Package 'SMAD'

CompPASS

Description

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 (http://besra.hms.harvard.edu/ipmsmsdbs/cgi-bin/tutorial.cgi). The output includes Z-score, S-score, D-score and WD-score. This function also computes entropy and normalized WD-score. The source code for this function was based on the source code. https://github.com/dnusinow/cRomppass

Usage

CompPASS(datInput)

Arguments

datInput

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

Value

A data frame consists of unique bait-prey pairs with Z-score, S-score,D-score and WD-score indicating interacting probabilities.

Author(s)

Qingzhou Zhang, [email protected]

References

Sowa, Mathew E., et al. "Defining the human deubiquitinating enzyme interaction landscape." Cell 138.2 (2009): 389-403. https://doi.org/10.1016/j.cell.2009.04.042

Huttlin, Edward L., et al. "The BioPlex network: a systematic exploration of the human interactome." Cell 162.2 (2015): 425-440. https://doi.org/10.1016/j.cell.2015.06.043

Huttlin, Edward L., et al. "Architecture of the human interactome defines protein communities and disease networks." Nature 545.7655 (2017): 505. https://www.nature.com/articles/nature22366

Examples

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

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DICE

Description

DICE The Dice coefficient is used to score the interaction affinity between two proteins.

Usage

DICE(datInput)

Arguments

datInput

A dataframe with column names: idRun, idPrey. 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 DICE scores.

Author(s)

Qingzhou Zhang, [email protected]

References

Bing Zhang et al., From pull-down data to protein interaction networks and complexes with biological relevance, Bioinformatics, Volume 24, Issue 7, 1 April 2008, Pages 979–986, https://doi.org/10.1093/bioinformatics/btn036

Examples

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

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Hart

Description

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

Usage

Hart(datInput)

Arguments

datInput

A dataframe with column names: idRun, idPrey. 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 Hart scores indicating interacting probabilities computed from negative log transformed Hypergeometric test P-values.

Author(s)

Qingzhou Zhang, [email protected]

References

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

Examples

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

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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, [email protected]

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)

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PE

Description

PE Incorporated both spoke and matrix model.

Usage

PE(datInput, rBait = 0.37, cntPseudo = 1)

Arguments

datInput	A dataframe with column names: idRun, idBait, idPrey, Each row represent one unique protein captured in one pull-down experiment.
rBait	The value of the 'r' parameter as desribed in the publication.
cntPseudo	The value of the 'pseudo count' parameter.

Value

A dataframe consists of protein-protein interactions from both Spoke and Matrix model.

Author(s)

Qingzhou Zhang, [email protected]

References

Collins, Sean R., et al. "Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae." Molecular & Cellular Proteomics 6.3 (2007): 439-450. https://doi.org/10.1074/mcp.M600381-MCP200

Examples

data(TestDatInput)
datScore <- PE(TestDatInput, 0.37, 1)
head(datScore)

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Test data for SMAD

Description

It is a subset of unfiltered BioPlex 2.0 consisting of apoptosis as bait proteins

Usage

data(TestDatInput)

Format

A data frame with 5000 rows and 5 variables

Details

• idRun A unique identifier for one AP-MS run

• idBait A unique identifier for the bait protein

• idPrey A unique identifier for the prey protein

• countPrey Sepctra/Peptider count for the prey protein

• lenPrey Protein length for the prey protein

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