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<title>The mcxarray manual</title>
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<p style="text-align:right">
16 May 2014&nbsp;&nbsp;&nbsp;
<a class="local" href="mcxarray.ps"><b>mcxarray</b></a>
14-137
</p>
<div class=" itemize " style="margin-top:1em; font-size:100%">
<div class=" item_compact"><div class=" item_rightalign nowrap " style="right:-3em">1.</div></div>
<div class=" item_text " style="margin-left:4em">
<a class="intern" href="#name">NAME</a>
</div>
<div class=" item_compact"><div class=" item_rightalign nowrap " style="right:-3em">2.</div></div>
<div class=" item_text " style="margin-left:4em">
<a class="intern" href="#synopsis">SYNOPSIS</a>
</div>
<div class=" item_compact"><div class=" item_rightalign nowrap " style="right:-3em">3.</div></div>
<div class=" item_text " style="margin-left:4em">
<a class="intern" href="#description">DESCRIPTION</a>
</div>
<div class=" item_compact"><div class=" item_rightalign nowrap " style="right:-3em">4.</div></div>
<div class=" item_text " style="margin-left:4em">
<a class="intern" href="#options">OPTIONS</a>
</div>
<div class=" item_compact"><div class=" item_rightalign nowrap " style="right:-3em">5.</div></div>
<div class=" item_text " style="margin-left:4em">
<a class="intern" href="#author">AUTHOR</a>
</div>
<div class=" item_compact"><div class=" item_rightalign nowrap " style="right:-3em">6.</div></div>
<div class=" item_text " style="margin-left:4em">
<a class="intern" href="#seealso">SEE ALSO</a>
</div>
</div>

<a name="name"></a>
<h2>NAME</h2>
<p style="margin-bottom:0" class="asd_par">
mcxarray &mdash; Transform array data to MCL matrices</p>

<a name="synopsis"></a>
<h2>SYNOPSIS</h2>
<p style="margin-bottom:0" class="asd_par">
<b>mcxarray</b> [options]</p>
<p style="margin-bottom:0" class="asd_par">
<b>mcxarray</b>
<a class="intern" href="#opt-data"><b>[-data</b> fname (<i>input data file</i>)<b>]</b></a><br>
<a class="intern" href="#opt-imx"><b>[-imx</b> fname (<i>input matrix file</i>)<b>]</b></a><br>
<a class="intern" href="#opt-co"><b>[-co</b> num (<i>(absolute) cutoff for output values (required)</i>)<b>]</b></a><br>
<a class="intern" href="#opt-skipr"><b>[-skipr</b> &lt;num&gt; (<i>skip &lt;num&gt; data rows</i>)<b>]</b></a><br>
<a class="intern" href="#opt-skipc"><b>[-skipc</b> &lt;num&gt; (<i>skip &lt;num&gt; data columns</i>)<b>]</b></a><br>
<a class="intern" href="#opt-o"><b>[-o</b> fname (<i>output file fname</i>)<b>]</b></a><br>
<a class="intern" href="#opt--text-table"><b>[--text-table</b> (<i>write output in full text table format</i>)<b>]</b></a><br>
<a class="intern" href="#opt-write-tab"><b>[-write-tab</b> &lt;fname&gt; (<i>write row labels to file</i>)<b>]</b></a><br>
<a class="intern" href="#opt-l"><b>[-l</b> &lt;num&gt; (<i>take labels from column &lt;num&gt;</i>)<b>]</b></a><br>

<a class="intern" href="#opt--pearson"><b>[--pearson</b> (<i>use Pearson correlation (default)</i>)<b>]</b></a><br>
<a class="intern" href="#opt--spearman"><b>[--spearman</b> (<i>use Spearman rank correlation</i>)<b>]</b></a><br>
<a class="intern" href="#opt--dot"><b>[--dot</b> (<i>use dot product</i>)<b>]</b></a><br>
<a class="intern" href="#opt--cosine"><b>[--cosine</b> (<i>use cosine (similarity)</i>)<b>]</b></a><br>
<a class="intern" href="#opt--slow-cosine"><b>[--slow-cosine</b> (<i>use cosine(0.5 alpha) (similarity)</i>)<b>]</b></a>

