Metadata-Version: 2.1
Name: strainpycon
Version: 1.0
Summary: Strain disambiguation methods for mixed DNA samples
Home-page: https://www.ymsir.com/strainpycon/
Author: Ymir Vigfusson, Lars Ruthotto, Rebecca M. Mitchell, Lauri Mustonen, Xiangxi Gao
Author-email: ymir.vigfusson@emory.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown

# Introduction

StrainPycon is a Python 3 package that can be used to disambiguate multiple
strains in mixed samples of DNA. Mathematically, StrainPycon can solve binary
blind source separation problems and compute certain high-dimensional integrals
involving binary variables. The connection between these mathematical concepts
and strain identification is discussed in the following journal article:  

  L. Mustonen, X. Gao, A. Santana, R.M. Mitchell, Y. Vigfusson, and L. Ruthotto,  
  A Bayesian framework for molecular strain identification from mixed diagnostic samples,  
  *Inverse Problems* 34(10), 105009, 2018,  
  https://doi.org/10.1088/1361-6420/aad7cd

StrainPycon builds on the StrainRecon.jl package written in Julia:
https://github.com/lruthotto/StrainRecon.jl

# Motivation

As a motivating example, suppose you have a blood sample infected by multiple
Plasmodium falciparum malaria parasites. Assuming you have done PCR on chosen
SNP sites, the number of calls that differ from the reference genome are
indicative of what proportion of the strains have mutated at that SNP.
StrainPycon is an approach for identifying the strains in the sample through
disambiguation (deconvolution) without requiring any prior knowledge about the
sample or the parasite. The process can also help assess the multiplicity of
infection in the sample, which can aid malaria surveillance efforts, for
instance.

# Citation

If you use StrainPycon in your project, please cite the journal article above.

# Full documentation

Please refer to the full documentation of StrainPycon at:
https://www.ymsir.com/strainpycon/

# Requirements

StrainPycon was tested in the following environment:
+ 64-bit Linux
+ Python 3.6.5 with NumPy 1.14.3

# Basic usage

Usually, the user only wants to access a few methods from the StrainRecon class:

``` py
import strainpycon
S = strainpycon.StrainRecon()
```

Let us generate synthetic measurement data with three strains and 24 SNP sites
and solve the inverse problem:
``` py
(measurements, strains, freq) = S.random_data(24, 3)
(strains_recon, freq_recon) = S.compute(measurements, 3)
```
Here, `strains_recon` should equal `strains` and `freq_recon` should equal
`freq`.

Next, let us draw another random measurement, now with Gaussian additive noise.
We compute the *misfit*, or negative log-likelihood, when the number of strains
in the reconstruction varies from one to seven. Moreover, we compute posterior
statistics to quantify uncertainty:
``` py
gamma = 0.1 # standard deviation of Gaussian noise
(measurements, strains, freq) = S.random_data(18, 4, gamma=gamma)
misfits = S.misfits(measurements, range(1,8))
(strains_mean, freq_mean, strains_dev, freq_dev) = S.posterior_stats(measurements, 4, gamma)
```

A complete description of the methods and detailed examples can be found on:
See https://www.ymsir.com/strainpycon/

# Known issues

StrainPycon does not support multi-threading yet.

# Contacts

Please direct questions to:
Ymir Vigfusson, Emory University, ymir.vigfusson@emory.edu



