mcl(1)                          USER COMMANDS                           mcl(1)



  NAME
      mcl - The Markov Cluster Algorithm, aka the MCL algorithm.

      mcl  is  a  cluster  algorithm  for graphs. A single option controls the
      granularity of the output clustering. This is the  -I inflation  option,
      described further below.

  GETTING STARTED
      There  are  two  main  modes of invocation. The most accessible is label
      mode that assumes label input. The input is then a  file  or  stream  in
      which  each  line encodes an edge in terms of two labels and a numerical
      value, separated by white space. The most  basic  example  of  usage  is
      this:

         mcl <-|fname> --abc -o fname-out

      The  output is then a file where each line is a cluster of tab-separated
      labels. MCL works natively with a numerical encoding of its input.  This
      matrix  input  is  the second mode of operation. Label mode can save its
      input for later use in matrix input. Here is how to do it.

         mcl <-|fname> --abc -o fname-out\
            -cache-tab map-name -cache-graph graph-name

      Native mode (matrix input) is entered simply by  not  specifying  --abc.
      The input file should then have been created by an earlier invocation of
      -cache-graph fname or by other means, e.g. mcxload(1). It is possible to
      obtain  label  output  in  native mode by specifying a label dictionary,
      possibly one that was saved earlier using the  -cache-tab fname  option.
      An example is this:

         mcl <-|fname> --yield-abc -o fname-out\
            -strict-tab fname-map

      Here  -strict-tab  tells  mcl  that  the input should not contain labels
      other than found in the file saved-mapping. It is possible to relax this
      by using -restrict-tab fname-map or -extend-tab fname-map.

      Label  mode  is very convenient for easy and fast exploration. A decided
      advantage of full native mode (where both graph input and cluster output
      are  in  matrix format) is that the data can easily be analyzed and sub-
      jected to further processing. Second, input that  is  stored  in  native
      binary format loads much faster than label data when the input data size
      grows large. If neither of these is of concern then label  mode  may  be
      entirely sufficient.  For more information on label mode and native mode
      refer to examples in this manual and mcxio(5).

      Granularity
      If you want to explore cluster structure in graphs with MCL, do use  the
      -I inflation  option  with  varying  parameters to obtain clusterings at
      different levels of granularity.

      Clustering from blast files
      Refer to the group of options discussed with --abc. By way  of  a  small
      example, consider these.

      mcxdeblast --abc-out=- hsfsp.blast | mcl - --abc -o -
      mcxdeblast --m9 --abc-out=- hsfsp.blast | mcl - --abc -o -

      The single hyphens in this example (other than word-separators) indicate
      that output is written to STDOUT or read from STDIN.   The  blast  files
      are  respectively  in  default and column (-m8 or -m9) format.  They are
      parsed, the output is sent to mcl, and mcl sends a list of tab-separated
      labels to STDOUT. All the abc strings serve to indicate that the mode of
      communication between these programs is label format.  It is possible to
      cache the input graph in native mcl matrix format:

      mcxdeblast --abc-out=- hsfsp.blast | \
         mcxload -abc - --mirror -o hsfsp.mcx -cache-tab hsfsp.tab --binary
      mcl hsfsp.mcx -use-tab hsfsp.tab -o  hsfsp.my-nice-clustering

      The matrix is written in binary format to speed up subsequent reads. The
      speed-up factor is approximately ten-fold.  For large graphs it is  rec-
      ommended  to  use  binary  format.   Keep  in mind though that it is not
      portable across machines and is not garantueed  to  be  portable  across
      different  versions of mcl or differently compiled versions of mcl.  mcl
      also accepts the --binary option,  ensuring  that  graphs  written  with
      -cache-graph  are  output  in  binary  format.   The mcxload(1) --mirror
      option ensures that the resulting graph is undirected. Input graphs that
      are cached with mcl's -cache-graph option get the same treatment.

  SYNOPSIS
      The  example  invocation below assumes matrix input, as described in the
      mcxio(5) section. Switching to label mode requires the input file to  be
      in label format and the addition of the --abc option.

      mcl  <-|fname>  [-I f (inflation)] [-o str (fname)] [-scheme k (resource
      scheme)]

      These options are sufficient in 95 percent of the  cases  or  more.  The
      first  argument  must be the name of a file containing a graph/matrix in
      the mcl input format, or a hyphen to read from STDIN.  With  respect  to
      clustering,  only  the -I option and -scheme option are relevant and the
      rest is immaterial.

      A mechanism for pipelines is supported (as of the first 1.003  release).
      Refer  to  the  PIPELINES  section  for more information.  A prepackaged
      pipeline for BLAST data is present in the form of mclblastline(1). As of
      release  1.006  a  lightweight  BLAST  clustering mechanism is also sup-
      ported. GETTING STARTED is a small introduction, with some  examples  of
      using BLAST results.

      The  full  listing  of  mcl options is shown below, separated into parts
      corresponding with functional aspects  such  as  clustering,  threading,
      verbosity, pruning and resource management, automatic output naming, and
      dumping.  The -scheme parameter provides a single access  point  to  the
      pruning  options,  and  should  be sufficient in most cases.  mcl allows
      comprehensive tuning and access to  its  internals  for  those  who  are
      interested, so it has many options.

      Baseline clustering options
      [-I f (inflation)] [-o str (fname)] [-scheme k (resource scheme)]

      Stream options
      [--abc  fname  (expect/write  labels)]  [--expect-abc  (expect  labels)]
      [--yield-abc fname (write  labels)]  [-use-tab  fname  (use  mapping  to
      write)]  [-strict-tab  fname  (use  mapping  to enforce)] [-restrict-tab
      fname (use mapping to restrict)] [-extend-tab  fname  (use  mapping  and
      extend)] [-cache-tab fname (write mapping)]

      Transform options
      [-tf  <tf-spec>  (transform  input matrix values)] [-stream-tf <tf-spec>
      (transform input stream values)] [--stream-log (take logarithm of stream
      values)] [--stream-neg-log (take negative logarithm of stream values)]

      Cache options
      [-cache-graph   fname   (write  graph)]  [-cache-expanded  fname  (write
      expanded graph)] [-ei inflation (use with expanded input)]

      Additional clustering options
      [-l n (initial iteration number)] [-L n (main iteration number)]  [-i  f
      (initial inflation)]

      Input manipulation options
      [-c  f  (centering)]  [-pi  f  (pre-inflation)] [-pp n (preprune count)]
      [-in-gq f (filter threshold)]

      Clustering result options
      [-sort str (sort mode)] [--keep-overlap=y/n (retain overlap)] [--output-
      limit=y/n  (write  limit matrix)] [--force-connected=y/n (analyze compo-
      nents)]  [--check-connected=y/n  (analyze  components)]   [--analyze=y/n
      (performance  criteria)]  [--show-log=y/n  (show log)] [--append-log=y/n
      (append log)]

      Verbosity options
      [-v str (verbosity type on)] [-V str  (verbosity  type  off)]  [--silent
      (very)]  [--verbose (very)] [-progress k (gauge)] [--show (print (small)
      matrices to screen)]

      Thread options
      [-te  k  (#expansion  threads)]  [-ti  k  (#inflation  threads)]  [-t  k
      (#threads)]

      Output file name options
      [-o  str  (fname)]  [-ap  str  (use  str  as file name prefix)] [-aa str
      (append str to suffix)] [-az (show output  file  name  and  exit)]  [-ax
      (show output suffix and exit)]

      Dump options
      [-dump-interval  i:j  (dump  interval)]  [-dump-modulo  k (dump modulo)]
      [-dump-stem stem (dump file stem)] [-dump str (type)]  [-dump-subi  spec
      (index  list for submatrix dump)] [-dump-subd spec (domain list for sub-
      matrix dump)] [-dump-dom fname (domain matrix file)] [-digits n  (print-
      ing precision)]

      Info options
      [--jury-charter  (explains jury)] [--version (show version)] [-how-much-
      ram k (RAM upper bound)] [-h (most important options)] [--apropos  (one-
      line  description  for  all  options)] [-z (show current settings)] [-az
      (show output file name and exit)] [-ax (show output  suffix  and  exit)]
      [--show-schemes (show resource schemes)]

