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.
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) |
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.
Physical resolution: 0.65 micron/pixel.
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.
- August 2020: initial release
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