TFA.estimate           package:plsgenomics           R Documentation

_P_r_e_d_i_c_t_i_o_n _o_f _T_r_a_n_s_c_r_i_p_t_i_o_n _F_a_c_t_o_r _A_c_t_i_v_i_t_i_e_s _u_s_i_n_g _P_L_S

_D_e_s_c_r_i_p_t_i_o_n:

     The function 'TFA.estimate' estimates the transcription factor
     activities from gene expression data and ChIP data using the PLS
     multivariate regression approach described in Boulesteix and
     Strimmer (2005).

_U_s_a_g_e:

     TFA.estimate(CONNECdata, GEdata, ncomp=NULL, nruncv=0, alpha=2/3, unit.weights=TRUE)

_A_r_g_u_m_e_n_t_s:

CONNECdata: a (n x p) matrix containing the ChIP data for the n genes
          and the p predictors. The n genes must be the same as the n
          genes of 'GEdata' and the ordering of the genes must also be
          the same. Each row of 'ChIPdata' corresponds to a gene, each
          column to a transcription factor. 'CONNECdata' might have
          either binary (e.g. 0-1) or numeric entries.

  GEdata: a (n x m) matrix containing the gene expression levels of the
          n considered genes for m samples. Each row of 'GEdata'
          corresponds to a gene, each column to a sample.

   ncomp: if 'nruncv=0', 'ncomp' is the number of latent  components to
          be  constructed. If 'nruncv>0', the  number of  latent
          components to be used for PLS regression is chosen from
          1,...,'ncomp' using the cross-validation procedure described
          in Boulesteix and Strimmer (2005). If 'ncomp=NULL', 'ncomp'
          is set to min(n,p).

  nruncv: the number of cross-validation iterations to be performed for
          the choice of the number of latent components. If 'nruncv=0',
          cross-validation is not performed and 'ncomp' latent
          components are used.

   alpha: the proportion of genes to be included in the training set
          for the cross-validation procedure.

unit.weights: If 'TRUE' then the latent components will be constructed
          from weight vectors that are standardized to length 1, 
          otherwise the weight vectors do not have length 1 but the
          latent components have norm 1.

_D_e_t_a_i_l_s:

     The gene expression data as well as the ChIP data are assumed to
     have been properly normalized. However, they do not have to be
     centered or scaled, since centering and scaling are performed by
     the function 'TFA.estimate' as a preliminary step. 

     The matrix 'ChIPdata' containing the ChIP data for the n genes and
     p transcription  factors might be replaced by  any 'connectivity'
     matrix whose entries give the strength of the interactions between
     the genes and transcription factors. For instance, a connectivity
     matrix obtained by aggregating qualitative information from
     various genomic databases  might be used as argument  in place of
     ChIP data.

_V_a_l_u_e:

     A list with the following components: 

     TFA: a (p x m) matrix containing the estimated transcription
          factor activities for the p transcription factors and the m
          samples.

metafactor: a (m x 'ncomp') matrix containing the metafactors for the m
          samples. Each row corresponds to a sample, each column to a
          metafactor.

   ncomp: the number of latent components used in the PLS regression.

_A_u_t_h_o_r(_s):

     Anne-Laure Boulesteix (<URL:
     http://www.statistik.lmu.de/~socher/>)  and  Korbinian Strimmer
     (<URL: http://www.statistik.lmu.de/~strimmer/>).

_R_e_f_e_r_e_n_c_e_s:

     A. L. Boulesteix and K. Strimmer (2005). Predicting Transcription
     Factor Activities  from Combined Analysis of Microarray and ChIP
     Data: A Partial Least Squares Approach.

     S. de Jong (1993). SIMPLS: an alternative approach to partial
     least squares  regression, Chemometrics Intell. Lab. Syst. *18*,
     251-263.

_S_e_e _A_l_s_o:

     'pls.regression', 'pls.regression.cv'.

_E_x_a_m_p_l_e_s:

     # load plsgenomics library
     library(plsgenomics)

     # load Ecoli data
     data(Ecoli)

     # estimate TFAs based on 3 latent components
     TFA.estimate(Ecoli$CONNECdata,Ecoli$GEdata,ncomp=3,nruncv=0)

     # estimate TFAs and determine the best number of latent components simultaneously
     TFA.estimate(Ecoli$CONNECdata,Ecoli$GEdata,ncomp=1:5,nruncv=20)

