calculateFC               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 _F_C

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

     Automatically creates design and contrast matrices if not
     specified. This function is useful for comparing fold change
     results with those of other differential expression (DE) methods
     such as 'pumaDE'.

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

     calculateFC(
             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', 'calculateLimma', 'calculateTtest',
     'createDesignMatrix' and 'createContrastMatrix' and class
     'DEResult'

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

             data(affybatch.example)
             varLabels(affybatch.example) <- c("level")
             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)
             FCRes <- calculateFC(eset_rma)
             topGeneIDs(FCRes,numberOfGenes=6)
             plotErrorBars(eset_rma, topGenes(FCRes))

