mMsMatrix {PAA} | R Documentation

## Compute a reference minimum M statistic (n1 x n2)-matrix.

### Description

Computes a reference minimum M statistic (n1 x n2)-matrix (mMs matrix).

### Usage

    mMsMatrix(x, y)

### Arguments

`x` | integer, first dimension (i.e., number of samples in group 1) of the mMs matrix to be computed (mandatory).
`y` | integer, second dimension (i.e., number of samples in group 2) of the mMs matrix to be computed (mandatory).

### Details

For feature preselection the "minimum M Statistic" (mMs) proposed by Love B. can be used. The mMs is a univariate measure that is sensitive to population subgroups. To avoid redundant mMs computations for a large number of features (e.g., ca. 9500 features on ProtoArray v5) a reference matrix containing all relevant mMs values can be precomputed. For this purpose, only two parameters are needed: the number of samples in group 1 (`n1`) and the number of samples in group 2 (`n2`). According to mMs definition for each matrix element (i,m) a mMs value (= the probability of) for having m values in group 1 larger than the i-th largest value in group 2 is computed.

### Value

A (n1 x n2)-matrix containing all mMs values for group 1 and group 2.

### Note

To check whether a feature is more prevalent in group 1 or in group 2, PAA needs both the mMs for having m values in group 1 larger than the i-th largest element in group 2 as well as the mMs for having m values in group 2 larger than the i-th largest element in group 1. Hence, always both must be computed: `mMsMatrix(n1,n2)` and `mMsMatrix(n2,n1)`.

### Author(s)

Michael Turewicz, michael.turewicz@rub.de

### References

Love B: The Analysis of Protein Arrays. In: Functional Protein Microarrays in Drug Discovery. CRC Press; 2007: 381-402.

### Examples

    #exemplary computation for a group 1 comprising 10 arrays and a group 2
    #comprising 12 arrays
    mMs.matrix1 <- mMsMatrix(x=10, y=12)
    mMs.matrix2 <- mMsMatrix(x=12, y=10)

[Package PAA version 1.18.0 Index]