quantileAdjust             package:edgeR             R Documentation

_N_o_r_m_a_l_i_z_e_s _a _d_a_t_a_s_e_t _b_y _u_s_i_n_g _a _q_u_a_n_t_i_l_e _a_d_j_u_s_t_m_e_n_t

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

     The function adjusts (you might say normalizes) a dataset,
     creating pseudodata that represents quantile-adjusted data as if
     all samples had the same library size, while estimating the
     dispersion parameter.

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

      
     quantileAdjust(object, N = prod(object$lib.size)^(1/ncol(object$data)), alpha = 0, null.hypothesis = FALSE, n.iter = 5, r.init = NULL, tol = 0.001, verbose=TRUE) 

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

  object: list containing the raw data with elements 'data' (table of
          counts), 'group' (vector indicating group) and 'lib.size'
          (vector of library sizes)

       N: library size to normalize to; default is the geometric mean
          of the original library sizes

   alpha: weight to put on the individual tag's likelihood

null.hypothesis: logical, whether to calculate the means and percentile
          under the null hypothesis; default is 'TRUE'

  n.iter: number of iterations in estimating the size parameter

  r.init: initialized value of the size parameter; if 'NULL', then the
          common value on unadjusted data is used

     tol: tolerance in estimating the size parameter

 verbose: whether to write comments, default 'true'

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

     list containing several elements used in downstream function
     calls.  'r' is the dispersion estimate, 'pseudo' is the
     quantile-adjusted pseudodata, 'ps' is a list containing the
     abundance estimates, 'N' is the common library size and 'p' and
     'mu' are the percentiles and means, respectively that the quantile
     is based on

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

     Mark Robinson

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

     set.seed(0)
     y<-matrix(rnbinom(40,size=1,mu=10),ncol=4)
     d<-list(data=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
     qA<-quantileAdjust(d,alpha=100)