      Pruning options
      The following options all pertain to the various pruning strategies that
      can be employed by mcl. They are described in the PRUNING  OPTIONS  sec-
      tion, accompanied by a description of the mcl pruning strategy.  If your
      graphs are huge and you have an appetite for tuning, have a look at  the
      following:

      [-p  f  (cutoff)]  [-P  n  (1/cutoff)]  [-S  n (selection number)] [-R n
      (recovery number)] [-pct f (recover percentage)] [-my-scheme n (tag cus-
      tom  scheme)]  [-warn-pct  n  (prune  warn  percentage)] [-warn-factor n
      (prune warn factor)] [--dense (allow matrices to fill)] [--adapt  (prun-
      ing)]  [--rigid  (pruning)]  [-ae  f (adaptive pruning exponent)] [-af f
      (adaptive pruning factor)] [-nx x (x window index)]  [-ny  y  (y  window
      index)]  [-nj  j (jury window index)] [-nw w (nr of windows)] [-nl w (nr
      of iterations)] [--thick (expect dense input graph)]

      The first argument of mcl must be a file  name,  but  some  options  are
      allowed  to  appear as the first argument instead. These are the options
      that cause mcl to print out information of some  kind,  after  which  it
      will gracefully exit. The full list of these options is

      -z,  -h,  --apropos, --version, --show-settings, --show-schemes, --jury-
      charter, -how-much-ram k.

  DESCRIPTION
      mcl implements the MCL algorithm, short for  the  Markov  cluster  algo-
      rithm,  a  cluster algorithm for graphs developed by Stijn van Dongen at
      the Centre for  Mathematics  and  Computer  Science  in  Amsterdam,  the
      Netherlands.  The  algorithm  simulates  flow using two simple algebraic
      operations on matrices.  The inception of this flow process and the the-
      ory behind it are described elsewhere (see REFERENCES). Frequently asked
      questions are answered in the mclfaq(7) section.  The program  described
      here  is  a fast threaded implementation written by the algorithm's cre-
      ator with contributions by several others. Anton Enright  co-implemented
      threading;  see the HISTORY/CREDITS section for a complete account.  See
      the APPLICABILITY section for a description of the  type  of  graph  mcl
      likes  best,  and  for  a  qualitative  assessment of its speed.  mcl is
      accompanied by several other utilities  for  analyzing  clusterings  and
      performing matrix and graph operations; see the SEE ALSO section.

      The  first  argument  is the input file name, or a single hyphen to read
      from stdin. The rationale for making the name of the input file a  fixed
      parameter  is  that you typically do several runs with different parame-
      ters. In command line mode it is pleasant if you do  not  have  to  skip
      over an immutable parameter all the time.

      The  -I f  option  is  the  main control, affecting cluster granularity.
      Using mcl is as simple as typing (assuming a file  proteins  contains  a
      matrix/graph in native matrix format)

      mcl proteins -I 2.0

      The above will result in a clustering written to the file named out.pro-
      teins.I20s2. It is - of course - possible to explicitly specify the name
      of  the  output  file using the -o fname option. Refer to the -ap option
      for a description of mcl's procedure in automatically constructing  file
      names from it parameters.

      In native mode clusterings are stored as matrices - this is discussed in
      the mcxio(5) section.  You presumably want  to  convert  the  output  to
      something  that  is easier to interpret. The native mcl matrix format is
      perhaps unpleasant to parse in the quick and dirty way. You can use

      mcl proteins -I 2.0 -use-tab proteins.tab --yield-abc

      to get a line/tab based output format, each line containing a cluster in
      the  form  of  tab-separated  labels.  Here proteins.tab should be a tab
      file previously created by mcxdeblast(1) or mcl.  Refer to mcxio(5)  for
      more  information  on tab files, and the entries grouped under --abc for
      an extensive discussion of the various ways in  which  mcl  can  combine
      label format and matrix format in input and output.

      In  finding  good  mcl parameter settings for a particular domain, or in
      finding cluster structure at different levels of granularity, one  typi-
      cally  runs  mcl  multiple  times  for varying values of f (refer to the
      -I inflation option for further information).

      NOTE
      mcl interprets the matrix entries or graph edge weights as similarities,
      and  it  likes  undirected  input  graphs  best.  It can handle directed
      graphs, but any node pair (i,j) for which w(i,j) is  much  smaller  than
      w(j,i)  or vice versa will presumably have a slightly negative effect on
      the clusterings output by mcl. Many such node pairs  will  have  a  dis-
      tinctly  negative  effect,  so try to make your input graphs undirected.
      How your edge weights are computed may affect mcl's performance. In pro-
      tein clustering, one way to go is to choose the negated logarithm of the
      BLAST probabilities (see REFERENCES).

      mcl's default parameters should make it quite fast under almost all cir-
      cumstances.  Taking  default  parameters,  mcl has been used to generate
      good protein clusters on 133k proteins, taking 10 minutes  running  time
      on  a  Compaq  ES40 system with four alpha EV6.7 processors. It has been
      applied (with good results) to graphs with 800k nodes, and if  you  have
      the  memory  (and  preferably CPUs as well) nothing should stop you from
      going further.

      For large graphs, there are several groups of parameters  available  for
      tuning  the  mcl  computing process, should it be necessary. The easiest
      thing to do is just vary the -scheme  option.  This  triggers  different
      settings  for  the  group of pruning parameters -p/-P, -R, -S, and -pct.
      The default setting corresponds with -scheme 6.  There is an  additional
      group  of  control  parameters  --adapt, --rigid, -ae, -af, which may be
      helpful in speeding up mcl.  When doing multiple mcl runs for  the  same
      graphs  with different -I settings (for obtaining clusterings at differ-
      ent levels of granularity), it can be useful to factor out the first bit
      of  computation that is common to all runs, by using the -cache-expanded
      option one time and then using -ei inflation for each run  in  the  set.
      Whether  mcl considers a graph large depends mainly on the graph connec-
      tivity; a highly connected graph on 50,000 nodes is  large  to  mcl  (so
      that  you  might  want  to  tune resources) whereas a sparsely connected
      graph on 500,000 nodes may be business as usual.

      mcl is a memory munger. Its precise appetite  depends  on  the  resource
      settings.  You  can get a rough (and usually much too pessimistic) upper
      bound for the amount of RAM that is needed by  using  the  -how-much-ram
      option.  The corresponding entry in this manual page contains the simple
      formula via which the upper bound is computed.

      Two other groups of interest are the  thread-related  options  (you  can
      specify  the  number of threads to use) -t, -te, -ti, and the verbosity-
      related options --verbose, --silent, -v, -V.  The  actual  settings  are
      shown  with  -z, and for graphs with at most 12 nodes or so you can view
      the MCL matrix iterands on screen by supplying  --show  (this  may  give
      some more feeling).

      MCL  iterands allow a generic interpretation as clusterings as well. The
      clusterings associated with early iterands may contain a fair amount  of
      overlap.  Refer to the -dump option, the mclfaq(7) manual, and the clmi-
      mac(1) utility (Interpret Matrices As Clusterings).  Use clmimac only if
      you  have  a  special  reason; the normal usage of mcl is to do multiple
      runs for varying -I parameters and use the  clusterings  output  by  mcl
      itself.

      Under  very rare circumstances, mcl might get stuck in a seemingly infi-
      nite loop. If the number of iterations exceeds a hundred and  the  chaos
      indicator  remains  nearly  constant (presumably around value 0.37), you
      can force mcl to stop by sending it the ALRM  signal  (usually  done  by
      kill  -s  ALRM pid). It will finish the current iteration, and interpret
      the last iterand a clustering. Alternatively, you can wait and mcl might
      converge  by  itself  or  it will certainly stop after 10,000 iterations
      (the default value for the -L option). The most probable explanation for
      such  an  infinite  loop  is that the input graph contains the flip-flop
      graph of node size three as a subgraph.

      The creator of  this  page  feels  that  manual  pages  are  a  valuable
      resource,  that  online html documentation is also a good thing to have,
      and that info pages are way way ahead of their time. The  NOTES  section
      explains how this page was created.

      In  the  OPTIONS section options are listed in order of importance, with
      related options grouped together.

