pvalues.manova {Pigengene} | R Documentation

## Computes pvalues for multi-class differential expression

### Description

Passes the arguments to `manova`, which performs multi-class analysis of variance.

### Usage

    pvalues.manova(Data, Labels)

### Arguments

`Data` | A matrix or data frame containing the (expression) data, with genes corresponding to columns and rows corresponding to samples. Rows and columns must be named.
`Labels` | A (preferably named) vector containing the Labels (condition types). Names must agree with rows of `Data`

### Value

A list with following elements:

`call` | The call that created the results
`pvals` | The matrix of pvalues with columns "pValue", "FDR", "Bonferroni". Rows are named according to genes, the columns of `Data`.
`manovaFit` | The full output of `manova` function.

### Note

`oneway.test` function is a better generalizatoion to Welch's t-tst from 2-calsses to multi-class because it dose not assume that the variaces are necessarly equal. However, in practice, with "enough number of samples", the two approaches will lead to similar p-values.

### Author(s)

Amir Foroushani

### References

Krzanowski, W. J. (1988) _Principles of Multivariate Analysis. A User's Perspective._ Oxford.

Hand, D. J. and Taylor, C. C. (1987) _Multivariate Analysis of Variance and Repeated Measures._ Chapman and Hall.

B. L. Welch (1951), On the comparison of several mean values: an alternative approach.

### See Also

`oneway.test`, `manova`, `compute.pigengene`

### Examples

         data(eigengenes33)
         d1 <- rbind(eigengenes33$aml,eigengenes33$mds)
         Labels <- c(rep("AML",nrow(eigengenes33$aml)),rep("MDS",nrow(eigengenes33$mds)))
         names(Labels) <- rownames(d1)
         ps <- pvalues.manova(Data=d1, Labels=Labels)
         plot(log10(ps$pvals[,"Bonferroni"]))

[Package Pigengene version 1.16.0 Index]