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 will be created for each contrast of interest.

     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:

             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)
             eset_mmgmos_normd <- pumaNormalize(eset_mmgmos)
             eset_comb <- pumaComb(eset_mmgmos_normd)
             esetDE <- pumaDE(eset_comb)
             esetDE$genes[1:6,]
             esetDE$p[1:6,]