<a class="intern" href="#opt--angle"><b>[--angle</b> (<i>use angle between vectors (note: a metric distance)</i>)<b>]</b></a><br>
<a class="intern" href="#opt--acute-angle"><b>[--acute-angle</b> (<i>use acute angle between vectors</i>)<b>]</b></a><br>
<a class="intern" href="#opt--angle-norm"><b>[--angle-norm</b> (<i>use normalised angle between vectors (by pi)</i>)<b>]</b></a><br>
<a class="intern" href="#opt--acute-angle-norm"><b>[--acute-angle-norm</b> (<i>use normalised acute angle between vectors&nbsp;(by&nbsp;pi/2)</i>)<b>]</b></a><br>
<a class="intern" href="#opt--sine"><b>[--sine</b> (<i>use sine (note: a metric distance)</i>)<b>]</b></a><br>
<a class="intern" href="#opt--slow-sine"><b>[--slow-sine</b> (<i>use sine(0.5 alpha) (note: a metric distance)</i>)<b>]</b></a><br>
<a class="intern" href="#opt--euclid"><b>[--euclid</b> (<i>use Euclidean distance between vectors</i>)<b>]</b></a><br>
<a class="intern" href="#opt--max"><b>[--max</b> (<i>use L-oo, aka Chebyshev distance</i>)<b>]</b></a><br>
<a class="intern" href="#opt--taxi"><b>[--taxi</b> (<i>use L-1, aka taxi, aka city-block distance</i>)<b>]</b></a><br>
<a class="intern" href="#opt-minkowski"><b>[-minkowski</b> &lt;num&gt; (<i>use Minkowski distance with power &lt;num&gt;</i>)<b>]</b></a><br>
<a class="intern" href="#opt-fp"><b>[-fp</b> &lt;mode&gt; (<i>use fingerprint measure</i>)<b>]</b></a><br>

<a class="intern" href="#opt-digits"><b>[-digits</b> &lt;num&gt; (<i>output precision</i>)<b>]</b></a><br>
<a class="intern" href="#opt--write-binary"><b>[--write-binary</b> (<i>write output in binary format</i>)<b>]</b></a><br>
<a class="intern" href="#opt-t"><b>[-t</b> &lt;int&gt; (<i>use &lt;int&gt; threads</i>)<b>]</b></a>
<br>
<a class="intern" href="#opt-J"><b>[-J</b> &lt;intJ&gt; (<i>a total of &lt;intJ&gt; jobs are used</i>)<b>]</b></a>
<br>
<a class="intern" href="#opt-j"><b>[-j</b> &lt;intj&gt; (<i>this job has index &lt;intj&gt;</i>)<b>]</b></a>
<br>
<a class="intern" href="#opt-start"><b>[-start</b> &lt;int&gt; (<i>start at column &lt;int&gt; inclusive</i>)<b>]</b></a><br>
<a class="intern" href="#opt-end"><b>[-end</b> &lt;int&gt; (<i>end at column &lt;int&gt; EXclusive</i>)<b>]</b></a><br>
<a class="intern" href="#opt--transpose-data"><b>[--transpose-data</b> (<i>work with the transposed data matrix</i>)<b>]</b></a><br>
<a class="intern" href="#opt--rank-transform"><b>[--rank-transform</b> (<i>rank transform the data first</i>)<b>]</b></a><br>
<a class="intern" href="#opt-tf"><b>[-tf</b> spec (<i>transform result network</i>)<b>]</b></a><br>
<a class="intern" href="#opt-table-tf"><b>[-table-tf</b> spec (<i>transform input table before processing</i>)<b>]</b></a><br>
<a class="intern" href="#opt-n"><b>[-n</b> mode (<i>normalize input</i>)<b>]</b></a><br>
<a class="intern" href="#opt--zero-as-na"><b>[--zero-as-na</b> (<i>treat zeroes as missing data</i>)<b>]</b></a><br>
<a class="intern" href="#opt--sparse"><b>[--sparse</b> (<i>do not store zero values</i>)<b>]</b></a><br>
<a class="intern" href="#opt-write-data"><b>[-write-data</b> &lt;fname&gt; (<i>write data to file</i>)<b>]</b></a><br>
<a class="intern" href="#opt-write-na"><b>[-write-na</b> &lt;fname&gt; (<i>write NA matrix to file</i>)<b>]</b></a><br>
<a class="intern" href="#opt--job-info"><b>[--job-info</b> (<i>print index ranges for this job</i>)<b>]</b></a><br>
<a class="intern" href="#opt--help"><b>[--help</b> (<i>print this help</i>)<b>]</b></a><br>
<a class="intern" href="#opt-h"><b>[-h</b> (<i>print this help</i>)<b>]</b></a><br>
<a class="intern" href="#opt--version"><b>[--version</b> (<i>print version information</i>)<b>]</b></a>
</p>

