smoothByRegion              package:reb              R Documentation

_r_e_b

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

     This function smooths gene expression data to assist in the
     identification of regional expression biases.

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

     reb(eset, genome, chrom = "ALL", ref = NULL, center = FALSE,
     aggrfun=absMax, method = c("movbin", "supsmu", "lowess","movt"), ...)

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

    eset: the expression set to analyze 

  genome: an associated chromLoc annotation object 

   chrom: a character vector specifying the chromosomes to analyze

     ref: a vector containing the index of reference samples from which
          to make comparisons. Defaults to NULL (internally referenced
          samples

  center: boolean - re-center gene expression matrix columns. Helpful
          if 'ref' is used

 aggrfun: a function to summarizes/aggregates gene expression values
          that map to the same locations. Defaults to the maximum
          absolute value 'absMax'. If NULL, all values are included.

  method: smoothing function to use - either '"supmu"', '"lowess"', 
          '"movbin"' or '"movt"'.

     ...: additional paramaters to pass along to the smoothing function 

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

     'reb' returns an eset that contains predictions of regional
     expression bias using data smoothing approachs. The exprSet is
     separated into subsets based on the 'genome' chromLocation object
     and the gene expression data within the subsets is organized by
     genomic location and smoothed. In addition, the 'approx' function
     is used to estimate data between any missing values. This was
     implimented so the function follows the principles of least
     astonishment.    

     Smoothing approachs are most straightforwardly applied by
     comparing a set of test samples to a set of control samples.  For
     single color experiments, the control samples can be specified
     using the 'ref' argument and the comparisons are generated
     internal to the 'reb' function. This argument can also be used for
     two-color experiments provided both the test and control samples
     were run against a common reference.

     If multiple clones map to the same genomic locus the 'aggrfun'
     argument can be used to summarize the overlapping expression
     values to a single summarized value. This is can be helpful in two
     situtations. First, the 'supsum' and 'lowess' smoothing functions
     do not allow for duplicate values. Currently, if duplicate values
     are found and these smoothing functions are used, the duplicate
     values are simply discard. Second, if 50 copies of the actin gene
     are present on a the array and actin changes expression under a
     given condition, it may appear as though a regional expression
     bias exists as 50 values within a region change expression.
     Summarizing the 50 expression values to a single value can
     partially correct for this effect. 

     The idiogram package can be used to plot the regional expression
     bias.

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

     An exprSet

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

     Kyle A. Furge, kyle.furge@vai.org Karl J. Dykema,
     karl.dykema@vai.org

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

     Furge KA, Dykema KJ, Ho C, Chen X. Comparison of array-based
     comparative genomic hybridization with gene expression-based
     regional expression biases to identify genetic abnormalities in
     hepatocellular carcinoma. BMC Genomics. 2005 May 9;6(1):67. PMID:
     1588246

     MCR eset data was obtained with permission. See PMID: 15377468

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

     'movbin','idiogram'

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

     # The mcr.eset is a two-color gene expression exprSet
     # with cytogenetically complex (MCR) and normal 
     # control (MNC) samples which are a pooled-cell line reference.

     data("mcr.eset")
     data(idiogramExample)

     ## Create a vector with the index of normal samples
     norms <- grep("MNC",colnames(mcr.eset@exprs))

     ## Smooth the data using the default 'movbin' method,
     ## with the normal samples as reference

     cset <- reb(mcr.eset@exprs,vai.chr,ref=norms,center=TRUE)

     ## Display the results with midiogram
     midiogram(cset@exprs[,-norms],vai.chr,method="i",dlim=c(-5,5),col=.rwb)

