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)))

