View source: R/chooseCandGenes.R
chooseCandGenes | R Documentation |
This function can be used to independently select candidate genes from a given real RNA-srq data (bulk/single) for the SPsimSeq simulation. It chooses genes with various chracteristics, such as log-fold-change above a certain thereshold.
chooseCandGenes(
cpm.data,
group,
lfc.thrld,
llStat.thrld,
t.thrld,
w = w,
max.frac.zeror.diff = Inf,
pDE,
n.genes,
prior.count
)
cpm.data |
logCPM transformed matrix (if log.CPM.transform=FALSE, then it is the source gene expression data) |
group |
a grouping factor |
lfc.thrld |
a positive numeric value for the minimum absolute log-fold-change for selecting candidate DE genes in the source data (when group is not NULL and pDE>0) |
llStat.thrld |
a positive numeric value for the minimum squared test statistics from the log-linear model to select candidate DE genes in the source data (when group is not NULL and pDE>0) containing X as a covariate to select DE genes |
t.thrld |
a positive numeric value for the minimum absolute t-test statistic for the log-fold-changes of genes for selecting candidate DE genes in the source data (when group is not NULL and pDE>0) |
w |
a numeric value between 0 and 1. The number of classes to construct the probability distribution will be round(w*n), where n is the total number of samples/cells in a particular batch of the source data |
max.frac.zeror.diff |
a numeric value >=0 indicating the maximum absolute difference in the fraction of zero counts between the groups for DE genes. |
pDE |
fraction of DE genes |
n.genes |
total number of genes |
prior.count |
a positive constant to be added to the CPM before log transformation, to avoid log(0). The default is 1. |
a list object contating a set of candidate null and non-null genes and additional results
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