Perform pathway analysis

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Description

Performs pathway analysis

Usage

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runSigPathway(G, minNPS = 20, maxNPS = 500,
              tab, phenotype, nsim = 1000,
              weightType = c("constant", "variable"), ngroups = 2,
              npath = 25, verbose = FALSE, allpathways = FALSE,
              annotpkg = NULL, alwaysUseRandomPerm = FALSE)

Arguments

G

a list containing the source, title, and probe sets associated with each curated pathway

minNPS

an integer specifying the minimum number of probe sets in tab that should be in a gene set

maxNPS

an integer specifying the maximum number of probe sets in tab that should be in a gene set

tab

a numeric matrix of expression values, with the rows and columns representing probe sets and sample arrays, respectively

phenotype

a numeric (or character if ngroups >= 2) vector indicating the phenotype

nsim

an integer indicating the number of permutations to use

weightType

a character string specifying the type of weight to use when calculating NEk statistics

ngroups

an integer indicating the number of groups in the matrix

npath

an integer indicating the number of top gene sets to consider from each statistic when ranking the top pathways

verbose

a boolean to indicate whether to print debugging messages to the R console

allpathways

a boolean to indicate whether to include the top npath pathways from each statistic or just consider the top npath pathways (sorted by the sum of ranks of both statistics) when generating the summary table

annotpkg

a character vector specifying the name of the BioConductor annotation package to use to fetch accession numbers, Entrez Gene IDs, gene name, and gene symbols

alwaysUseRandomPerm

a boolean to indicate whether the algorithm can use complete permutations for cases where nsim is greater than the total number of unique permutations possible with the phenotype vector

Details

runSigPathway is a wrapper function that

(1) Selects the gene sets to analyze using selectGeneSets

(2) Calculates NTk and NEk statistics using calculate.NTk and calculate.NEK

(3) Ranks the top npath pathways from each statistic using rankPathways

(4) Summarizes the means, standard deviation, and individual statistics of each probe set in each of the above pathways using getPathwayStatistics

Value

A list containing

gsList

a list containing three vectors from the output of the selectGeneSets function

list.NTk

a list from the output of calculate.NTk

list.NEk

a list from the output of calculate.NEk

df.pathways

a data frame from rankPathways which contains the top pathways' indices in G, gene set category, pathway title, set size, NTk statistics, NEk statistics, the corresponding q-values, and the ranks.

list.gPS

a list from getPathwayStatistics containing nrow(df.pathways) data frames corresponding to the pathways listed in df.pathways. Each data frame contains the name, mean, standard deviation, the test statistic (e.g., t-test), and the corresponding unadjusted p-value. If ngroups = 1, the Pearson correlation coefficient is also returned. If a valid annotpkg is specified, the probes' accession numbers, Entrez Gene IDs, gene name, and gene symbols are also returned.

parameters

a list of parameters (e.g., nsim) used in the analysis

Author(s)

Lu Tian, Peter Park, and Weil Lai

References

Tian L., Greenberg S.A., Kong S.W., Altschuler J., Kohane I.S., Park P.J. (2005) Discovering statistically significant pathways in expression profiling studies. Proceedings of the National Academy of Sciences of the USA, 102, 13544-9.

http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102

Examples

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## Load in filtered, expression data
data(MuscleExample)

## Prepare the pathways to analyze and run analysis with 1 wrapper function

nsim <- 1000
ngroups <- 2
verbose <- TRUE
weightType <- "constant"
npath <- 25
allpathways <- FALSE
annotpkg <- "hgu133a.db"

res.muscle <- runSigPathway(G, 20, 500, tab, phenotype, nsim,
                            weightType, ngroups, npath, verbose,
                            allpathways, annotpkg)

## Summarize results
print(res.muscle$df.pathways)

## Get more information about the probe sets' means and other statistics
## for the top pathway in res.pathways
print(res.muscle$list.gPS[[1]])

## Write table of top-ranked pathways and their associated probe sets to
## HTML files
writeSigPathway(res.muscle, tempdir(), "sigPathway_rSP",
                "TopPathwaysTable.html")