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

AFLFast (extends AFL with Power Schedules)

A Machine Teaching Framework for Scalable Recognition

Configure SRX interfaces with Scrapli

FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR

POT : Python Optimal Transport

My take on a practical implementation of Linformer for Pytorch.

Fully Connected DenseNet for Image Segmentation

Repo for EchoVPR: Echo State Networks for Visual Place Recognition

Revisiting Self-Training for Few-Shot Learning of Language Model.

A toolkit for developing and comparing reinforcement learning algorithms.

Hand tracking demo for DIY Smart Glasses with a remote computer doing the work

Automatically measure the facial Width-To-Height ratio and get facial analysis results provided by Microsoft Azure

Artificial Intelligence search algorithm base on Pacman

TorchIO is a Medical image preprocessing and augmentation toolkit for deep learning. Part of the PyTorch Ecosystem.

[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

System Design course at HSE (2021)

TDN: Temporal Difference Networks for Efficient Action Recognition

AbelNN: Deep Learning Python module from scratch

This code implements constituency parse tree aggregation

WHENet - ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L