HG: HGScore

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

Description

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.

Usage

HG(datInput)

Arguments

datInput

A dataframe with column names: idRun, idPrey, countPrey, lenPrey. Each row represent one unique protein captured in one pull-down experiment.

Value

A dataframe consists of pairwise combindation of preys identified in the input with HG scores indicating interacting probabilities computed from negative log transformed Hypergeometric test P-values.

Author(s)

Qingzhou Zhang, [e-mail]

References

Guruharsha, K. G., et al. 'A protein complex network of Drosophila melanogaster.' Cell 147.3 (2011): 690-703. https://doi.org/10.1016/j.cell.2011.08.047

Hart, G. Traver, Insuk Lee, and Edward M. Marcotte. 'A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality.' BMC bioinformatics 8.1 (2007): 236. https://doi.org/10.1186/1471-2105-8-236

Zybailov, Boris, et al. 'Statistical analysis of membrane proteome expression changes in Saccharomyces c erevisiae.' Journal of proteome research 5.9 (2006): 2339-2347. https://doi.org/10.1021/pr060161n

Examples

data(TestDatInput)
datScore <- HG(TestDatInput)
head(datScore)

SMAD documentation built on Nov. 8, 2020, 8:24 p.m.