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
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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
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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
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Supplementary Fig. 12	link1 link2	path1 path2
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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

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Dataset and Code for ICCV 2021 paper "Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme"

An easy-to-use app to visualise attentions of various VQA models.

Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

StarGAN - Official PyTorch Implementation (CVPR 2018)

HGCAE Pytorch implementation. CVPR2021 accepted.

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

Codes for [NeurIPS'21] You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership.

Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

This code provides various models combining dilated convolutions with residual networks

The pure and clear PyTorch Distributed Training Framework.

Smart edu-autobooking - Johnson @ DMI-UNICT study room self-booking system

GANSketchingJittor - Implementation of Sketch Your Own GAN in Jittor

MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

Node-level Graph Regression with Deep Gaussian Process Models

Implementation of the SUMO (Slim U-Net trained on MODA) model

Code for "Causal autoregressive flows" - AISTATS, 2021