clmprotocols(5)                  FILE FORMATS                  clmprotocols(5)



   1. NAME
   2. DESCRIPTION
   3. Network representation
   4. Clustering similarity graphs encoded in BLAST results
   5. Clustering expression data
   6. SEE ALSO
   7. AUTHOR

  NAME
      clmprotocols - Work flows and protocols for mcl and friends

  DESCRIPTION
      A guide to doing analysis with mcl and its helper programs.

  Network representation
      The clustering program mcl expects the name of file as its first
      argument.  If the --abc option is used, the file is assumed to adhere to
      a simple format where a network is specified edge by edge, one line and
      one edge at a time.  Each line describes an edge as two labels and a
      numerical value, all separated by white space. The labels and the value
      respectively identify the two nodes and the edge weight. The format is
      called ABC-format, where 'A' and 'B' represent the two labels and 'C'
      represents the edge weight. The latter is optional; if omitted the edge
      weight is set to one.  If ABC-format is used, the output is returned as
      a listing of clusters, each cluster given as a line of white-space
      separated labels.

      MCL can also utilize a second representation, which is a stringent and
      unambiguous format for both input and output.  This is called matrix
      format and it is required when using other programs in the mcl suite,
      for example when comparing and analysing clusterings using clm(1) or
      when extracting and transforming networks using mcx(1).  Native mode
      (matrix format) is entered simply by not specifying --abc.

      The recommended approach using mcl is to convert an external format to
      ABC-format. The program mcxload(1) reads the latter and creates a native
      network file and a dictionary file that maps network nodes to labels.
      All applications in the MCL suite, including mcl itself, can read this
      native network file format. Label output can be obtained using
      mcxdump(1). The workflow is thus:

         #  External format has been converted to file data.abc (abc format)

         mcxload -abc data.abc --stream-mirror -write-tab data.tab -o data.mci

         mcl data.mci -I 1.4
         mcl data.mci -I 2
         mcl data.mci -I 4

         mcxdump -icl out.data.mci.I14 -tabr data.tab -o dump.data.mci.I14
         mcxdump -icl out.data.mci.I20 -tabr data.tab -o dump.data.mci.I20
         mcxdump -icl out.data.mci.I40 -tabr data.tab -o dump.data.mci.I40

      In this example the cluster output is stored in native format and dumped
      to labels using mcxdump. The stored output can now be used to learn more
      about the clusterings. An example is the following, where clm(1) is
      applied in mode dist to gauge the distance between different
      clusterings.

         clm dist --chain out.data.mci.I{14,20,40}


  Clustering similarity graphs encoded in BLAST results
      A specific instance of the workflow above is the clustering of proteins
      based on their sequence similarities. In the most typical scenario the
      external format is BLAST output, which needs to be transformed to ABC
      format.  In the examples below the input is in columnar blast format
      obtained with the blast -m8 option.  It requires a version of mcl at
      least as recent as 09-061.  First we create an ABC-formatted file using
      the external columnar BLAST format, which is assumed to be in a file
      called seq.cblast.

         cut -f 1,2,11 seq.cblast > seq.abc

      The columnar format in the file seq.cblast has, for a given BLAST hit,
      the sequence labels in the first two columns and the asssociated E-value
      in column 11. It is parsed by the standard UNIX cut(1) utility. The
      format must have been created with the BLAST -m8 option so that no
      comment lines are present. Alternatively these can be filtered out using
      grep.  The newly created seq.abc file is loaded by mcxload(1), which
      writes both a network file seq.mci and a dictionary file seq.tab.

         mcxload -abc seq.abc --stream-mirror --stream-neg-log10 -abc-tf 'ceil(200)'
               -o seq.mci -write-tab seq.tab

      The --stream-mirror option ensures that the resulting network will be
      undirected, as recommended when using mcl. Omitting this option would
      result in a directed network as BLAST E-values generally differ between
      two sequences. The default course of action for mcxload(1) is to use the
      best value found between a pair of labels. The next option, --abc-neg-
      log10 tranforms the numerical values in the input (the BLAST E-values)
      by taking the logarithm in base 10 and subsequently negating the sign.
      Finally, the transformed values are capped so that any E-value below
      1e-200 is set to a maximum allowed edge weight of 200.

