evalScoring {macat}R Documentation

Score differential expression, assess significance, and smooth scores along the chromosome

Description

This function computes for all genes on one chromosome the regularized t-statistic to score differential gene expression for two given groups of samples. Additionally these scores are computed for a number of permutations to assess significance. Afterwards these scores are smoothed with a given kernel along the chromosome to give scores for chromosomal regions.

Usage

evalScoring(data,class,chromosome,nperms=1000,permute="labels",subset=NULL,
     newlabels=NULL,kernel=rbf,kernelparams=NULL,cross.validate=TRUE,
     paramMultipliers=2^(-4:4),ncross=10,step.width=100000,
     memory.limit=TRUE)

Arguments

data Gene expression data in the MACAT list format. See data(stjude) for an example.
class Which of the given class labels is to be analyzed
chromosome Chromosome to be analyzed
nperms Number of permutations
permute Method to do permutations. Default 'labels' does permutations of the class labels, which is the common and faster way to assess significance of differential expression. The altenative 'locations' does permutations of gene locations, is much slower and right only preliminary.
subset If a subset of samples is to be used, give vector of column- indices of these samples in the original matrix here.
newlabels If other labels than the ones in the MACAT-list-structure are to be used, give them as character vector/factor here. Make sure argument 'class' is one of them.
kernel Choose kernel to smooth scores along the chromose. Available are 'kNN' for k-Nearest-Neighbors, 'rbf' for radial-basis-function (Gaussian), 'basePairDistance' for a kernel, which averages over all genes within a given range of base pairs around a position.
kernelparams Additional parameters for the kernel as list, e.g., kernelparams=list(k=5) for taking the 5 nearest neighbours in the kNN-kernel. If NULL some defaults are set within the function.
cross.validate Logical. Should the paramter settings for the kernel function be optimized by a cross-validation?
paramMultipliers Numeric vector. If you do cross-validation of the kernel parameters, specify the multipliers of the given (standard) parameters to search over for the optimal one.
ncross Integer. If you do cross-validation, specify how many folds.
step.width Defines the resolution of smoothed scores on the chromosome, is in fact the distance in base pairs between 2 positions, for which smoothed scores are to be calculated.
memory.limit If you have a computer with lots of RAM, setting this to FALSE will increase speed of computations.

Details

See the documentation for detailed theory and explanations of this functions.

Value

List of class 'MACATevalScoring' with 11 components:

original.geneid Gene IDs of the genes on the chosen chromosome, sorted according to their position on the chromosome
original.loc Location of genes on chromosome in base pairs from 5'end
original.score Regularized t-score of genes on chromosome
original.pvalue Empirical p-value of genes on chromosome. How often was a higher score observed than this one with random permutations? In other words, how significant seems this score to be?
steps Positions on the chromosome in bp from 5', for which smoothed scores have been computed.
sliding.value Smoothed regularized t-scores at step-positions.
lower.permuted.border Smoothed scores from permutations, lower significance border, currently 2.5%-quantile of permutation scores.
upper.permuted.border Smoothed scores from permutations, upper significance border, currently 97.5%-quantile of permutation scores.
chromosome Chromosome, which has been analyzed
class Class, which has been analyzed
chip Identifier for used microarray

Author(s)

MACAT development team

See Also

scoring,plot.MACATevalScoring, getResults

Examples

   
    #loaddatapkg("stjudem")
    data(stjd)

    # T-lymphocyte versus B-lymphocyte on chromosome 1, 
    #  smoothed with k-Nearest-Neighbours kernel(k=15), 
    #  few permutations for higher speed
    chrom1Tknn <- evalScoring(stjd,"T",chromosome="1",permute="labels",
    nperms=100,kernel=kNN,kernelparams=list(k=15),step.width=100000)

    # plotting on x11:
    plot(chrom1Tknn)

    # plotting on HTML:
    plot(chrom1Tknn,"html")

[Package macat version 1.6.0 Index]