ExpressionSet            package:Biobase            R Documentation

_C_l_a_s_s _t_o _C_o_n_t_a_i_n _a_n_d _D_e_s_c_r_i_b_e _H_i_g_h-_T_h_r_o_u_g_h_p_u_t _E_x_p_r_e_s_s_i_o_n _L_e_v_e_l _A_s_s_a_y_s.

_D_e_s_c_r_i_p_t_i_o_n:

     Container for high-throughput assays and experimental metadata.
     'ExpressionSet' class is derived from 'eSet', and requires a
     matrix named 'exprs' as assayData member.

_E_x_t_e_n_d_s:

     Directly extends class 'eSet'.

_C_r_e_a_t_i_n_g _O_b_j_e_c_t_s:

     'new("ExpressionSet")'

     'new("ExpressionSet", phenoData = new("AnnotatedDataFrame"),
     featureData = new("AnnotatedDataFrame"), experimentData =
     new("MIAME"), annotation = character(0), exprs = new("matrix")) '

     This creates an 'ExpressionSet' with 'assayData' implicitly
     created to contain 'exprs'. Additional named matrix arguments with
     the same dimensions as 'exprs' are added to 'assayData'; the row
     and column names of these additional matricies should match those
     of 'exprs'. 

     'new("ExpressionSet", assayData =
     assayDataNew(exprs=new("matrix")), phenoData =
     new("AnnotatedDataFrame"), featureData =
     new("AnnotatedDataFrame"), experimentData = new("MIAME"),
     annotation = character(0)) '

     This creates an 'ExpressionSet' with 'assayData' provided
     explicitly. In this form, the only required named argument is
     'assayData'.

     'as([exprSet],"ExpressionSet")'

     'ExpressionSet' instances are usually created through
     'new("ExpressionSet", ...)'. Usually the arguments to 'new'
     include 'exprs' (a matrix of expression data, with features
     corresponding to rows and samples to columns), 'phenoData',
     'featureData', 'experimentData', and 'annotation'.  'phenoData',
     'featureData', 'experimentData', and 'annotation' can be missing,
     in which case they are assigned default values.

_S_l_o_t_s:

     Inherited from 'eSet':

     '_a_s_s_a_y_D_a_t_a': Contains matrices with equal dimensions, and with
          column number equal to 'nrow(phenoData)'. 'assayData' must
          contain a matrix 'exprs' with rows represening features
          (e.g., reporters) and columns representing samples.
          Additional matrices of identical size (e.g., representing
          measurement errors) may also be included in 'assayData'.
          Class:'AssayData-class'

     '_p_h_e_n_o_D_a_t_a': See 'eSet'

     '_f_e_a_t_u_r_e_D_a_t_a': See 'eSet'

     '_e_x_p_e_r_i_m_e_n_t_D_a_t_a': See 'eSet'

     '_a_n_n_o_t_a_t_i_o_n': See 'eSet'

_M_e_t_h_o_d_s:

     Class-specific methods.

     '_a_s(_e_x_p_r_S_e_t,"_E_x_p_r_e_s_s_i_o_n_S_e_t")' Coerce objects of 'exprSet-class' to
          'ExpressionSet'

     '_a_s(_o_b_j_e_c_t,"_d_a_t_a._f_r_a_m_e")' Coerce objects of 'ExpressionSet-class'
          to 'data.frame' by transposing the expression matrix and
          concatenating 'phenoData'

     '_e_x_p_r_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_e_x_p_r_s(_E_x_p_r_e_s_s_i_o_n_S_e_t,_m_a_t_r_i_x)<-' Access and
          set elements named 'exprs' in the 'AssayData-class' slot.

     '_e_s_A_p_p_l_y(_E_x_p_r_e_s_s_i_o_n_S_e_t, _M_A_R_G_I_N, _F_U_N,          ...)' 'apply'-like
          function to conveniently operate on 'ExpressionSet' objects.
          See 'esApply'.

     '_w_r_i_t_e._e_x_p_r_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)' Write expression values to a text
          file. It takes the same arguments as 'write.table'

     Derived from 'eSet':

     '_u_p_d_a_t_e_O_b_j_e_c_t(_o_b_j_e_c_t, ..., _v_e_r_b_o_s_e=_F_A_L_S_E)' Update instance to
          current version, if necessary. See 'updateObject' and 'eSet'

     '_i_s_C_u_r_r_e_n_t(_o_b_j_e_c_t)' Determine whether version of object is
          current. See 'isCurrent'

     '_i_s_V_e_r_s_i_o_n_e_d(_o_b_j_e_c_t)' Determine whether object contains a
          'version' string describing its structure . See 'isVersioned'

     '_a_s_s_a_y_D_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t)': See 'eSet'

     '_s_a_m_p_l_e_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)' _a_n_d '_s_a_m_p_l_e_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)<-': 
          See 'eSet'

     '_f_e_a_t_u_r_e_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_f_e_a_t_u_r_e_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t, _v_a_l_u_e)<-': 
          See 'eSet'

     '_d_i_m_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)': See 'eSet'

     '_p_h_e_n_o_D_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_p_h_e_n_o_D_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)<-': 
          See 'eSet'

     '_v_a_r_L_a_b_e_l_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_v_a_r_L_a_b_e_l_s(_E_x_p_r_e_s_s_i_o_n_S_e_t, _v_a_l_u_e)<-': 
          See 'eSet'

