runSigPathway {sigPathway} | R Documentation |
Performs pathway analysis
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)
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 |
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
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 |
Lu Tian, Peter Park, and Weil Lai
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
## 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" 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")