R: Provide table of genes with read-enriched regions, and their...
genesWithPeaks
R Documentation
Provide table of genes with read-enriched regions, and their location
Description
Provide table of genes with read-enriched regions, and their location
Usage
genesWithPeaks(distances)
Arguments
distances
data.frame structure obtained by distances2Genes
Details
This function report for each gene, the maximum peak score in different regions near of the gene. The input of the function is the distances between genes and peaks calculated by distance2Genes
Value
data.frame structure with each coloumn being:
name
name of the gene
max3kb1kb
maximum score value for the region 3Kb upstream to 1Kb dowstream
u3000
maximum score value for the region 3Kb upstream to 2Kb upstream
u2000
maximum score value for the region 2Kb upstream to 1Kb upstream
u1000
maximum score value for the region 1Kb upstream to 0Kb upstream
d0
maximum score value for the region 0Kb upstream to 0Kb dowstream
d1000
maximum score value for the region 0Kb dowstream to 1Kb dowstream
Muino et al. (submitted). Plant ChIP-seq Analyzer: An R package for the statistcal detection of protein-bound genomic regions.
Kaufmann et al.(2009).Target genes of the MADS transcription factor SEPALLATA3: integration of developmental and hormonal pathways in the Arabidopsis flower. PLoS Biology; 7(4):e1000090.
See Also
distance2Genes,CSAR-package
Examples
##For this example we will use the a subset of the SEP3 ChIP-seq data (Kaufmann, 2009)
data("CSAR-dataset");
##We calculate the number of hits for each nucleotide posotion for the control and sample. We do that just for chromosome chr1, and for positions 1 to 10kb
nhitsS<-mappedReads2Nhits(sampleSEP3_test,file="sampleSEP3_test",chr=c("CHR1v01212004"),chrL=c(10000))
nhitsC<-mappedReads2Nhits(controlSEP3_test,file="controlSEP3_test",chr=c("CHR1v01212004"),chrL=c(10000))
##We calculate a score for each nucleotide position
test<-ChIPseqScore(control=nhitsC,sample=nhitsS)
##We calculate the candidate read-enriched regions
win<-sigWin(test)
##We calculate relative positions of read-enriched regions regarding gene position
d<-distance2Genes(win=win,gff=TAIR8_genes_test)
##We calculate table of genes with read-enriched regions, and their location
genes<-genesWithPeaks(d)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(CSAR)
Loading required package: S4Vectors
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, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, 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, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/CSAR/genesWithPeaks.Rd_%03d_medium.png", width=480, height=480)
> ### Name: genesWithPeaks
> ### Title: Provide table of genes with read-enriched regions, and their
> ### location
> ### Aliases: genesWithPeaks
>
> ### ** Examples
>
>
> ##For this example we will use the a subset of the SEP3 ChIP-seq data (Kaufmann, 2009)
> data("CSAR-dataset");
> ##We calculate the number of hits for each nucleotide posotion for the control and sample. We do that just for chromosome chr1, and for positions 1 to 10kb
> nhitsS<-mappedReads2Nhits(sampleSEP3_test,file="sampleSEP3_test",chr=c("CHR1v01212004"),chrL=c(10000))
mappedReads2Nhits has just finished CHR1v01212004 ...
> nhitsC<-mappedReads2Nhits(controlSEP3_test,file="controlSEP3_test",chr=c("CHR1v01212004"),chrL=c(10000))
mappedReads2Nhits has just finished CHR1v01212004 ...
>
>
> ##We calculate a score for each nucleotide position
> test<-ChIPseqScore(control=nhitsC,sample=nhitsS)
CHR1v01212004 done...
>
> ##We calculate the candidate read-enriched regions
> win<-sigWin(test)
CHR1v01212004 done...
>
> ##We calculate relative positions of read-enriched regions regarding gene position
> d<-distance2Genes(win=win,gff=TAIR8_genes_test)
Starting CHR1v01212004 ...
>
> ##We calculate table of genes with read-enriched regions, and their location
> genes<-genesWithPeaks(d)
>
>
>
>
>
>
>
> dev.off()
null device
1
>