ComBat: ComBat-adjusted microarray gene expression data

In survJamda: Survival Prediction by Joint Analysis of Microarray Gene Expression Data

Description

Compute ComBat-adjusted microarray gene expression data.

Usage

ComBat(expression_xls, sample_info_file, type = "txt", write = TRUE, 
covariates = "all", par.prior = TRUE, filter = FALSE, skip = 0, 
prior.plots = TRUE)

Arguments

expression_xls	A character string specifying gene expression file.
sample_info_file	A character string specifying sample file.
type	A character string specifying the type of the file, "txt" or "csv".
write	A Boolean variable indicating whether the output (adjusted data) should be written into a file.
covariates	A vector of integers or "all" if all covariates should be used. covariates=all will use all of the columns in your sample info file in the modeling (except array/sample name), if you only want use a some of the columns in your sample info file, specify these columns here as a vector (you must include the Batch column in this list).
par.prior	A Boolean character indicating whether the parametric adjustment should be applied.
filter	A Boolean variable indicating whether presence/absence call is used in the gene expression file.
skip	An integer value indicating the number of columns that contain the gene names. skip = 1 implies the first expression values start from column 2.
prior.plots	A Boolean variable indicating whether the prior plots should be given where black is a kernel density estimate of the batch effects. Quantile-quantile plots are also included. If the red and black lines do not match up well, use the nonparametric adjustment.

Value

Matrix of adjusted expression data.

Warning

This function is not called by the user directly.

Author(s)

WE Johnson

References

W. Johnson E., L. Chen, Rabinovic, and A. Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics, 8(1):118-127, January2007. ISSN 1465-4644.

survJamda documentation built on May 1, 2019, 8:50 p.m.