In zqzneptune/SMAD: Statistical Modelling of AP-MS Data (SMAD)

knitr::opts_chunk$set(
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Introduction

This R package implements statistical modelling of affinity purification–mass spectrometry (AP-MS) data to compute confidence scores to identify bona fide protein-protein interactions (PPI).

Prepare Input Data

Prepare input data into the dataframe datInput with the following format:

|idRun|idBait|idPrey|countPrey|lenPrey| |-----|:----:|:----:|:-------:|:-------:| |AP-MS run ID|Bait ID|Prey ID|Prey peptide count|Prey protein length|

library(SMAD)
data("TestDatInput")
head(TestDatInput)

The test data is subset from the unfiltered BioPlex 2.0 data, which consists of apoptosis proteins as baits.

Methods

CompPASS

Comparative Proteomic Analysis Software Suite (CompPASS) is based on spoke model. This algorithm was developed by Dr. Mathew Sowa for defining the human deubiquitinating enzyme interaction landscape (Sowa, Mathew E., et al., 2009). The implementation of this algorithm was inspired by Dr. Sowa's online tutorial. The output includes Z-score, S-score, D-score and WD-score. In its implementation in BioPlex 1.0 (Huttlin, Edward L., et al., 2015) and BioPlex 2.0 (Huttlin, Edward L., et al., 2017), a naive Bayes classifier that learns to distinguish true interacting proteins from non-specific background and false positive identifications was included in the compPASS pipline. This function was optimized from the source code.

scoreCompPASS <- CompPASS(TestDatInput)
head(scoreCompPASS)

Based on the scores, bait-prey interactions could be ranked and ready for downstream analyses.

par(mfrow = c(2, 2))
plot(sort(scoreCompPASS$scoreZ, decreasing = TRUE), pch = 16,
     xlab = "Ranked bait-prey interactions",
     ylab = "Z-score")
plot(sort(scoreCompPASS$scoreS, decreasing = TRUE), pch = 16,
     xlab = "Ranked bait-prey interactions",
     ylab = "S-score")
plot(sort(scoreCompPASS$scoreD, decreasing = TRUE), pch = 16,
     xlab = "Ranked bait-prey interactions",
     ylab = "D-score")
plot(sort(scoreCompPASS$scoreWD, decreasing = TRUE), pch = 16,
     xlab = "Ranked bait-prey interactions",
     ylab = "WD-score")

HGScore

HGScore Scoring algorithm based on a hypergeometric distribution error model (Hart et al., 2007) with incorporation of NSAF (Zybailov, Boris, et al., 2006). This algorithm was first introduced to predict the protein complex network of Drosophila melanogaster (Guruharsha, K. G., et al., 2011). This scoring algorithm was based on matrix model. Unlike CompPASS, we need protein length for each prey in the additional column.

scoreHG <- HG(TestDatInput)
head(scoreHG)

plot(sort(scoreHG$HG, decreasing = TRUE), pch = 16,
     xlab = "Ranked prey-prey interactions",
     ylab = "HGscore")

Noted that HG scoring implements matrix models which leads to significant increase of inferred protein-protein interactions.

zqzneptune/SMAD documentation built on Nov. 6, 2024, 1:53 p.m.