     '_v_a_r_M_e_t_a_d_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_v_a_r_M_e_t_a_d_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)<-': 
          See 'eSet'

     '_p_D_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_p_D_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)<-': See 'eSet'

     '_v_a_r_M_e_t_a_d_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_v_a_r_M_e_t_a_d_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)' 
          See 'eSet'

     '_e_x_p_e_r_i_m_e_n_t_D_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t)','_e_x_p_e_r_i_m_e_n_t_D_a_t_a(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)<-': 
          See 'eSet'

     '_p_u_b_M_e_d_I_d_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_p_u_b_M_e_d_I_d_s(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)' See
          'eSet'

     '_a_b_s_t_r_a_c_t(_E_x_p_r_e_s_s_i_o_n_S_e_t)': See 'eSet'

     '_a_n_n_o_t_a_t_i_o_n(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_a_n_n_o_t_a_t_i_o_n(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)<-' 
          See 'eSet'

     '_c_o_m_b_i_n_e(_E_x_p_r_e_s_s_i_o_n_S_e_t,_E_x_p_r_e_s_s_i_o_n_S_e_t)': See 'eSet'

     '_s_t_o_r_a_g_e_M_o_d_e(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_s_t_o_r_a_g_e_M_o_d_e(_E_x_p_r_e_s_s_i_o_n_S_e_t,_c_h_a_r_a_c_t_e_r)<-': 
          See 'eSet'

     Standard generic methods:

     '_i_n_i_t_i_a_l_i_z_e(_E_x_p_r_e_s_s_i_o_n_S_e_t)': Object instantiation, used by 'new';
          not to be called directly by the user.

     '_u_p_d_a_t_e_O_b_j_e_c_t(_E_x_p_r_e_s_s_i_o_n_S_e_t)': Update outaded versions of
          'ExpressionSet' to their current definiton. See
          'updateObject', 'Versions-class'.

     '_v_a_l_i_d_O_b_j_e_c_t(_E_x_p_r_e_s_s_i_o_n_S_e_t)': Validity-checking method, ensuring
          that 'exprs' is a member of 'assayData'.
          'checkValidity(ExpressionSet)' imposes this validity check,
          and the validity checks of 'eSet'.

     '_m_a_k_e_D_a_t_a_P_a_c_k_a_g_e(_o_b_j_e_c_t, _a_u_t_h_o_r, _e_m_a_i_l, _p_a_c_k_a_g_e_N_a_m_e, _p_a_c_k_a_g_e_V_e_r_s_i_o_n, _l_i_c_e_n_s_e, _b_i_o_c_V_i_e_w_s, _f_i_l_e_P_a_t_h, _d_e_s_c_r_i_p_t_i_o_n=_p_a_s_t_e(_a_b_s_t_r_a_c_t(_o_b_j_e_c_t), _c_o_l_l_a_p_s_e="_n_n"), ...)' 
          Create a data package based on an ExpressionSet object. See
          'makeDataPackage'.

     '_a_s(_e_x_p_r_S_e_t,_E_x_p_r_e_s_s_i_o_n_S_e_t)': Coerce 'exprSet' to 'ExpressionSet'.

     '_a_s(_e_S_e_t,_E_x_p_r_e_s_s_i_o_n_S_e_t)': Coerce the 'eSet' portion of an object
          to 'ExpressionSet'.

     '_s_h_o_w(_E_x_p_r_e_s_s_i_o_n_S_e_t)' See 'eSet'

     '_d_i_m(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_n_c_o_l' See 'eSet'

     '_E_x_p_r_e_s_s_i_o_n_S_e_t[(_i_n_d_e_x)': See 'eSet'

     '_E_x_p_r_e_s_s_i_o_n_S_e_t$', '_E_x_p_r_e_s_s_i_o_n_S_e_t$<-' See 'eSet'

     '_E_x_p_r_e_s_s_i_o_n_S_e_t[[_i]]', '_E_x_p_r_e_s_s_i_o_n_S_e_t[[_i]]<-' See 'eSet'

_A_u_t_h_o_r(_s):

     Biocore team

_S_e_e _A_l_s_o:

     'eSet-class', 'ExpressionSet-class'.

_E_x_a_m_p_l_e_s:

     # create an instance of ExpressionSet
     new("ExpressionSet")

     new("ExpressionSet",
         exprs=matrix(runif(1000), nrow=100, ncol=10))

     # update an existing ExpressionSet
     data(sample.ExpressionSet)
     updateObject(sample.ExpressionSet)

     # information about assay and sample data
     featureNames(sample.ExpressionSet)[1:10]
     sampleNames(sample.ExpressionSet)[1:5]
     phenoData(sample.ExpressionSet)
     experimentData(sample.ExpressionSet)

     # subset: first 10 genes, samples 2, 4, and 10
     expressionSet <- sample.ExpressionSet[1:10,c(2,4,10)]

     # named features and their expression levels
     subset <- expressionSet[c("AFFX-BioC-3_at","AFFX-BioDn-5_at"),]
     exprs(subset)

     # samples with above-average 'score' in phenoData
     highScores <- expressionSet$score > mean(expressionSet$score)
     expressionSet[,highScores]

     # (automatically) coerce to data.frame
     lm(score~AFFX.BioDn.5_at + AFFX.BioC.3_at, data=subset)

