Published June 5, 2023
| Version 1.0
Dataset
Open
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
Creators
- 1. Broad Institute of MIT and Harvard
- 2. Boston University
- 3. Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
Description
We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers.
Files
Files
(28.1 GB)
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md5:fe52ad9f1d05e2aa31526fbe0f4c83dd
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28.1 GB | Download |
Additional details
Funding
- Collaborative Research: Image-based Readouts of Cellular State using Universal Morphology Embeddings 2134695
- U.S. National Science Foundation
References
- Bray, M.-A. et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience 6, 1–5 (2017).
- Gustafsdottir, S. M. et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One 8, e80999 (2013).
- Thul, P. J. et al. A subcellular map of the human proteome. Science 356, (2017).
- Viana, M. P. et al. Integrated intracellular organization and its variations in human iPS cells. Nature 613, 345–354 (2023).
- Way, G. P. et al. Morphology and gene expression profiling provide complementary information for mapping cell state. bioRxiv 2021.10.21.465335 (2021).