<a name="description"></a>
<h2>DESCRIPTION</h2>
<p style="margin-bottom:0" class="asd_par">
<b>mcxarray</b> can either read a flat file containing array data (<b>-data</b>)
or a matrix file satisfying the mcl input format (<b>-imx</b>). In the
former case it will by default work with the rows as the data vectors. In
the latter case it will by default work with the columns as the data
vectors (note that mcl matrices are presented as a listing of columns).
This can be changed for both using the
<a class="intern" href="#opt--transpose-data"><b>--transpose-data</b> option</a>.
</p>
<p style="margin-bottom:0" class="asd_par">
The input data may contain missing data in the form of empty columns,
NA values (not available/applicable), or NaN values (not a number).
The program keeps track of these, and when computing the correlation
between two rows or columns ignores all positions where any one of
the two has missing data.
</p>

<a name="options"></a>
<h2>OPTIONS</h2>
<div class=" itemize " style="margin-top:1em; font-size:100%">
<div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-data"></a><b>-data</b> fname (<i>input data file</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Specify the data file containing the expression values.
It should be tab-separated.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-imx"></a><b>-imx</b> fname (<i>input matrix file</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
The expression values are read from a file in mcl matrix format.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--pearson"></a><b>--pearson</b> (<i>use Pearson correlation (default)</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--spearman"></a><b>--spearman</b> (<i>use Spearman rank correlation</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--cosine"></a><b>--cosine</b> (<i>use cosine</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--slow-cosine"></a><b>--slow-cosine</b> (<i>use cosine(0.5 alpha) (similarity)</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--dot"></a><b>--dot</b> (<i>use the dot product</i>)</div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
All these measures express the level of similarity or correlation
between two vectors.
Note that the dot product is not normalised and should only be used with
very good reason. A few more similarity measures are provided by
the fingerprint option <b>-fp</b> described below.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-fp"></a><b>-fp</b> &lt;mode&gt; (<i>specify fingerprint measure</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">Fingerprints are used to define an entity in terms of it having
or not having certain traits. This means that a fingerprint can be
represented by a boolean vector, and a set of fingerprints can be represented
by an array of such vectors. In the presence of many traits and entities the dimensions
of such a matrix can grow large. The sparse storage employed by <span class="smallcaps">MCL</span>-edge is
ideally suited to this, and mcxarray is ideally suited to the computation
of all pairwise comparisons between such fingerprints.
Currently mcxarray supports five different types of fingerprint, described below.
Given two fingerprints, the number of traits unique to the first is denoted by <i>a</i>,
the number unique to the second is denoted by <i>b</i>, and the number that they
have in common is denoted by <i>c</i>.
</p>
<div class=" itemize " style="margin-top:1em; font-size:100%">
<div class=" item_cascade"><div class=" item_leftalign nowrap " >hamming</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">The Hamming distance, defined as <i>a</i>+<i>b</i>.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " >tanimoto</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">The Tanimoto similarity measure, <i>c</i>/(<i>a</i>+<i>b</i>+<i>c</i>).</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " >cosine</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">The cosine similarity measure, <i>c</i>/sqrt((<i>a</i>+<i>c</i>)*(<i>b</i>+<i>c</i>)).</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " >meet</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">Simply the number of shared traits, identical to <i>c</i>.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " >cover</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">A normalised and non-symmetric similarity measure, representing the fraction
of traits shared relative to the number of traits by a single entity.
This gives the value <i>c</i>/(<i>a</i>+<i>c</i>) in one direction, and the value
<i>c</i>/(<i>b</i>+<i>c</i>) in the other.
</p>
</div>
