lpeAdj                package:LPEadj                R Documentation

_H_i_g_h _l_e_v_e_l _l_p_e_A_d_j _f_u_n_c_t_i_o_n _t_h_a_t _e_x_e_c_u_t_e_s _t_h_e _a_d_j_u_s_t_e_d _l_o_c_a_l _p_o_o_l_e_d
_e_r_r_o_r _s_i_g_n_i_f_i_c_a_n_c_e _t_e_s_t. _I_f _m_o_r_e _c_o_n_t_r_o_l _o_v_e_r _p_a_r_a_m_e_t_e_r_s _i_s _n_e_e_d_e_d
_t_h_e_n _s_e_e _d_o_c_u_m_e_n_t_a_t_i_o_n _f_o_r _c_a_l_c_u_l_a_t_e_L_p_e_A_d_j.

_D_e_s_c_r_i_p_t_i_o_n:

     Applies the LPE algorithm with two additional options. The first
     is that the original LPE method sets all variances below the max
     variance in the ordered distribution of variances to the maximum
     variance. in LPEadj this option is turned off by default.  The
     second option is to use a variance adjustment based on sample size
     rather than pi/2. By default the LPEadj uses the sample size based
     variance adjustment.  It is recommended to keep both of these
     options to the default.

_U_s_a_g_e:

       lpeAdj(dat, labels=NULL, doMax=FALSE, doAdj=TRUE, q=.01)

_A_r_g_u_m_e_n_t_s:

     dat: Replicated data of experiment containing two groups (as
          matrix  or data-frame)

  labels: vector of group labels that correspond to the columns of dat.
          eg. labels=c(0,0,0,1,1,1) describes two groups with three
          replicates each

   doMax: boolean: if T then all variances below the max variance in
          the ordered distribution of variances are set to the maximum
          variance. It is recommended to use the default value of
          False.

   doAdj: If T then run LPE with using variance adjustment value based
          on number of replicates (hardcoded in adjValues) rather than
          pi/2.

       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.

_D_e_t_a_i_l_s:

     The LPE test statistic numerator is the difference in medians
     between the two experimental conditions. The test statistic
     denominator is the combined pooled standard error for the two
     experimental conditions obtained by looking up the var.M from each
     baseOlig.error variance function. The conversion to p-values is
     based on the Gaussian distribution for difference if order
     statistics (medians).

_V_a_l_u_e:

     Data frame including x, median of x, y, median of y, median
     difference of (x,y), pooled standard deviation of difference, LPE
     p-value, outlier flag, probability of an outlier within x or y, .

_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.

_E_x_a_m_p_l_e_s:

        
      # Loading the library and null dataset (two groups with three
      # replicates each)
      library(LPEadj)
      dat <- matrix(rnorm(6000), ncol=6)
      
      # Applying LPE
      lpe.result <- lpeAdj(dat, labels=c(0,0,0,1,1,1))
       

