For more information about RxRx19a please visit RxRx.ai and read the asscociated paper, Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2.
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)|
|disease_condition||The disease condition tested in the well (mock, irradiated or viral)|
|treatment||Compound tested in the well|
|treatment_conc||Compound concentration tested (in uM)|
The images are found in
images/* and can be downloaded from here (n.b. this is 445GB).
The image data are 1024x1024 8-bit
png files. The image paths, such as
HRCE-1/Plate1/AA02_s2_w3.png, can be read as:
Experiment Name: Cell type and experiment number (HRCE experiment 1)
Plate Number (1)
Well location on plate (column AA, row 2)
All five channels (
w5) 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 1.4GB).
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.
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