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. _Methods in Molecular Biology_ 224,
     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.