summarizeByRegion            package:reb            R Documentation

_C_o_m_p_u_t_e _S_u_m_m_a_r_y _S_t_a_t_i_s_t_i_c_s _o_f _G_e_n_o_m_e _R_e_g_i_o_n_s

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

     Splits the data into subsets based on genome mapping information,
     computes summary statistics for each region, and returns the
     results in a convenient form. (cgma stands for Comparative Genomic
     Microarray Analysis)

     This function supplies a t.test function at the emperically
     derived significance threshold (p.value = 0.005)

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

     cgma(eset, genome, chrom="ALL",ref=NULL,center=TRUE,aggrfun=NULL, p.value=0.005, FUN=t.test, explode=FALSE ,...) 

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

    eset: an exprSet object

  genome: an chromLocation object, such as on produced by
          buildChromLocation or buildChromMap

   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. If NULL, all values are
          included. Also see 'absMax'

 p.value: p.value cutoff or NA

     FUN: function by which to summarize the data

 explode: boolean - explode summary matrix into a full expression set?

     ...: further arguments pass to or used by the function

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

     Gene expression values are separated into subsets that based on
     the 'chromLocation' object argument.  For example, buildChromMap
     can be used to produce a 'chromLocation' object composed of the
     genes that populate human chromosome 1p and chromosome 1q. The
     gene expression values from each of these regions are extracted
     from the 'exprSet' and a summary statistic is computed for each
     region.

     'cgma' is most straightforwardly used to identify regional gene
     expression biases when comparing a test sample to a reference
     sample. For example, a number of simple tests can be used to
     determine if a genomic region contains a disproportionate number
     of positive or negative log transformed gene expression ratios.
     The presence of such a regional expression bias can indicates an
     underlaying genomic abnormality.

     If multiple clones map to the same genomic locus the
     aggregate.by.loc argument can be used to include a summary value
     for the overlapping expression values rather then include all of
     the individual gene expression values. For example, if 50 copies
     of the actin gene are on a particular array and actin changes
     expression under a given condition, it may appear as though a
     regional expression bias exists as 50 values in a small region
     change expression.

     'regmap' is usually the best way to plot results of this function.
     'idiogram'  can also be used if you set the "explode" argument to
     TRUE.

     'buildChromLocation.2' can be used to create a chromLocation
     object in  which the genes can be divided a number of different
     ways. Separating the data by chromosome  arm was the original
     intent. If you use 'buildChromLocation.2'  with the "arms"
     argument to build your chromLocation object, set the "chrom"
     argument  to "arms" in this function.

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

       m: A matrix of summary statistics

_N_o_t_e:

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

     Kyle A. Furge

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

     Crawley and Furge, Genome Biol. 2002;3(12):RESEARCH0075. Epub 2002
     Nov 25.

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

     'buildChromMap','tBinomTest','regmap','buildChromLocation.2'

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

     ## 
     ## NOTE: This requires an annotation package to work.
     ##       In this example packages "hu6800" and "golubEsets" are used.
     ##       They can be downloaded from http://www.bioconductor.org
     ##       "hu6800" is under MetaData, "golubEsets" is under Experimental Data.

     if(require(hu6800) && require(golubEsets)) {
        data(Golub_Train)
        cloc <- buildChromMap("hu6800",c("1p","1q","2p","2q","3p","3q"))

        ## For one-color expression data
        ## compare the ALL samples to the AML samples
        ## not particularly informative in this example

        aml.ix <- which(Golub_Train$"ALL.AML" == "AML")
        bias <- cgma(eset=Golub_Train,ref=aml.ix,genome=cloc)
        regmap(bias,col=.rwb) 
     } else print("This example requires the hu6800 and golubEsets data
        packages.")

     ## A more interesting example

     ## The mcr.eset is a two-color gene expression exprSet
     ## where cytogenetically complex (MCR), 
     ## cytogenetically simple (CN) leukemia samples
     ## and normal control (MNC) samples were profiled against
     ## a pooled-cell line reference
     ## The MCR eset data was obtained with permission. See PMID: 15377468

     ## Notice the dimished expression on chromosome 5 in the MCR samples
     ## and the enhanced expression on chromosome 11
     ## This reflects chromosome gains and losses as validated by CGH

        data("mcr.eset")
        data(idiogramExample)
        norms <- grep("MNC",colnames(mcr.eset@exprs))
        bias <- cgma(mcr.eset@exprs,vai.chr,ref=norms)
        regmap(bias,col=topo.colors(50)) 

