plotcmarrt {Starr} | R Documentation |
Plot the histograms of p-values and normal QQ plots under correlation structure and independence.
plotcmarrt(cmarrt.ma, freq=FALSE)
cmarrt.ma |
output object from |
freq |
see ?hist |
Diagnostic plots for comparing the distribution of standardized MA statistics under correlation and independence.
Histogram of p-values and normal QQ plots under correlation structure and independence.
If the normal quantile-quantile plot deviates from the reference line for unbound probes, this indicates that Gaussian approximation is not suitable for analyzing this data.
Pei Fen Kuan, Adam Hinz
P.F. Kuan, H. Chun, S. Keles (2008). CMARRT: A tool for the analysiz of ChIP-chip data from tiling arrays by incorporating the correlation structure. Pacific Symposium of Biocomputing13:515-526.
# dataPath <- system.file("extdata", package="Starr") # bpmapChr1 <- readBpmap(file.path(dataPath, "Scerevisiae_tlg_chr1.bpmap")) # cels <- c(file.path(dataPath,"Rpb3_IP_chr1.cel"), file.path(dataPath,"wt_IP_chr1.cel"), # file.path(dataPath,"Rpb3_IP2_chr1.cel")) # names <- c("rpb3_1", "wt_1","rpb3_2") # type <- c("IP", "CONTROL", "IP") # rpb3Chr1 <- readCelFile(bpmapChr1, cels, names, type, featureData=TRUE, log.it=TRUE) # ips <- rpb3Chr1$type == "IP" # controls <- rpb3Chr1$type == "CONTROL" # rpb3_rankpercentile <- normalize.Probes(rpb3Chr1, method="rankpercentile") # description <- c("Rpb3vsWT") # rpb3_rankpercentile_ratio <- getRatio(rpb3_rankpercentile, ips, controls, description, fkt=median, featureData=FALSE) # probeAnnoChr1 <- bpmapToProbeAnno(bpmapChr1) # peaks <- cmarrt.ma(rpb3_rankpercentile_ratio, probeAnnoChr1, chr=NULL, M=NULL,250,window.opt='fixed.probe') # plotcmarrt(peaks)