cn_calcPRs: assess performance of GRN predictions based on zscores

cn_calcPRsR Documentation

assess performance of GRN predictions based on zscores

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_calcPRs(zscores, tfs, goldStandard, universe = NULL, tRange = seq(3,
  6, by = 0.25), random = FALSE, randomIter = 10,
  funType = "intersect")

Arguments

zscores

matrix of zscores

tfs

vector of tf(s) perturbed

goldStandard

gold stanrd gene list

universe

genes tested

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

Value

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

Examples

grnPR<-cn_calcPRs(zscs, "Sox2", aGoldStandard[["Sox2"]], allGenes, tRange=seq(0,8, by=.5), random=TRUE, randomIter=3);


pcahan1/CellNet documentation built on July 5, 2022, 5:06 a.m.