heterozygousSNPs        package:beadarraySNP        R Documentation

_R_e_t_r_i_e_v_e _h_e_t_e_r_o_z_y_g_o_u_s _S_N_P_s

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

     Heterozygous SNPs are determined based on quality score criteria

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

       heterozygousSNPs(object, threshold=0.9, useQuality=TRUE, relative=TRUE, 
                        percentile=FALSE)

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

  object: class SnpSetIllumina

threshold: numeric (0:1) minimum quality score to be called
          heterozygous

useQuality: logical, use quality score

relative: logical, use quality score relative to GTS, see details

percentile: logical, use percentage of probes above threshold

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

     This function presumes that the specificity for determining
     heterozygity is  more important than the sensitivity, and will
     therefore only call probes heterozygous  if that can be done with
     high certainty.
      The Illumina genotyping software calculates two quality measures:
     gen train score (GTS)  and gen call score (GCS). The GTS is a
     measure for how well clusters can be recognized  in a training
     set. This value is probe specific, and the same for all samples in
     an experiment. The GCS is a probe-specific, sample specific value
     that measures how close  a probe in a sample is to the clusters
     determined in the training step. This value is  always lower than
     the GTS for a probe.
      'read.SnpSetIllumina' will put GCS into the 'callProbability'
     element  of the 'assaydata' slot and the GTS into the
     'featureData' slot. The  function uses these locations to retrieve
     the necessary information.
      If 'relative' is 'FALSE' then the raw GCS values are compared to
     the 'threshold'. In this case a 'threshold' of around 0.5 should
     be used. If  'relative' is 'TRUE' then GCS/GTS is compared to the
     'threshold' and 'threshold' should be around 0.9.
      With 'percentile=TRUE' the 'threshold' quantile is calculated for
     each sample, and only probes with higher scores can be called
     heterozygous. A 'threshold' of around 0.2 seems to work fine
     usually.

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

     This function returns a 'logical matrix' with same dimensions as
     'object'.

_N_o_t_e:

     The purpose of the function is to separate heterozygous probes
     from non-heterozygous probes. In tumor samples the determination
     of the genotype can be difficult,  because of aneuploidy and the
     fact that a sample is often a mixture of normal and tumor cells.

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

     Jan Oosting

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

     'SnpSetIllumina-class'

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

       data(chr17.260)
       plot(heterozygosity(heterozygousSNPs(chr17.260[,"514TV"])),col="red",pch="x")
       points(heterozygosity(exprs(chr17.260)[,"514TV"]))

