computeThresholdNeighborhood: Probability threshold for neighborhood classification

In TanerArslan/SubCellBarCode: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome

Description

Thresholds for each neighborhood are decided to get confident predictions.

Usage

computeThresholdNeighborhood(test.repA, test.repB)

Arguments

test.repA	data.frame; test predictions, observation and probablity vectors for each protein in replicate A
test.repB	data.frame; test predictions, observation and probablity vectors for each protein in replicate B

Value

threshold.neighborhood.df

Examples

{

df <- loadData(SubCellBarCode::hcc827Ctrl)

c.prots <- calculateCoveredProtein(rownames(df), markerProteins[,1])

set.seed(7)
c.prots <- sample(c.prots, 600)
cls <- svmClassification(c.prots, df, markerProteins)

test.A <- cls[[1]]$svm.test.prob.out
test.B <- cls[[2]]$svm.test.prob.out

t.n.df <- computeThresholdNeighborhood(test.A, test.B)
}

TanerArslan/SubCellBarCode documentation built on June 7, 2021, 9 a.m.