lpe                   package:LPE                   R Documentation

_E_v_a_l_u_a_t_e_s _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

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

     The local pooled error test attempts to reduce dependence on the
     within-gene estimates in tests for differential expression, by
     pooling error estimates within regions of similar intensity. Note
     that with the large number of genes there will be genes with low
     within-gene error estimates by chance, so that some
     signal-to-noise ratios will be large regardless of mean expression
     intensities and fold-change. The local pooled error attempts to
     avert this by combining within-gene error estimates with those of
     genes with similar expression intensity.

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

       lpe(x, y, basevar.x,basevar.y, df=10, array.type="olig", 
           probe.set.name=NULL, trim.percent=5)

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

       x: Replicated data from first experimental condition (as matrix 
          or data-frame)

       y: Replicated data from second experimental condition (as matrix
           or data-frame)

basevar.x: Baseline distribution of first condition obtained from 
          function baseOlig.error

basevar.y: Baseline distribution of second condition obtained from 
          function baseOlig.error

      df: Degrees of freedom used in fitting smooth.spline to estimates
          of var.M for bins in A

array.type: Currently supports oligo arrays

probe.set.name: Gene IDs. By default if they are not provided then
          1,2,3,... is assigned as GeneID

trim.percent: Percent of (A, var.M) estimates to trim from low  end of
          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). The user may select bith the smoother
     degrees of freedom (smaller is smoother) and the trim percent to
     obtain a variance fucntion to suit particular  issues i.e.
     variability of genes with low expression intensity.

_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):

     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.

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

        
      # Loading the library and the data
      library(LPE)
      data(Ley)
      
      dim(Ley)
      # Gives 12488*7 
      # First column is ID.


      # Subsetting the data
      subset.Ley <- Ley[1:1000,]
       
       subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5")
       
      # Finding the baseline distribution of condition 1 and 2.
     var.11 <- baseOlig.error.step1(subset.Ley[,2:4]) 
      var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
      var.2 <- baseOlig.error(subset.Ley[,5:7], q=0.01)
      
      # Applying LPE
      lpe.result <- lpe(subset.Ley[,2:4],subset.Ley[,5:7], var.1, var.2,
                     probe.set.name=subset.Ley[,1])
       

