quantify_pathways_deregulation {pathifier} | R Documentation

## Quantify deregulation of pathways in cancer

### Description

Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample.

### Usage

    quantify_pathways_deregulation(data, allgenes, syms, pathwaynames, normals = NULL,
    ranks = NULL, attempts = 100, maximize_stability = TRUE, logfile = "", samplings = NULL,
    min_exp = 4, min_std = 0.4)

### Arguments

`data` | The n x m mRNA expression matrix, where n is the number of genes and m the number of samples.
`allgenes` | A list of n identifiers of genes.
`syms` | A list of p pathways, each pathway is a list of the genes it contains (as appear in "allgenes").
`pathwaynames` | The names of the p pathways.
`normals` | A list of m logicals, true if a normal sample, false if tumor.
`ranks` | External knowledge on the ranking of the m samples, if exists (to use initial guess)
`attempts` | Number of runs to determine stability.
`maximize_stability` | If true, throw away components leading to low stability of sampling noise.
`logfile` | Name of the file the log should be written to (use stdout if empty).
`samplings` | A matrix specifying the samples that should be chosen in each sampling attempt, chooses a random matrix if samplings is NULL.
`min_exp` | The minimal expression considered as a real signal. Any values below are thresholded to be min_exp.
`min_std` | The minimal allowed standard deviation of each gene. Genes with lower standard deviation are divided by min_std instead of their actual standard deviation. (Recommended: set min_std to be the technical noise).

### Value

`scores` | The deregulation scores, the main output of pathifier
`genesinpathway` | The genes of each pathway used to devise its dregulation score
`newmeanstd` | Average standart devaition after omitting noisy components
`origmeanstd` | Originial average standart devaition, before omitting noisy components
`pathwaysize` | The number of components used to devise the pathway score
`curves` | The prinicipal curve learned for every pathway
`curves_order` | The order of the points of the prinicipal curve learned for every pathway
`z` | Z-scores of the expression matrix used to learn prinicpal curve
`compin` | The components not omitted due to noise
`xm` | The average expression over all normal samples
`xs` | The standart devation of expression over all normal samples
`center` | The centering used by the PCA
`rot` | The matrix of variable loadings of the PCA
`pctaken` | The number of principal components used
`samplings` | A matrix specifying the samples that should be chosen in each sampling attempt
`sucess` | Pathways for which a deregulation score was sucessfully computed
`logfile` | Name of the file the log was written to

### Author(s)

Yotam Drier <drier.yotam@mgh.harvard.edu> Maintainer: Assif Yitzhaky <assif.yitzhaky@weizmann.ac.il>

### References

Drier Y, Sheffer M, Domany E. Pathway-based personalized analysis of cancer. Proceedings of the National Academy of Sciences, 2013, vol. 110(16) pp:6388-6393. (www.pnas.org/cgi/doi/10.1073/pnas.1219651110)

See more information on : http://www.weizmann.ac.il/pathifier/

### Examples

    data(KEGG) # Two pathways of the KEGG database
    data(Sheffer) # The colorectal data of Sheffer et al.
    PDS<-quantify_pathways_deregulation(sheffer$data, sheffer$allgenes,
      kegg$gs, kegg$pathwaynames, sheffer$normals, attempts = 100,
      logfile="sheffer.kegg.log", min_exp=sheffer$minexp, min_std=sheffer$minstd)

[Package pathifier version 1.30.0 Index]