racExSet-class            package:GGtools            R Documentation

_C_l_a_s_s "_r_a_c_E_x_S_e_t" _f_o_r _c_o_m_b_i_n_i_n_g _R_a_r_e_A_l_l_e_l_e_C_o_u_n_t _r_e_p_r_e_s_e_n_t_a_t_i_o_n_s
_o_f _S_N_P_s, _g_e_n_e _e_x_p_r_e_s_s_i_o_n _d_a_t_a, _a_n_d _o_t_h_e_r _p_h_e_n_o_t_y_p_e _d_a_t_a

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

     A container for combining RareAlleleCount representations  of
     SNPs, gene expression data, and other phenotype data

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

     Objects can be created by calls of the form 'new("racExSet",
     phenoData, experimentData, annotation, exprs, racs, ...)'.

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

     '_r_a_c_A_s_s_a_y_s': Object of class '"AssayData"' SxN matrix of 0, 1, 2,
          or NA, rownames are RS numbers from dbSNP, column names are
          sampleNames 

     '_a_s_s_a_y_D_a_t_a': Object of class '"AssayData"' GxN matrix of
          expression values 

     '_r_a_r_e_b_a_s_e': character vector of length G stating the rare base of
          the SNP

     '_S_N_P_a_l_l_e_l_e_s': character vector of length G stating the alleles in
          the form "A/B"

     '_p_h_e_n_o_D_a_t_a': Object of class '"AnnotatedDataFrame"' standard
          phenoData instance 

     '_e_x_p_e_r_i_m_e_n_t_D_a_t_a': Object of class '"MIAME"' experiment level
          metadata 

     '_a_n_n_o_t_a_t_i_o_n': Object of class '"character"' tag 

     '.___c_l_a_s_s_V_e_r_s_i_o_n__': Object of class '"Versions"' R class version
          control metadata

_E_x_t_e_n_d_s:

     Class '"eSet"', directly. Class '"VersionedBiobase"', by class
     '"eSet"'. Class '"Versioned"', by class '"eSet"'.

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

     _s_n_p_s 'signature(x = "racExSet")': accessor for the racAssay matrix 

     _s_n_p_N_a_m_e_s 'signature(x = "racExSet")': give RS numbers for rows of
          racAssay matrix 

     _r_a_c_A_s_s_a_y_s 'signature(x = "racExSet")': accessor for the racAssays
          slot

     _o_n_e_F_i_t 'signature(racExSet = "racExSet", geneid = "genesym", snpid
          = "character", fitfun = "function")': use a genesym instance
          (e.g., 'genesym("DDR1")' to identify the expression values,
          an rs number to identify the snp genotype information, and
          fitfun to fit a model with expression as dependent variable
          and genotype (rare allele counts) as predictor.  Any fitting
          function accepting a formula and data frame should succeed
          (e.g., 'lm'). 

     _o_n_e_F_i_t 'signature(racExSet = "racExSet", geneid = "character",
          snpid = "character", fitfun = "function")': as above, but can
          avoid the lookup of the gene symbol by supplying the probe
          set identifier directly.  This is problematic at present as
          most probe set identifier strings are not valid R variable
          names, and so cannot serve directly in the formula. Some
          backtick coding is needed to get this to work generally. 

     _o_n_e_F_i_t 'signature(racExSet = "racExSet", geneid = "list", snpid =
          "character", fitfun = "function")': the 'geneid' parameter
          must be a named list of length one with content a numeric
          vector of expression values conforming to the snp values. 
          Avoids lookup of gene name and of expression values.

     _o_n_e_F_i_t 'signature(racExSet = "racExSet", geneid = "list" ,
          snpid="formula", fitfun="function")': the 'geneid' parameter
          must be bound to a named list of length 1 with content a
          numeric vector with one element per sample; the name of the
          list element is the gene symbol on which the numeric vector
          is a vector of expression values; the 'snpid' parameter is
          bound to a formula template, either '~.' or '~factor(.)'
          expressing choice between an additive genetic model and a
          general model.

_N_o_t_e_s:

     Helper functions 'SNPalleles' and 'rarebase' are accessors. 
     'snps5PrimeTo(gene,rad)' will obtain rs numbers for SNPs in
     selected locations relative to the specified gene.  'snps3PrimeTo'
     and 'snpsNear' have similar purposes.

_N_o_t_e:

     The 'genesym' class works in conjunction with annotation to allow
     translation from HUGO symbols to affy probe set names according to
     Bioconductor  annotation environments.  The 'getpsid' function
     carries out the translation; the 'genesym' class/wrapper just
     tells us that a string will be serving the role of a HUGO symbol
     and allows us to dispatch accordingly.  The 'snpID' class has
     similar role.  It just lets us know that a string is intended to
     be regarded as an rs number.

     'hgfocusSYMMEMO' is a lookup table quickly establishing the
     probeset ID based on a HUGO symbol.

     'chr20GGdem' is a demonstration example of chromosome 20 hapmap
     snps and hgfocus expression data from Cheung.

     The 'exclMono' function eliminates loci that happen to be
     monomorphic in the sample.

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

     VJ Carey <stvjc@channing.harvard.edu>

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

     'make_racExSet' for construction; 'eSet' ancestor class

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

     example(make_racExSet)
     dem
     snpNames(dem)[1:4]
     featureNames(dem)[1:4]
     oneFit(dem, genesym("DDR1"), "rs4814683", lm)
     ddr1 = list(DDR1=exprs(dem)["1007_s_at",])
     oneFit(dem, ddr1, "rs4814683", aov)
     data(chr20GGdem)
     ddr1 = list(DDR1=exprs(chr20GGdem)["1007_s_at",])
     summary(oneFit(chr20GGdem, ddr1, ~rs4814683+factor(rs6060535), lm))
     dim(snps(chr20GGdem))
     dim(snps(exclMono(chr20GGdem)))

