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', phenoData = [AnnotatedDataFrame], experimentData = [MIAME], annotation = [character], exprs = [matrix], ...) ' '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', 'experimentData', and 'annotation'. 'phenoData', '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' '_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' '_a_s._d_a_t_a._f_r_a_m_e(_o_b_j_e_c_t)' Coerce 'ExpressionSet-class' object to 'data.frame' '_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. 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' '_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)<-': S ee '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' '_g_e_n_e_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)' _a_n_d '_g_e_n_e_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t, _v_a_l_u_e)<-': Depre cated. Has the same effect as 'featureNames' which is the preferred accessor of expression matrix row names. '_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)<-': Se e '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)<-': S ee '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)' S ee '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)<-' S ee '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' '_r_e_p_o_r_t_e_r_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t)', '_r_e_p_o_r_t_e_r_N_a_m_e_s(_E_x_p_r_e_s_s_i_o_n_S_e_t,_v_a_l_u_e)<-': DEPRE CATED 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'. '_a_s(_e_x_p_r_S_e_t,_E_x_p_r_e_s_s_i_o_n_S_e_t)': Coerce 'exprSet' too '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") # update an existing ExpressionSet data(sample.ExpressionSet) updateObject(sample.ExpressionSet) # update existing exprSet-like class to ExpressionSet data(sample.exprSet) expressionSet <- as(sample.exprSet,"ExpressionSet") expressionSet # information about assay and sample data featureNames(expressionSet)[1:10] sampleNames(expressionSet)[1:5] phenoData(expressionSet) experimentData(expressionSet) # subset: first 10 genes, samples 2, 4, and 10 expressionSet <- as(sample.exprSet,"ExpressionSet") 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)