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RxRx2

For more information about RxRx2 please visit RxRx.ai and read the asscociated paper, Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery.

RxRx2 is part of a larger set of Recursion datasets that can be found at RxRx.ai and on GitHub. For questions about this dataset and others please email info@rxrx.ai.

Metadata

The metadata can be found in metadata.csv and downloaded from here. The schema of the metadata is as follows:

Attribute Description
site_id Unique identifier of a given site
well_id Unique identifier of a given well
cell_type Cell type tested
experiment Experiment identifier
plate Plate number within the experiment
well Location on the plate
site Indication of the location in the well where image was taken (1, 2, 3 or 4)
treatment Soluble factor added to the well
treatment_conc Soluble factor concentration tested (in mg/mL)

Images

The images are found in images/* and can be downloaded from here (n.b. this is 185GB). The image data are 1024x1024 8-bit png files. The image paths, such as HUVEC-1/Plate1/AA02_s2_w3.png, can be read as:

Experiment Name: Cell type and experiment number (HUVEC experiment 1)
Plate Number (1)
Well location on plate (column AA, row 2)
Site (2)
Channel (3)

All six channels (w1 - w6) make up an single image of a given site.

Deep Learning Embeddings

The deep learning embeddings can be found in embeddings.csv and downloaded from here (n.b. this is 76MB).

Each row in the csv has a site_id as described in the metadata schema. The remaining 1024 columns is the embedding for that respective site.

Changelog:

  • August 2020: initial release

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.