cn_assessGRN: assess performance of GRN predictions based on zscores. this...

In pcahan1/CellNet: Assess and improve cell fate engineering with network biology

assess performance of GRN predictions based on zscores. this function runs cn_calcPRs on all TFs in gol_standard

Description

Recall (Sensitivity): fraction of gold standard regulatory relationships detected by GRN. Precision (1-FPR): Proportion of called relationships that are in the gold standard

Usage

cn_assessGRN(zscores, goldStandard, tRange = seq(3, 6, by = 0.25),
  random = FALSE, randomIter = 10, funType = "intersect")

Arguments

zscores	matrix of zscores
goldStandard	gold stanard gene list
tRange	threshold sequence
random	boolean, whether to compute a random selection of calls, too, based on the number of genes actually predicted
randomIter	numer of random selects to make, returns only the average
funType	how to treat >1 TF, intersect or union
universe	genes tested

Value

List of performance results of form: data frame of Score (cutoff), Precision, Recall, Predictions (n), obs = real GRN, rand = list of random GRNs, one per

Examples

prList<-cn_assessGRN(zscores, gs, random=TRUE)

pcahan1/CellNet documentation built on May 18, 2023, 4:58 p.m.