</div>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--sine"></a><b>--sine</b> (<i>use sine (note: a metric distance)</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--slow-sine"></a><b>--slow-sine</b> (<i>use sine(0.5 alpha) (note: a metric distance)</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--angle"></a><b>--angle</b> (<i>use angle between vectors (note: a metric distance)</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--acute-angle"></a><b>--acute-angle</b> (<i>use acute angle between vectors</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--angle-norm"></a><b>--angle-norm</b> (<i>use normalised angle between vectors (by pi)</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--acute-angle-norm"></a><b>--acute-angle-norm</b> (<i>use normalised acute angle between vectors (by pi/2)</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--euclid"></a><b>--euclid</b> (<i>use Euclidean distance between vectors</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--max"></a><b>--max</b> (<i>use L-oo, aka Chebyshev distance</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--taxi"></a><b>--taxi</b> (<i>use L-1, aka taxi, aka city-block, aka Manhattan distance</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-minkowski"></a><b>-minkowski</b> &lt;num&gt; (<i>use Minkowski distance with power &lt;num&gt;</i>)</div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
All these measures express the level of dissimilarity or distance
between two vectors.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-skipr"></a><b>-skipr</b> &lt;num&gt; (<i>skip &lt;num&gt; data rows</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Skip the first <i>&lt;num&gt;</i> data rows.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-skipc"></a><b>-skipc</b> &lt;num&gt; (<i>skip &lt;num&gt; data columns</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Ignore the first <i>&lt;num&gt;</i> data columns.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-l"></a><b>-l</b> &lt;num&gt; (<i>take labels from column &lt;num&gt;</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Specifies to construct a tab of labels from this data column.
The tab can be written to file using <a class="intern" href="#opt-write-tab"><b>-write-tab</b>&nbsp;<i>fname</i></a>.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-write-tab"></a><b>-write-tab</b> &lt;fname&gt; (<i>write row labels to file</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Write a tab file. In the simple case where the labels are in the first
data column it is sufficient to issue <b>-skipc</b>&nbsp;<b>1</b>.
If more data columns need to be skipped one must explicitly specify
the data column to take labels from with <b>-l</b>&nbsp;<i>l</i>.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-t"></a><b>-t</b> &lt;int&gt; (<i>use &lt;int&gt; threads</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-J"></a><b>-J</b> &lt;intJ&gt; (<i>a total of &lt;intJ&gt; jobs are used</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-j"></a><b>-j</b> &lt;intj&gt; (<i>this job has index &lt;intj&gt;</i>)</div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Computing all pairwise correlations is time-intensive for large input.
If you have multiple CPUs available consider using as
many threads. Additionally it is possible to spread the computation over
multiple jobs/machines.
These three options are described in the <a class="local sibling" href="clmprotocols.html">clmprotocols</a> manual page.
The following set of options, if given to as many commands, defines three jobs, each running four threads.
</p>
<div class="verbatim">-t 4 -J 3 -j 0 -o out.0
-t 4 -J 3 -j 1 -o out.1
-t 4 -J 3 -j 2 -o out.2</div>
<p style="margin-top:0em; margin-bottom:0em">
The output can then be collected with
</p>
<div class="verbatim">mcx collect --add-matrix -o out.all out.[0-2]</div>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--job-info"></a><b>--job-info</b> (<i>print index ranges for this job</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-start"></a><b>-start</b> &lt;int&gt; (<i>start at column &lt;int&gt; inclusive</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-end"></a><b>-end</b> &lt;int&gt; (<i>end at column &lt;int&gt; EXclusive</i>)</div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
<b>--job-info</b> can be used to list the set of column
ranges to be processed by the job as a result of the command
line options <b>-t</b>, <b>-J</b>, and <b>-j</b>.
If a job has failed, this option can be used to manually
split those ranges into finer chunks, each to be processed
as a new sub-job specified with <b>-start</b> and <b>-end</b>.
With the latter two options, it is impossible to use
parallelization of any kind
(i.e. any of the <b>-t</b>, <b>-J</b>, and <b>-j</b> options).
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-o"></a><b>-o</b> fname (<i>output file fname</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Output file name.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt--text-table"></a><b>--text-table</b> (<i>write output in full text table format</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
