twilight.filtering         package:twilight         R Documentation

_A _f_u_n_c_t_i_o_n _f_o_r _p_e_r_m_u_t_a_t_i_o_n _f_i_l_t_e_r_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     The function call invokes the filtering for permutations of class
     labels that produce a set of complete null scores. Depending on
     the test setting, the algorithm iteratively generates valid
     permutations of the class labels and computes scores. These are
     transformed to pooled p-values and each set of permutation
     p-values is tested for uniformity. Permutations with acceptable
     uniform p-value distributions are kept. The search stops if either
     the number 'num.perm' of wanted permutations is reached or if the
     number of possible unique(!) permutations is smaller than
     'num.perm'. The default values are similar to function
     'twilight.pval' but please note the details below.

_U_s_a_g_e:

     twilight.filtering(xin, yin, method = "fc", paired = FALSE, s0 = 0, verbose = TRUE, num.perm = 1000, num.take = 50)

_A_r_g_u_m_e_n_t_s:

     xin: Either an expression set ('ExpressionSet') or a data matrix
          with rows corresponding to features and columns corresponding
          to samples. 

     yin: A numerical vector containing class labels. The higher label
          denotes the case, the lower label the control samples to test
          case vs. control. For correlation scores, 'yin' can be any
          numerical vector of length equal to the number of samples. 

  method: Character string: '"fc"' for fold change equivalent test
          (that is log ratio test), '"t"' for t-test, and '"z"' for
          Z-test. With '"pearson"' or '"spearman"', the test statistic
          is either Pearson's correlation coefficient or Spearman's
          rank correlation coefficient.

  paired: Logical value. Depends on whether the samples are paired.
          Ignored if 'method="pearson"' or 'method="spearman"'.

      s0: Fudge factor for variance correction in the Z-test. Takes
          effect only if 'method="z"'. If 's0=0': The fudge factor is
          set to the median of the pooled standard deviations.

 verbose: Logical value for message printing. 

num.perm: Number of permutations. Within 'twilight.pval', 'num.perm' is
          set to 'B'. 

num.take: Number of permutations kept in each step of the iterative
          filtering. Within 'twilight.pval', 'num.take' is set to the
          minimum of 50 and 'ceiling(num.perm/20)'. 

_D_e_t_a_i_l_s:

     See vignette.

_V_a_l_u_e:

     Returns a matrix with permuted input labels 'yin' as rows. Please
     note that this matrix is already translated into binary labels for
     two-sample testing or to ranks if Spearman's correlation was
     chosen. The resulting permutation matrix can be directly passed
     into function 'twilight.pval'. Please note that the first row
     always contains the original input 'yin' to be consistent with the
     other permutation functions in package 'twilight'.

_A_u_t_h_o_r(_s):

     Stefanie Scheid <URL: http://www.molgen.mpg.de/~scheid>

_R_e_f_e_r_e_n_c_e_s:

     Scheid S and Spang R (2004): A stochastic downhill search
     algorithm for estimating the local false discovery rate, _IEEE
     TCBB_ *1(3)*, 98-108.

     Scheid S and Spang R (2005): twilight; a Bioconductor package for
     estimating the local false discovery rate, _Bioinformatics_
     *21(12)*, 2921-2922. 

     Scheid S and Spang R (2006): Permutation filtering: A novel
     concept for significance analysis of large-scale genomic data, in:
     Apostolico A, Guerra C, Istrail S, Pevzner P, and Waterman M
     (Eds.): _Research in Computational Molecular Biology: 10th Annual
     International Conference, Proceedings of RECOMB 2006, Venice,
     Italy, April 2-5, 2006_. Lecture Notes in Computer Science vol.
     3909, Springer, Heidelberg, pp. 338-347.

_S_e_e _A_l_s_o:

     'twilight.pval'

_E_x_a_m_p_l_e_s:

     ## Not run: 
     ### Leukemia data set of Golub et al. (1999).
     library(golubEsets)
     data(Golub_Train)

     ### Variance-stabilizing normalization of Huber et al. (2002).
     library(vsn)
     golubNorm <- vsn(exprs(Golub_Train))

     ### A binary vector of class labels.
     id <- as.numeric(Golub_Train$ALL.AML)

     ### Do an unpaired t-test.
     ### Let's have a quick example with 50 filtered permutations only.
     ### With num.take=10, we only need 5 iteration steps.
     yperm <- twilight.filtering(golubNorm,id,method="t",num.perm=50,num.take=10)
     dim(yperm)

     ### Let's see if the filtered permutations really produce uniform p-value distributions.
     ### The first row is the original labeling, so we try the second permutation.
     yperm <- yperm[-1,]
     b <- twilight.pval(golubNorm,yperm[1,],method="t",yperm=yperm)
     hist(b$result$pvalue)
     ## End(Not run)

