Zscore: Meta-analysis of Microarray Data from Different Platforms

Description Usage Arguments Value Author(s) References Examples

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

1
  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 <[email protected]>, Hyungwon Choi <[email protected]>

References

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

Examples

1
 # Zscore(merged, pheno=NULL, permute=10000, verbose=FALSE)

metaArray documentation built on Nov. 1, 2018, 2:27 a.m.