boxplt.cpd {compdiagTools} | R Documentation |
Draws a boxplot for each chip given in a gene expression matrix or standard errors given in plmset-object
boxplt.cpd(fr, filename = NULL, pointsize = 14, height = 1.6 + ncol(fr) * pointsize/48, call = "", ...) boxplt.plmset.cpd(plmset, filename = NULL, pointsize = 14, height = 1.6 + ncol(coefs(plmset)) * pointsize/48, ...)
fr |
a gene expression matrix. |
plmset |
affyPLM-object of which standard error estimates are extracted. |
filename |
store a bitmap/pdf here is this argument is specified. |
pointsize |
gives the font size |
height |
height in inches |
call |
either 'summary' or 'normalized' |
... |
further arguments passed to bitmap command for writing files. |
Draws a boxplot of gene expression levesl after summary or after normalization. Given the hypothesis that gene expression levels are distributed simmilarly across chips from the same experiment, we want to see similar boxplots as well.
boxplt.plmset.cpd
does the same for estimated standard errors
on the residuals. We want to see low residuals, because this means
that the fitting of the linear model went fine.
Use type="pdfwrite" as parameter to generate PDF
Stefan Bentink
library(golubEsets) data(golubTest) expr.mat <- exprs(vsn(golubTest, describe.preprocessing=FALSE)) boxplt.cpd(expr.mat)