gsva
Description
Gene Set Variation Analysis
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  gsva(expr, gset.idx.list, ...)
## S4 method for signature 'ExpressionSet,list'
gsva(expr, gset.idx.list, annotation,
method = c("gsva", "ssgsea", "zscore", "plage"), rnaseq = FALSE,
abs.ranking = FALSE, min.sz = 1, max.sz = Inf, no.bootstraps = 0,
bootstrap.percent = 0.632, parallel.sz = 0, parallel.type = "SOCK",
mx.diff = TRUE, tau = switch(method, gsva = 1, ssgsea = 0.25, NA),
kernel = TRUE, ssgsea.norm = TRUE, verbose = TRUE,
is.gset.list.up.down = FALSE)
## S4 method for signature 'ExpressionSet,GeneSetCollection'
gsva(expr, gset.idx.list,
annotation, method = c("gsva", "ssgsea", "zscore", "plage"),
rnaseq = FALSE, abs.ranking = FALSE, min.sz = 1, max.sz = Inf,
no.bootstraps = 0, bootstrap.percent = 0.632, parallel.sz = 0,
parallel.type = "SOCK", mx.diff = TRUE, tau = switch(method, gsva = 1,
ssgsea = 0.25, NA), kernel = TRUE, ssgsea.norm = TRUE, verbose = TRUE,
is.gset.list.up.down = FALSE)
## S4 method for signature 'matrix,GeneSetCollection'
gsva(expr, gset.idx.list, annotation,
method = c("gsva", "ssgsea", "zscore", "plage"), rnaseq = FALSE,
abs.ranking = FALSE, min.sz = 1, max.sz = Inf, no.bootstraps = 0,
bootstrap.percent = 0.632, parallel.sz = 0, parallel.type = "SOCK",
mx.diff = TRUE, tau = switch(method, gsva = 1, ssgsea = 0.25, NA),
kernel = TRUE, ssgsea.norm = TRUE, verbose = TRUE,
is.gset.list.up.down = FALSE)
## S4 method for signature 'matrix,list'
gsva(expr, gset.idx.list, annotation,
method = c("gsva", "ssgsea", "zscore", "plage"), rnaseq = FALSE,
abs.ranking = FALSE, min.sz = 1, max.sz = Inf, no.bootstraps = 0,
bootstrap.percent = 0.632, parallel.sz = 0, parallel.type = "SOCK",
mx.diff = TRUE, tau = switch(method, gsva = 1, ssgsea = 0.25, NA),
kernel = TRUE, ssgsea.norm = TRUE, verbose = TRUE,
is.gset.list.up.down = FALSE)

Arguments
expr 
Gene expression data which can be given either as an 
gset.idx.list 
Gene sets provided either as a 
... 
other optional arguments. 
annotation 
In the case of calling 
method 
Method to employ in the estimation of geneset enrichment scores per sample. By default
this is set to 
rnaseq 
Flag to inform whether the input gene expression data comes from microarray
( 
abs.ranking 
Flag to determine whether genes should be ranked according to
their sign ( 
min.sz 
Minimum size of the resulting gene sets. 
max.sz 
Maximum size of the resulting gene sets. 
no.bootstraps 
Number of bootstrap iterations to perform. 
bootstrap.percent 
.632 is the ideal percent samples bootstrapped. 
parallel.sz 
Number of processors to use when doing the calculations in parallel.
This requires to previously load either the 
parallel.type 
Type of cluster architecture when using 
mx.diff 
Offers two approaches to calculate the enrichment statistic (ES)
from the KS random walk statistic. 
tau 
Exponent defining the weight of the tail in the random walk performed by both the 
kernel 
Logical, set to 
ssgsea.norm 
Logical, set to 
verbose 
Gives information about each calculation step. Default: 
is.gset.list.up.down 
logical. Is the gene list divided into up/down sublists? Please note that it is important to name the upregulated gene set list 'up', and the downregulated gene set list to 'down', if this argument is used (e.g gset = list(up = up_gset, down = down_gset)) 
Value
returns gene set enrichment scores for each sample and gene set
Methods (by class)

expr = ExpressionSet,gset.idx.list = list
: Method for ExpressionSet and list 
expr = ExpressionSet,gset.idx.list = GeneSetCollection
: Method for ExpressionSet and GeneSetCollection 
expr = matrix,gset.idx.list = GeneSetCollection
: Method for matrix and GeneSetCollection 
expr = matrix,gset.idx.list = list
: Method for matrix and list
See Also
Hanzelmann, S., Castelo, R., & Guinney, J. (2013). GSVA: gene set variation analysis for microarray and RNASeq data. BMC Bioinformatics, 14, 7. http://doi.org/10.1186/14712105147
Examples
1 2 3 4 5 6 7 8 9  data("Maupin")
names(maupin)
geneSet< maupin$sig$EntrezID #Symbol ##EntrezID # both up and down genes:
up_sig< maupin$sig[maupin$sig$upDown == "up",]
d_sig< maupin$sig[maupin$sig$upDown == "down",]
u_geneSet< up_sig$EntrezID #Symbol # up_sig$Symbol ## EntrezID
d_geneSet< d_sig$EntrezID
es.dif < gsva(maupin$data, list(up = u_geneSet, down= d_geneSet), mx.diff=1,
verbose=TRUE, abs.ranking=FALSE, is.gset.list.up.down=TRUE, parallel.sz = 1 )$es.obs
