exprSet               package:Biobase               R Documentation

_C_l_a_s_s _f_o_r _M_i_c_r_o_a_r_r_a_y _D_a_t_a _a_n_d _M_e_t_h_o_d_s _f_o_r _P_r_o_c_e_s_s_i_n_g _T_h_e_m

_D_e_s_c_r_i_p_t_i_o_n:

     This is a class representation for microarray data

_E_x_t_e_n_d_s:

     Directly extends class 'annotatedDataset'.

_C_r_e_a_t_i_n_g _O_b_j_e_c_t_s:

     ' new('exprSet', exprs = [exprMatrix], se.exprs = [exprMatrix],
     phenoData = [phenoData], annotation = [character], description =
     [characterORMIAME], notes = [character])'

_S_l_o_t_s:

     Derived from 'annotatedDataset':

     '_r_e_p_o_r_t_e_r_I_n_f_o' class:'data.frameOrNULL'

     '_p_h_e_n_o_D_a_t_a': Object of class 'phenoData' containing the patient
          (or case) level data. The columns of the pData slot of this
          entity represent variables and the rows represent patients or
          cases. 

     Introduced in 'exprSet':

     '_e_x_p_r_s': Object of class 'exprMatrix'. The observed expression
          levels. This is a matrix with columns representing patients
          or cases and rows representing genes. 

     '_s_e._e_x_p_r_s': Object of class 'exprMatrix'. This is a matrix of the
          same dimensions as 'exprs' which contains standard error
          estimates for the estimated expression levels. 

     '_a_n_n_o_t_a_t_i_o_n' A character string identifying the annotation that
          may be used for the 'exprSet' instance.

     '_d_e_s_c_r_i_p_t_i_o_n': Object of class 'characterORMIAME'. For
          compatibility with previous version of this class description
          can also be a 'character'. The clase 'characterOrMIAME' has
          been defined just for this.

     '_n_o_t_e_s': Object of class 'character' of explanatory text 

_M_e_t_h_o_d_s:

     Derived from 'annotatedDataset':

     '$(_e_x_p_r_S_e_t)' _a_n_d '$(_e_x_p_r_S_e_t, _v_a_l_u_e)<-' An old-style method. It is
          'pData(eset)[[as.character(val)]]' which does not quite have
          the right semantics but it is close. This operator extracts
          the named component of the 'pData' slot in 'phenoData'.

     '[[(_i_n_d_e_x)' _a_n_d '[[(_i_n_d_e_x, _v_a_l_u_e)<-': see 'annotatedDataset'

     '_p_h_e_n_o_D_a_t_a(_e_x_p_r_S_e_t)' _a_n_d '_p_h_e_n_o_D_a_t_a(_e_x_p_r_S_e_t, _v_a_l_u_e)<-' see
          'annotatedDataset'

     '_r_e_p_o_r_t_e_r_I_n_f_o(_e_x_p_r_S_e_t)' _a_n_d '_r_e_p_o_r_t_e_r_I_n_f_o(_e_x_p_r_S_e_t, _v_a_l_u_e)<-' see
          'annotatedDataset'

     '_p_D_a_t_a(_e_x_p_r_S_e_t)' _a_n_d '_p_D_a_t_a(_e_x_p_r_S_e_t, _v_a_l_u_e)<-' see
          'annotatedDataset'

     '_v_a_r_L_a_b_e_l_s(_e_x_p_r_S_e_t)' see 'annotatedDataset'

     Class-specific methods:

     '_u_p_d_a_t_e_2_M_I_A_M_E(_e_x_p_r_S_e_t)': Converts 'exprSet's from previous
          versions, that have a 'character' in description to an object
          that has an instance of the class 'MIAME' in the description
          slot. The old description is stored in the 'title' slot. If
          the object already has a 'MIAME' description the same object
          is returned.

