SAM-class              package:siggenes              R Documentation

_C_l_a_s_s _S_A_M

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

     This is a class representation for several versions of the SAM
     (Significance Analysis of Microarrays) procedure proposed by
     Tusher et al. (2001).

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created using the functions 'sam', 'sam.dstat', 
     'sam.wilc' and 'sam.snp'.

_S_l_o_t_s:


     '_d': Object of class '"numeric"' representing the expression
          scores of the genes.

     '_d._b_a_r': Object of class '"numeric"' representing the expected
          expression scores under the null hypothesis.

     '_v_e_c._f_a_l_s_e': Object of class '"numeric"' containing the one-sided
          expected number of falsely called genes.

     '_p._v_a_l_u_e': Object of class '"numeric"' consisting of the p-values
          of the genes.

     '_s': Object of class '"numeric"' representing the standard
          deviations of the genes. If the standard deviations are not
          computed, 's' will be set to 'numeric(0)'. 

     '_s_0': Object of class '"numeric"' representing the value of the
          fudge factor. If not computed, 's0' will be set to
          'numeric(0)'.

     '_m_a_t._s_a_m_p': Object of class '"matrix"' containing the permuted
          group labels used in the estimation of the null distribution.
          Each row represents one permutation, each column one
          observation (pair). If no permutation procedure has been
          used, 'mat.samp' will be set to 'matrix(numeric(0))'.

     '_p_0': Object of class '"numeric"' representing the prior
          probability that a gene is not differentially expressed.

     '_m_a_t._f_d_r': Object of class '"matrix"' containing general
          information as the number of significant genes and the
          estimated FDR for several values of Delta. Each row
          represents one value of Delta, each of the 9 columns one
          statistic.

     '_q._v_a_l_u_e': Object of class '"numeric"' consisting of the q-values
          of the genes. If not computed, 'q.value' will be set to
          'numeric(0)'.

     '_f_o_l_d': Object of class '"numeric"' representing the fold changes
          of the genes. If not computed, 'fold' will be  set to
          'numeric(0)'.

     '_m_s_g': Object of class '"character"' containing information about,
          e.g., the type of analysis. 'msg' is printed when the
          functions 'print' and 'summary', respectively, are called.

     '_c_h_i_p': Object of class '"character"' naming the microarray used
          in the analysis. If no information about the chip is
          available, 'chip' will be set to '""'.

_M_e_t_h_o_d_s:


     _i_d_e_n_t_i_f_y 'signature(x = "SAM")': After generating a SAM plot,
          'identify' can be used to obtain information about the genes
          by clicking on the symbols in the SAM plot. For details, see 
          'help.sam(identify)'. Arguments are listed by
          'args.sam(identify)'.

     _p_l_o_t 'signature(x = "SAM")': Generates a SAM plot or the Delta
          plots. If the specified 'delta' in 'plot(object,delta)' is a
          numeric value, a SAM plot will be generated. If 'delta' is
          either not specified or a numeric vector, the Delta plots
          will be generated. For details, see '?sam.plot2',
          '?delta.plot' or  'help.sam(plot)',respectively. Arguments
          are listed by 'args.sam(plot)'.

     _p_r_i_n_t 'signature(x = "SAM")': Prints general information such as 
          the number of significant genes and the estimated FDR for a
          set of  Delta. For details, see 'help.sam(print)'. Arguments
          are listed by 'args.sam(print)'.

     _s_h_o_w 'signature(object = "SAM")': Shows the output of the SAM
          analysis.

     _s_u_m_m_a_r_y 'signature(object = "SAM")': Summarizes the results of a
          SAM analysis. If 'delta' in 'summary(object,delta)' is not
          specified or a numeric vector, the information shown by print
          and some additional information will be shown. If 'delta' is
          a numeric vector, the general information for the specific
          Delta is shown and additionally gene-specific information
          about the genes called  significant using this value of
          Delta. The output of summary is an object of class sumSAM
          which has the slots 'row.sig.genes', 'mat.fdr', 'mat.sig' and
          'list.args'. For details,  see 'help.sam(summary)'. All
          arguments are listed by 'args.sam(summary)'.

_N_o_t_e:

     SAM was developed by Tusher et al. (2001).

     !!! There is a patent pending for the SAM technology at Stanford
     University. !!!

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

     Holger Schwender, holger.schw@gmx.de

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

     Schwender, H., Krause, A. and Ickstadt, K. (2003). Comparison of
     the Empirical Bayes and the Significance Analysis of Microarrays.
     _Technical Report_, SFB 475, University of Dortmund, Germany.
     <URL: http://www.sfb475.uni-dortmund.de/berichte/tr44-03.pdf>.

     Schwender, H. (2004). Modifying Microarray Analysis Methods for 
     Categorical Data - SAM and PAM for SNPs. To appear in:
     _Proceedings of the the 28th Annual Conference of the GfKl_.

     Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance
     analysis of microarrays applied to the ionizing radiation
     response. _PNAS_, 98, 5116-5121.

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

     'sam','args.sam','sam.plot2', 'delta.plot'

_E_x_a_m_p_l_e_s:

     ## Not run: 
       # Load the package multtest and the data of Golub et al. (1999)
       # contained in multtest.
       library(multtest)
       data(golub)
       
       # Perform a SAM analysis for the two class unpaired case assuming
       # unequal variances.
       sam.out <- sam(golub, golub.cl, B=100, rand=123)
       sam.out
       
       # Alternative ways to show the output of sam.
       show(sam.out)
       print(sam.out)
       
       # Obtain a little bit more information.
       summary(sam.out)
       
       # Print the results of the SAM analysis for other values of Delta.
       print(sam.out, seq(.2, 2, .2))
       
       # Again, the same with additional information.
       summary(sam.out, seq(.2, 2, .2))
         
       # Obtain the Delta plots for the default set of Deltas.
       plot(sam.out)
       
       # Generate the Delta plots for Delta = 0.2, 0.4, 0.6, ..., 2.
       plot(sam.out, seq(0.2, 0.4, 2))
       
       # Obtain the SAM plot for Delta = 2.
       plot(sam.out, 2)
       
       # Get information about the genes called significant using 
       # Delta = 3 (since neither the gene names nor the chip type
       # has been specified ll is set to FALSE to avoid a warning).
       sam.sum3 <- summary(sam.out, 3, ll=FALSE)
       sam.sum3
       
       # Obtain the rows of the Golub et al. (1999) data set containing
       # the genes called differentially expressed
       sam.sum3@row.sig.genes
       
       # and their names
       golub.gnames[sam.sum3@row.sig.genes, 3] 

       # The matrix containing the d-values, q-values etc. of the
       # differentially expressed genes can be obtained by
       sam.sum3@mat.sig
     ## End(Not run)