  OPTIONS
      -I f (inflation)
         Sets the main inflation value to f. This value is the main handle for
         affecting  cluster granularity. It is usually chosen somewhere in the
         range [1.2-5.0]. -I 5.0 will tend to result in fine-grained  cluster-
         ings,  and -I 1.2 will tend to result in very coarse grained cluster-
         ings. Your mileage will vary depending on the characteristics of your
         data. That is why it is a good idea to test the quality and coherency
         of your clusterings using clmdist(1) and clminfo(1). This  will  most
         likely reveal that certain values of -I are simply not right for your
         data. The clmdist(1) section contains a discussion of how to use  the
         cluster validation tools shipped with mcl (see the SEE ALSO section).

         A second option for affecting cluster granularity is the  -c  option.
         It may possibly increase granularity.

         With  low  values  for -I, like -I 1.2, you should be prepared to use
         more resources in order to  maintain  quality  of  clusterings,  i.e.
         increase the argument to the -scheme option.

      -o str (fname)
         Output  the  clustering  to file named fname.  It is possible to send
         the clustering to stdout by supplying -o -. If either one  of  --abc,
         --yield-abc, or -use-tab tab-file is used the output will be in label
         format - provided a tab file is specified or the input  is  in  label
         format.  Otherwise the clustering is output in the mcl matrix format;
         see the mcxio(5) section for more information on this.  Refer also to
         the group of options discussed at --abc.

         Look at the -ap prefix option and its siblings for the automatic nam-
         ing constructions employed by mcl if the -o option is not used.

      --abc fname (expect/write labels)
      --expect-abc (expect labels)
      --yield-abc fname (write labels)
      -use-tab fname (use mapping to write)
      -strict-tab fname (use mapping to enforce)
      -restrict-tab fname (use mapping to restrict)
      -extend-tab fname (use mapping and extend)
      -cache-tab fname (write mapping)
         These items all relate to label input  and/or  label  output.   --abc
         tells mcl to expect label input and output clusters in terms of those
         labels.  It is equivalent to  the  combination  of  --expect-abc  and
         --yield-abc.

         -restrict-tab  and  -strict-tab can be used both with label input and
         native format.  When label input is used, they restrict, respectively
         require  labels  to be present in the tab file.  When native input is
         used they restrict, respectively require indices to be present in the
         tab  domain.  -strict-tab fails in the face of exceptions, -restrict-
         tab will simply ignore them.  -strict-tab and  -restrict-tab  do  not
         automatically yield label output. You need --abc or --yield-abc.

         -extend-tab  is only useful when label input is used.  It will create
         a new label/index mapping when a label is not found in the tab  file.
         Presumably you want to use the -cache-tab as well then.

         -use-tab  is  only useful when matrix input is used.  It will use the
         tab file to convert the output  to  labels;  it  does  not  hitch  on
         indices  missing from the tab file. Take a pick from -restrict-tab or
         -strict-tab and --abc or --yield-abc if that is what you want.

         -cache-tab can be used to preserve the tab file that was  constructed
         from label input, either from scratch or by extension from a previous
         tab file in case -extend-tab was used.

         NOTE
         in all its dealings with tab files, mcl will only  accept  those  for
         which  the associated domain is canonical, that is, domains for which
         the indices range from zero to some number N without omissions. It is
         possible  to  hook up any tab file to mcl, but it requires mcxdump to
         act as an intermediary - mcxdump(1) has  no  such  limitations.  This
         need in general not be of concern to you. If a tab file is created by
         mcl or mcxdeblast it will always be canonical.

      -tf <tf-spec> (transform input matrix values)
      -stream-tf <tf-spec> (transform input stream values)
      --stream-log (take logarithm of stream values)
      --stream-neg-log (take negative logarithm of stream values)
         -tf transforms the values of the input matrix according to <tf-spec>.
         -stream-tf  transforms  the stream values (when --abc or --expect-abc
         is used) according to <tf-spec>.  --neg-stream-log  and  --stream-log
         respectively imply that the stream input values are first replaced by
         their (negative) logarithm.  The reason for their existence is  docu-
         mented  in  mcxio(5).   For  a  description of the transform language
         excpected/accepted in <tf-spec> refer to the same.

      -cache-graph fname (write graph)
      -cache-expanded fname (write expanded graph)
      -ei inflation (use with expanded input)
         If you work with label input, -cache-graph can be used  to  save  the
         matrix  mcl  constructs.  In  a following mcl invocation, you can use
         this graph rather than the label input. This should greatly speed  up
         matters.  Presumably  the  first time around you have use the -cache-
         tab my.cache option. You can put that my.cache file to  good  use  by
         passing  it in as -use-tab my.cache the second time around.  The ses-
         sion below puts everything together.

         mcl xyz.data --abc -I 2.0 -o xyz.cls-I20 --binary\
                     -cache-graph xyz.mci -cache-tab xyz.tab
         mcl xyz.mci -I 2.4 -use-tab xyz.tab -o xyz.cls-I24

         The --binary option is useful for large graphs as it will drastically
         speed  up  subsequent  load times. Otherwise it is not necessary, and
         bear in mind that binary format is not portable across  machines  and
         it  is not garantueed to be portable across different version of mcl.
         For very large graphs it could be a bit wasteful to load a large  tab
         structure into memory. The second line can then be replaced by

         mcl xyz.mci -I 2.4 -o -|\
         mcxdump -imx - --no-values --dump-rlines\
                     -tabr xyz.tab -o xyz.cls-I24

         Admittedly this is beginning to look like black magic, but truthfully
         it is not, my dear paduan. mcxdump(1) simply needs to be told how  it
         should  format its output. It needs to know it should dump the matrix
         columns (clusters in this case) on a per-line basis, that  it  should
         not  dump the index that identifies the cluster (an arbitrary rank in
         our case), and that there is no need to output values.  mcxdump  fur-
         thermore  does not know how the tab file relates to its input matrix,
         whereas mcl previously had the advantage of knowing.  Hence -tabr  is
         telling mcxdump that the row domain in the clustering matrix identify
         the nodes.

         The first step in almost any mcl run is that the  matrix  constructed
         from the input is squared or expanded. This is a rather costly opera-
         tion if the input size is large. When you are doing multiple runs  it
         can thus be useful to cache the expanded matrix at the cost of a lit-
         tle more hassle.  Use -cache-expanded fname to write this  matrix  to
         fname.   In subsequent runs supply fname as the input argument to mcl
         and use -ei num to indicate that the first thing to apply  should  be
         inflation  with  parameter  num.  Combining our previous two examples
         then yields

         mcl xyz.data --abc -I 2.0 -o xyz.cls-I20 --binary\
                     -cache-expanded xyz.mxp -cache-tab xyz.tab
         mcl xyz.mxp -ei 2.4 -I 2.4 -o -|\
         mcxdump -imx - --no-values --dump-rlines\
                     -tabr xyz.tab -o xyz.cls-I24

         Behold, this is a very time and space efficient setup.

      -scheme k (use a preset resource scheme)
         There are currently seven different resource schemes,  indexed  1..7.
         High  schemes result in more expensive computations that may possibly
         be more accurate. The default scheme is 4. When mcl is done, it  will
         give  a grade (the so called jury synopsis) to the appropriateness of
         the scheme used. A low grade does  not  necessarily  imply  that  the
         resulting  clustering is bad - but anyway, a low grade should be rea-
         son to try for a higher scheme. The grades are listed in the  PRUNING
         OPTIONS section under the -nj option.

         The  PRUNING OPTIONS section contains an elaborate description of the
         way mcl manages resources, should you be interested.  In case you are
         worried  about  the  validation  of  the  resulting  clusterings, the
         mclfaq(7) section has several entries discussing this issue. The bot-
         tom  line  is that you have to compare the clusterings resulting from
         different schemes (and otherwise identical parameters)  using  utili-
         ties  such  as  clmdist(1),  clminfo(1) on the one hand, and your own
         sound judgment on the other hand.

         If your input graph is extremely dense, with an average  node  degree
         (i.e. the number of neighbours per node) that is somewhere above 500,
         you may need to filter the input graph by removing the nodes of high-
         est  degree  (and  projecting them back onto the resulting clustering
         afterwards) or by using the -pp option.

      --show-schemes (show preset resource schemes)
         Shows the explicit settings to which  the  different  preset  schemes
         correspond.

         The  characteristics are written in the same format (more or less) as
         the output triggered by -v pruning.

