Statistical Modelling of AP-MS Data (SMAD)

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).

Installation

The development version can be installed through github: {r} devtools::install_github(repo="zqzneptune/SMAD") library(SMAD)

Input Data

A demo data.frame was provided as a hint how the input data should strcutured in order to run the scoring functions:

```{r} data(TestDatInput) colnames(TestDataInput)

1 "idRun" "idBait" "idPrey" "countPrey" "lenPrey" ```

|idRun|idBait|idPrey|countPrey|lenPrey| |-----|:----:|:----:|:-------:|-------| |Unique ID of one AP-MS run|Bait ID|Prey ID|Prey peptide count|Protein sequence length of the prey|

In case of duplcates, a suffix or prefix of e.g. "A", "B" could be added to idRun in order to make "idRun-idBait" combination unique to each replicate.

Run scoring

1. 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.

The input data.frame, datInput, should include:idRun, idBait, idPrey and countPrey.

{r} datScore <- CompPASS(datInput)

2. DICE

The Dice coefficient is used to score the interaction scores across prey pair-wise combinations, which was proposed by (Bing Zhang et al., 2008)

The input data.frame, datInput, should include:idRun and idPrey.

{r} datScore <- DICE(datInput)

3. Hart

Hart scoring algorithm is based on a hypergeometric distribution error model (Hart et al., 2007).

The input data.frame, datInput, should include:idRun and idPrey.

{r} datScore <- Hart(datInput)

4. HGScore

HGScore algorithm is 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.

The input data.frame, datInput, should include:idRun, idPrey, countPrey and lenPrey.

{r} datScore <- HG(datInput)

5. PE

PE incorporated both spoke and matrix model as repored in (Sean R. Collins, et al., 2007).

The input data.frame, datInput, should include:idRun, idBait and idPrey.

{r} datScore <- PE(datInput)

License

MIT @ Qingzhou Zhang