pamr.knnimpute             package:pamr             R Documentation

_A _f_u_n_c_t_i_o_n _t_o _i_m_p_u_t_e _m_i_s_s_i_n_g _e_x_p_r_e_s_s_i_o_n _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     A function to impute missing expression data, using nearest
     neighbor averaging.

_U_s_a_g_e:

     pamr.knnimpute(data ,k = 10, rowmax = 0.5, colmax = 0.8, maxp = 1500)

_A_r_g_u_m_e_n_t_s:

    data: The PAM input data. A list with components: 'x', an
          expression matrix with  genes in the rows, samples in the
          columns, and 'y', a vector of the class labels for each
          sample. Same form as used by pamr.train, and same as that
          produced by 'pamr.from.excel'

       k: Number of neighbors to be used in the imputation (default=10)

  rowmax: The maximum percent missing data allowed in any row (default
          50%). For any rows with more than 'rowmax'% missing are
          imputed using the overall mean per sample.

  colmax: The maximum percent missing data allowed in any column
          (default 80%). If any column has more than 'colmax'% missing
          data, the program halts and reports an error.

    maxp: The largest block of genes imputed using the knn algorithm
          inside 'pamr.knnimpute' (default 1500); larger blocks are
          divided by two-means clustering (recursively) prior to
          imputation. If 'maxp=p', only knn imputation is done.

_D_e_t_a_i_l_s:

     'pamr.knnimpute'  uses k-nearest neighbors in the space of genes
     to impute missing expression values.

     For each gene with missing values, we find the k nearest neighbors
     using a Euclidean metric, confined to the columns for which that
     gene is NOT missing. Each candidate neighbor might be missing some
     of the coordinates used to calculate the distance. In this case we
     average the distance from the non-missing coordinates. Having
     found the k nearest neighbors for a gene, we impute the missing
     elements by averaging those (non-missing) elements of its
     neighbors. This can fail if ALL the neighbors are missing in a
     particular element. In this case we use the overall column mean
     for that block of genes.

     Since nearest neighbor imputation costs O(p*log(p)) operations per
     gene, where p is the number of rows, the computational time can be
     excessive for large p and a large number of missing rows. Our
     strategy is to break blocks with more than 'maxp' genes into two
     smaller blocks using two-mean clustering. This is done recursively
     till all blocks have less than 'maxp' genes. For each block, knn
     imputation is done separately. We have set the default value of
     'maxp' to 1500. Depending on the speed of the machine, and number
     of samples, this number might be increased. Making it too small is
     counter-productive, because the number of two-mean clustering
     algorithms will increase.

_V_a_l_u_e:

    data: 

     {The input data list, with x replaced by the  imputed version of
     x}

_A_u_t_h_o_r(_s):

     Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, and
     Gilbert Chu

_R_e_f_e_r_e_n_c_e_s:

     Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and
     Botstein, D., Imputing Missing Data for Gene Expression Arrays,
     Stanford University Statistics Department Technical report (1999),
     http://www-stat.stanford.edu/~hastie/Papers/missing.pdf

     Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown,
     Trevor Hastie, Robert Tibshirani, David Botstein and Russ B.
     Altman, Missing value estimation methods for DNA microarrays
     BIOINFORMATICS Vol. 17 no. 6, 2001 Pages 520-525

