preselect {PAA} | R Documentation

## Score and preselect features.

### Description

Iterates all features to score them via mMs, Student's t-test, or mRMR. Optionally, a list of not informative features can be obtained (for discarding them).

### Usage

    preselect(elist=NULL, columns1=NULL, columns2=NULL, label1="A", label2="B",
     log=NULL, discard.threshold=0.5, fold.thresh=1.5, discard.features=TRUE,
     mMs.above=1500, mMs.between=400, mMs.matrix1=NULL,
     mMs.matrix2=NULL, method=NULL)

### Arguments

`elist` | `EListRaw` or `EList` object (mandatory).
`columns1` | column name vector (string vector) of group 1 (mandatory).
`columns2` | column name vector (string vector) of group 2 (mandatory).
`label1` | class label of group 1.
`label2` | class label of group 2.
`log` | indicates whether the data is in log scale (mandatory; note: if TRUE log2 scale is expected).
`discard.threshold` | positive numeric between 0 and 1 indicating the maximum mMs or, respectively, the maximum t-test p-value for features to be included for further analysis. Default is `"0.5"`.
`fold.thresh` | numeric indicating the minimum fold change for features to be included for further analysis. Default is `"1.5"`.
`discard.features` | boolean indicating whether merely feature scores (i.e., mMs or t-test p-values) (=`"FALSE"`) or feature scores and a discard list (=`"TRUE"`) should be returned. Default is `"TRUE"`.
`mMs.above` | mMs above parameter (integer). Default is `"1500"`.
`mMs.between` | mMs between parameter (integer). Default is `"400"`.
`mMs.matrix1` | precomputed mMs reference matrix (see `mMsMatrix()`) for group 1 (mandatory).
`mMs.matrix2` | precomputed mMs reference matrix (see `mMsMatrix()`) for group 2 (mandatory).
`method` | preselection method ( `"mMs"`, `"tTest"`, `"mrmr"`). Default is `"mMs"`.

### Details

This function takes an `EListRaw` or `EList` object and group-specific column vectors. Furthermore, the class labels of group 1 and group 2 are needed. If `discard.features` is `"TRUE"` (default), all features that are considered as not differential will be collected and returned for discarding.

If `method = "mMs"`, additionally precomputed mMs reference matrices (see `mMsMatrix()`) for group 1 and group 2 will be needed to compute mMs values (see Love B.) as scoring method. All mMs parameters (`mMs.above` and `mMs.between`) can be set. The defaults are `"1500"` for `mMs.above` and `"400"` for `mMs.between`. Features having an mMs value larger than `discard.threshold` (here: numeric between 0.0 and 1.0) or do not satisfy the minimal absolute fold change `fold.thresh` are considered as not differential.

If `method = "tTest"`, Student's t-test will be used as scoring method. Features having a p-value larger than `discard.threshold` (here: numeric between 0.0 and 1.0) or do not satisfy the minimal absolute fold change `fold.thresh` are considered as not differential.

If `method = "mrmr"`, mRMR scores for all features will be computed as scoring method (using the function `mRMR.classic()` of the CRAN R package `mRMRe`). Features that are not the `discard.threshold` (here: integer indicating a number of features) best features regarding their mRMR score are considered as not differential.

### Value

If `discard.features` is `"FALSE"`: matrix containing metadata, feature scores and intensity values for the whole data set.

If `discard.features` is `"TRUE"`, a list containing:

`results` | matrix containing metadata, feature scores and intensity values for the whole data set.
`discard` | vector containing row indices (= features) for discarding features considered as not differential.

### 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.

The software `"Prospector"` for ProtoArray analysis can be downloaded from the Thermo Fisher Scientific web page (https://www.thermofisher.com).

The R package mRMRe can be downloaded from CRAN. See also: De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, Haibe-Kains B. mRMRe: an R package for parallelized mRMR ensemble feature selection. Bioinformatics 2013.

The package `limma` by Gordon Smyth et al. can be downloaded from Bioconductor (https://www.bioconductor.org).

Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420.

### Examples

    cwd <- system.file(package="PAA")
    load(paste(cwd, "/extdata/Alzheimer.RData", sep=""))
    elist <- elist[elist$genes$Block < 10,]
    c1 <- paste(rep("AD",20), 1:20, sep="")
    c2 <- paste(rep("NDC",20), 1:20, sep="")
    preselect(elist, columns1=c1, columns2=c2, label1="AD", label2="NDC", log=FALSE,
     discard.threshold=0.5, fold.thresh=1.5, discard.features=TRUE, method="tTest")

[Package PAA version 1.16.0 Index]