Detecting differential growth of microbial populations with Gaussian process regression.

Peter Tonner, Cynthia Darnell, Barbara E Engelhardt, Amy Schmid

Code repository corresponding to paper "Detecting differential growth of microbial populations with Gaussian process regression."

Setup

Requirements

git (or download repository as tarball)
python (virtualenv recommended)

Download this repository

git clone https://github.com/ptonner/gp_growth_phenotype.git

Setup a local python environment (optional)

virtualenv .

source bin/activate

Install requirements

pip install -r requirements.txt

Examples

Input to the B-GREAT method should come from two dataframes, data (n x p) and meta (p x k) where n, p, and k are:

n: number of time-points
p: number of time-course measurements (replicates)
k: number of covariates to be tested, e.g. strain, condition.

The index of data is assumed to be the time column. Each row of meta should correspond to a single column of data, e.g. the number of columns in data should equal the number of rows in meta.

import pandas as pd
data = pd.read_csv("path/to/data.csv",index_col=0)
meta = pd.read_csv("path/to/meta.csv")

assert data.shape[1] == meta.shape[0]

These two variables should then be given to bgreat for use in analysis.

import bgreat
bgreat.setGlobals(_data=data,_meta=meta)

Testing strain effects

For a single effect on growth, a single column in meta will be used for testing differential growth. meta should include a column called strain that defines the strain being growth in each column of data. A specific value in the strain column should correspond to the 'parent' or 'control' strain, and is given to bgreat through the setGlobals function. Additionally, a column should be added to meta called "strain-regression" which represents the column used in building the GP regression model. It should contain 1's and 0's corresponding to a non-parent or parent strain sample, respectively.

parent = 'my-parent'
meta['strain-regression'] = (meta.strain!=parent).astype(int)
bgreat.setGlobals(_parent=parent,_control=control)

A list of mutants can then be given to calculate the Bayes factor score, with permutation testing and FDR calculation.

mutants = ['m1','m2',...]
results = testMutants(mutants)

Testing interactions

Interaction testing requires an additional variable, the control condition, as well as two additional columns in meta - "condition" and "interaction"

control = 'my-control'
meta['interaction'] = ((meta.Condition!=control) & (meta.strain!=parent)).astype(int)
meta['condition'] = (meta.Condition!=control).astype(int)
bgreat.setGlobals(_control=control,_meta=meta)

results = bgreat.testMutantCondition(mutants)

Ravenclawer/gp_growth_phenotype