      -c f (centering)
         The larger the value of f the more nodes are attached  to  themselves
         rather  than  their  neighbours, the more expansion (the spreading of
         flow through the graph) is opposed, and the more  fine-grained  clus-
         terings  tend to be. f should be chosen greater than or equal to 1.0.
         The default is f=1.0. This option has a much weaker effect  than  the
         -I option, but it can be useful depending on your data.

      -v str (verbosity type on)
         See the --verbose option below.

      -V str (verbosity type off)
         See the --verbose option below.

      --silent (very)
         See the --verbose option below.

      --verbose (very)
         These are the different verbosity modes:

         progress
         pruning
         explain
         clusters
         all

         where  all means all three previous modes.  --verbose and -v all turn
         them all on, --silent and -V all turn them all off. -v str and -V str
         turn  on/off  the  single mode str (for str equal to one of progress,
         pruning, or explain).  Each verbosity mode is  given  its  own  entry
         below.

      -v progress
         This mode causes mcl to emit a gauge for each single matrix multipli-
         cation. It uses some default length  for  the  gauge,  which  can  be
         altered  by the -progress k option. Simply using the latter will also
         turn on this verbosity mode.  This mode can give you quickly an  idea
         how  long  an  mcl  run  might take. If you use threading (see the -t
         option and its friends), this option may slow down the program a lit-
         tle  (relative to -V progress, not relative to a single-CPU mcl run).

      -v explain
         This mode causes the output of explanatory headers  illuminating  the
         output generated with the pruning verbosity mode.

      -v pruning
         This mode causes output of resource-related quantities. It has a sep-
         arate entry in the PRUNING OPTIONS section.

      -v clusters
         This mode prints a terse list of characteristics of  the  clusterings
         associated with intermediate iterands.

      -progress k (gauge)
         If  k>0  then  for each matrix multiplication mcl will print an ascii
         gauge telling how far it is. The gauge will be (in some cases approx-
         imately) k characters long. If k<0 then mcl will emit a gauge that is
         extended by one character after every |k| vectors computed. For large
         graphs, this option has been known to ease the pain of impatience. If
         k=0 then mcl will print a message only after every matrix multiplica-
         tion,  and  not during matrix multiplication. This can be useful when
         you want mcl to be as speedy as possible, for example when using par-
         allellized  mode  (as  monitoring progress requires thread communica-
         tion).  For parallellization (by threading) see the -t option.

      -aa str (append str to suffix)
         See the -ap option below.

      -ap str (use str as file name prefix)
         If the -o fname option is not used, mcl will create a file name  (for
         writing  output  to)  that should uniquely characterize the important
         parameters used in the current invocation of mcl. The default  format
         is  out.fname.suf,  where out is simply the literal string out, fname
         is the first argument containing the  name  of  the  file  (with  the
         graph) to be clustered, and where suf is the suffix encoding a set of
         parameters (described further below).

         The -ap str option specifies a prefix to use rather than out.fname as
         sketched  above.   However,  mcl will interpret the character '=', if
         present in str, as a placeholder for the input file name.

         If the -aa str option is used, mcl will append str to the suffix  suf
         created by itself.  You can use this if you need to encode some extra
         information in the file name suffix.

         The suffix is constructed as follows. The -I f and -scheme  parameter
         are always encoded.  The -pi f, -l k, -i f, and -c f options are only
         encoded if they are used.  Any  real  argument  f  is  encoded  using
         exactly one trailing digit behind the decimal separator (which itself
         is not written). The setting -I 3.14 is  thus  encoded  as  I31.  The
         -scheme  option  is  encoded  using the letter 's', all other options
         mentioned here are encoded as themselves (stripped  of  the  hyphen).
         For example

         mcl small.mci -I 3 -c 2.5 -pi 0.8 -scheme 5

         results  in the file name out.small.mci.I30s5c25pi08.  If you want to
         know beforehand what file name will be produced, use the -az  option.

      -az (show output file name and exit)
      -ax (show output suffix and exit)
         If  mcl  automatically  constructs  a file name, it can be helpful to
         known beforehand what that file name will be. Use -az  and  mcl  will
         write  the  file  name to STDOUT and exit. This can be used if mcl is
         integrated into other software for which the  automatic  creation  of
         unique file names is convenient.

         By  default MCL incorporates the input file name into the output file
         name and appends a short suffix describing the most important  option
         settings.  Use -ax to find out what that suffix is.  This can be use-
         ful in wrapper pipeline scripts such as clxcoarse.

      -te k (#expansion threads)
         See the -t k option below.

      -ti k (#inflation threads)
         See the -t k option below.

      -t k (#threads)
         The -t options are self-explanatory. Note that threading inflation is
         hardly useful, as inflation is orders of magnitude faster than expan-
         sion. Also note that threading is only useful if you  have  a  multi-
         processor system.

         When  threading,  it is best not to turn on pruning verbosity mode if
         you are letting mcl run unattended, unless you want to scrutinize its
         output  later.  This  is  because  it  makes mcl run somewhat slower,
         although the difference is not dramatic.

      -l n (initial iteration number) (small letter ell)
         The number of times mcl will use a different inflation  value  before
         it  switches  to  the  (main) inflation given by the -I (capital eye)
         option. The different value is called initial inflation and  is  tun-
         able  using  the -i f option (default value f=2.0). The default value
         (to -l) is zero. This option supplies new ways of  affecting  cluster
         granularity, e.g. by supplying

         mcl proteins -i 1.4 -l 2 -I 4.0

         one  lets expansion prevail during the first two iterations, followed
         by inflation catching up (in a figurative way of writing).  This  may
         be  useful in certain cases, but this type of experiment is certainly
         secondary to simply varying -I (capital eye).

      -L n (main iteration number)
         Normally, mcl computes the MCL process until it has  converged  fully
         to  a  doubly idempotent matrix. The number of iterations required is
         typically somewhere in the range 10-100.  The  first  few  iterations
         generally  take the longest time.  The -L option can be used to spec-
         ify the number of iterations mcl may do at most. When this number  is
         reached,  mcl  will output the clustering associated with the iterand
         last computed.

      -i f (initial inflation)
         The inflation value used during the first n iterations,  where  n  is
         specified by the -l (ell) option.  By default, n=0 and f=2.0.

      -pi f (pre-inflation)
         If  used, mcl will apply inflation one time to the input graph before
         entering the main process. This can be useful  for  making  the  edge
         weights  in a graph either more homogeneous (which may result in less
         granular clusterings) or more heterogeneous (which may result in more
         granular  clusterings).   Homogeneity  is  achieved for values f less
         than one, heterogeneity for values larger than one.   Values  to  try
         are normally in the range [2.0,10.0].

      -di i:j (dump interval)
      -di all
      -dump-interval i:j
         Dump during iterations i..j-1. Use all to dump in all iterations. See
         the -dump str option below.

      -dm k (dump i+0..i+k..)
      -dump-modulo k
         Sampling rate: select only these iterations  in  the  dump  interval.
         See the -dump str option below.

      -ds stem (file stem)
      -dump-stem stem
         Set  the the stem for file names of dumped objects (default mcl). See
         the -dump str option below.

      -dump-subi spec (index list for submatrix dump)
      -dump-subd spec (domain list for submatrix dump)
      -dump-dom fname (domain matrix file)
         -dump-subi specifies a range of indices which will be used to  select
         the extended principal submatrix.  Argument spec can be a comma-sepa-
         rated list of single integers and integer ranges. Ranges are  denoted
         by two integers separated by a hyphen.

         If  -dump-dom is used and specifies a matrix file, the indices speci-
         fied in the -dump-subd option should index columns  in  that  matrix.
         These  columns  are  merged  and added to the list of entries used in
         selecting the extended principal submatrix.

      -dump str (type)
         str is checked for substring occurrences of  the  following  entries.
         Repeated use of -dump is also allowed.

         ite
         dag
         cls
         chr
         lines
         cat

         lines  and cat change the mode of dumping. The first changes the dump
         format to a line based pairwise format rather than  the  default  mcl
         matrix format. The second causes all dumped items to be dumped to the
         default stream used for the output clustering, which is  appended  at
         the end.

