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.
The metadata can be found in
metadata.csv and downloaded from here. The schema of the metadata is as follows:
|site_id||Unique identifier of a given site|
|well_id||Unique identifier of a given well|
|cell_type||Cell type tested|
|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)
All six channels (
w6) make up an single image of a given
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.
- August 2020: initial release
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.