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<a href="Bio-module.html">Package Bio</a> ::
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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="base-tree">
object --+
|
<strong class="uidshort">Record</strong>
</pre>
<hr />
<pre class="literalblock">
Store gene expression data.
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 href="Bio.Cluster.Record-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">handle</span>=<span class="summary-sig-default">None</span>)</span><br />
Read gene expression data from the file handle and return a Record.</td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.__init__">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.Cluster.Record-class.html#treecluster" class="summary-sig-name">treecluster</a>(<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><br />
Apply hierarchical clustering and return a Tree object.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.treecluster">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.Cluster.Record-class.html#kcluster" class="summary-sig-name">kcluster</a>(<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><br />
Apply k-means or k-median clustering.</td>
<td align="right" valign="top">
<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 href="Bio.Cluster.Record-class.html#somcluster" class="summary-sig-name">somcluster</a>(<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><br />
Calculate a self-organizing map on a rectangular grid.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.somcluster">source code</a></span>
</td>
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<td><span class="summary-sig"><a href="Bio.Cluster.Record-class.html#clustercentroids" class="summary-sig-name">clustercentroids</a>(<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><br />
Calculate the cluster centroids and return a tuple (cdata, cmask).</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 href="Bio.Cluster.Record-class.html#clusterdistance" class="summary-sig-name">clusterdistance</a>(<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>,
<span class="summary-sig-arg">dist</span>=<span class="summary-sig-default">'e'</span>,
<span class="summary-sig-arg">transpose</span>=<span class="summary-sig-default">0</span>)</span><br />
Calculate the distance between two clusters.</td>
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<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.clusterdistance">source code</a></span>
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<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.Cluster.Record-class.html#distancematrix" class="summary-sig-name">distancematrix</a>(<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><br />
Calculate the distance matrix and return it as a list of arrays</td>
<td align="right" valign="top">
<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">self</span>,
<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 />
Save 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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<span class="summary-type"> </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>
<td align="right" valign="top">
<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>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Cluster-pysrc.html#Record._savedata">source code</a></span>
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<td colspan="2" class="summary">
<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__delattr__</code>,
<code>__format__</code>,
<code>__getattribute__</code>,
<code>__hash__</code>,
<code>__new__</code>,
<code>__reduce__</code>,
<code>__reduce_ex__</code>,
<code>__repr__</code>,
<code>__setattr__</code>,
<code>__sizeof__</code>,
<code>__str__</code>,
<code>__subclasshook__</code>
</p>
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<!-- ==================== PROPERTIES ==================== -->
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<p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
<code>__class__</code>
</p>
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<!-- ==================== METHOD DETAILS ==================== -->
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<a name="__init__"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">handle</span>=<span class="sig-default">None</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.__init__">source code</a></span>
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<p>Read gene expression data from the file handle and return a
Record.</p>
<p>The file should be in the format defined for Michael Eisen's
Cluster/TreeView program.</p>
<dl class="fields">
<dt>Overrides:
object.__init__
</dt>
</dl>
</td></tr></table>
</div>
<a name="treecluster"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">treecluster</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">transpose</span>=<span class="sig-default">0</span>,
<span class="sig-arg">method</span>=<span class="sig-default">'m'</span>,
<span class="sig-arg">dist</span>=<span class="sig-default">'e'</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.treecluster">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Apply hierarchical clustering and return a Tree object.
The pairwise single, complete, centroid, and average linkage hierarchical
clustering methods are available.
transpose: if equal to 0, genes (rows) are clustered;
if equal to 1, microarrays (columns) are clustered.
dist : specifies the distance function to be used:
dist=='e': Euclidean distance
dist=='b': City Block distance
dist=='c': Pearson correlation
dist=='a': absolute value of the correlation
dist=='u': uncentered correlation
dist=='x': absolute uncentered correlation
dist=='s': Spearman's rank correlation
dist=='k': Kendall's tau
method : specifies which linkage method is used:
method=='s': Single pairwise linkage
method=='m': Complete (maximum) pairwise linkage (default)
method=='c': Centroid linkage
method=='a': Average pairwise linkage
See the description of the Tree class for more information about the Tree
object returned by this method.
