HG {SMAD} | R Documentation

## HGScore

### 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, zqzneptune@hotmail.com

### 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)

[Package SMAD version 1.8.0 Index]