si                  package:exonmap                  R Documentation

_C_a_l_c_u_l_a_t_e _t_h_e _s_p_l_i_c_i_n_g _i_n_d_e_x

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

     Calculates the splicing index for the probesets in one or more
     genes, as defined in the Affymetrix white paper "Alternative
     Transcript Analysis Methods for Exon Arrays".

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

      si(x, v, group, gps, median.gene=FALSE,median.probeset=FALSE,unlogged=TRUE)

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

       x: eSet containing expression data 

       v: Character vector of Ensembl gene names 

   group: If defined, the column name in the ExpressionSet's pData
          object in which to look for 'gps' 

     gps: The two sets of arrays to compare 

median.gene: Use the median instead of the mean when calculating
          averages across genes

median.probeset: Use the median instead of the mean when calculating
          averages across probesets in each replicate group  

unlogged: Unlog the expression data before calculating the splicing
          index (and then re-log afterwards) 

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

     The splicing index gives a measure of the difference in expression
     level for each probeset in a gene between two sets of arrays,
     relative to the gene-level average in each set. This is calculated
     only for those probesets that are defined as exon targeting and
     non-multitargetted (See 'select.probewise' and 'exclude.probewise'
     for more details of how this filtering is performed.

     The two sets of arrays can be specified in two ways: First, by
     using numeric indices defining the appropriate columns in the
     expression data. This is done by supplying these as a list to
     'gps' (e.g. 'gps=list(1:3,4:6)' will calculate the splicing index
     between arrays 1,2,3 and 4,5,6. Alternatively, the annotation in
     the 'pData' object from 'x' can be used (e.g.
     'group="treatment",gps=c("a","b")', will compare between the
     arrays labelled "a", and "b" in the "treatment" column of
     'pData(x)').

     The implementation also calculates a 'p.value' and 't.statistic'
     for each probeset; these are returned alongside the splicing
     index.

     By default, the splicing index is calculated using the mean across
     genes and samples. Specifing 'median.gene=TRUE' or
     'median.probeset=TRUE' will use the median instead (for the gene
     or probeset level averages, respectively). It is calculated using
     the unlogged data, unless 'unlogged=FALSE'. This only affects the
     internal calculations; values in 'x' are always assumed to be
     logged, and the splicing index is always returned on the log2
     scale.

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

     A 'list', one element for each gene. Each element contains a
     'data.frame', with the results for a given gene. Each row
     corresponds to a probeset, and there are four columns in the
     'data.frame': '"si","p.value","t.statistic"' and '"gene.av"'.

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

     Crispin J Miller with contributions from Carla Moller Levet and
     Michal J Okoniewski

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

     <URL:  http://bioinformatics.picr.man.ac.uk/>

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

     'splanova'

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

      
       if(interactive()) {
         xmapConnect()
         data(exonmap)
         gg <- probeset.to.gene(c("2326780","2326822" ))
         spl.idx <-  si(x, gg, "group", c("a","b"))
         spl.idx <-  si(x, gg, gps=list(1:3,4:6))
       }