         The  ite  option  writes  mcl iterands to file. The cls option writes
         clusterings associated with mcl iterands to file.  These clusters are
         obtained  from  a  particular  directed acyclic graph (abbreviated as
         DAG) associated with each iterand. The dag option writes that DAG  to
         file.  The  DAG  can  optionally  be further pruned and then again be
         interpreted as a clustering using clmimac(1), and clmimac(1) can also
         work  with  the  matrices written using the ite option.  It should be
         noted that clusterings associated with intermediate iterands may con-
         tain  overlap,  which  is  interesting in many applications. For more
         information refer to mclfaq(7) and the REFERENCES section below.

         The result option dumps the usual MCL clustering.

         The chr option says, for each iterand I, to output a  matrix  C  with
         characteristics of I. C has the same number of columns as I. For each
         column k in C, row entry 0 is the diagonal or 'loop' value of  column
         k  in I after expansion and pruning, and before inflation and rescal-
         ing. Entry 1 is the loop value after inflation and rescaling.   Entry
         2 is the center of column k (the sum of its entries squared) computed
         after expansion and before pruning, entry  3  is  the  maximum  value
         found  in that column at the same time. Entry 4 is the amount of mass
         kept for that column after pruning.

         The -ds option sets  the  stem  for  file  names  of  dumped  objects
         (default  mcl). The -di and -dm options allow a selection of iterands
         to be made.

      -digits n (printing precision)
         This has two completely different uses. It sets the number  of  deci-
         mals  used  for  pretty-printing  mcl  iterands when using the --show
         option (see below), and it sets the number of decimals used for writ-
         ing the expanded matrix when using the -cache-expanded option.

      --show (print matrices to screen)
         Print  matrices  to  screen.  The  number of significant digits to be
         printed can be tuned  with  -digits n.  An  80-column  screen  allows
         graphs (matrices) of size up to 12(x12) to be printed with three dig-
         its precision (behind the comma), and of size up to 14(x14) with  two
         digits.  This  can give you an idea of how mcl operates, and what the
         effect of pruning is.  Use e.g. -S 6 for such a small graph and  view
         the MCL matrix iterands with --show.

      --binary (output format)
         Write matrix dump output in binary mcl format rather than interchange
         mcl format (the default). Note that mcxconvert(1) can be used to con-
         vert  each  one  into  the other.  See mcxio(5) and mcxconvert(1) for
         more information.

      -sort str (sort mode)
         str can be one of lex, size, revsize, or none. The default  is  'rev-
         size',  in  which  the  largest  clusters  come first. If the mode is
         'size', smallest clusters come first, if the mode is 'lex',  clusters
         are  ordered  lexicographically, and if the mode is 'none', the order
         is the same as produced by the procedure used by mcl to map  matrices
         onto clusterings.

      --keep-overlap y/n (retain overlap)
         The  keep-overlap  action  causes  mcl  to retain overlap should this
         improbable event occur. In theory, mcl may generate a clustering that
         contains  overlap, although this almost never happens in practice, as
         it requires some particular type of symmetry to  be  present  in  the
         input graph (not just any symmetry will do). Mathematically speaking,
         this is a conjecture and not a theorem, but the  present  author  wil
         eat his shoe if it fails to be true (for marzipan values of shoe). It
         is easy though to construct an input graph for which certain mcl set-
         tings  result  in overlap - for example a line graph on an odd number
         of nodes. The default is to remove overlap should it occur.

         This option has more than theoretical use because mcl is able to gen-
         erate  clusterings  associated with intermediate iterands.  For these
         clusterings, overlap is more than a theoretical possibility, and will
         often  occur.  If  you  specify  the -L k option, mcl will output the
         clustering associated with the last iterand computed, and it may well
         contain overlap.

         This  option  has  no  effect on the clusterings that are output when
         using -dump cls - the default  for  those  is  that  overlap  is  not
         touched, and this default can not yet be overridden.

      --force-connected=y/n (analyze components)
      --check-connected=y/n (analyze components)
         If  the  input  graph  has  strong bipartite characteristics, mcl may
         yield clusters that do not correspond to connected components in  the
         input  graph.  Turn  one  of  these modes on to analyze the resultant
         clustering.

         If loose clusters are found they will be split into subclusters  cor-
         responding  to  connected  components.   With --force-connected=y mcl
         will write the corrected clustering to the normal  output  file,  and
         the  old clustering to the same file with suffix orig.  With --check-
         connected=y mcl will write the loose clustering to the normal  output
         file, and the corrected clustering to the same file with suffix coco.

         These options are not on by default, as  the  analysis  is  currently
         (overly) time-consuming and mcl's behaviour actually makes some sense
         (when taking bipartite characteristics into account).

      --output-limit=y/n (write limit matrix)
         This will write the limit matrix to a file named base-limit.

      --analyze=y/n (performance criteria)
         With this mode turned on, mcl will reread the input matrix  and  com-
         pute  a  few performance criteria and attach them to the output file.
         Off by default.

      --append-log=y/n (append log)
         Appends a log with the process characteristics to  the  output  file.
         By default, this mode is on.

      --show-log=y/n (show log)
         Shows  the  log  with process characteristics on STDOUT.  By default,
         this mode is off.

      -in-gq f (filter threshold)
         mcl will remove any edges in the input graph  (equivalently,  entries
         in the input matrix) for which the weight is below f.

      -pp n (preprune count)
         For  each  column  vector (node) in the input matrix (graph) mcl will
         keep the n entries (outgoing edges) of that vector (node)  that  have
         largest weight and remove the rest.

      --jury-charter (explains jury)
         Explains how the jury synopsis is computed from the jury marks.

      --version (show version)
         Show version.

      -how-much-ram n (RAM upper bound)
         n  is interpreted as the number of nodes of an input graph.  mcl will
         print the maximum amount of RAM it needs for its  computations.   The
         formula for this number in bytes is:

            2 * c * k * n

            2  :  two matrices are concurrently held in memory.
            c  :  mcl cell size (as shown by -z).
            n  :  graph cardinality (number of nodes).
            k  :  MAX(s, r).
            s  :  select number (-S, -scheme options).
            r  :  recover number (-R, -scheme options).

         This  estimate will usually be too pessimistic. It does assume though
         that the average node degree of the input graph does  not  exceed  k.
         The  -how-much-ram  option  takes  other  command-line arguments into
         account (such as -S and -R), and it expresses the amount  of  RAM  in
         megabyte units.

      -h (show help)
         Shows a selection of the most important mcl options.

      --apropos (show help)
         Gives a one-line description for all options.

      --show-settings (show settings)
         A synonym for the -z option.

      -z (show settings)
         Show  current  settings for tunable parameters.  --show-settings is a
         synonym.

  PRUNING OPTIONS
      -p f (cutoff)
      -P n (1/cutoff)
      -S s (selection number)
      -R r (recover number)
      -pct pct (recover percentage)
      -my-scheme n (tag custom scheme)
         After computing a new (column stochastic) matrix vector during expan-
         sion  (which  is  matrix multiplication c.q. squaring), the vector is
         successively exposed to different pruning strategies. The  intent  of
         pruning  is  that many small entries are removed while retaining much
         of the stochastic mass of the original vector. After pruning, vectors
         are  rescaled  to be stochastic again. MCL iterands are theoretically
         known to be sparse in a weighted sense, and this manoever effectively
         perturbs  the  MCL  process a little in order to obtain matrices that
         are genuinely sparse, thus  keeping  the  computation  tractable.  An
         example  of  monitoring  pruning  can  be  found in the discussion of
         -v pruning at the end of this section.

         mcl proceeds as follows. First, entries that are smaller than  cutoff
         are removed, resulting in a vector with at most 1/cutoff entries. The
         cutoff can be supplied either by -p, or as the inverse value  by  -P.
         The latter is more intuitive, if your intuition is like mine (and the
         P stands for precision or pruning  by  the  way).   The  cutoff  just
         described  is  rigid;  it  is  the  same for all vectors. The --adapt
         option causes the computation of a cutoff that depends on a  vector's
         homogeneity  properties, and this option may or may not speed up mcl.

         Second, if the remaining stochastic mass (i.e. the sum of all remain-
         ing entries) is less than pct/100 and the number of remaining entries
         is less than r (as specified by the -R flag), mcl will try to  regain
         ground  by recovering the largest discarded entries. The total number
         of entries is not allowed to grow larger than r.  If recovery was not
         necessary,  mcl  tries  to prune the vector further down to at most s
         entries (if applicable), as specified by the -S flag. If this results
         in  a  vector  that satisfies the recovery condition then recovery is
         attempted, exactly as described above. The latter will not  occur  of
         course if r <= s.

