plotCoexpression       package:GeneRegionScan       R Documentation

_P_l_o_t _C_o_e_x_p_r_e_s_s_i_o_n _o_f _p_r_o_b_e_s _i_n _a _P_r_o_b_e_L_e_v_e_l_S_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     Function that will investigate all possible pairings in a set of
     probes, calculate the Pearson correlation coefficient and plot
     them in a meaningful way

_U_s_a_g_e:

         plotCoexpression(object, gene, probeData=NULL, verbose=TRUE, directions="all", correlationCutoff=0.5,
         probeLevelInfo=c("probeid"))

_A_r_g_u_m_e_n_t_s:

  object: A ProbeLevelSet object or a regular ExpressionSet object (in
          which case a probeData argument is required). See
          getLocalProbeIntensities and related functions on how to
          create a ProbeLevelSet.

    gene: A number of gene sequences as DNAstring, vectors of
          DNAStrings, character-vectors or readFASTA outputs.

probeData: Optional if a ProbeLevelSet is submitted as object argument.
          Otherwise it must be a data frame with rownames corresponding
          to the featureNames of the ExpressionSet and a column named
          "sequence" with the probe sequences as character strings

 verbose: TRUE or FALSE

directions: A character vector of the matching-directions that should
          be scanned (which combinations of complementary and reverse).
          Defaults to "all" which is shorthand for all possible
          directions, but can take anything from:
          c("matchForwardSense", "matchForwardAntisense",
          "matchReverseSense", "matchReverseAntisense")

correlationCutoff: A number between 0 and 1. The limit at which Pearson
          correlation (in absolute values) should not be plotted below.
          Defaults to 0.5

probeLevelInfo: The information about each probe to include in the
          plot. Should be a vector of one or more of the following
          elements: probeid, probesetid, sequence. Default is only
          probeid.

_D_e_t_a_i_l_s:

     This function takes a ProbeLevelSet or an ExpressionSet +
     probeData and the sequence of a gene. It then calculates pairwise
     Pearson correlation coefficients between all possible combinations
     of probes. Then it assigns each probe to a location  along the
     length of the gene and plots a relational graph showing which
     probes has high correlation coefficients. The correlation
     coefficients are sorted by absolute values meaning that it will
     also include the negative correlations.

_V_a_l_u_e:

     No value, but plots a hapmap style plot of correlation values
     between all probes

_A_u_t_h_o_r(_s):

     Lasse Folkersen

_S_e_e _A_l_s_o:

     'geneRegionScan', 'plotOnGene'

_E_x_a_m_p_l_e_s:

             
             data(exampleProbeLevelSet)
         plotCoexpression(exampleProbeLevelSet, mrna, correlationCutoff=0.7, probeLevelInfo=c("probeid","sequence"))

