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Viewing changes to doc/examples/perform_PCoA_analysis.rst

  • Committer: Bazaar Package Importer
  • Author(s): Steffen Moeller
  • Date: 2010-12-04 22:30:35 UTC
  • mfrom: (1.1.1 upstream)
  • Revision ID: james.westby@ubuntu.com-20101204223035-j11kinhcrrdgg2p2
Tags: 1.5-1
* Bumped standard to 3.9.1, no changes required.
* New upstream version.
  - major additions to Cookbook
  - added AlleleFreqs attribute to ensembl Variation objects.
  - added getGeneByStableId method to genome objects.
  - added Introns attribute to Transcript objects and an Intron class.
  - added Mann-Whitney test and a Monte-Carlo version
  - exploratory and confirmatory period estimation techniques (suitable for
    symbolic and continuous data)
  - Information theoretic measures (AIC and BIC) added
  - drawing of trees with collapsed nodes
  - progress display indicator support for terminal and GUI apps
  - added parser for illumina HiSeq2000 and GAiix sequence files as 
    cogent.parse.illumina_sequence.MinimalIlluminaSequenceParser.
  - added parser to FASTQ files, one of the output options for illumina's
    workflow, also added cookbook demo.
  - added functionality for parsing of SFF files without the Roche tools in
    cogent.parse.binary_sff
  - thousand fold performance improvement to nmds
  - >10-fold performance improvements to some Table operations

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doc/examples/perform_PCoA_analysis.rst
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.. doctest::
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    >>> d = distance.EstimateDistances(al, submodel= HKY85())
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    >>> d.run(show_progress=False)
 
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    >>> d.run()
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Now use this matrix to perform principal coordinates analysis.
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    ======================================================================================
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            Type              Label  vec_num-0  vec_num-1  vec_num-2  vec_num-3  vec_num-4
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    --------------------------------------------------------------------------------------
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    Eigenvectors          NineBande      -0.02       0.01      -0.04       0.01       0.00
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    Eigenvectors           DogFaced      -0.04      -0.06       0.01       0.00       0.00
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    Eigenvectors          HowlerMon      -0.07       0.01      -0.01      -0.02       0.00
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    Eigenvectors              Mouse       0.20       0.01       0.01      -0.00       0.00
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    Eigenvectors              Human      -0.07       0.04       0.03       0.01       0.00
 
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    Eigenvectors          NineBande      -0.02       0.01       0.04       0.01       0.00
 
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    Eigenvectors           DogFaced      -0.04      -0.06      -0.01       0.00       0.00
 
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    Eigenvectors          HowlerMon      -0.07       0.01       0.01      -0.02       0.00
 
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    Eigenvectors              Mouse       0.20       0.01      -0.01      -0.00       0.00
 
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    Eigenvectors              Human      -0.07       0.04      -0.03       0.01       0.00
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     Eigenvalues        eigenvalues       0.05       0.01       0.00       0.00      -0.00
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     Eigenvalues  var explained (%)      85.71       9.60       3.73       0.95      -0.00
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    --------------------------------------------------------------------------------------
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    >>> PCoA_result.writeToFile('PCoA_results.txt',sep='\t')
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Cleanup
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-------
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We don't actually want to keep that file now, so I'm importing the ``os`` module to delete it.
 
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    We don't actually want to keep that file now, so I'm importing the ``os`` module to delete it.
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.. doctest::
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