The output will be written in tabular format rather than native <b>mcl-edge</b> format.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-digits"></a><b>-digits</b> &lt;num&gt; (<i>output precision</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Specify the precision to use in native interchange format.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt--write-binary"></a><b>--write-binary</b> (<i>write output in binary format</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Write output matrices in native binary format.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-co"></a><b>-co</b> num (<i>(absolute) cutoff for output values</i>)</div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Output values of magnitude smaller than <i>num</i> are removed (set to zero).
Thus, negative values are removed only if their positive counterpart
is smaller than <i>num</i>.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt--transpose-data"></a><b>--transpose-data</b> (<i>work with the transpose</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
Work with the transpose of the input data matrix.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt--rank-transform"></a><b>--rank-transform</b> (<i>rank transform the data first</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
The data is rank-transformed prior to the computation of pairwise measures.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-write-data"></a><b>-write-data</b> &lt;fname&gt; (<i>write data to file</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
This writes the data that was read in to file.
If <b>--spearman</b> is specified the data will
be rank-transformed.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-write-na"></a><b>-write-na</b> &lt;fname&gt; (<i>write NA matrix to file</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
This writes all positions for which no data was found
to file, in native mcl matrix format.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt--zero-as-na"></a><b>--zero-as-na</b> (<i>treat zeroes as missing data</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
This option can be useful when reading data with the <b>-imx</b> option,
for example after it has been loaded from label input by <a class="local sibling" href="mcxload.html">mcxload</a>.
An example case is the processing of a large number of probe rankings,
where not all rankings contain all probe names. The rankings can be loaded
using <a class="local sibling" href="mcxload.html">mcxload</a> with a tab file containing all probe names.
Probes that are present in the ranking are given a positive ordinal
number reflecting the ranking, and probes that are absent are implicitly
given the value zero. With the present option mcxarray will handle
the correlation computation in a reasonable way.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt--sparse"></a><b>--sparse</b> (<i>do not store zero data value</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
With this option internal calculations are performed on compressed
data where zeroes are not stored. This can be useful when the input
data is very large.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt-n"></a><b>-n</b> mode (<i>normalization mode</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
If <i>mode</i> is set to <b>z</b> the data will be normalized
based on z-score. No other modes are currently supported.</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-tf"></a><b>-tf</b> spec (<i>transform result network</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-table-tf"></a><b>-table-tf</b> spec (<i>transform input table before processing</i>)</div>
<div class=" item_text " style="margin-left:2em">
<p style="margin-top:0em; margin-bottom:0em">
The transformation syntax is described in <a class="local sibling" href="mcxio.html">mcxio</a>.
</p>
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt--help"></a><b>--help</b> (<i>print help</i>)</div><div class=" item_cascade item_leftalign nowrap" ><a name="opt-h"></a><b>-h</b> (<i>print help</i>)</div>
<div class=" item_text " style="margin-left:2em">
</div>
<div style="margin-top:0em">&nbsp;</div><div class=" item_cascade"><div class=" item_leftalign nowrap " ><a name="opt--version"></a><b>--version</b> (<i>print version information</i>)</div></div>
<div class=" item_text " style="margin-left:2em">
</div>
</div>

<a name="author"></a>
<h2>AUTHOR</h2>
<p style="margin-bottom:0" class="asd_par">
Stijn van Dongen.</p>

<a name="seealso"></a>
<h2>SEE ALSO</h2>
<p style="margin-bottom:0" class="asd_par">
<a class="local sibling" href="mcl.html">mcl</a>,
<a class="local sibling" href="mclfaq.html">mclfaq</a>,
and <a class="local sibling" href="mclfamily.html">mclfamily</a> for an overview of all the documentation
and the utilities in the mcl family.</p>
</body>
</html>
