geneSetTest              package:limma              R Documentation

_G_e_n_e _S_e_t _T_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     Test whether a set of genes is enriched for differential
     expression. Genes are assumed to be independent.

_U_s_a_g_e:

     geneSetTest(selected,statistics,alternative="mixed",type="auto",ranks.only=TRUE,nsim=10000)
     barcodeplot(selected,statistics,type="auto",...)

_A_r_g_u_m_e_n_t_s:

selected: vector specifying the elements of 'statistic' in the test
          group.  This can be a vector of indices, or a logical vector
          of the same length as 'statistics', or any vector such as
          'statistic[selected]' contains the statistic values for the
          selected group.

statistics: numeric vector giving the values of the test statistic for
          every gene or probe in the reference set, usually every probe
          on the microarray.

alternative: character string specifying the alternative hypothesis,
          must be one of '"mixed"' (default), '"either"', '"up"' or
          '"down"'. 'two.sided', '"greater"' and '"less"' are also
          permitted as synonyms for '"either"', '"up"' and '"down"'
          respectively.

    type: character string specifying whether the statistics are t-like
          ('"t"'), F-like '"f"' or whether the function should make an
          educated guess ('"auto"')

ranks.only: logical, if 'TRUE' only the ranks of the 'statistics' are
          used.

    nsim: number of random samples to take in computing the p-value.
          Not used if 'ranks.only=TRUE'.

     ...: other arguments are passed to 'plot'.

_D_e_t_a_i_l_s:

     This function computes a p-value to test the hypothesis that the
     selected set of genes tends to be more highly ranked in
     differential expression compared to randomly selected genes. This
     function can be used to detect differential expression for a group
     of genes, even when the effects are too small or there is too
     little data to detect the genes individually. It is also provides
     a means to compare the results between different experiments.

     The mean-rank gene set test of Michaud et al (2008) corresponds to
     'geneSetTest' with 'ranks.only=TRUE'.

     'geneSetTest' is a competitive test in the sense that genes in the
     test set are compared to other genes (Goeman and Buhlmann, 2007).
     A self-contained gene set test is performed by 'roast'.

     Because it is based on permuting genes, 'geneSetTest' assumes that
     the different genes (or probes) are independent. (Strictly
     speaking, it assumes that the genes in the set are no more
     correlated on average than randomly selected genes.) This
     assumption may be reasonable if the gene set is relatively small
     and if there is relatively little genotypic variation in the data,
     for example if the data is obtained from genetically identical
     inbred mice. The independence assumption may be misleading if the
     gene set is large or if the data contains a lot of genotypic
     variation, for example for human cancer samples. These
     assumptions, when valid, permit a much quicker and more powerful
     significance test to be conducted.

     The 'statistics' are usually a set of probe-wise statistics
     arising for some comparison from a microarray experiment. They may
     be t-statistics, meaning that the genewise null hypotheses would
     be rejected for large positive or negative values, or they may be
     F-statistics, meaning that only large values are significant. Any
     set of signed statistics, such as log-ratios, M-values or
     moderated t-statistics, are treated as t-like. Any set of unsigned
     statistics, such as F-statistics, posterior probabilities or
     chi-square tests are treated as F-like. If 'type="auto"' then the
     statistics will be taken to be t-like if they take both positive
     and negative values and otherwise will be taken to be F-like.

     There are four possible alternatives to test for.
     'alternative=="up"' means the genes in the set tend to be
     up-regulated, with positive t-statistics. 'alternative=="down"'
     means the genes in the set tend to be down-regulated, with
     negative t-statistics. 'alternative=="either"' means the set is
     either up or down-regulated as a whole. 'alternative=="mixed"'
     test whether the genes in the set tend to be differentially
     expressed, without regard for direction. In this case, the test
     will be significant if the set contains mostly large test
     statistics, even if some are positive and some are negative.

     The latter three alternatives are appropriate if you have a prior
     expection that all the genes in the set will react in the same
     direction. The '"mixed"' alternative is appropriate if you know
     only that the genes are involved in the relevant pathways,
     possibly in different directions. The '"mixed"' alternative is the
     only one possible with F-like statistics.

     The test statistic used for the gene-set-test is the mean of the
     statistics in the set. If 'ranks.only' is 'TRUE' the only the
     ranks of the statistics are used. In this case the p-value is
     obtained from a Wilcoxon test. If 'ranks.only' is 'FALSE', then
     the p-value is obtained by simulation using 'nsim' random selected
     sets of genes.

     The 'barcodeplot' is a graphical representation of the gene set
     test using ranks.

_V_a_l_u_e:

     Numeric value giving the estimated p-value.

_A_u_t_h_o_r(_s):

     Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Goeman, JJ, and Buhlmann P (2007). Analyzing gene expression data
     in terms of gene sets: methodological issues. _Bioinformatics_ 23,
     980-987. 

     Michaud, J, Simpson, KM, Escher, R, Buchet-Poyau, K, Beissbarth,
     T, Carmichael, C, Ritchie, ME, Schutz, F, Cannon, P, Liu, M, Shen,
     X, Ito, Y, Raskind, WH, Horwitz, MS, Osato, M, Turner, DR, Speed,
     TP, Kavallaris, M, Smyth, GK, and Scott, HS (2008). Integrative
     analysis of RUNX1 downstream pathways and target genes. _BMC
     Genomics_ 9, 363. <URL:
     http://www.biomedcentral.com/1471-2164/9/363>

_S_e_e _A_l_s_o:

     'roast', 'wilcox.test'

_E_x_a_m_p_l_e_s:

     stat <- -9:9
     sel <- c(2,4,5)
     geneSetTest(sel,stat,alternative="down")
     geneSetTest(sel,stat,alternative="either")
     geneSetTest(sel,stat,alternative="down",ranks=FALSE)
     sel <- c(1,19)
     geneSetTest(sel,stat,alternative="mixed")
     geneSetTest(sel,stat,alternative="mixed",ranks=FALSE)

