candidateRelocatedProteins: Identify candidate relocated proteins

In TanerArslan/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)


}

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