     '_a_s_s_a_y_D_a_t_a(_e_x_p_r_S_e_t)': Method to access 'exprs' slot

     '_e_x_p_r_s(_e_x_p_r_S_e_t)' _a_n_d '_e_x_p_r_s(_e_x_p_r_S_e_t)<-': Methods to access/update
          'exprs' slot 

     '_s_e._e_x_p_r_s(_e_x_p_r_S_e_t)' _a_n_d '_s_e._e_x_p_r_s(_e_x_p_r_S_e_t)<-': Methods to
          access/update 'se.exprs' slot 

     '_d_e_s_c_r_i_p_t_i_o_n(_e_x_p_r_S_e_t)' _a_n_d '_d_e_s_c_r_i_p_t_i_o_n(_e_x_p_r_S_e_t, _v_a_l_u_e)<-': 
          Methods to access/update 'description' slot 

     '_a_n_n_o_t_a_t_i_o_n(_e_x_p_r_S_e_t)' _a_n_d '_a_n_n_o_t_a_t_i_o_n(_e_x_p_r_S_e_t, _v_a_l_u_e)<-': Methods
          to access/update 'annotation' slot

     '_n_o_t_e_s(_e_x_p_r_S_e_t)' _a_n_d '_n_o_t_e_s(_e_x_p_r_S_e_t, _v_a_l_u_e)<-': Methods to
          access/update 'notes' slot 

     '_a_b_s_t_r_a_c_t(_e_x_p_r_S_e_t)': Not documented: 'function(object)
          abstract(description(object))'

     '_s_a_m_p_l_e_N_a_m_e_s(_e_x_p_r_S_e_t)' _a_n_d '_s_a_m_p_l_e_N_a_m_e_s(_e_x_p_r_S_e_t, _v_a_l_u_e)<-': 
          Methods to access/update 'dimnames' of the 'exprs' slot 

     '_g_e_n_e_N_a_m_e_s(_e_x_p_r_S_e_t)' _a_n_d '_g_e_n_e_N_a_m_e_s(_e_x_p_r_S_e_t, _v_a_l_u_e)<-': Methods to
          access/update 'row.names' of the 'exprs' slot - gene names 

     '_f_e_a_t_u_r_e_N_a_m_e_s(_e_x_p_r_S_e_t)' _a_n_d '_f_e_a_t_u_r_e_N_a_m_e_s(_e_x_p_r_S_e_t, _v_a_l_u_e)<-': 
          Methods to access/update 'row.names' of the 'exprs' slot; use
          'featureNames' in preference to 'geneNames', to allow easier
          transition to new Biobase classes like 'ExpressionSet-class'.

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

     '_w_r_i_t_e._e_x_p_r_s(_e_x_p_r_S_e_t,...)': Writes the expression levels to file.
          It takes the same arguments as 'write.table'. If called with
          no arguments it is equivalent to
          'write.table(exprs(exprSet),file="tmp.txt",quote=FALSE,sep="\t")'.

     '_e_x_p_r_s_2_e_x_c_e_l(_e_x_p_r_S_e_t,...)': Writes the expression levels to 'csv'
          file. This file will open nicely in excel. It takes the same
          arguments as 'write.table'. If called with no arguments it is
          equivalent to 'write.table(exprs(exprSet),file="tmp.csv", sep
          = ",", col.names = NA)'.

     '_a_s._d_a_t_a._f_r_a_m_e._e_x_p_r_S_e_t(_e_x_p_r_S_e_t, _r_o_w._n_a_m_e_s = _N_A, _o_p_t_i_o_n_a_l = _N_A)': 
          Converts 'exprSet' into a 'data.frame'. In the return value,
          the first column is called 'exprs' and contains the values
          returned by the method 'exprs()'. The second column is called
          genenames and contains the values returned by the method
          'geneNames()'. The other columns will depend on the contents
          of the 'phenoData' slot.