      To obtain clusterings from seq.mci and seq.tab one has two choices. The
      first is to generate an abstract clustering representation and from that
      obtain the label output, as follows.  Below the -o option is not used,
      so mcl will create meaningful and unique output names by itself. The
      default way of doing this is to preprend the prefix out. and to append a
      suffix encoding the inflation value used, with inflation encoded using
      two digits of precision and the decimal separator removed.

         mcl seq.mci -I 1.4
         mcl seq.mci -I 2
         mcl seq.mci -I 4
         mcl seq.mci -I 6

         mcxdump -icl out.seq.mci.I14 -tab seq.tab -o dump.seq.mci.I14
         mcxdump -icl out.seq.mci.I20 -tab seq.tab -o dump.seq.mci.I20
         mcxdump -icl out.seq.mci.I40 -tab seq.tab -o dump.seq.mci.I40
         mcxdump -icl out.seq.mci.I60 -tab seq.tab -o dump.seq.mci.I60

      Now the file out.seq.tab.I14 and its associates can be used for example
      to compute the distances between the encoded clusterings with clm dist,
      to compute a set of strictly reconciled nested clusterings with clm
      order, or to compute an efficiency criterion with clm info.

      Alternatively, label output can be obtained directly from mcl as
      follows.

         mcl seq.mci -I 1.4  -use-tab seq.tab
         mcl seq.mci -I 2  -use-tab seq.tab
         mcl seq.mci -I 4  -use-tab seq.tab
         mcl seq.mci -I 6  -use-tab seq.tab


  Clustering expression data
      The clustering of expression data constitutes another workflow. In this
      case the external format usually is a tabular file format containing
      labels for genes or probes and numerical values measuring the expression
      values or fold changes across a series of conditions or experiments.
      Such tabular files can be processed by mcxarray(1), which comes
      installed with mcl. The program computes correlations (either Pearson or
      Spearmann) between genes, and creates an edge between genes if their
      correlation exceeds the specified cutoff. From this mcxarray(1) creates
      both a network file and a dictionary file. In the example below, the
      file expr.data is in tabular format with one row of column headers (e.g.
      tags for experiments) and one column of row identifiers (e.g. probe or
      gene identifiers).

         mcxarray -data expr.data -skipr 1 -skipc 1 -o expr.mci -write-tab expr.tab
            --pearson -co 0.7 -tf 'abs(),add(-0.7)'


      This uses the Pearson correlation, ignoring values below 0.7.  The
      remaining values in the interval [0.7-1] are remapped to the interval
      [0-0.3]. This is recommended so that the edge weights will have
      increased contrast between them, as mcl is affected by relative
      differences (ratios) between edge weights rather than absolute
      differences. To illustrate this, values 0.75 and 0.95 are mapped to 0.05
      and 0.25, with respective ratios 0.79 and 0.25.  The network file
      expr.mci and the dictionary file expr.tab can now be used as before.

      It is possible to investigate the effect of the correlation cutoff as
      follows.  First a network is generated at a very low threshold, and this
      network is analysed using mcxquery.

         mcxarray -data expr.data -skipr 1 -skipc 1 -o expr20.mcx --write-binary
            --pearson -co 0.2 -tf 'abs()'
         mcx query -imx expr20.mcx --vary-correlation


      The output is in a tabular format describing the properties of the
      network at increasing correlation thresholds. Examples are the size of
      the biggest component, the number of orphan nodes (not connected to any
      other node), and the mean and median node degrees.  A good way to choose
      the cutoff is to balance the number of singletons and the median node
      degree. Both should preferably not be too high.  For example the number
      of orphan nodes should be less than ten percent of the total number of
      nodes, and the median node degree should be at most one hundred
      neighbours.

  SEE ALSO
      mcxio(5).

  AUTHOR
      Stijn van Dongen.



  clmprotocols 1.008, 09-308        4 Nov 2009                   clmprotocols(5)
