CellRank's reproducibility repository.

Last update: Oct 08, 2022

Overview

CellRank's reproducibility repository

We believe that reproducibility is key and have made it as simple as possible to reproduce our results. Please either open an issue or contact as at [email protected] should you experience difficulties reproducing any result.

Manuscript, code and data

CellRank is published in Nature Methods and the software package can be found at our main website, cellrank.org. Raw published data is available from the Gene Expression Omnibus (GEO) under accession codes:

Processed data, including spliced and unspliced count abundances, is available on figshare. To ease reproducibility, our data examples can also be accessed through CellRank's dataset interface.

Navigating this repository

We've organized this repository along the categories below. For each item, you can click the link under nbviewer to open the notebook in the browser using nbviewer. There is no 1-1 mapping from figures to notebooks - some notebooks produce panels for several figures, and some figures contain panels from several notebooks. The tables we provide here make the connection between figures and notebooks explicit. At the top of each notebook, we indicate the package versions we use.

Results

Main Figures

Figure	nbviewer	Notebook Path
Fig. 1	link	path
Fig. 2	link	path
Fig. 3	link	path
Fig. 4	link	path
Fig. 5 Palantir	link	path
Fig. 5 STEMNET	link	path
Fig. 5 Velocyto	link	path
Fig. 5 FateID	link	path
Fig. 6	link	path

Extended Data Figures

Figure	nbviewer	Notebook Path
Extended Data Fig. 1	NA (toy data)	NA (toy data)
Extended Data Fig. 2	link	path
Extended Data Fig. 3	link	path
Extended Data Fig. 4	link	path
Extended Data Fig. 5	link	path
Extended Data Fig. 6	link	path
Extended Data Fig. 7	link	path
Extended Data Fig. 8	link	path
Extended Data Fig. 9	link	path
Extended Data Fig. 10	link	path

Supplementary Figures

Figure	nbviewer	Notebook Path
Supplementary Fig. 1	link	path
Supplementary Fig. 2	link	path
Supplementary Fig. 3	link	path
Supplementary Fig. 4	link	path
Supplementary Fig. 5	link	path
Supplementary Fig. 6	link	path
Supplementary Fig. 7	link	path
Supplementary Fig. 8	link	path
Supplementary Fig. 9	link	path
Supplementary Fig. 10	link	path
Supplementary Fig. 11	link	path
Supplementary Fig. 12	link1 link2	path1 path2
Supplementary Fig. 13	link	path
Supplementary Fig. 14	link	path
Supplementary Fig. 15	link	path
Supplementary Fig. 16	link	path
Supplementary Fig. 17	NA (microscopy results)	NA (microscopy results)

Supplementary Tables

Table	nbviewer	Notebook Path
Supplementary Tab. 1	link	path
Supplementary Tab. 2	link	path
Supplementary Tab. 3	link	path

CellRank's reproducibility repository.

Related tags

Overview

CellRank's reproducibility repository

Manuscript, code and data

Navigating this repository

Results

Owner

Theis Lab

A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Deep Multimodal Neural Architecture Search

The challenge for Quantum Coalition Hackathon 2021

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax

Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019). Including competition model, online demo, etc.

Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

TLXZoo - Pre-trained models based on TensorLayerX

Only valid pull requests will be allowed. Use python only and readme changes will not be accepted.

Code release for "COTR: Correspondence Transformer for Matching Across Images"

MANO hand model porting for the GraspIt simulator

A project for developing transformer-based models for clinical relation extraction

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Code for testing various M1 Chip benchmarks with TensorFlow.

[SIGGRAPH 2021 Asia] DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning

Official implementation of VQ-Diffusion

Reproduce partial features of DeePMD-kit using PyTorch.

Tensor-Based Quantum Machine Learning