CellRank's reproducibility repository.

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
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Extended Data Figures
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Supplementary Figures
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Supplementary Fig. 17 NA (microscopy results) NA (microscopy results)
Supplementary Tables
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Owner
Theis Lab
Institute of Computational Biology
Theis Lab
Probabilistic Cross-Modal Embedding (PCME) CVPR 2021

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Task-related Saliency Network For Few-shot learning

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A sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes

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Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

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Official code for paper "ISNet: Costless and Implicit Image Segmentation for Deep Classifiers, with Application in COVID-19 Detection"

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