Description Usage Arguments Details Value Author(s) See Also Examples
This function computes for all genes on one chromosome the regularized tstatistic 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.
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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 now should be considered preliminary at best. 
pcompute 
Method to determine the pvalue for differential
expression of each gene. Is only evaluated if the argument

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 MACATliststructure 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 kNearestNeighbors, 'rbf' for radialbasisfunction (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 kNNkernel. If NULL some defaults are set within the function. 
cross.validate 
Logical. Should the paramter settings for the kernel function be optimized by a crossvalidation? 
paramMultipliers 
Numeric vector. If you do crossvalidation of the kernel parameters, specify the multipliers of the given (standard) parameters to search over for the optimal one. 
ncross 
Integer. If you do crossvalidation, 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. 
verbose 
logical; should function's progress be reported to STDOUT ?; default: TRUE. 
Please see the package vignette for more details on this function.
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 tscore of genes on chromosome 
original.pvalue 
Empirical pvalue 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 tscores at steppositions. 
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 
MACAT development team
scoring
,plot.MACATevalScoring
,
getResults
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  data(stjd) # load example data
# if you have the data package 'stjudem' installed,
# you should work on the full data therein, of which
# the provided example data, is just a piece
#loaddatapkg("stjudem")
#data(stjude)
# Tlymphocyte versus Blymphocyte on chromosome 1,
# smoothed with kNearestNeighbours 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:
if (interactive())
plot(chrom1Tknn)
# plotting on HTML:
if (interactive())
plot(chrom1Tknn,"html")

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