preprocess                package:LPE                R Documentation

_P_r_e_p_r_o_c_e_s_s_i_n_g _t_h_e _d_a_t_a (_I_Q_R _n_o_r_m_a_l_i_z_a_t_i_o_n, _t_h_r_e_s_h_o_l_d_i_n_g, _l_o_g-
_t_r_a_n_s_f_o_r_m_a_t_i_o_n, _a_n_d _l_o_w_e_s_s _n_o_r_m_a_l_i_z_a_t_i_o_n)

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

     Finds inter-quartile range of the data = (75th percentile - 25th
     percentile), thresholds low intensity MAS4, MAS5 and dChip data to
     1, then log transforms the data (base 2), and dows lowess
     normalization

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

       preprocess(x, data.type="MAS5",threshold=1,LOWESS=FALSE)

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

       x: x is the data-set which needs preprocessing.

data.type: Three types of data accepted in the current version : MAS4
          (Microarray suite software) , MAS5 and dChip

threshold: threshold is the 'thresholding value' below which all data
          would be thresholded (default = 1).

  LOWESS: LOWESS is a logical variable which determines if lowess 
          normalization needs to be performed.

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

     Returns a data-set of same dimensions as that of the input data.
     It has  IQR normalization for MAS4 and MAS5 data. Low intensities
     of MAS4, MAS5 and dChip data are thresholded to 1. Then data is
     transformed to base 2. If LOWESS normalization parameter is set as
     TRUE, then lowess   normalization is performed.

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

     Nitin Jainnitin.jain@pfizer.com

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

     J.K. Lee and M.O.Connell(2003). _An S-Plus library for the
     analysis of differential expression_. In The Analysis of Gene
     Expression Data: Methods and Software. Edited by G. Parmigiani, ES
     Garrett, RA Irizarry ad SL Zegar. Springer, NewYork.

     Jain et. al. (2003) _Local pooled error test for identifying
     differentially expressed genes with a small number of replicated
     microarrays_, Bioinformatics, 1945-1951.

     Jain et. al. (2005) _Rank-invariant resampling based estimation of
     false discovery rate for analysis of small sample microarray
     data_, BMC Bioinformatics, Vol 6, 187.

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

     'lpe'

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

       library(LPE)
       # Loading the LPE library
      
       data(Ley)
       # Loading the data set
       dim(Ley) #gives 12488 * 7
       Ley[1:3,]

       Ley[1:1000,2:7] <- preprocess(Ley[1:1000,2:7],data.type="MAS5",
             threshold=1, LOWESS=TRUE)
       Ley[1:3,]
      

