genefinder package:genefilter R Documentation _F_i_n_d_s _g_e_n_e_s _t_h_a_t _h_a_v_e _s_i_m_i_l_a_r _p_a_t_t_e_r_n_s _o_f _e_x_p_r_e_s_s_i_o_n. _D_e_s_c_r_i_p_t_i_o_n: Given an 'exprSet' or a matrix of gene expressions, and the indices of the genes of interest, 'genefinder' returns a list of the 'numbResults' closest genes. The user can specify one of the standard distance measures listed below. The number of values to return can be specified. The return value is a list with two components: genes (measured through the desired distance method) to the genes of interest (where X is the number of desired results returned) and their distances. _U_s_a_g_e: genefinder(X, ilist, numResults=25, scale="none", weights, method="euclidean") _A_r_g_u_m_e_n_t_s: X: A numeric matrix where columns represent patients, and rows represent genes. ilist: Vector of genes of interest. Contains indices of genes in matrix X. numResults: Number of results to display, starting from the least distance to the greatest. scale: one of 'none', 'range', or 'zscore'. Scaling is carried out separately on each row. weights: A vector of weights applied across the columns of 'X'. If no weights are supplied, no weights are applied method: one of "euclidean", "maximum", "manhattan", "canberra", "correlation", "binary". _D_e_t_a_i_l_s: If the scale option is "range", then the input matrix is scaled using genescale(). If it is "zscore", then the input matrix is scaled using the 'scale' builtin with no arguments. The method option specifies the metric used for gene comparisons. The metric is applied, row by row, for each gene specified in 'ilist'. The "correlation" option for the distance method will return a value equal to 1-correlation(x). See 'dist' for a more detailed description of the distances. _V_a_l_u_e: The returned value is a list containing an entry for each gene specified in ilist. Each list entry contains an array of distances for that gene of interest. _A_u_t_h_o_r(_s): J. Gentry and M. Kajen _S_e_e _A_l_s_o: 'genescale' _E_x_a_m_p_l_e_s: set.seed(12345) #create some fake expression profiles m1 <- matrix (1:12, 4, 3) v1 <- 1 nr <- 2 #find the 2 rows of m1 that are closest to row 1 genefinder (m1, v1, nr, method="euc") v2 <- c(1,3) genefinder (m1, v2, nr) genefinder (m1, v2, nr, scale="range") genefinder (m1, v2, nr, method="manhattan") m2 <- matrix (rnorm(100), 10, 10) v3 <- c(2, 5, 6, 8) nr2 <- 6 genefinder (m2, v3, nr2, scale="zscore")