hybridization            package:MixThres            R Documentation

_H_y_b_r_i_d_i_z_a_t_i_o_n  : _e_s_t_i_m_a_t_i_o_n _a_n_d _s_e_l_e_c_t_i_o_n _o_f _t_h_e _m_i_x_t_u_r_e _m_o_d_e_l

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

     This function estimates the probe intensity values distribution
     using a particular mixture model in order to define in a second
     step a hybridization threshold.

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

     hybridization(x, leftT = c(TRUE, FALSE, TRUE, FALSE), rightT = c(TRUE, TRUE, FALSE, FALSE), stop.crit = 1e-06, Kmax = 5)

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

x               : A dataframe containing a vector of probenames and a
          vector of probe intensity values.

leftT           : A vector of boolean specifying the presence of a left
          truncature. 

rightT          : A vector of boolean specifying the presence of a
          right truncature of the same length than leftT.

stop.crit       : The stopping relative precision limit for stopping EM
          algorithm. 

Kmax            : The maximal number of components in the mixture
          model. 

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

     Truncated gaussian distributions are used. An EM algorithm with a
     supplement M-step corresponding to a fixed-point algorithm (in
     order to correct bias) is used to estimate the parameters of the
     mixture. A vector epsilon algorithm is used to accelerate the EM
     algorithm. The model is selected according to the BIC.

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

     An object of class MixThres, 

GeneID          : The vector of probenames.

InputData       : The vector of input intensity values.

ModelInfo       : A list concerning the selected model : nb of
          components, parameters estimation, loglikelihood, nb of
          parameters, aic, bic

Prob            : The matrix of posterior probabilities.

Trunc           : A vector of the two truncatures values.

Bic             : A vector of the BIC calculated for all the models.

NbParam         : A vector of the number of parameters for all the
          models.

Crit            : NULL. Will be filled with the use of the threshold
          function.

Threshold       : NULL. Will be filled with the use of the threshold
          function.

Hybrid          : NULL. Will be filled with the use of the threshold
          function.

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

     J. Aubert

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

     Picard et al., Using mixture models to define a hybridization
     threshold in DNA microarray experiments.

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

     'threshold', 'print.MixThres', 'plot.MixThres', 'hist.MixThres'

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

     ## Data simulation
     Signal <- c(rnorm(100,5,1),rnorm(700,8,1),rnorm(200,12,1))
     fileIN <- data.frame(Gene=paste("Gene",1:1000,sep=""),Signal=Signal)

     ## Mixture models
     out <- hybridization(fileIN)

