calculateLimma             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 _u_s_i_n_g _l_i_m_m_a

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

     Runs a default analysis using the 'limma' package. Automatically
     creates design and contrast matrices if not specified. This
     function is useful for comparing 'limma' results with those of
     other differential expression (DE) methods such as 'pumaDE'.

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

     calculateLimma(
             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:

     The 'eset' argument must be supplied, and must be a valid
     'ExpressionSet' object. Design and contrast matrices can be
     supplied, but if not, default matrices will be used. These should
     usually be sufficient for most analyses.

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

     An object of class 'DEResult'.

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

     Richard D. Pearson

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

     Related methods 'pumaDE', 'calculateTtest', 'calculateFC',
     'createDesignMatrix' and 'createContrastMatrix' and class
     'DEResult'

_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_rma <- rma(affybatch.example)
             limmaRes <- calculateLimma(eset_rma)
             topGeneIDs(limmaRes,numberOfGenes=6)
             plotErrorBars(eset_rma, topGenes(limmaRes))

