intensityOutliersPlot(GWASTools)
intensityOutliersPlot()所属R语言包:GWASTools
Plot mean intensity and highlight outliers
绘制平均强度和亮点离群
译者:生物统计家园网 机器人LoveR
描述----------Description----------
intensityOutliersPlot is a function to plot mean intensity for chromosome i vs mean of intensities for autosomes (excluding i) and highlight outliers
intensityOutliersPlot是一个函数来绘制染色体我亦或平均强度是指染色体(不包括我)和亮点离群的强度
用法----------Usage----------
intensityOutliersPlot(mean.intensities, sex, outliers,
sep = FALSE, label, ...)
参数----------Arguments----------
参数:mean.intensities
scan x chromosome matrix of mean intensities
扫描平均强度的X染色体矩阵
参数:sex
vector with values of "M" or "F" corresponding to scans in the rows of mean.intensities
向量的“M”或“F”对应的值扫描mean.intensities行
参数:outliers
list of outliers, each member corresponds to a chromosome (member "X" is itself a list of female and male outliers)
离群值的列表,每个成员都对应一个染色体(成员的“X”本身就是一种男性和女性的离群)
参数:sep
plot outliers within a chromosome separately (TRUE) or together (FALSE)
图分别在染色体异常值(TRUE)或(假)
参数:label
list of plot labels (to be positioned below X axis) corresponding to list of outliers
图标签列表(以下X轴定位)列出相应的离群
参数:...
additional arguments to plot
额外的参数plot
Details
详情----------Details----------
Outliers must be determined in advance and stored as a list, with one element per chromosome. The X chromosome must be a list of two elements, "M" and "F". Each element should contain a vector of ids corresponding to the row names of mean.intensities.
离群必须事先确定并存储为一个列表,每一个染色体元素。在X染色体上必须有两个元素的列表“M”和“F”。每个元素应包含一个相应的行名mean.intensitiesIDS的向量。
If sep=TRUE, labels must also be specified. labels should be a list that corresponds exactly to the elements of outliers.
如果sep=TRUE,labels也必须被指定。 labels应该是一个完全对应outliers元素的列表。
作者(S)----------Author(s)----------
Cathy Laurie
参见----------See Also----------
meanIntensityByScanChrom
meanIntensityByScanChrom
举例----------Examples----------
# calculate mean intensity[计算平均强度]
library(GWASdata)
file <- system.file("extdata", "affy_qxy.nc", package="GWASdata")
nc <- NcdfIntensityReader(file)
data(affy_scan_annot)
scanAnnot <- ScanAnnotationDataFrame(affy_scan_annot)
intenData <- IntensityData(nc, scanAnnot=scanAnnot)
meanInten <- meanIntensityByScanChrom(intenData)
intenMatrix <- meanInten$mean.intensity
# find outliers[发现离群]
outliers <- list()
sex <- scanAnnot$sex
id <- scanAnnot$scanID
allequal(id, rownames(intenMatrix))
for (i in colnames(intenMatrix)) {
if (i != "X") {
imean <- intenMatrix[,i]
imin <- id[imean == min(imean)]
imax <- id[imean == max(imean)]
outliers[[i]] <- c(imin, imax)
} else {
idf <- id[sex == "F"]
fmean <- intenMatrix[sex == "F", i]
fmin <- idf[fmean == min(fmean)]
fmax <- idf[fmean == max(fmean)]
outliers[[i]][["F"]] <- c(fmin, fmax)
idm <- id[sex == "M"]
mmean <- intenMatrix[sex == "M", i]
mmin <- idm[mmean == min(mmean)]
mmax <- idm[mmean == max(mmean)]
outliers[[i]][["M"]] <- c(mmin, mmax)
}
}
par(mfrow=c(2,4))
intensityOutliersPlot(intenMatrix, sex, outliers)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
注1:为了方便大家学习,本文档为生物统计家园网机器人LoveR翻译而成,仅供个人R语言学习参考使用,生物统计家园保留版权。
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