evalScoring              package:macat              R Documentation

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_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     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, verbose=TRUE)

_A_r_g_u_m_e_n_t_s:

    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.

 verbose: logical; should function's progress be reported to STDOUT ?;
          default: TRUE.

_D_e_t_a_i_l_s:

     Please see the package vignette for more details on this function.

_V_a_l_u_e:

     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

_A_u_t_h_o_r(_s):

     MACAT development team

_S_e_e _A_l_s_o:

     'scoring','plot.MACATevalScoring', 'getResults'

_E_x_a_m_p_l_e_s:

         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")

         # 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:
         if (interactive())
            plot(chrom1Tknn)

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

