makeTable {qsea} | R Documentation |
This function creates a table from the qsea objects qseaSet and qseaTvN_glm
makeTable(qs,glm,norm_methods="counts",samples,groupMeans, keep, ROIs, annotation, minPvalSummarize, CNV=FALSE, verbose=TRUE, minEnrichment=3, chunksize=1e5)
qs |
a qseaSet object (mandatory) |
glm |
a list of one or more qseaGLM objects (optional) |
norm_methods |
ether a character vector of pre-defined normalization combinations, or a list defining normalization combinations. This affects both individual and mean values. |
samples |
The indices of the samples for which individual values are to be written out in the specified order |
groupMeans |
a named list of indices vectors, defining groups for which mean values are to be written out |
keep |
a vector of indices of the windows that are considered (as created by isSignificant) |
ROIs |
A GRanges object, containing regions of interest (ROIs). Only windows overlapping ROIs are considered. |
annotation |
a named list of GRange objects, containing annotations (e.g. genes, CpG islands, ...) that are added to the table. |
minPvalSummarize |
If ROIs are given, you can specify a QseaTvN_glm object. For each ROI the window with the most significant differential coverage is written out |
CNV |
If set TRUE, the CNV logFC for the samples specified by samples are written out. |
verbose |
verbosity level |
minEnrichment |
for transformation to absolute methylation level, you can specify the minimal number of expected reads for a fully methylated window. This avoids inaccurate estimates, due to low enrichment. |
chunksize |
For efficient memory usage, the table is built up in chunks. With this parameter, the maximum number of windows processed in one chunk is specified. |
Note that, if overlapping ROIs are specified, windows might emerge in the table several times.
A result table containing the specified normalized values for the selected windows and samples/groups
Mathias Lienhard
isSignificant
#create example set qs=getExampleQseaSet() design=model.matrix(~group, getSampleTable(qs)) TvN_glm=fitNBglm(qs, design, norm_method="beta") TvN_glm=addContrast(qs,TvN_glm, coef=2, name="TvN") sig=isSignificant(TvN_glm, fdr_th=0.01) ##Table containing all significant windows tab1=makeTable(qs=qs, glm=TvN_glm, keep=sig, samples=getSampleNames(qs)) ##additional CNV logFC for the selected samples tab2=makeTable(qs=qs, glm=TvN_glm, keep=sig, samples=getSampleNames(qs), CNV=TRUE) ##explicit selection of normalization: ##counts (i.e. no normalization, only counts) tab3=makeTable(qs=qs, glm=TvN_glm, keep=sig, samples=getSampleNames(qs), norm_method="counts") ##counts AND %methylation values for individual samples and group means tab4=makeTable(qs=qs, glm=TvN_glm, keep=sig, samples=getSampleNames(qs), groupMeans=getSampleGroups(qs), norm_method=c("counts", "beta"))