candidateRelocatedProteins: Identify candidate relocated proteins

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

Description

Identify candidate condition-dependent relocated proteins by comparing neighborhood classifications with respect to protein-protein pearson correlation and minumum PSM, peptide spectrum matching, count.

Usage

candidateRelocatedProteins(sampleCls1, s1PSM, s1Quant, sampleCls2, s2PSM,
  s2Quant, annotation = FALSE, min.psm = 2, pearson.cor = 0.8)

Arguments

sampleCls1	data.frame; merged classification, combination of compartment and neighborhood classification.
s1PSM	data.frame; minimum PSM count table across ten TMT channel
s1Quant	data.frame; fractionation quantification data
sampleCls2	data.frame; merged classification, combination of compartment and neighborhood classification.
s2PSM	data.frame; minimum PSM count table across ten TMT channel
s2Quant	data.frame; fractionation quantification data
annotation	boolean; labeling the selected proteins
min.psm	numeric; minimum psm, peptide spectra matching value
pearson.cor	numeric; pearson correlation threshold

Value

candidate.df

Examples

{

candidate.df <- candidateRelocatedProteins(hcc827GEFClass, hcc827GefPSMCount,
hcc827GEF, hcc827GEFClass, hcc827GefPSMCount, hcc827GEF,
annotation = FALSE)


}

SubCellBarCode documentation built on Nov. 8, 2020, 5:26 p.m.