Zscore: Meta-analysis of Microarray Data from Different Platforms

In metaArray: Integration of Microarray Data for Meta-analysis

Description

This function calculates Z-score for each matched gene across all datasets. In each dataset, it performs local regression smoothing of mean vs variance. Z score is constructed by taking the ratio of weighted mean difference and combined standard deviation according to Box and Tiao (1992).

Usage

  Zscore(merged, pheno = NULL, permute = 0, verbose = TRUE)

Arguments

merged	mergeExprSet object that contains gene expression and class label with all datasets. Class label should consist of two unique elements. If pheno is NULL, first columns of phenoData from each ExpressionSet is sought as class labels. If a vector of particular column number in each data is specified, corresponding columns of phenoData will be considered for class labels.
pheno	A numeric vector specifying the location of class labels in phenoData from each ExpressionSet, a unit of mergeExprSet representing one dataset.
permute	If permute is 0, weighted Z-score will be referenced to standard normal distribution for two-sided p-value. Otherwise, columns of all datasets (each dataset separately) will be shuffled at random, from which a permutation distribution of Z-scores are formed and Z-scores are referenced to this distribution.
verbose	If verbose is TRUE, the progress of permutation will be reported.

Value

A data.frame with matched genes, Z-scores and p-values will result.

Author(s)

Debashis Ghosh <[e-mail]>, Hyungwon Choi <[e-mail]>

References

J.Wang et al, Bioinformatics 2004 Nov 22;20(17):3166-78

Examples

 # Zscore(merged, pheno=NULL, permute=10000, verbose=FALSE)

Example output

metaArray documentation built on April 29, 2020, 2:12 a.m.