# 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) ``` ## Quick start ### 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)][1]. The implementation of this algorithm was inspired by Dr. Sowa's [online tutorial][2]. 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)][3] and BioPlex 2.0 [(Huttlin, Edward L., et al., 2017)][4], 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][5]. Prepare input data into the dataframe *datInput* with the following format: |idRun|idBait|idPrey|countPrey| |-----|:----:|:----:|:-------:| |Unique ID of one AP-MS run|Bait ID|Prey ID|Prey peptide count| Then run: ```{r} CompPASS(datInput) ``` ### 2. HGScore HGScore Scoring algorithm based on a hypergeometric distribution error model [(Hart et al., 2007)][6] with incorporation of NSAF [(Zybailov, Boris, et al., 2006)][7]. This algorithm was first introduced to predict the protein complex network of Drosophila melanogaster [(Guruharsha, K. G., et al., 2011)][8]. This scoring algorithm was based on matrix model. Unlike CompPASS, we need protein length for each prey in the additional column. Prepare input data into the dataframe *datInput* with the following format: |idRun|idBait|idPrey|countPrey|lenPrey| |-----|:----:|:----:|:-------:|:-------:| |Unique ID of one AP-MS run|Bait ID|Prey ID|Prey peptide count|Prey protein length| Then run: ```{r} HG(datInput) ``` ## License MIT @ Qingzhou Zhang [1]: https://doi.org/10.1016/j.cell.2009.04.042 [2]: http://besra.hms.harvard.edu/ipmsmsdbs/cgi-bin/tutorial.cgi [3]: https://doi.org/10.1016/j.cell.2015.06.043 [4]: https://www.nature.com/articles/nature22366 [5]: https://github.com/dnusinow/cRomppass [6]: https://doi.org/10.1186/1471-2105-8-236 [7]: https://doi.org/10.1021/pr060161n [8]: https://doi.org/10.1016/j.cell.2011.08.047