cepa.univariate.all: Apply centrality-extented GSA on a list of pathways

In CePa: Centrality-Based Pathway Enrichment

Apply centrality-extented GSA on a list of pathways

Description

Apply centrality-extented GSA on a list of pathways

Usage

cepa.univariate.all(mat, label, pc, cen = default.centralities,
    cen.name = sapply(cen, function(x) ifelse(mode(x) == "name", deparse(x), x)),
    nlevel = "tvalue_abs", plevel = "mean", iter = 1000)

Arguments

mat	expression matrix in which rows are genes and columns are samples
label	a sampleLabel object identify the design of the microarray experiment
pc	a pathway.catalogue object storing information of pathways
cen	centrality measuments, it can ce a string, or a function
cen.name	centrality measurement names. By default it is parsed from cen argument
nlevel	node level transformation, should be one of "tvalue", "tvalue_sq", "tvalue_abs". Also self-defined functions are allowed
plevel	pathway level transformation, should be one of "max", "min", "median", "sum", "mean", "rank". Also, self-defined functions are allowed
iter	number of simulations

Details

The traditional gene-set analysis (GSA) to find significant pathways uses the whole expression matrix. GSA methods are implemented via either a univariate or a multivariate procedure. In univariate analysis, node level statistics are initially calculated from fold changes or statistical tests (e.g., t-test). These statistics are then combined into a pathway level statistic by summation or averaging. Multivariate analysis considers the correlations between genes in the pathway and calculates the pathway level statistic directly from the expression value matrix using Hotelling's T^2 test or MANOVA models. The function implement univariate procedure of GSA with network centralities.

If users use the PID.db data, all genes should be formatted in gene symbol.

If the centrality measurement is set as a string, only pre-defined "equal.weight", "in.degree", "out.degree", "degree", "betweenness", "in.reach", "out.reach", "reach", "in.spread", "out.spread" and "spread" are allowed. More centrality measurements can be used by setting it as a function (such as closeness, cluster coefficient). In the function, we recommand users choose at least two centrality measurements. Note that the self-defined function should only contain one argument which is an igraph object. The default centralities are "equal.weight", "in.degree", "out.degree", "betweenness", "in.reach" and "out.reach".

The node level statistic can be self-defined. The self-defined function should contain two arguments: a vector for expression value in treatment class and a vector for expression value in control class.

The pathway level statistic can be self-defined. The self-defined function should only contain one argument: the vector of node-level statistic.

However, in most circumstance, the function is called by cepa.all.

We are sorry that only the univariate procedures in GSA are extended. We are still trying to figure out the extension for the multivariate procedures in GSA.

Value

A cepa.all class object

Author(s)

Zuguang Gu <[e-mail]>

See Also

cepa

Examples

## 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("http://mcube.nju.edu.cn/jwang/lab/soft/cepa/P53_symbol.gct")
label = read.cls("http://mcube.nju.edu.cn/jwang/lab/soft/cepa/P53.cls", 
    treatment="MUT", control="WT")
# will spend about 45 min
res.gsa = cepa.univariate.all(mat = eset, label = label, pc = PID.db$NCI)

## End(Not run)

CePa documentation built on June 12, 2022, 1:05 a.m.