applyThresholdNeighborhood: Apply thresholds to neighborhood classification

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

Description

Apply thresholds for all predictions at the neighborhood level to increase the true positive rate and remove poor classification.

Usage

applyThresholdNeighborhood(all.repA, all.repB, threshold.df)

Arguments

all.repA	data.frame; all predictions and probablity vectors for each protein in replicate A
all.repB	data.frame; all predictions and probablity vectors for each protein in replicate B
threshold.df	data.frame; collection od precision and recall values for each neighborhood

Value

n.cls.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)

all.A <- cls[[1]]$all.prot.pred
all.B <- cls[[2]]$all.prot.pred

n.cls.df <- applyThresholdNeighborhood(all.A, all.B, t.n.df)
}

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