detectOutlier              package:lumi              R Documentation

_D_e_t_e_c_t _t_h_e _o_u_t_l_i_e_r _s_a_m_p_l_e (_o_r _g_e_n_e)

_D_e_s_c_r_i_p_t_i_o_n:

     Detect the outlier sample (or gene) based on distance to the
     cluster center

_U_s_a_g_e:

     detectOutlier(x, metric = "euclidean", standardize = TRUE, Th = 2, ifPlot = FALSE)

_A_r_g_u_m_e_n_t_s:

       x: a LumiBatch object, ExpressionSet object or a matrix with
          each column corresponding to a sample or other profile 

  metric: the distance matric

standardize: standardize the profile or not 

      Th: the threshold of outlier,  

  ifPlot: to plot the result (as a hierarchical tree) or not 

_D_e_t_a_i_l_s:

     The current outlier detection is based on the distance from the
     sample to the center (average of all samples). The assumption of
     the outlier detection is that there is only one single cluster and
     the distance from the sample to the center is Gaussian
     distributed.

     The outlier is detected when its distance to the center is larger
     than a certain threshold. The threshold is calculated as Th *
     median distances to the center.

     The profile relations can be visualized as a hierarchical tree.

_V_a_l_u_e:

     Plot the results or     return the outlier (a logic vector) with
     the distance matrix and threshold as attributes.

_A_u_t_h_o_r(_s):

     Pan Du

_S_e_e _A_l_s_o:

     'lumiQ'

_E_x_a_m_p_l_e_s:

     ## load example data
     data(example.lumi)

     ## detect the outlier (Further improvement needed.)
     temp <- detectOutlier(example.lumi, ifPlot=TRUE)

