Description Usage Arguments Details Value Author(s) References Examples
Iterates all features to score them via mMs, Student's ttest, or mRMR. Optionally, a list of not informative features can be obtained (for discarding them).
1 2 3 4 
elist 

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 ttest pvalue for features to be
included for further analysis. Default is 
fold.thresh 
numeric indicating the minimum fold change for
features to be included for further analysis. Default is 
discard.features 
boolean indicating whether merely feature scores
(i.e., mMs or ttest pvalues) (= 
mMs.above 
mMs above parameter (integer). Default is 
mMs.between 
mMs between parameter (integer). Default is

mMs.matrix1 
precomputed mMs reference matrix (see 
mMs.matrix2 
precomputed mMs reference matrix (see 
method 
preselection method ( 
This function takes an EListRaw
or EList
object and groupspecific
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 ttest will be used as scoring method.
Features having a pvalue 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.
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. 
Michael Turewicz, michael.turewicz@rub.de
Love B: The Analysis of Protein Arrays. In: Functional Protein Microarrays in Drug Discovery. CRC Press; 2007: 381402.
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, PapillonCavanagh S, Olsen C, ElHachem N, Bontempi G, HaibeKains 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 397420.
1 2 3 4 5 6 7  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")

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