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academic-kickstart

batch-effect-removal-benchmarking

A benchmark of batch-effect correction methods for single-cell RNA sequencing data

bipy

DEPRECATED Lightweight bioinformatics pipeline tools using iPython

cpa

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

deeppacket

a Deep learning toolbox for network traffic classification

ganhacks

starter from "How to Train a GAN?" at NIPS2016

graph-planner

A java based Implementation of "Graph-Plan" Algorithm

lotfollahi.com

m0hammadl.github.io

perturbseq_gi

Code for reproducing "Exploring genetic interaction manifolds constructed from rich single-cell phenotypes"

really-awesome-gan

A list of papers on Generative Adversarial (Neural) Networks

rst-cheatsheet

A two-page cheatsheet for restructured text

scanpy

Single-Cell Analysis in Python. Scales to very high cell numbers.

scgen

Single cell perturbation prediction - https://scgen.readthedocs.io

scgen_notebooks

This repository contains example noteboks for scGen

scvi

Deep generative modeling for single-cell transcriptomics