Skip to main content

MIOFlow is a Python package for modeling and analyzing single-cell RNA-seq data using optimal flows.

Project description

MIOFlow

Setup

Create conda environment

$ conda create -n mioflow python=3.10
$ conda activate mioflow

install pytorch according to instructions https://pytorch.org/get-started/

we used

$ conda install pytorch pytorch-cuda=11.6 -c pytorch -c nvidia

install requirements and MIOFlow

$ pip install -e .

Remove unused packages and caches.

$ conda clean --all

Install MIOFlow for developers and internal use:

$ cd path/to/this/repository
$ pip install -e MIOFlow

Data

Most datasets are available in the directory data. The file eb_v4_df_pca200.npy can be downloaded here.

Add kernel to Jupyter Notebook

automatic conda kernels

For greater detail see the official docs for nb_conda_kernels. In short, install nb_conda_kernels in the environment from which you launch JupyterLab / Jupyter Notebooks from (e.g. base) via:

$ conda install -n <notebook_env> nb_conda_kernels

to add a new or exist conda environment to Jupyter simply install ipykernel into that conda environment e.g.

$ conda install -n <python_env> ipykernel

manual ipykernel

add to your Jupyter Notebook kernels via

$ python -m ipykernel install --user --name sklab-mioflow

It can be removed via:

$ jupyter kernelspec uninstall sklab-mioflow

list kernels found by Jupyter

kernels recognized by conda

$ python -m nb_conda_kernels list

check which kernels are discovered by Jupyter:

$ jupyter kernelspec list

How to use

This repository consists of our python library MIOFlow as well as a directory of scripts for running and using it.

Scripts

To recreate our results with MMD loss and density regulariazation you can run the following command:

python scripts/run.py -d petals -c mmd -n petal-mmd

This will generate the directory results/petals-mmd and save everything there.

For a full list of parameters try running:

python scripts/run.py --help

Python Package

One could simply import everything and use it piecemeal:

from MIOFlow.ode import *
from MIOFlow.losses import *
from MIOFlow.utils import *
from MIOFlow.models import *
from MIOFlow.plots import *
from MIOFlow.train import *
from MIOFlow.constants import *
from MIOFlow.datasets import *
from MIOFlow.exp import *
from MIOFlow.geo import *
from MIOFlow.eval import *

Tutorials

One can also consult or modify the tutorial notebooks for their uses: - EB Bodies tutorial - Dyngen tutorial - Petals tutorial

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mioflow-0.1.3.tar.gz (37.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mioflow-0.1.3-py3-none-any.whl (41.3 kB view details)

Uploaded Python 3

File details

Details for the file mioflow-0.1.3.tar.gz.

File metadata

  • Download URL: mioflow-0.1.3.tar.gz
  • Upload date:
  • Size: 37.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.17

File hashes

Hashes for mioflow-0.1.3.tar.gz
Algorithm Hash digest
SHA256 c80cb3bbd35010ac4ce12729e58365900ca7875e0d67677d0ff84d898094a4ed
MD5 7c61c14a56978f761fc908ab6ce799cf
BLAKE2b-256 acc2a826a4110a79a12c218ede6b5fc0b50788f1cc267729e63a6f478686dfce

See more details on using hashes here.

File details

Details for the file mioflow-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: mioflow-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 41.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.17

File hashes

Hashes for mioflow-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 0bf33d1a96f4b3b35c602c58baa02921255884552741cb271a02bb0460349bf8
MD5 d3037af81a1f7a6d9886dcc65a8bc7bd
BLAKE2b-256 89372ac66ae2accaba741b400debb72f734d69e5ce91a1f19d8c61efeb31695d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page