GlobalAncova.decomp       package:GlobalAncova       R Documentation

_G_l_o_b_a_l_A_n_c_o_v_a _w_i_t_h _s_e_q_u_e_n_t_i_a_l _a_n_d _t_y_p_e _I_I_I _s_u_m _o_f _s_q_u_a_r_e_s _d_e_c_o_m_p_o_s_i_t_i_o_n _a_n_d _a_d_j_u_s_t_m_e_n_t _f_o_r _g_l_o_b_a_l _c_o_v_a_r_i_a_t_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computation of a F-test for the association between expression
     values and clinical entities.  The test is carried out by
     comparison of corresponding linear models via the extra sum of
     squares principle. In models with various influencing factors
     extra sums of squares can be treated with sequential and type III
     decomposition. Adjustment for global covariates, e.g. gene
     expression values in normal tissue as compared to tumour tissue,
     can be applied.   Given theoretical p-values may not be
     appropriate due to correlations and non-normality. The functions
     are hence seen more as a descriptive tool.

_U_s_a_g_e:

     GlobalAncova.decomp(xx, formula, model.dat = NULL, method = c("sequential", "type3", "all"),  test.genes = NULL, genewise = FALSE, zz = NULL, zz.per.gene = FALSE)

_A_r_g_u_m_e_n_t_s:

      xx: Matrix of gene expression data, where columns correspond to
          samples and rows to genes. The data should be properly
          normalized beforehand (and log- or otherwise transformed).
          Missing values are not allowed. Gene and sample names can be
          included as the row and column names of 'xx'.

 formula: Model formula for the linear model.

model.dat: Data frame that contains all the variable information for
          each sample.

  method: Whether sequential or type III decomposition or both should
          be calculated.

test.genes: Vector of gene names or a list where each element is a
          vector of gene names.

genewise: Shall the sequential decomposition be displayed for each
          single gene in a (small) gene set?

      zz: Global covariate, i.e. matrix of same dimensions as 'xx'.

zz.per.gene: If set to 'TRUE' the adjustment for the global covariate
          is applied on a gene-wise basis.

_V_a_l_u_e:

     Depending on parameters 'test.genes', 'method' and 'genewise'
     ANOVA tables, or lists of ANOVA tables for each  decomposition
     and/or gene set, or lists with components of ANOVA tables for each
     gene are returned.

_N_o_t_e:

     This work was supported by the NGFN project 01 GR 0459, BMBF,
     Germany.

_A_u_t_h_o_r(_s):

     Ramona Scheufele ramona.scheufele@charite.de 
      Reinhard Meister meister@tfh-berlin.de
      Manuela Hummel hummel@ibe.med.uni-muenchen.de 
      Urlich Mansmann mansmann@ibe.med.uni-muenchen.de

_S_e_e _A_l_s_o:

     'Plot.sequential', 'pair.compare', 'GlobalAncova'

_E_x_a_m_p_l_e_s:

     data(vantVeer)
     data(phenodata)
     data(pathways)

     # sequential or type III decomposition
     GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "sequential", test.genes = pathways[1:3])
     GlobalAncova.decomp(xx = vantVeer, formula = ~ grade + metastases + ERstatus, model.dat = phenodata, method = "type3", test.genes = pathways[1:3]) 

     # adjustment for global covariate
     data(colon.tumour)
     data(colon.normal)
     data(colon.pheno)
     GlobalAncova.decomp(xx = colon.tumour, formula = ~ UICC.stage + sex + location, model.dat = colon.pheno, method = "all", zz = colon.normal)

