ebam                package:siggenes                R Documentation

_E_m_p_i_r_i_c_a_l _B_a_y_e_s _A_n_a_l_y_s_i_s _o_f _M_i_c_r_o_a_r_r_a_y_s

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

     Performs an Empirical Bayes Analysis of Microarrays for a
     specified value of the fudge factor a0. Modified versions of the t
     statistics are used.

_U_s_a_g_e:

         ebam(a0.out,data,a0=NA,p0=NA,delta=NA,stable=TRUE,number.int=139,local.bin=.1,
         col.accession=NA,col.gene.name=NA,q.values=TRUE,R.fold=TRUE,R.dataset=data,na.rm=FALSE,
         file.out=NA)

_A_r_g_u_m_e_n_t_s:

  a0.out: the object to which the output of a previous analysis with
          'find.a0' was assigned.

    data: the data set that should be analyzed. Each row of this data
          set must correspond to a gene. It has to be the same data set
          that was used in  'find.a0'.

      a0: the fudge factor. If 'NA', the value suggested by 'find.a0'
          will be used.

      p0: prior probability that a gene is differentially expressed. If
          not specified (i.e. 'NA'), it will automatically be computed.

   delta: a gene will be called differentially expressed, if its
          posterior probability of being differentially expressed is
          large than or equal to 'delta'. By default, the same 'delta'
          is used as in 'find.a0'.

  stable: if 'TRUE' (default), p0 will be computed by the algorithm of
          Storey and Tibshirani (2003). If 'FALSE', the (unstable)
          estimate will be computed that ensures that the posterior
          probability of being differentially expressed is always
          nonnegative.

number.int: the number of equally spaced intervals that is used in the 
          logistic regression for the estimation of the ratio of the
          null density to the mixture density.

local.bin: specifies the interval used in the estimation of the local
          FDR for the expression score z. By default, this interval is
          [z-0.1,z+0.1].

col.accession: the column of 'data' containing the accession numbers of
          the genes. If specified, the accession numbers of the
          significant genes will be added to the output.

col.gene.name: the column of 'data' that contains the names of the
          genes. If specified, the names of the significant genes will
          be added to the output.

q.values: if 'TRUE' (default), the q-value for each gene will be
          computed.

  R.fold: if 'TRUE' (default), the fold change for each differentially
          expressed gene will be computed.

R.dataset: the data set used in the computation of the fold change.
          This data set can be a transformed version of 'data'.

   na.rm: if 'FALSE' (default), the fold change of genes with at least
          one missing value will be set to 'NA'. If 'TRUE', missing
          values will be replaced by the genewise mean.

file.out: if specified, general information like the number of
          significant  genes and the estimated FDR and gene-specific
          information like the expression scores, the q-values, the R
          fold etc. of the differentially expressed genes are stored in
          this file.

_V_a_l_u_e:

     a plot of the expression scores against their posterior
     probability of being differentially expressed, and (optional) a
     file containing general information like the estimated FDR and the
     number of differentially expressed genes and  gene-specific
     information about the differentially expressed genes like their
     names, their expression scores, q values and their fold changes.

     FDR: vector containing the estimated p0, the number of significant
          genes, the number of falsely called genes and the estimated
          FDR.

ebam.out: table containing gene-specific information about the
          differentially expressed genes.

row.sig.genes: vector consisting of the row numbers that belong to the
          differentially expressed genes.

     ...: 

_N_o_t_e:

     The number of false positives are computed by p0 times the number
     of falsely called genes.

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

     Holger Schwender, holger.schw@gmx.de

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

     Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001).
     Empirical Bayes Analysis of a Microarray Experiment, _JASA_, 96,
     1151-1160.

_S_e_e _A_l_s_o:

     'find.a0'   'ebam.wilc'

