puma-package              package:puma              R Documentation

_p_u_m_a - _P_r_o_p_a_g_a_t_i_n_g _U_n_c_e_r_t_a_i_n_t_y _i_n _M_i_c_r_o_a_r_r_a_y _A_n_a_l_y_s_i_s

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

     Most analyses of Affymetrix GeneChip data are based on point
     estimates of expression levels and ignore the uncertainty of such
     estimates. By propagating uncertainty to downstream analyses we
     can improve results from microarray analyses. For the first time,
     the puma package makes a suite of uncertainty propagation methods
     available to a general audience. puma also offers improvements in
     terms of scope and speed of execution over previously available
     uncertainty propagation methods. Included are summarisation,
     differential expression detection, clustering and PCA methods,
     together with useful plotting functions.

_D_e_t_a_i_l_s:


       Package:  puma
       Type:     Package
       Version:  1.2.1
       Date:     2007-06-26
       License:  LGPL excluding donlp2

     For details of using the package please refer to the Vignette

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

     Richard Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil
     D. Lawrence, Magnus Rattray

     Maintainer: Richard Pearson
     <richard.pearson@postgrad.manchester.ac.uk>

_R_e_f_e_r_e_n_c_e_s:

     Milo, M., Niranjan, M., Holley, M. C., Rattray, M. and Lawrence,
     N. D. (2004) A probabilistic approach for summarising
     oligonucleotide gene expression data, technical report available
     upon request.

     Liu, X., Milo, M., Lawrence, N. D. and Rattray, M. (2005) A
     tractable probabilistic model for Affymetrix probe-level analysis
     across multiple chips, Bioinformatics, 21(18):3637-3644.

     Sanguinetti, G., Milo, M., Rattray, M. and Lawrence, N. D. (2005)
     Accounting for probe-level noise in principal component analysis
     of microarray data, Bioinformatics, 21(19):3748-3754.

     Rattray, M., Liu, X., Sanguinetti, G., Milo, M. and Lawrence, N.
     D. (2006) Propagating uncertainty in Microarray data analysis,
     Briefings in Bioinformatics, 7(1):37-47. 

     Liu, X., Milo, M., Lawrence, N. D. and Rattray, M. (2006)
     Probe-level measurement error improves accuracy in detecting
     differential gene expression, Bioinformatics, 22(17):2107-2113.

     Liu, X. Lin, K., Andersen, B. Rattray, M. (2007) Including
     probe-level uncertainty in model-based gene expression clustering,
     BMC Bioinformatics, 8(98).

     Pearson, R. D., Liu, X., Sanguinetti, G., Milo, M., Lawrence, N.
     D., Rattray, M. (2007) In preparation.

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

             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)
             pumapca_mmgmos <- pumaPCA(eset_mmgmos)
             plot(pumapca_mmgmos)
             eset_mmgmos_normd <- pumaNormalize(eset_mmgmos)
             eset_comb <- pumaComb(eset_mmgmos_normd)
             esetDE <- pumaDE(eset_comb)

