CSAR (version 1.24.0)

CSAR-package: Statistical tools for the analysis of ChIP-seq data

Description

Statistical tools for ChIP-seq data analysis. The package is oriented to plant organisms, and compatible with standard file formats in the plant research field.

Details

Package:

CSAR

Type:

Package

Version:

1.0

Date:

2009-11-09

License:

Artistic-2.0

LazyLoad:

yes

References

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.

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 generate a wig file of the results to visualize tehm in a genome browser
score2wig(test,file="test.wig")

##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)

##We calculate two sets of read-enrichment scores through permutation
permutatedWinScores(nn=1,sample=sampleSEP3_test,control=controlSEP3_test,fileOutput="test",chr=c("CHR1v01212004"),chrL=c(100000))
permutatedWinScores(nn=2,sample=sampleSEP3_test,control=controlSEP3_test,fileOutput="test",chr=c("CHR1v01212004"),chrL=c(100000))

###Next function will get all permutated score values generated by permutatedWinScores function. 
##This represent the score distribution under the null hypotesis and therefore it can be use to control the error of our test.
nulldist<-getPermutatedWinScores(file="test",nn=1:2)

##From this distribution, several cut-off values can be calculated to control the error of our test. 
##Several functions  in R can be used for this purpose.
##In this package we had implemented a simple method for the control of the error based on FDR"
getThreshold(winscores=values(win)$score,permutatedScores=nulldist,FDR=.01)