adjBaseOlig.error           package:LPEadj           R Documentation

_E_v_a_l_u_a_t_e_s _L_P_E _v_a_r_i_a_n_c_e _f_u_n_c_t_i_o_n _o_f _M _f_o_r _q_u_a_n_t_i_l_e_s _o_f _A _w_i_t_h_i_n _a_n_d 
_e_x_p_e_r_i_m_e_n_t_a_l _c_o_n_d_i_t_i_o_n _a_n_d _t_h_e_n _i_n_t_e_r_p_o_l_a_t_e_s _i_t _f_o_r _a_l_l _g_e_n_e_s.

_D_e_s_c_r_i_p_t_i_o_n:

     Calls adjBaseOlig.error.step1 and adjBaseOlig.error.step2
     functions in order to calculate the baseline distribution.

_U_s_a_g_e:

       adjBaseOlig.error(y, stats=median, q=0.01, min.genes.int=10,div.factor=1,
     setMax1=FALSE)

_A_r_g_u_m_e_n_t_s:

       y: y is a preprocessed matrix or data frame of expression
          intensities in which columns are expression intensities for a
          particular experimental condition and rows are genes.

   stats: It determines whether mean or median is to be used for the
          replicates

       q: q is the quantile width; q=0.01 corresponds to 100 quantiles
          i.e. percentiles. Bins/quantiles have equal number of genes
          and are split according to the average intensity A.

min.genes.int: Determines the minimum number of genes in a subinterval
          for selecting the adaptive intervals.

div.factor: Determines the factor by which sigma needs to be divided
          for selecting adaptive intervals.

 setMax1: If T then all variances below the max variance in the ordered
          distribution of variances are set to the maximum variance. If
          F then variances are left as is (recommended)

_V_a_l_u_e:

     Returns object of class baseOlig comprising a data frame with 2
     columns: A and var M, and rows for each quantile specified. The A
     column contains the median values of A for each quantile/bin and
     the M columns contains  the pooled variance of the replicate chips
     for genes within each quantile/bin.

_A_u_t_h_o_r(_s):

     Carl Murie carl.murie@mcgill.ca, Nitin Jain nitin.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:

     'lpeAdj'

_E_x_a_m_p_l_e_s:

       # Loading the library and the data
       library(LPEadj)
       data(Ley)
       
       dim(Ley)
       # Gives 12488 by 7
       Ley[1:3,]
       # Returns 
       #       ID           c1   c2   c3    t1    t2    t3
       # 1  AFFX-MurIL2_at 4.06 3.82 4.28 11.47 11.54 11.34
       # 2 AFFX-MurIL10_at 4.56 2.79 4.83  4.25  3.72  2.94
       # 3  AFFX-MurIL4_at 5.14 4.10 4.59  4.67  4.71  4.67

       Ley[,2:7] <- preprocess(Ley[,2:7],data.type="MAS5")
       
       subset <- 1:1000
       Ley.subset <- Ley[subset,]
       
       # Finding the baseline distribution of subset of the data
       # condition one (3 replicates)
       var.1 <- adjBaseOlig.error(Ley.subset[,2:4], q=0.01, setMax1=FALSE)
       dim(var.1)
       # Returns a matrix of 1000 by 2 (A,M) format, equal to the nrow(data) 

