pumaDE                 package:puma                 R Documentation

_C_a_l_c_u_l_a_t_e _d_i_f_f_e_r_e_n_t_i_a_l _e_x_p_r_e_s_s_i_o_n _b_e_t_w_e_e_n _c_o_n_d_i_t_i_o_n_s

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

     The function generates lists of genes ranked by probability of
     differential expression (DE). This uses the PPLR method.

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

     pumaDE(
             eset
     ,       design.matrix = createDesignMatrix(eset)
     ,       contrast.matrix = createContrastMatrix(eset)
     )

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

    eset: An object of class 'ExpressionSet'. 

design.matrix: A design matrix 

contrast.matrix: A contrast matrix 

_D_e_t_a_i_l_s:

     A separate list of genes will be created for each contrast of
     interest.

     Note that this class returns a 'DEResult-class' object. This
     object contains information on both the PPLR statistic values
     (which should generally be used to rank genes for differential
     expression), as well as fold change values (which are generally
     not recommended for ranking genes, but which might be useful, for
     example, to use as a filter). To understand more about the object
     returned see 'DEResult-class', noting that when created a DEResult
     object with the pumaDE function, the 'statistic' method should be
     used to return PPLR values. Also note that the 'pLikeValues'
     method can be used on the returned object to create values which
     can more readily be compared with p-values returned by other
     methods such as variants of t-tests (limma, etc.).

     While it is possible to run this function on data from individual
     arrays, it is generally recommended that this function is run on
     the output of the function 'pumaComb' (which combines information
     from replicates).

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

     An object of class 'DEResult-class'.

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

     Richard D. Pearson

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

     Related methods 'calculateLimma', 'calculateFC', 'calculateTtest',
     'pumaComb', 'mmgmos', 'pplr', 'createDesignMatrix' and
     'createContrastMatrix'

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

             #       Next 4 lines commented out to save time in package checks, and saved version used
         # if (require(affydata)) {
             #       data(Dilution)
             #       eset_mmgmos <- mmgmos(Dilution)
             # }
             data(eset_mmgmos)

             #       Next line shows that eset_mmgmos has 4 arrays, each of which is a different
             #   condition (the experimental design is a 2x2 factorial, with both liver and
             #       scanner factors)
             pData(eset_mmgmos)
             
             #       Next line shows expression levels of first 3 probe sets
             exprs(eset_mmgmos)[1:3,]

             #       Next line used so eset_mmgmos only has information about the liver factor
             #       The scanner factor will thus be ignored, and the two arrays of each level
             #       of the liver factor will be treated as replicates
             pData(eset_mmgmos) <- pData(eset_mmgmos)[,1,drop=FALSE]

             #       To save time we'll just use 100 probe sets for the example
             eset_mmgmos_100 <- eset_mmgmos[1:100,]
             eset_comb <- pumaComb(eset_mmgmos_100)

             pumaDEResults <- pumaDE(eset_comb)

             topGeneIDs(pumaDEResults,6) # Gives probeset identifiers
             topGenes(pumaDEResults,6) # Gives row numbers
             statistic(pumaDEResults)[topGenes(pumaDEResults,6),] # PPLR scores of top six genes
             FC(pumaDEResults)[topGenes(pumaDEResults,6),] # Fold-change of top six genes