         The  default  setting  is something like -P 4000 -S 500 -R 600. Check
         the -z flag to be sure. There is a set of precomposed settings, which
         can  be  triggered  with  the  -scheme k  option.  k=4 is the default
         scheme; higher values for k result in costlier and more accurate com-
         putations  (vice  versa  for lower, cheaper, and less accurate).  The
         schemes are listed using the --show-schemes option. It  is  advisable
         to  use  the  -scheme option only in interactive mode, and to use the
         explicit expressions when doing batch processing. The reason is  that
         there  is  no  guarantee  whatsoever that the schemes will not change
         between different releases. This is because the scheme options should
         reflect  good  general purpose settings, and it may become appararent
         that other schemes are better.

         Note that 'less accurate' or 'more accurate' computations  may  still
         generate  the  same output clusterings. Use clmdist to compare output
         clusterings for different resource parameters.  Refer  to  clmdist(1)
         for a discussion of this issue.

         The  -my-scheme n  option sets a tag that is used in constructing the
         default output naming file. If not used, mcl will create a relatively
         long  string  describing  the  settings  of  the -P, -pct, -R, and -S
         parameters, e.g. P600Q85R1000S1200 (where Q tags  the  pct  setting).
         If used, mcl will simply use the tag sn.

      -warn-pct k (prune warn percentage)
      -warn-factor k (prune warn factor)
         The  two  options  -warn-pct and -warn-factor relate to warnings that
         may be triggered once the initial pruning of a vector  is  completed.
         The  idea  is  to issue warnings if initial pruning almost completely
         destroys a computed vector, as this may be a sign  that  the  pruning
         parameters  should be changed. It depends on the mass remaining after
         initial pruning whether a warning will be issued.  If  that  mass  is
         less  than  warn-pct or if the number of remaining entries is smaller
         by a factor warn-factor than both the number  of  entries  originally
         computed  and  the  recovery  number,  in that case, mcl will issue a
         warning.

         -warn-pct takes an integer between 0 and 100 as parameter, -warn-fac-
         tor  takes  a real positive number. They default to something like 30
         and 50.0. If you want to see less  warnings,  decrease  warn-pct  and
         increase  warn-factor.  Set  warn-factor to zero if you want no warn-
         ings.

      --dense (allow matrices to fill)
         This renders all pruning options useless except for one.  After  each
         expansion step, mcl will remove all entries that are smaller than the
         threshold specified by -p or -P, which acts like a precision in  this
         case. After removal, the matrix columns are rescaled to be stochastic
         again.

         If the -p threshold (precision) is zero or very  small,  the  --dense
         option  results  in  a rather accurate and very costly computation of
         the MCL process. Do not use this option for  graphs  with  more  than
         several  thousands  of entries, or you will have trouble digging your
         processor out of swap.

      --rigid (pruning)
         See the --adapt option below.

      -ae f (adaptive pruning exponent)
         See the --adapt option below.

      -af f (adaptive pruning factor)
         See the --adapt option below.

      --adapt (pruning)
         The default mcl pruning behaviour as described under the -P option is
         called rigid pruning (it being the default renders the switch --rigid
         currently useless), refering to the fact  that  the  first  stage  of
         pruning  removes entries smaller than a fixed threshold.  The options
         discussed here enable the computation of a threshold that depends  on
         the  homogeneity characteristics of a vector. This behaviour is trig-
         gered by supplying --adapt.

         The --adapt behaviour only affects the first pruning stage, c.q.  the
         computation  of  the first threshold (see the discussion under the -P
         option). It does not interfere with either selection or recovery.  It
         is  affected  however by the threshold as specified by the -P option.
         When using --adapt, you typically use the -P option as well, and  you
         can  and  should  use  a  higher  value  then you would without using
         --adapt.

         All that said, --adapt triggers this behaviour:  Given  a  stochastic
         vector  v, its mass center of order two is computed, which is the sum
         of each entry squared. The mass center of v, call it c,  is  strongly
         related to its homogeneity properties (see REFERENCES). The threshold
         T is computed as 1/f * pow(c, e), where e and f are the arguments  to
         the -af f and -ae e options respectively (check -z for the respective
         defaults).  For either e or f decreasing  it  means  that  T  becomes
         larger.   Finally,  T  is maxed with the rigid threshold value, which
         can be altered using either -p f or -P n.   The  latter  is  why  you
         should  increase  the  -P parameter n (so that the rigid threshold is
         decreased) once you switch to adaptive pruning. The adaptive  thresh-
         old  should  be  the  main factor controlling pruning, with the rigid
         threshold acting as a safeguard that does not take over too often.

         This may seem complicated, but the rules are actually  quite  simple,
         and  you  may  just  disregard the definition of T. The usefulness of
         these options will vary. If you want to speed up mcl, try it out  and
         add --adapt to your settings.

      --thick (expect dense input graph)
         This  option is somewhat esoteric. It does not affect the matrices as
         computed by mcl, but it affects the way in which they  are  computed.
         If the input graph is very dense, this may speed up mcl a little.

      -v pruning
         Pruning  verbosity mode causes mcl to emit several statistics related
         to the pruning  process,  each  of  which  is  described  below.  Use
         -v explain  to get explanatory headers in the output as well (or sim-
         ply use -v all).

         Selection and recovery
         The number of selections and recoveries mcl  had  to  perform  during
         each  iteration  is  shown.  It  also shows the number of vectors for
         which the mass after final pruning was below the fraction defined  by
         the -pct option as a percentage (default probably 90 or 95).

         Initial and pruned vector footprint distributions
         The distribution of the vector footprints (i.e. the number of nonzero
         entries) before and after pruning is shown. This is  assembled  in  a
         terse (horrid if you will) format, looking as follows (with some con-
         text stripped, noting that the data  for  three  expansion  steps  is
         shown):

         ----------------------------------------------------
          mass percentages  | distr of vec footprints       |
                  |         |____ expand ___.____ prune ____|
           prune  | final   |e4   e3   e2   |e4  e3   e2    |
         all ny nx|all ny nx|8532c8532c8532c|8532c8532c8532c|
         ---------.---------.---------------.---------.-----.
          98 88 86  98 91 86 _________022456 ___________0234
          98 89 86  98 94 91 _______00245678 ___________0234
          98 90 89  99 95 94 _______00235568 ___________0234
          ...

         This  particular  output  was  generated  (and  truncated after three
         rounds of expansion and inflation) from clustering a protein graph on
         9058  nodes  with  settings  -I 1.4,  -P 2000,  -S 500,  -R 600,  and
         -pct 95.

         The header entries 8532c85.. indicate thresholds  going  from  80000,
         50000, 20000, 12500, 8000, all the way down to 300, 200, and 125. The
         character 'c' signifies the base 1.25 (for no apparent  reason).  The
         second entry '2' (after '0') on the first line signifies that roughly
         20 percent of  all  the  vectors  had  footprint  (#nonzero  entries)
         between 800 and 1250.  Likewise, 40 percent had footprint between 300
         and 500. The '0' entries signify a fraction somewhere  below  5  per-
         cent,  and the '@' entries signify a fraction somewhere above 95 per-
         cent.

         Two columns are listed, one for the expansion vector footprints (i.e.
         after  squaring), and the other for the vector footprints right after
         initial pruning took place (i.e. before selection and recovery, after
         either  adaptive  or  rigid  pruning).   This may give an idea of the
         soundness of the initial pruning process (overly  severe,  or  overly
         mild),  and  the  extent  to which you want to apply selection and/or
         recovery.

         Initial and final mass windows
         The mass averages of the pruned vectors  after  the  first  selection
         stage are shown, and the mass averages of the vectors as finally com-
         puted, i.e. after selection and recovery. Note that the latter corre-
         sponds  to  a different stage than what is shown for the vector foot-
         prints, if either selection or  recovery  is  turned  on.   For  both
         cases,  three  averages  are shown: the average over all vectors, the
         average over the worst x cases, and the  average  over  the  worst  y
         cases.  The mass averages are shown as percentages: '98' on the first
         line under the 'prune/all' column means that overall  98  percent  of
         the stochastic mass of the matrix was kept after pruning.