     Iterator-series methods:
      This is a set of methods to iterate over different types of
     objects. The behaviour of the methods is similar to that of the
     'apply' family.

     '_i_t_e_r(_e_x_p_r_S_e_t, _m_i_s_s_i_n_g, _f_u_n_c_t_i_o_n)': An iterator over genes.
          Returns the result of applying 'function' to the matrix of
          expressions on margin 1 (see 'apply')

     '_i_t_e_r(_e_x_p_r_S_e_t, _m_i_s_s_i_n_g, _l_i_s_t)': A multi-iterator over genes.
          Concatenates result of applying each function in the list
          'list' in a matrix (assumes result of each function
          evaluation is a scalar).

     '_i_t_e_r(_e_x_p_r_S_e_t, _c_h_a_r_a_c_t_e_r, _f_u_n_c_t_i_o_n)': An iterator over genes.
          'function' is assumed to have arguments x and y; the pData
          element named by covlab will be bound to x, the gene
          expression values will be iteratively bound to y

     Split-series methods:

     '_s_p_l_i_t(_e_x_p_r_S_e_t, _f_a_c_t_o_r)': See method for 'vector'

     '_s_p_l_i_t(_e_x_p_r_S_e_t, _v_e_c_t_o_r)': Splits the exprSet. The returned value
          is a list, each component of which is an 'exprSet'. If the
          length of 'vector' is a divisor of the number of rows of the
          phenoData data frame then the split is made on this.

     Standard generic methods:

     '_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'

     '_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_h_o_w(_e_x_p_r_S_e_t)': Renders information about the exprSet in a
          concise way on stdout. 

     '[(_e_x_p_r_S_e_t)': A subset operator. Ensures that both 'exprs' and
          'phenoData' are subset properly.

     '_v_a_l_i_d_O_b_j_e_c_t(_e_x_p_r_S_e_t)': Validity-checking method, ensuring the
          number and names of 'phenoData' rows match the number and
          names of 'exprs' columns

_S_e_e _A_l_s_o:

     'MIAME', 'annotatedDataset', 'phenoData', 'class:exprMatrix',
     'class:characterORMIAME', 'read.exprSet', 'esApply'

_E_x_a_m_p_l_e_s:

       data(geneData)
       data(geneCov)
       covdesc<- list("Covariate 1", "Covariate 2", "Covariate 3")
       names(covdesc) <- names(geneCov)
       pdata <- new("phenoData", pData=geneCov, varLabels=covdesc)
       pdata[1,]
       pdata[,2]

       expr <- new("exprSet", exprs=geneData, phenoData=pdata)
       expr
       expr[,1:10]
       expr[,1]
       expr[1,]
       expr[1,1]
       expr[1:100,]
       expr[1:44,c(2,4,6)]
       Means <- iter(expr, f=mean)

       chkdich <- function(x) if(length(unique(x))!=2) stop("x not dichotomous")
       mytt <- function(x,y) {
          chkdich(x)
          d <- split(y,x)
          t.test(d[[1]],d[[2]])$p.val
       }

       Tpvals <- iter(expr, "cov1", mytt )

       sp1 <- split(expr, c(1,2))
       sp2 <- split(expr, c(rep(1,6), rep(2,7)))

       sampleNames(expr)
       sampleNames(expr) <- letters
       featureNames(expr)[1:10]

       # as.data.frame.exprSet - example
       data(sample.exprSet)
       sd.genes <- esApply(sample.exprSet, 1, sd)
       dataf <- as.data.frame(sample.exprSet)
       dataf <- cbind(dataf, sd.genes=rep(unname(sd.genes), length=nrow(dataf)))
       coplot(sd.genes ~ exprs | sex+type, data=dataf)

       # update existing exprSet-like object
       data(sample.exprSet)
       updateObject(sample.exprSet) # to match class definition of same (exprSet) class
       expressionSet <- as(sample.exprSet, "ExpressionSet") # to different class

