences from an unspecified genome
getFeatureCounts: find out how many features are in a neighborhood
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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus NumOfGene.1Kb NumOfGene.10Kb NumOfGene.1Mb
## <character> <character> <integer> <integer> <integer>
## [1] + HIV 1 1 52
## [2] - HIV 8 8 71
## [3] - HIV 0 1 24
## [4] + MLV 1 1 22
## [5] - MLV 1 1 22
## [6] + MLV 0 0 13
## -------
## seqinfo: 27 sequences from an unspecified genome
# 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)
getSitesInFeature: find out a position within a feature
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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus InGene InGeneOrt
## <character> <character> <character> <character>
## [1] + HIV LOC643837 +
## [2] - HIV CDK11B,CDK11A -
## [3] - HIV FALSE <NA>
## [4] + MLV MEGF6 -
## [5] - MLV MEGF6 -
## [6] + MLV FALSE <NA>
## -------
## seqinfo: 27 sequences from an unspecified genome
## 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 Chr
## <Rle> <IRanges> <Rle> | <character> <numeric> <character>
## [1] chr1 773398 + | Burgess-HIV-HeLa-03e07 773398 chr1
## [2] chr1 1636200 - | Burgess-HIV-HeLa-10d11 1636200 chr1
## [3] chr1 2503557 - | Burgess-HIV-HeLa-04h05 2503557 chr1
## [4] chr1 3409787 + | Burgess-MLV-HeLa-nj1.. 3409787 chr1
## [5] chr1 3420249 - | Burgess-MLV-HeLa-nj1.. 3420249 chr1
## [6] chr1 8103835 + | Burgess-MLV-HeLa-oi0.. 8103835 chr1
## Ort virus InGene
## <character> <character> <logical>
## [1] + HIV TRUE
## [2] - HIV TRUE
## [3] - HIV FALSE
## [4] + MLV TRUE
## [5] - MLV TRUE
## [6] + MLV FALSE
## -------
## seqinfo: 27 sequences from an unspecified genome
# InGenes <- getSitesInFeature(alldata.rd, genes.rd, "InGene", asBool=TRUE, parallel=TRUE)
doAnnotation
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")) )
Plotting Results
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
Ways to load parallel backends
-
Load one of the following libraries depending on machine/OS:
doMC,doSMP,doSNOW,doMPI -
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)
- 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)