gnr

granger causality analysis

This file will become your README and also the index of your documentation.

Developer Guide

Setup

# create conda environment
$ mamba env create -f env.yml

# update conda environment
$ mamba env update -n gnr --file env.yml
# $ mamba env update -n gnr --file env.mac.yml

Install

pip install -e .

# install from pypi
pip install gnr

nbdev

# activate conda environment
$ conda activate gnr

# make sure the gnr package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to the gnr package
$ nbdev_prepare

Note: it might be useful to use the following snippet to enable hot reloading:

%load_ext autoreload
%autoreload 2

Publishing

# publish to pypi
$ nbdev_pypi

# publish to conda
$ nbdev_conda --build_args '-c conda-forge'

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/dsm-72/gnr.git

or from conda

$ conda install -c dsm-72 gnr

or from pypi

$ pip install gnr

df_trj = make_mock_genes_x_tbins()
df_trj.head()

	0	1	2	3	4	5	6	7	8	9	...	90	91	92	93	94	95	96	97	98	99
wasf	0	0	0	0	0	0	0	0	0	0	...	9	9	9	9	9	9	9	9	9	9
colq	9	9	9	9	9	9	9	9	9	9	...	1	0	0	0	0	0	0	0	0	0
gpr1	0	0	0	0	0	0	0	0	0	0	...	9	9	9	9	9	9	9	9	9	9
chrm3	9	9	9	9	9	9	9	9	9	9	...	1	1	0	0	0	0	0	0	0	0
lmod2	0	0	0	0	0	0	0	1	1	1	...	8	8	8	9	9	9	9	9	9	9

5 rows × 100 columns

gc_op = GrangerCausality(n_jobs=2)

df_res = gc_op.fit_transform(df_trj, fit_params={'standard_scaler':True, 'signed_correlation': True})

df_res.head()

	wasf_y	colq_y	gpr1_y	chrm3_y	lmod2_y	tek_y	kank3_y	oca2_y	taz_y	map4k1_y
wasf_x	1.000000	0.683091	0.314458	0.144127	0.000818	1.000000	1.000000	0.000066	0.102470	0.006449
colq_x	1.000000	1.000000	0.779284	1.000000	1.000000	0.001091	0.192685	0.675090	1.000000	0.806543
gpr1_x	0.805541	0.042286	1.000000	0.892251	0.795418	0.823063	1.000000	0.542452	0.001091	0.852052
chrm3_x	0.001091	0.073638	0.168425	1.000000	0.632585	1.000000	0.102470	0.542452	1.000000	0.367649
lmod2_x	0.683091	0.000104	0.031086	0.220671	1.000000	0.683091	0.000818	0.367649	1.000000	0.017608

gc_op.plot_df_org(figsize=(4,4))

gc_op.plot_df_res(figsize=(4,4))

Documentation

Documentation can be found hosted on GitHub repository pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.