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
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
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Supplementary Fig. 12	link1 link2	path1 path2
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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

Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning

GAN-based 3D human pose estimation model for 3DV'17 paper

Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at [email protected]

Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

PyTorch implementation of Deep HDR Imaging via A Non-Local Network (TIP 2020).

Semantic graph parser based on Categorial grammars

Viewmaker Networks: Learning Views for Unsupervised Representation Learning

SMIS - Semantically Multi-modal Image Synthesis(CVPR 2020)

Garbage Detection system which will detect objects based on whether it is plastic waste or plastics or just garbage.

Official PyTorch Implementation of Rank & Sort Loss [ICCV2021]

Training BERT with Compute/Time (Academic) Budget

Using some basic methods to show linkages and transformations of robotic arms

Deep GPs built on top of TensorFlow/Keras and GPflow

Equivariant layers for RC-complement symmetry in DNA sequence data

ROS-UGV-Control-Interface - Control interface which can be used in any UGV

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Dynamic Bottleneck for Robust Self-Supervised Exploration

JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction

[ACM MM2021] MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification