View source: R/univariate_extension.R
cepa.univariate | R Documentation |
Apply centrality-extended GSA on a single pathway
cepa.univariate(mat, label, pc, pathway = NULL, id = NULL, cen = "equal.weight", cen.name = if(is.function(cen)) deparse(substitute(cen)) else if(mode(cen) == "name") deparse(cen) else cen, iter = 1000, nlevel = "tvalue_abs", plevel = "mean", node.level.from.expr = NULL, node.level.t.value = NULL, r.node.level.from.expr = NULL)
mat |
expression matrix in which rows are genes and columns are samples |
label |
a |
pc |
a |
pathway |
|
id |
identify the number of the pathway in the catalogue |
cen |
centrality measuments, it can ce a string, or function has been quote |
cen.name |
centrality measurement names |
nlevel |
node level transformation, should be one of "tvalue", "tvalue_sq", "tvalue_abs". Also self-defined functions are allowed, see |
plevel |
pathway level transformation, should be one of "max", "min", "median", "sum", "mean", "rank". Also, self-defined functions are allowed, see |
node.level.from.expr |
for simplicity of computing |
node.level.t.value |
for simplicity of computing |
r.node.level.from.expr |
for simplicity of computing |
iter |
number of simulations |
The function is always called by cepa.univariate.all
. But you can still
use it if you realy want to analysis just one pathway under one centrality.
A cepa
class object
Zuguang Gu <z.gu@dkfz.de>
## Not run: data(PID.db) # GSA extension # P53_symbol.gct and P53_cls can be downloaded from # http://mcube.nju.edu.cn/jwang/lab/soft/cepa/ eset = read.gct("P53_symbol.gct") label = read.cls("P53.cls", treatment="MUT", control="WT") # will spend about 45 min res.gsa = cepa.univariate(mat = eset, label = label, pc = PID.db$NCI, id = 2) ## End(Not run)
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