         This  example  demonstrates  that many entries could be removed while
         retaining much of the stochastic mass. The  effect  of  the  recovery
         (-R)  parameter is also clear: the final averages are higher than the
         initial averages, as a result of mcl undoing  some  overenthousiastic
         pruning.

         An  average  over  the  worst  k cases is called a window of width k;
         internally, mcl tracks many more such windows. The result of this can
         be  seen when using the --append-log=y option (which appends a log to
         the cluster output) or the --show-log=y option (which sends  the  log
         to  STDOUT).   From  a fixed set of windows those that are applicable
         are tracked, that is, all those windows for which the width does  not
         exceed  the  graph  cardinality.  The  windows  in the fixed set have
         respective sizes 1, 2, 5, 10, 20, 50, and  so  on  up  until  5000000
         (which makes 15 windows in all).

      -nx i (x window index)
      -ny j (y window index)
         The  options  -nx  and -ny both take an index in the range 1..15. The
         default values for -nx and -ny are respectively 4 and 7, denoting the
         fourth  and  seventh window of respective widths 10 and 100. They are
         used in the verbosity output as described above.

      -nj i (jury window index)
         The -nj denotes a window index in the same way as  -nx  and  -ny  do.
         This  particular  window  is used for computing the jury marks, which
         are the three number reported by mcl when it  is  done.  They  are  a
         reminder  of  the  existence  of  pruning and its importance for both
         speed and accuracy, and they are indicative rather than  authorative.

         These  jury marks are simply the respective mass averages in the jury
         window for the first three iterations. The  marks  are  even  further
         simplified  and  mapped to the jury synopsis, which is a single grade
         expressed as an adjective. The grades are,  in  decreasing  order  of
         achievement,  perfect  exceptional superior excellent good acceptable
         mediocre poor bad lousy miserable  awful  wretched  atrocious.  Doing
         'mcl  --jury-charter'  will  tell you how the jury marks map onto the
         jury synopsis.

         The jury marks should preferably be higher than 70. If  they  are  in
         the vicinity of 80 or 90, mcl is doing fine as far as pruning is con-
         cerned.  Choose a higher scheme if you think them too low.  For  very
         dense  graphs  that  do have strong cluster structure, the jury marks
         can sink as low as to the 30's and 40's, but the  clusterings  gener-
         ated  by  mcl may still be good. The marks and the synopsis grade the
         severity of pruning, not cluster quality. Note that the jury  becomes
         friendlier, resp. harsher when the -nj option is increased/decreased.

      -nw w (nr of windows)
         Normally, mcl will use all windows that have width smaller  than  the
         cardinality of the input graph. This option limits the set of windows
         to those w windows of smallest width.  This affects the  output  when
         setting --append-log=y output.

      -nl l (number of iterations)
         By  default,  mcl will log the window mass averages for the first ten
         iterations. This options sets  that  number  to l.   It  affects  the
         --append-log=y output.

  PIPELINES
      As of the 1.006 release, label data can be directly streamed into MCL as
      described in GETTING STARTED and EXAMPLES.   For  BLAST  input  this  is
      achieved by hooking up mcxdeblast(1) --abc-out=- with mcl - --abc. Refer
      to GETTING STARTED for examples.

      The classic mode of operation is more heavyweight, and the remainder  of
      this section describes the underlying design.  Much of the code now used
      for streaming directly into mcl was derived from this earlier framework.

      In  general,  clustering  requires  several stages; creating the matrix,
      running mcl, and displaying the result. The display stage  is  supported
      by clmformat(1). The matrix creation stage often needs only be done once
      for a given data collection, followed by repeated runs of the other  two
      stages for varying inflation values and scheme settings.

      The  matrix  creation  stage  can  often be split up in two more stages,
      namely parsing a data file in some given format, and assembling a matrix
      from  the data bits and pieces, such as node indices and edge weights or
      even edge weight contributions.  The assembly step can be done by mcxas-
      semble(1),  which  allows  a  very general input format and customizable
      behaviour in how the bits and pieces should be transformed to the  input
      graph.  This leaves the parse stage to be filled in.

      The  mclpipeline  script  implements a generic and customizable pipeline
      encapsulating the four stages  distinguished  here  (parsing,  assembly,
      clustering, display). It is possible to let only part of the pipeline be
      active, and many other features  are  supported.  The  IO  mechanism  is
      entirely  file based, and files are associated with parametrizations via
      file name extensions (by all means a simple mechanism).

      mclpipeline(1) requires a single parse script to be specified.  It  will
      be  plugged  into  the pipeline and you should be set to run.  The parse
      script  must  satisfy  the  interface  requirements  described  in   the
      mclpipeline manual page.

      For   BLAST   input,   the  mclblastline  script  provides  a  dedicated
      mclpipeline interface. It uses the mcxdeblast script that comes prepack-
      aged with mcl.

  EXAMPLES
      The following is an example of label input

      ---8<------8<------8<------8<------8<---
      cat hat  0.2
      hat bat  0.16
      bat cat  1.0
      bat bit  0.125
      bit fit  0.25
      fit hit  0.5
      hit bit  0.16
      --->8------>8------>8------>8------>8---

      It can be clustered like this:

      mcl cathat --abc -o out.cathat

      The file out.cathat should now like like this

      ---8<------8<------8<------8<------8<---
      cat hat bat
      bit fit hit
      --->8------>8------>8------>8------>8---

      A  few things to note. First, MCL will symmetrize any arrow it finds. If
      it sees bat cat 1.0 it will act as if it also saw cat bat 1.0.  You  can
      explicitly specify cat bat 1.0, mcl will in the first parse stage simply
      end up with duplicate entries. Second, MCL deduplicates  repeated  edges
      by taking the one with the maximum value. So,

      ---8<------8<------8<------8<------8<---
      cat hat  0.2
      hat cat  0.16
      hat cat  0.8
      --->8------>8------>8------>8------>8---

      Will result in two arrows cat-hat and hat-cat both with value 0.8.

  APPLICABILITY
      mcl  will work very well for graphs in which the diameter of the natural
      clusters is not too large. The presence of many edges between  different
      clusters  is not problematic; as long as there is cluster structure, mcl
      will find it. It is less likely to work well for  graphs  with  clusters
      (inducing  subgraphs)  of  large diameter, e.g. grid-like graphs derived
      from Euclidean data. So mcl in its canonical form is certainly  not  fit
      for boundary detection or image segmentation. I experimented with a mod-
      ified mcl and boundary detection in the thesis  pointed  to  below  (see
      REFERENCES).  This  was  fun and not entirely unsuccesful, but not some-
      thing to be pursued further.

      mcl likes undirected input graphs best, and it  really  dislikes  graphs
      with  node pairs (i,j) for which an arc going from i to j is present and
      the counter-arc from j to i is absent. Try  to  make  your  input  graph
      undirected.   Furthermore,  mcl  interprets  edge  weights  in graphs as
      similarities. If you are used to working with dissimilarities, you  will
      have to convert those to similarities using some conversion formula. The
      most important thing is that you feel confident  that  the  similarities
      are  reasonable, i.e. if X is similar to Y with weight 2, and X is simi-
      lar to Z with weight 200, then this should mean that the similarity of Y
      (to X) is neglectible compared with the similarity of Z (to X).

      mcl  is  probably not suited for clustering tree graphs. This is because
      mcl works best if there are multiple paths between  different  nodes  in
      the  natural clusters, but in tree graphs there is only one path between
      any pair of nodes. Trees are too sparse a structure for mcl to work  on.

      mcl  may  well  be suited for clustering lattices. It will depend on the
      density characteristics of the lattice, and the conditions  for  success
      are  the same as those for clustering graphs in general: The diameter of
      the natural clusters should not be too large.  NOTE  when  clustering  a
      lattice,  you  have  to cluster the underlying undirected graph, and not
      the directed graph that represents the lattice  itself.  The  reason  is
      that one has to allow mcl (or any other cluster algorithm) to 'look back
      in time', so to speak. Clustering and  directionality  bite  each  other
      (long discussion omitted).

      mcl  has a worst-case time complexity O(N*k^2), where N is the number of
      nodes in the graph, and k is the maximum number  of  neighbours  tracked
      during  computations.  k  depends  on  the  -P and -S options. If the -S
      option is used (which is the default setting) then k  equals  the  value
      corresponding  with  this  option. Typical values for k are in the range
      500..1000. The average case is much better than the worst  case  though,
      as  cluster  structure  itself  has  the effect of helping mcl's pruning
      schemes, certainly if the diameter of natural clusters is not large.

