calculate.GSEA {sigPathway} | R Documentation |
Calculates the 2-sided statistics based on the GSEA algorithm.
calculate.GSEA(tab, phenotype, gsList, nsim = 1000, verbose = FALSE, alwaysUseRandomPerm = FALSE)
tab |
a numeric matrix of expression values, with the rows and columns representing probe sets and sample arrays, respectively |
phenotype |
a numeric or character vector indicating the phenotype |
gsList |
a list containing three vectors from the output of
the selectGeneSets function |
nsim |
an integer indicating the number of permutations to use |
verbose |
a boolean to indicate whether to print debugging messages to the R console |
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 |
This function assumes 2 distinct types of phenotypes in the data. It calculates a variant of the GSEA statistics (Mootha et al.) with the following modifications: (a) GSEA was changed from a 1-sided to a 2-sided approach. (b) The 2-group t-statistics is used as the difference metric.
The function also normalizes the GSEA statistic and calculates the
corresponding q-values for each gene set as described in Tian
et al. (2005) The function's output can be used for further analysis
in other functions such as rankPathways.NGSk
or
getPathwayStatistics.NGSk
.
A list containing
ngs |
number of gene sets |
nsim |
number of permutations performed |
t.set |
a numeric vector of Tk statistics |
t.set.new |
a numeric vector of NTk statistics |
p.null |
the proportion of nulls |
p.value |
a numeric vector of p-values |
q.value |
a numeric vector of q-values |
Lu Tian, Peter Park, and Weil Lai
Mootha V.K., Lindgren C.M., Eriksson K.F., Subramanian A., Sihag S., Lehar J., Puigserver P., Carlsson E., Ridderstrale M., Laurila E., Houstis N., Daily M.J., Patterson N., Mesirov J.P., Golud T.R., Tamayo P., Spiegelman B., Lander E.S., Hirshhorn J.N., Altshuler D., Groop L.C. (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics, 34, 267-73.
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