pumaNormalize              package:puma              R Documentation

_N_o_r_m_a_l_i_z_e _a_n _E_x_p_r_e_s_s_i_o_n_S_e_t

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

     This is used to apply a scaling normalization to set of arrays.
     This normalization can be at the array scale (thus giving all
     arrays the same mean or median), or at the probeset scale (thus
     giving all probesets the same mean or median).

     It is generally recommended that the default option (median array
     scaling) is used after running 'mmgmos' and before running
     'pumaComb' and/or 'pumaDE'. There are however, situations where
     this might not be the recommended, for example in time series
     experiments where it is expected than there will be general
     up-regulation or down-regulation in overall gene expression levels
     between time points.

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

     pumaNormalize(
             eset
     ,       arrayScale = c("median", "none", "mean", "meanlog")
     ,       probesetScale = c("none", "mean", "median")
     ,       probesetNormalisation = NULL
     ,       replicates = list(1:dim(exprs(eset))[2])
     )

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

    eset: An object of class 'ExpressionSet'. 

arrayScale: A method of scale normalisation at the array level. 

probesetScale: A method of scale normalisation at the probe set level. 

probesetNormalisation: If not NULL normalises the expression levels to
          have zero mean and adjusts the variance of the gene
          expression according to the zero-centered normalisation. 

replicates: List of integer vectors indicating which arrays are
          replicates. 

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

     An object of class 'ExpressionSet' holding the normalised data.

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

     Richard D. Pearson

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

     Methods 'mmgmos', 'pumaComb' and 'pumaDE'

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

             library(puma)
             data(affybatch.example)
             pData(affybatch.example) <- data.frame("level"=c("twenty","twenty","ten")
                 , "batch"=c("A","B","A"), row.names=rownames(pData(affybatch.example)))
             eset_mmgmos <- mmgmos(affybatch.example)
             apply(exprs(eset_mmgmos),2,median)
             eset_mmgmos_normd <- pumaNormalize(eset_mmgmos)
             apply(exprs(eset_mmgmos_normd),2,median)

