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
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Supplementary Fig. 10	link	path
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Supplementary Fig. 12	link1 link2	path1 path2
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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

Pytorch implementation of DeepMind's differentiable neural computer paper.

Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr.

Official implementation of Densely connected normalizing flows

Monocular 3D Object Detection: An Extrinsic Parameter Free Approach (CVPR2021)

Depth image based mouse cursor visual haptic

Ladder Variational Autoencoders (LVAE) in PyTorch

Encoding Causal Macrovariables

Video-based open-world segmentation

Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.

Detectorch - detectron for PyTorch

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Camera calibration & 3D pose estimation tools for AcinoSet

Code & Data for Enhancing Photorealism Enhancement

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

Exploration of some patients clinical variables.

Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

PyTorch implementations of neural network models for keyword spotting

This repository contains tutorials for the py4DSTEM Python package