rob-knight
commented
10 years ago I think this is a good idea in principle. We are adding scikit-learn as a dependency anyway. Our experiments with isomap and LLE a few years ago were not encouraging but datasets are now much larger and it is worth revisiting. Thanks!
Rob
On May 4, 2014, at 9:29 PM, "Joshua Haas" notifications@github.com<mailto:notifications@github.com> wrote:
QIIME currently only implements Principal Coordinate Analysis (PCoA) for dimensionality reduction and subsequent viewing with Emperor. While PCoA is a tried and true method within the metagenomic community, it is inherently linear and thus misses nonlinear relationships. This can be a significant shortcoming, as bacteria tend to have nonlinear relationships, as explored in [1].
Many nonlinear methods for dimensionality reduction have been developed, a subset of which are known as Manifold Learning techniques (example: Isomap was used in [1]). Several of these unsupervised techniques have already been implemented in the Scikit-Learn python package [2].
Adding them to QIIME would require either another dependancy or inclusion of relevant files from their github [3]. As well as some simple python scripts that I have already written as part of school work (these currently assume Scikit-Learn is already installed).
[1] http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6392684 [2] http://scikit-learn.org/stable/modules/manifold.html [3] https://github.com/scikit-learn/scikit-learn/tree/master/sklearn
— Reply to this email directly or view it on GitHubhttps://github.com/biocore/qiime/issues/1530.
QIIME currently only implements Principal Coordinate Analysis (PCoA) for dimensionality reduction and subsequent viewing with Emperor. While PCoA is a tried and true method within the metagenomic community, it is inherently linear and thus misses nonlinear relationships. This can be a significant shortcoming, as bacteria tend to have nonlinear relationships, as explored in [1].
Many nonlinear methods for dimensionality reduction have been developed, a subset of which are known as Manifold Learning techniques (example: Isomap was used in [1]). Several of these unsupervised techniques have already been implemented in the Scikit-Learn python package [2].
Adding them to QIIME would require either another dependancy or inclusion of relevant files from their github [3]. As well as some simple python scripts that I have already written as part of school work (these currently assume Scikit-Learn is already installed).
[1] http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6392684 [2] http://scikit-learn.org/stable/modules/manifold.html [3] https://github.com/scikit-learn/scikit-learn/tree/master/sklearn