Metadata-Version: 1.0
Name: PySurvey
Version: 0.1.2
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Jonathan Friedman
Author-email: yonatanf@mit.edu
License: MIT
Description: **********************************************
        PySurvey: Interactive analysis of survey data 
        **********************************************
        
        ``PySurvey`` is a `Python <http://www.python.org>`__ package designed to perform interactive analysis of survey data, composed of counts of occurrence of different categories in a collection of samples. 
        Specifically, ``PySurvey`` is developed in the context of genomic surveys, such as 16S surveys, where one studies the occurrence of OTUs across samples.
        Though much of ``PySurvey``'s functionality is not unique to survey data, and equivalent features are implemented in many other packages, ``PySurvey`` is intended to serve as a 'one-stop-shop', and thus attempts to includes all the methods that are commonly used in the analysis of genomic survey data (often by wrapping around other packages), with a sensible choice of default parameters (e.g. distance metrics, etc').
        
        ``PySurvey`` is based on the powerful `pandas <http://pandas.pydata.org/>`__ package which offers rich data structures which are 
        tailored and optimized for interactive analysis of large data tables.
        
        --------------------------
        ``PySurvey`` Resources
        --------------------------
        - **Documentation:** http://yonatanfriedman.com/docs/survey/index.html
        - **Source Repository:** https://bitbucket.org/yonatanf/pysurvey
        
        
        --------------------------
        Key Features
        --------------------------
          - General utility:
        	- Metadata support.
        	- Filtering of samples/components.
        	- ML and Bayesian estimation of component fractions.
        
          - Exploratory analysis:
        	- Dimension reduction: PCoA.
        	- Clustering: hierarchical, gaussian mixture models GMM.
        	- Compositional correlations via `SparCC <https://bitbucket.org/yonatanf/sparcc>`__.
        	- Plotting: sorted heatmaps, stacked plots, ...
          
          - Ecological theory:
          	- Sample diversities (alpha diversity).	
        	- Rarefaction.
          	- Rank abundance plots. 
        
        
Platform: UNKNOWN
