cia                  package:made4                  R Documentation

_C_o_i_n_e_r_t_i_a _a_n_a_l_y_s_i_s: _E_x_p_l_o_r_e _t_h_e _c_o_v_a_r_i_a_n_c_e _b_e_t_w_e_e_n _t_w_o _d_a_t_a_s_e_t_s

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

     Performs CIA on two datasets as described by Culhane et al., 2003.
     Used for meta-analysis of two or more datasets.

_U_s_a_g_e:

     cia(df1, df2, cia.nf=2, cia.scan=FALSE, nsc=TRUE,...)
     plot.cia(x, nlab = 10, axis1 = 1, axis2 = 2, genecol = "gray25", 
              genelabels1 = rownames(ciares$co), genelabels2 = rownames(ciares$li), ...)

_A_r_g_u_m_e_n_t_s:

     df1: The first dataset.  A 'matrix', 'data.frame',  'exprSet' or
          'marrayRaw'.   If the input is gene expression data in a
          'matrix' or 'data.frame'. The  rows and columns are expected
          to contain the variables (genes) and cases (array samples) 
          respectively.

     df2: The second dataset.  A 'matrix', 'data.frame',  'exprSet' or
          'marrayRaw'.   If the input is gene expression data in a
          'matrix' or 'data.frame'. The  rows and columns are expected
          to contain the variables (genes) and cases (array samples) 
          respectively.

  cia.nf: Integer indicating the number of coinertia analysis axes to
          be saved. Default value is 2.

cia.scan: Logical indicating whether the coinertia analysis eigenvalue
          (scree) plot should be shown so that the number of axes, 
          'cia.nf' can be selected interactively. Default value is
          FALSE.

     nsc: A logical indicating whether coinertia analysis should be
          performed using two non-symmetric correspondence analyses
          'dudi.nsc'.  The default=TRUE is highly recommended. If
          FALSE, COA 'dudi.coa'  will be performed on df1, and row
          weighted COA 'dudi.rwcoa'  will be performed on df2 using the
          row weights from df1. 

       x: An object of class 'cia', containing the CIA projected
          coordinates to be plotted.  

    nlab: Numeric. An integer indicating the number of variables
          (genes) to be labelled on plots

   axis1: Integer, the column number for the x-axis. The default is 1

   axis2: Integer, the column number for the y-axis. The default is 2.

 genecol: Character, the colour of genes (variables). The default is
          "gray25".

genelabels1, genelabels2: A vector of variables labels, by default the
          row.names of each input matrix df1, and df2 are used

     ...: further arguments passed to or from other methods

_D_e_t_a_i_l_s:

     CIA has been successfully applied to the cross-platform comparison
     (meta-analysis) of microarray  gene expression datasets (Culhane
     et al., 2003). Please refer to this paper and the vignette for
     help in interpretation of the output from CIA.

     Co-inertia analysis (CIA) is a multivariate method that identifies
     trends or co-relationships in multiple datasets which contain the
     same samples. That is the rows or columns of the matrix have to 
     be weighted similarly and thus must be "matchable".  In 'cia', it
     is assumed that the analysis is being performed on the microarray
     cases, and thus the columns will be matched between the 2
     datasets. Thus please ensure that the order of cases (the columns)
     in df1 and df2 are equivalent before performing CIA.

     CIA simultaneously finds ordinations (dimension reduction
     diagrams) from the datasets that  are most similar. It does this
     by finding successive axes from the two datasets with maximum
     covariance.  CIA can be applied to datasets where the number of
     variables (genes) far exceeds the  number of samples (arrays) such
     is the case with microarray analyses.

     'cia' calls 'coinertia' in the ADE4 package. For more information
     on  coinertia analysis please refer to 'coinertia'  and several
     recent reviews (see below).

     In the paper by Culhane et al., 2003, the datasets df1 and df2 are
     transformed using COA and Row weighted COA respectively, before
     coinertia analysis.  It is now recommended to perform non
     symmetric correspondence analysis (NSC) rather than correspondence
     analysis  (COA) on both datasets.  

     *The RV coefficient*

     In the results, in the object 'cia' returned by the analysis,
     $coinertia$RV gives the RV coefficient. This is a measure of
     global similarity between the datasets, and is a number between 0
     and 1. The closer it is to 1 the greater the global similarity
     between the two datasets. 

     *Plotting and visualising cia results*

     'plot.cia' draws 3 plots.   

     The first plot uses 'S.match.col' to plots the projection
     (normalised scores $mY  and $mX) of the samples from each dataset
     onto the one space.  Cases (microarray samples) from one dataset
     are represented by circles,  and cases from the second dataset are
     represented by arrow tips. Each circle and arrow is joined by a
     line,  where the length of the line is proportional to the
     divergence between the gene expression profiles of that  sample in
     the two datasets.  A short line shows good agreement between the
     two  datasets.  

     The second two plots call 'plot.genes' are show the projection of
     the variables (genes, $li and $co)  from each dataset in the new
     space. It is important to note both the direction of project of
     Variables  (genes) and cases (microarray samples). Variables and
     cases that are projected in the same direction  from the origin
     have a positive correlation (ie those genes are upregulated in
     those microarray samples)

     _Please refer to the help on 'bga' for further discussion on
     graphing and visualisation functions in MADE4._

_V_a_l_u_e:

     An object of the class 'cia' which contains a list of length 4. 

    call: list of input arguments, df1 and df2

coinertia: A object of class "coinertia", sub-class 'dudi'. See
          'coinertia' 

    coa1: Returns an object of class "coa" or "nsc", with sub-class
          'dudi'. See 'dudi.coa' or 'dudi.nsc'

    coa2: Returns an object of class "coa" or "nsc", with sub-class
          'dudi'. See 'dudi.coa' or 'dudi.nsc'

_N_o_t_e:

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

     Aedin Culhane

_R_e_f_e_r_e_n_c_e_s:

     Culhane AC, et al., 2003 Cross platform comparison and
     visualisation of gene expression data using co-inertia analysis.
     BMC Bioinformatics. 4:59

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

     See also  'coinertia', 'plot.cia'

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

     data(NCI60)
     print("This will take a few minutes, please wait...")

     if (require(ade4, quiet = TRUE)) {
     # Example data are "G1_Ross_1375.txt" and "G5_Affy_1517.txt"
     coin <- cia(NCI60$Ross, NCI60$Affy)
     }
     coin
     # ciares$RV will give the RV-coefficient, the greater (scale 0-1) the better   
     cat(paste("The RV coefficient is a measure of global similarity between the datasets.\n",
     "The two datasets analysed are very similar. ",
     "The RV coefficient of this coinertia analysis is: ", coin$coinertia$RV,"\n", sep= ""))
     plot(coin)
     plot(coin, classvec=NCI60$classes[,2], clab=0, cpoint=3)