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="kcluster"></a>
<div>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">kcluster</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">nclusters</span>=<span class="sig-default">2</span>,
<span class="sig-arg">transpose</span>=<span class="sig-default">0</span>,
<span class="sig-arg">npass</span>=<span class="sig-default">1</span>,
<span class="sig-arg">method</span>=<span class="sig-default">'a'</span>,
<span class="sig-arg">dist</span>=<span class="sig-default">'e'</span>,
<span class="sig-arg">initialid</span>=<span class="sig-default">None</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.kcluster">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Apply k-means or k-median clustering.
This method returns a tuple (clusterid, error, nfound).
nclusters: number of clusters (the 'k' in k-means)
transpose: if equal to 0, genes (rows) are clustered;
if equal to 1, microarrays (columns) are clustered.
npass : number of times the k-means clustering algorithm is
performed, each time with a different (random) initial
condition.
method : specifies how the center of a cluster is found:
method=='a': arithmetic mean
method=='m': median
dist : specifies the distance function to be used:
dist=='e': Euclidean distance
dist=='b': City Block distance
dist=='c': Pearson correlation
dist=='a': absolute value of the correlation
dist=='u': uncentered correlation
dist=='x': absolute uncentered correlation
dist=='s': Spearman's rank correlation
dist=='k': Kendall's tau
initialid: the initial clustering from which the algorithm should start.
If initialid is None, the routine carries out npass
repetitions of the EM algorithm, each time starting from a
different random initial clustering. If initialid is given,
the routine carries out the EM algorithm only once, starting
from the given initial clustering and without randomizing the
order in which items are assigned to clusters (i.e., using
the same order as in the data matrix). In that case, the
k-means algorithm is fully deterministic.
Return values:
clusterid: array containing the number of the cluster to which each
gene/microarray was assigned in the best k-means clustering
solution that was found in the npass runs;
error: the within-cluster sum of distances for the returned k-means
clustering solution;
nfound: the number of times this solution was found.
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="somcluster"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">somcluster</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">transpose</span>=<span class="sig-default">0</span>,
<span class="sig-arg">nxgrid</span>=<span class="sig-default">2</span>,
<span class="sig-arg">nygrid</span>=<span class="sig-default">1</span>,
<span class="sig-arg">inittau</span>=<span class="sig-default">0.02</span>,
<span class="sig-arg">niter</span>=<span class="sig-default">1</span>,
<span class="sig-arg">dist</span>=<span class="sig-default">'e'</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.somcluster">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Calculate a self-organizing map on a rectangular grid.
The somcluster method returns a tuple (clusterid, celldata).
transpose: if equal to 0, genes (rows) are clustered;
if equal to 1, microarrays (columns) are clustered.
nxgrid : the horizontal dimension of the rectangular SOM map
nygrid : the vertical dimension of the rectangular SOM map
inittau : the initial value of tau (the neighborbood function)
niter : the number of iterations
dist : specifies the distance function to be used:
dist=='e': Euclidean distance
dist=='b': City Block distance
dist=='c': Pearson correlation
dist=='a': absolute value of the correlation
dist=='u': uncentered correlation
dist=='x': absolute uncentered correlation
dist=='s': Spearman's rank correlation
dist=='k': Kendall's tau
Return values:
clusterid: array with two columns, while the number of rows is equal to
the number of genes or the number of microarrays depending on
whether genes or microarrays are being clustered. Each row in
the array contains the x and y coordinates of the cell in the
rectangular SOM grid to which the gene or microarray was
assigned.
celldata: an array with dimensions (nxgrid, nygrid, number of
microarrays) if genes are being clustered, or (nxgrid,
nygrid, number of genes) if microarrays are being clustered.
Each element [ix][iy] of this array is a 1D vector containing
the gene expression data for the centroid of the cluster in
the SOM grid cell with coordinates (ix, iy).
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="clustercentroids"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">clustercentroids</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">clusterid</span>=<span class="sig-default">None</span>,
<span class="sig-arg">method</span>=<span class="sig-default">'a'</span>,
<span class="sig-arg">transpose</span>=<span class="sig-default">0</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.clustercentroids">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Calculate the cluster centroids and return a tuple (cdata, cmask).
