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<a href="Bio-module.html">Package Bio</a> ::
<a href="Bio.Cluster-module.html">Package Cluster</a> ::
Class Record
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class Record</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record">source code</a></span></p>
<pre class="literalblock">
A Record stores the gene expression data and related information
contained in a data file following the file format defined for
Michael Eisen's Cluster/TreeView program. A Record
has the following members:
data: a matrix containing the gene expression data
mask: a matrix containing only 1's and 0's, denoting which values
are present (1) or missing (0). If all elements of mask are
one (no missing data), then mask is set to None.
geneid: a list containing a unique identifier for each gene
(e.g., ORF name)
genename: a list containing an additional description for each gene
(e.g., gene name)
gweight: the weight to be used for each gene when calculating the
distance
gorder: an array of real numbers indicating the preferred order of the
genes in the output file
expid: a list containing a unique identifier for each experimental
condition
eweight: the weight to be used for each experimental condition when
calculating the distance
eorder: an array of real numbers indication the preferred order in the
output file of the experimental conditions
uniqid: the string that was used instead of UNIQID in the input file.
</pre>
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<td><span class="summary-sig"><a name="__init__"></a><span class="summary-sig-name">__init__</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">handle</span>=<span class="summary-sig-default">None</span>)</span><br />
Reads a data file in the format corresponding to Michael Eisen's
Cluster/TreeView program, and stores the data in a Record object</td>
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<td><span class="summary-sig"><a name="treecluster"></a><span class="summary-sig-name">treecluster</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">transpose</span>=<span class="summary-sig-default">0</span>,
<span class="summary-sig-arg">method</span>=<span class="summary-sig-default">'m'</span>,
<span class="summary-sig-arg">dist</span>=<span class="summary-sig-default">'e'</span>)</span></td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.treecluster">source code</a></span>
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<td><span class="summary-sig"><a name="kcluster"></a><span class="summary-sig-name">kcluster</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">nclusters</span>=<span class="summary-sig-default">2</span>,
<span class="summary-sig-arg">transpose</span>=<span class="summary-sig-default">0</span>,
<span class="summary-sig-arg">npass</span>=<span class="summary-sig-default">1</span>,
<span class="summary-sig-arg">method</span>=<span class="summary-sig-default">'a'</span>,
<span class="summary-sig-arg">dist</span>=<span class="summary-sig-default">'e'</span>,
<span class="summary-sig-arg">initialid</span>=<span class="summary-sig-default">None</span>)</span></td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.kcluster">source code</a></span>
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<td><span class="summary-sig"><a name="somcluster"></a><span class="summary-sig-name">somcluster</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">transpose</span>=<span class="summary-sig-default">0</span>,
<span class="summary-sig-arg">nxgrid</span>=<span class="summary-sig-default">2</span>,
<span class="summary-sig-arg">nygrid</span>=<span class="summary-sig-default">1</span>,
<span class="summary-sig-arg">inittau</span>=<span class="summary-sig-default">0.02</span>,
<span class="summary-sig-arg">niter</span>=<span class="summary-sig-default">1</span>,
<span class="summary-sig-arg">dist</span>=<span class="summary-sig-default">'e'</span>)</span></td>
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<td><span class="summary-sig"><a name="clustercentroids"></a><span class="summary-sig-name">clustercentroids</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">clusterid</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">method</span>=<span class="summary-sig-default">'a'</span>,
<span class="summary-sig-arg">transpose</span>=<span class="summary-sig-default">0</span>)</span></td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.clustercentroids">source code</a></span>
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<td><span class="summary-sig"><a name="clusterdistance"></a><span class="summary-sig-name">clusterdistance</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">index1</span>=<span class="summary-sig-default">[0]</span>,
<span class="summary-sig-arg">index2</span>=<span class="summary-sig-default">[0]</span>,
<span class="summary-sig-arg">method</span>=<span class="summary-sig-default">'a'</span>,
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<td><span class="summary-sig"><a name="distancematrix"></a><span class="summary-sig-name">distancematrix</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">transpose</span>=<span class="summary-sig-default">0</span>,
<span class="summary-sig-arg">dist</span>=<span class="summary-sig-default">'e'</span>)</span></td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.distancematrix">source code</a></span>
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<td><span class="summary-sig"><a href="Bio.Cluster.Record-class.html#save" class="summary-sig-name">save</a>(<span class="summary-sig-arg">jobname</span>,
<span class="summary-sig-arg">geneclusters</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">expclusters</span>=<span class="summary-sig-default">None</span>)</span><br />
saves the clustering results.</td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.save">source code</a></span>
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<td><span class="summary-sig"><a name="_savekmeans"></a><span class="summary-sig-name">_savekmeans</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">filename</span>,
<span class="summary-sig-arg">clusterids</span>,
<span class="summary-sig-arg">order</span>,
<span class="summary-sig-arg">transpose</span>)</span></td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record._savekmeans">source code</a></span>
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<td><span class="summary-sig"><a name="_savedata"></a><span class="summary-sig-name">_savedata</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">jobname</span>,
<span class="summary-sig-arg">gid</span>,
<span class="summary-sig-arg">aid</span>,
<span class="summary-sig-arg">geneindex</span>,
<span class="summary-sig-arg">expindex</span>)</span></td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record._savedata">source code</a></span>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">save</span>(<span class="sig-arg">jobname</span>,
<span class="sig-arg">geneclusters</span>=<span class="sig-default">None</span>,
<span class="sig-arg">expclusters</span>=<span class="sig-default">None</span>)</span>
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<pre class="literalblock">
saves the clustering results. The saved files follow the convention
for Java TreeView program, which can therefore be used to view the
clustering result.
Arguments:
jobname: The base name of the files to be saved. The filenames are
jobname.cdt, jobname.gtr, and jobname.atr for
hierarchical clustering, and jobname-K*.cdt,
jobname-K*.kgg, jobname-K*.kag for k-means clustering
results.
geneclusters=None: For hierarchical clustering results,
geneclusters is an (ngenes-1 x 2) array that describes
the hierarchical clustering result for genes. This array
can be calculated by the hierarchical clustering methods
implemented in treecluster.
For k-means clustering results, geneclusters is a vector
containing ngenes integers, describing to which cluster a
given gene belongs. This vector can be calculated by
kcluster.
expclusters=None: For hierarchical clustering results, expclusters
is an (nexps-1 x 2) array that describes the hierarchical
clustering result for experimental conditions. This array
can be calculated by the hierarchical clustering methods
implemented in treecluster.
For k-means clustering results, expclusters is a vector
containing nexps integers, describing to which cluster a
given experimental condition belongs. This vector can be
calculated by kcluster.
</pre>
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