CompareedgeRGLM {ChromSCape} | R Documentation |
Creates a summary table with the number of genes under- or overexpressed in each group and outputs several graphical representations
CompareedgeRGLM( dataMat = NULL, annot = NULL, ref_group = NULL, groups = NULL, featureTab = NULL, norm_method = "TMMwsp" )
dataMat |
reads matrix |
annot |
selected annotation of interest |
ref_group |
List containing one or more vectors of reference samples. Name of the vectors will be used in the results table. The length of this list should be 1 or the same length as the groups list |
groups |
List containing the IDs of groups to be compared with the reference samples. Names of the vectors will be used in the results table |
featureTab |
Feature annotations to be added to the results table |
norm_method |
Which method to use for normalizing ('upperquantile') |
A dataframe containing the foldchange and p.value of each feature
Eric Letouze & Celine Vallot
data("scExp") scExp_cf = correlation_and_hierarchical_clust_scExp(scExp) scExp_cf = choose_cluster_scExp(scExp_cf,nclust=2,consensus=FALSE) featureTab = as.data.frame(SummarizedExperiment::rowRanges(scExp_cf)) rownames(featureTab) = featureTab$ID ref_group = list("C1"=scExp_cf$cell_id[which(scExp_cf$cell_cluster=="C1")]) groups = list("C2"=scExp_cf$cell_id[which(scExp_cf$cell_cluster=="C2")]) myres = CompareedgeRGLM(as.matrix(SingleCellExperiment::counts(scExp_cf)), annot=as.data.frame(SingleCellExperiment::colData(scExp_cf)), ref_group=ref_group,groups=groups, featureTab=featureTab)