08.Tests                package:limma                R Documentation

_H_y_p_o_t_h_e_s_i_s _T_e_s_t_i_n_g _f_o_r _L_i_n_e_a_r _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     LIMMA provides a number of functions for multiple testing across
     both contrasts and genes. The starting point is an 'MArrayLM'
     object, called 'fit' say, resulting from fitting a linear model
     and running 'eBayes' and, optionally, 'contrasts.fit'. See
     06.LinearModels or 07.SingleChannel for details.

_M_u_l_t_i_p_l_e _t_e_s_t_i_n_g _a_c_r_o_s_s _g_e_n_e_s _a_n_d _c_o_n_t_r_a_s_t_s:

     The key function is 'decideTests'. This function writes an object
     of class 'TestResults', which is basically a matrix of '-1', '0'
     or '1' elements, of the same dimension as 'fit$coefficients',
     indicating whether each coefficient is significantly different
     from zero. A number of different multiple testing strategies are
     provided. The function calls other functions 'classifyTestsF',
     'classifyTestsP' and 'classifyTestsT' which implement particular
     strategies.  The function 'FStat' provides an alternative
     interface to 'classifyTestsF' to extract only the overall
     moderated F-statistic.

     A number of other functions are provided to display the results of
     'decideTests'. The functions 'heatDiagram' (or the older version
     'heatdiagram' displays the results in a heat-map style display.
     This allows visual comparison of the results across many different
     conditions in the linear model.

     The functions 'vennCounts' and 'vennDiagram' provide Venn diagram
     style summaries of the results.

     Summary and 'show' method exists for objects of class
     'TestResults'.

     The results from 'decideTests' can also be included when the
     results of a linear model fit are written to a file using
     'write.fit'.

_G_e_n_e _S_e_t _T_e_s_t_s:

     Competitive gene set testing is provided by 'geneSetTest', which
     permutes genes, while self-contained gene set testing is provided
     by 'roast', which randomly rotates arrays.

     The function 'alias2Symbol' is provided to help match gene sets
     with microarray probes by way of official gene symbols.

_O_t_h_e_r _F_u_n_c_t_i_o_n_s:

     Given a set of p-values, the function 'convest' can be used to
     estimate the proportion of true null hypotheses.

     When evaluating test procedures with simulated or known results,
     the utility function 'auROC' can be used to compute the area under
     the Receiver Operating Curve for the test results for a given
     probe.

_A_u_t_h_o_r(_s):

     Gordon Smyth