SeqKNN                package:SeqKnn                R Documentation

_S_e_q_u_e_n_t_i_a_l _K_N_N _i_m_p_u_t_a_t_i_o_n _m_e_t_h_o_d

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

     This function estimates missing values sequentially from the gene
     that has least missing rate in microarray data, using weighted
     mean of k nearest neighbors. This function requires 'nnmiss'.

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

     SeqKNN(data, k)

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

    data: matrix or dataframe, 1 row corresponds to 1 gene, 1 column to
          1 sample,colnames and rownames can be used

       k: number of nearest neighbors

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

     'SeqKNN' separates the dataset into incomplete and complete set
     that has or has not missing values respectively. The genes in
     incomplete set are imputed by the order of missing rate. Missing
     value is filled by the weighted mean value of corresponding column
     of the nearest neighbor genes in complete set. Once all missing
     values in a gene are imputed, the imputed gene is moved into the
     complete set and used for the imputation of the rest of genes in
     incomplete set. In this process, all missing values in one gene
     can be imputed simultaneously from the selected neighbor genes in
     complete set. This reduces execution time from previously
     developed KNN method that selects nearest neighbors for each
     imputation.

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

     Ki-Yeol Kim and Gwan-Su Yi

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

     Ki-Yeol Kim, Byoung-Jin Kim, Gwan-Su Yi (2004.Oct.26) "Reuse of
     imputed data in microarray analysis increases imputation
     efficiency", BMC Bioinformatics 5:160.

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

     ## Not run: 
     data(khan05)
     imputedData<-SeqKNN(khan05,10)
     ## End(Not run)

      