  FILES
      There are currently no resource nor configuration files.  The mcl matrix
      format is described in the mcxio(5) section.

  ENVIRONMENT
      MCLXINTERCHANGEDIGITS
         When  writing  matrices in interchange format, mcl will use the envi-
         ronment variable MCLXINTERCHANGEDIGITS (if present) as the  precision
         (number of digits) for printing the fractional part of values.

      MCLXIOVERBOSITY
         MCL  and  its  sibling  applications will usually report about matrix
         input/output from/to disk. The verbosity level can be  regulated  via
         MCLXIOVERBOSITY. These are the levels it can currently be set to.

          1  Silent but applications may alter this.
          2  Silent and applications can not alter this.
          4  Verbose but applications may alter this.
          8  Verbose and applications can not alter this (default).

      MCLXIOFORMAT
         MCL  and  its sibling applications will by default output matrices in
         interchange format rather than binary  format  (cf.  mcxio(5)).   The
         desired format can be controlled via the variable MCLXIOFORMAT. These
         are the levels it can currently be set to.

          1  Interchange format but applications may alter this.
          2  Interchange format and applications can not alter this (default).
          4  Binary format but applications may alter this.
          8  Binary format and applications can not alter this.

      MCLXINTERCHANGEFLAGS
         If  matrices  are output in interchange format, by default empty vec-
         tors will not be listed. Equivalently (during  input  time),  vectors
         for  which  no  listing  is present are understood to be empty - note
         that the presence of a vector is established using the domain  infor-
         mation  found  in the header part.  It is possible to enforce listing
         of empty vectors by setting bit '1' in the variable  MCLXINTERCHANGE-
         FLAGS.

      MCLXIOUNCHECKED
         MCL  and its sibling applications will always check a matrix for con-
         sistency while it is being read. If this variable is set, the consis-
         tency  check is omitted. For large graphs the speed up can be consid-
         erable. However, if the input graph is not conforming it will  likely
         crash the application that is using it.

  DIAGNOSTICS
      If  mcl  issues  a  diagnostic error, it will most likely be because the
      input matrix could not be parsed succesfully.  mcl tries to  be  helpful
      in  describing  the  kind  of  parse  error.   The  mcl matrix format is
      described in the mcxio(5) section.

  BUGS
      No  known  bugs  at  this  time.  Please  send  bug  reports   to   mcl-
      devel@micans.org.

  AUTHOR
      Stijn van Dongen.

  HISTORY/CREDITS
      The  MCL  algorithm  was conceived in spring 1996 by the present author.
      The first implementation of the MCL algorithm followed that  spring  and
      summer.  It  was  written  in Perl and proved the viability of the algo-
      rithm. The implementation described here began its life in autumn  1997.
      The  first versions of the vital matrix library were designed jointly by
      Stijn van Dongen and Annius Groenink in the period Oktober  1997  -  May
      1999.  The efficient matrix-vector multiplication routine was written by
      Annius. This routine is without significant changes  still  one  of  the
      cornerstones of this MCL implementation.

      Since May 1999 all MCL libraries have seen much development and redesign
      by the present author. Matrix-matrix multiplication has  been  rewritten
      several times to take full advantage of the sparseness properties of the
      stochastic matrices brought forth by the MCL algorithm. This mostly con-
      cerns  the  issue of pruning - removal of small elements in a stochastic
      column in order to keep matrices sparse.

      Very instructive was that around April 2001 Rob Koopman pointed out that
      selecting  the  k largest elements out of a collection of n is best done
      using a min-heap. This was the key to  the  second  major  rewrite  (now
      counting  three)  of  the  MCL pruning schemes, resulting in much faster
      code, generally producing a more accurate computation of  the  MCL  pro-
      cess.

      In May 2001 Anton Enright initiated the parallellization of the mcl code
      and threaded inflation. From this  example,  Stijn  threaded  expansion.
      This  was  great, as the MCL data structures and operands (normal matrix
      multiplication and Hadamard multiplication) just beg  for  parallelliza-
      tion.

      Onwards.   The  January  2003  03-010  release  introduced  support  for
      sparsely enumerated (i.e. indices need not  be  sequential)  graphs  and
      matrices,  the result of a major overhaul of the matrix library and most
      higher layers.  Conceptually, the library now sees matrices as  infinite
      quadrants  of  which  only  finite  subsections  happen  to have nonzero
      entries.

      The June 2003 03-154 release introduced unix-type pipelines for cluster-
      ing,  including the BLAST parser mcxdeblast and the mclblastline script.
      The April 2004 04-105 release revived binary format, which  has  been  a
      first class citizen every since.

      With the March 2005 05-090 release mcxsubs finally acquired a sane spec-
      ification syntax. The November 2005 05-314 release brought  the  ability
      to  stream  label input directly into mcl. The subsequent release intro-
      duced a transformation language shared  by  various  mcl  siblings  that
      allows arbitrary progressions of transformations to be applied to either
      stream values or matrix values.

      Joost van Baal set up the mcl CVS  tree  and  packaged  mcl  for  Debian
      GNU/Linux.  He  completely  autotooled  the  sources, so much so that at
      first I found it hard to find them back  amidst  bootstrap,  aclocal.m4,
      depcomp, and other beauties.

      Jan  van der Steen shared his elegant mempool code. Philip Lijnzaad gave
      useful comments.  Philip,  Shawn  Hoon,  Abel  Ureta-Vidal,  and  Martin
      Mokrejs sent helpful bug reports.

      Abel  Ureta-Vidal  and  Dinakarpandian  Deendayal  commented on and con-
      tributed to mcxdeblast and mcxassemble.

      Tim Hughes contributed several good bug reports for mcxassemble,  mcxde-
      blast and zoem (a workhorse for clmformat).

  SEE ALSO
      mclfaq(7) - Frequently Asked Questions.

      mcxio(5) - a description of the mcl matrix format.

      There  are  many more utilities. Consult mclfamily(7) for an overview of
      and links to all the documentation and the utilities in the mcl  family.

      mcl  development  is discussed on mcl-devel@lists.micans.org, (subscrib-
      tion) information is at  https://lists.micans.org:446/listinfo/mcl-devel
      ,  this  list is archived at https://lists.micans.org:446/pipermail/mcl-
      devel/.

      mcl's home at http://micans.org/mcl/.

  REFERENCES
      Stijn van Dongen, Graph Clustering by Flow Simulation.  PhD thesis, Uni-
      versity of Utrecht, May 2000.
      http://www.library.uu.nl/digiarchief/dip/diss/1895620/inhoud.htm

      Stijn van Dongen. A cluster algorithm for graphs.  Technical Report INS-
      R0010, National Research Institute for Mathematics and Computer  Science
      in the Netherlands, Amsterdam, May 2000.
      http://www.cwi.nl/ftp/CWIreports/INS/INS-R0010.ps.Z

      Stijn van Dongen. A stochastic uncoupling process for graphs.  Technical
      Report INS-R0011, National Research Institute for Mathematics  and  Com-
      puter Science in the Netherlands, Amsterdam, May 2000.
      http://www.cwi.nl/ftp/CWIreports/INS/INS-R0011.ps.Z

      Stijn  van  Dongen. Performance criteria for graph clustering and Markov
      cluster  experiments.  Technical  Report  INS-R0012,  National  Research
      Institute  for Mathematics and Computer Science in the Netherlands, Ams-
      terdam, May 2000.
      http://www.cwi.nl/ftp/CWIreports/INS/INS-R0012.ps.Z

      Enright A.J., Van Dongen S., Ouzounis C.A.  An efficient  algorithm  for
      large-scale  detection  of  protein  families,  Nucleic  Acids  Research
      30(7):1575-1584 (2002).

  NOTES
      This page was generated from ZOEM manual macros, http://micans.org/zoem.
      Both  html  and  roff  pages can be created from the same source without
      having to bother with all the usual conversion problems,  while  keeping
      some level of sophistication in the typesetting.



  mcl 1.006, 05-321                 17 Nov 2005                           mcl(1)
