copa                  package:copa                  R Documentation

_C_a_l_c_u_l_a_t_e _C_O_P_A _S_c_o_r_e_s _f_r_o_m _a _S_e_t _o_f _M_i_c_r_o_a_r_r_a_y_s

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

     This function calculates COPA scores from a set of microarrays.
     Input can be an 'exprSet', 'ExpressionSet', or a matrix or
     'data.frame'.

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

     copa(object, cl, cutoff = 5, max.overlap = 0, norm.count = 0, pct = 0.95)

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

  object: An 'exprSet','ExpressionSet', or a matrix or 'data.frame'.

      cl: A vector of classlabels indicating sample status (normal = 1,
          tumor = 2).

  cutoff: The cutoff to determine 'outlier' status. See details for
          more information.

max.overlap: The maximum number of samples that can be considered
          'outliers' when comparing two genes. The default is 0,
          indicating that there can be no overlap. See details for more
          information.

norm.count: The number of normal samples that can be considered
          'outliers'. The default is 0, meaning that no normals may be
          outliers.

     pct: The percentile to use for pre-filtering the data. A
          preliminary step is to compute the number of outlier samples
          for each gene. All genes with a number of outlier samples
          less than the (default 95th) percentile will be removed from
          further consideration.

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

     Cancer Outlier Profile Analysis is a method that is intended to
     find pairs of genes that may be involved in recurrent gene fusion
     with a third (unknown) gene. The underlying idea here is that in
     certain cancers it may be common for the promoter region of one
     gene to become fused to certain oncogenes. For instance, Tomlins
     et. al. showed that the promoter region of TMPRSS2 fused to either
     ERG or ETV1 in the majority of prostate cancer tumors tested.

     Since this fusion should only happen with one oncogene in a given
     sample, we look for pairs of genes where some samples have much
     higher expression values, but the samples for gene 'A' are
     mutually exclusive from the samples for gene 'B'.

     The cutoff argument for this function is used to determine how
     high the centered and scaled expression value has to be in order
     to be considered an outlier. The max.overlap argument allows one
     to relax the requirement of mutual exclusivity, although in
     practice this is probably not advisable.

     Note that this function computes all row-wise comparisons, which
     gets very large very quickly. The function will throw a warning
     for any data set containing > 1000 rows and query the user to see
     if he/she really wants to proceed. The number of genes to be
     considered can be adjusted by increasing/decreasing the 'pct'
     argument.

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

 ord.prs: A matrix with two columns containing the ordered row numbers
          from the original matrix of gene expression values.

 pr.sums: A numeric vector with the number of mutually exclusive
          outliers for each gene pair. This is the criterion for
          ranking the gene pairs; the assumption being that a pair of
          genes with more mutually exclusive outliers will be more
          interesting than a pair with relatively fewer outliers.

     mat: A matrix containing the filtered gene expression values.

      cl: The classlabel vector passed to 'copa'

  cutoff: The cutoff used

max.overlap: The value of max.overlap used

norm.count: The value of norm.count used

     pct: The percentile used in the pre-filtering step

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

     James W. MacDonald

_R_e_f_e_r_e_n_c_e_s:

     Tomlins, SA, et al. Recurrent fusion of TMPRSS2 and ETS
     transcription factor genes in prostate cancer. Science. 2005 Oct
     28;310(5748):644-8.

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

     library(Biobase)
     data(sample.ExpressionSet)
     cl <- abs(3 - as.numeric(pData(sample.ExpressionSet)[,2]))
     tmp <- copa(sample.ExpressionSet, cl)

