01.Introduction            package:limma            R Documentation

_I_n_t_r_o_d_u_c_t_i_o_n _t_o _t_h_e _L_I_M_M_A _P_a_c_k_a_g_e

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

     LIMMA is a library for the analysis of gene expression microarray
     data, especially the use of linear models for analysing designed
     experiments and the assessment of differential expression. LIMMA
     provides the ability to analyse comparisons between many RNA
     targets simultaneously in arbitrary complicated designed
     experiments. Empirical Bayesian methods are used to provide stable
     results even when the number of arrays is small. The normalization
     and data analysis functions are for two-colour spotted
     microarrays. The linear model and differential expression
     functions apply to all microarrays including Affymetrix and other
     multi-array oligonucleotide experiments.

     There are three types of documentation available. (1) The _LIMMA
     User's Guide_ can be reached through the "User Guides and Package
     Vignettes" links at the top of the LIMMA contents page. The
     function 'limmaUsersGuide' gives the file location of the User's
     Guide. (2) An overview of limma functions grouped by purpose is
     contained in the numbered chapters at the top of the LIMMA
     contents page, of which this page is the first. (3) The LIMMA
     contents page gives an alphabetical index of detailed help topics.

     The function 'changeLog' displays the record of changes to the
     package.

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

     Gordon Smyth

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

     Smyth, G. K., Yang, Y.-H., Speed, T. P. (2003). Statistical issues
     in microarray data analysis. In: _Functional Genomics: Methods and
     Protocols_, M. J. Brownstein and A. B. Khodursky (eds.), Methods
     in Molecular Biology Volume 224, Humana Press, Totowa, NJ, pages
     111-136.

     Smyth, G. K. (2005). Limma: linear models for microarray data. In:
     'Bioinformatics and Computational Biology Solutions using R and
     Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W.
     Huber (eds), Springer, New York, 2005. To appear October 2005.