The centroid is defined as either the mean or the median over all elements
for each dimension.
data : nrows x ncolumns array containing the expression data
mask : nrows x ncolumns array of integers, showing which data are
missing. If mask[i][j]==0, then data[i][j] is missing.
transpose: if equal to 0, gene (row) clusters are considered;
if equal to 1, microarray (column) clusters are considered.
clusterid: array containing the cluster number for each gene or
microarray. The cluster number should be non-negative.
method : specifies how the centroid is calculated:
method=='a': arithmetic mean over each dimension. (default)
method=='m': median over each dimension.
Return values:
cdata : 2D array containing the cluster centroids. If transpose==0,
then the dimensions of cdata are nclusters x ncolumns. If
transpose==1, then the dimensions of cdata are
nrows x nclusters.
cmask : 2D array of integers describing which elements in cdata,
if any, are missing.
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="clusterdistance"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">clusterdistance</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">index1</span>=<span class="sig-default">[0]</span>,
<span class="sig-arg">index2</span>=<span class="sig-default">[0]</span>,
<span class="sig-arg">method</span>=<span class="sig-default">'a'</span>,
<span class="sig-arg">dist</span>=<span class="sig-default">'e'</span>,
<span class="sig-arg">transpose</span>=<span class="sig-default">0</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.clusterdistance">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Calculate the distance between two clusters.
index1 : 1D array identifying which genes/microarrays belong to the
first cluster. If the cluster contains only one gene, then
index1 can also be written as a single integer.
index2 : 1D array identifying which genes/microarrays belong to the
second cluster. If the cluster contains only one gene, then
index2 can also be written as a single integer.
transpose: if equal to 0, genes (rows) are clustered;
if equal to 1, microarrays (columns) are clustered.
dist : specifies the distance function to be used:
dist=='e': Euclidean distance
dist=='b': City Block distance
dist=='c': Pearson correlation
dist=='a': absolute value of the correlation
dist=='u': uncentered correlation
dist=='x': absolute uncentered correlation
dist=='s': Spearman's rank correlation
dist=='k': Kendall's tau
method : specifies how the distance between two clusters is defined:
method=='a': the distance between the arithmetic means of the
two clusters
method=='m': the distance between the medians of the two
clusters
method=='s': the smallest pairwise distance between members
of the two clusters
method=='x': the largest pairwise distance between members of
the two clusters
method=='v': average of the pairwise distances between
members of the clusters
transpose: if equal to 0: clusters of genes (rows) are considered;
if equal to 1: clusters of microarrays (columns) are
considered.
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="distancematrix"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">distancematrix</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">transpose</span>=<span class="sig-default">0</span>,
<span class="sig-arg">dist</span>=<span class="sig-default">'e'</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.distancematrix">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Calculate the distance matrix and return it as a list of arrays
transpose: if equal to 0: calculate the distances between genes (rows);
if equal to 1: calculate the distances beteeen microarrays
(columns).
dist : specifies the distance function to be used:
dist=='e': Euclidean distance
dist=='b': City Block distance
dist=='c': Pearson correlation
dist=='a': absolute value of the correlation
dist=='u': uncentered correlation
dist=='x': absolute uncentered correlation
dist=='s': Spearman's rank correlation
dist=='k': Kendall's tau
Return value:
The distance matrix is returned as a list of 1D arrays containing the
distance matrix between the gene expression data. The number of columns
in each row is equal to the row number. Hence, the first row has zero
elements. An example of the return value is
matrix = [[],
array([1.]),
array([7., 3.]),
array([4., 2., 6.])]
This corresponds to the distance matrix
[0., 1., 7., 4.]
[1., 0., 3., 2.]
[7., 3., 0., 6.]
[4., 2., 6., 0.]
</pre>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="save"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">save</span>(<span class="sig-arg">self</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>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Cluster-pysrc.html#Record.save">source code</a></span>
</td>
</tr></table>
<pre class="literalblock">
Save the clustering results.
The saved files follow the convention for the 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 a Tree object as returned by the treecluster method.
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 a Tree object as returned by the treecluster method.
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>
<dl class="fields">
</dl>
</td></tr></table>
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