nudSegmentation           package:snapCGH           R Documentation

_A_s_s_i_g_n_s _e_a_c_h _s_e_g_m_e_n_t _o_f _e_a_c_h _s_a_m_p_l_e _t_o _e_i_t_h_e_r '_n_o_r_m_a_l', '_u_p', '_d_o_w_n', '_a_m_p_l_i_f_i_e_d', _o_r '_d_e_l_e_t_e_d'

_D_e_s_c_r_i_p_t_i_o_n:

     Classifies each segment in each sample in a SegList according to
     the absolute magnitude of the segment and the noisiness of the
     data for that sample.

_U_s_a_g_e:

     nudSegmentation(seg, factor.change = 0.75, amplified.max.width = 10,
     deleted.max.width = 10, amplified.magnitude = 1, deleted.magnitude = 1,
     cellularity)

_A_r_g_u_m_e_n_t_s:

     seg: Object of class 'SegList'

factor.change: Factor of estimated noise by which a segment should
          deviate from zero to be classified as gained or lost.

amplified.max.width: Maximum width permitted for a segment to be
          classified as an amplicon.

deleted.max.width: Maximum width permitted for a segment to be
          classified as a deletion.

amplified.magnitude: Minimum (absolute) segmentation value permitted
          for a segment to be classified as an amplicon.

deleted.magnitude: Minimum (absolute) segmentation value permitted for
          a segment to be classified as a deletion.

cellularity: If present, a vector of the same length as the number of
          samples in seg, giving cellularity values as a fraction
          between 0-1. NAs should be used where these are not known.

_D_e_t_a_i_l_s:

     The function classifies each segment as either 'normal', 'up',
     'down', 'amplified' or 'deleted'. A region is classified as 'up'
     or 'down' if the segmentation value is more (in absolute value)
     than 'factor.change' multiplied by the interquartile range of the
     difference between observed and predicted values for each
     observation on the genome. A region is classified as amplified or
     deleted if it falls within the constraints of the arguments given.
     It is felt that regions of amplification or deletion should be
     sufficiently striking not to be affected by the noise of the data.
     The 'cellularity' argument, if given, adjusts the thresholds for
     identifying 'up' and 'down' regions in terms of the level of
     cellularity given. Thus, for a sample of low observed cellularity,
     smaller changes should be identified as gains or losses. In cases
     of sufficiently low cellularity, this will inevitably lead to
     greater misclassification and so the classification on these
     samples should be checked and, if necessary, the sample discarded.

_V_a_l_u_e:

     Object of class 'SegList, now with a \$regions element. \$regions
     is a list of the same length as the number of samples in seg, each
     element of which is a data.frame containing an annotation of the
     classification for the associated sample.'

_A_u_t_h_o_r(_s):

     Tom Hardcastle

_S_e_e _A_l_s_o:

     'plotSegmentedGenome' 'SegList'

