MEDIA INQUIRIES
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Through recent technological developments, we can now obtain RNA sequencing data from whole tissue sections without losing cell location information. But the computational methods for analyzing these spatial datasets are still inadequate. In particular, we need methods that can seamlessly integrate images, sequencing data, and 3D coordinates from large-scale spatial transcriptomic studies. To that end, the Eric and Wendy Schmidt Center is developing causal representation learning methods that allow us to integrate different kinds of data to uncover the mechanisms of how tissues are organized in health and disease.
Mayer, A. T., Holman, D. R., Sood, A., Tandon, U., Bhate, S. S., Bodapati, S., Barlow, G. L., Chang, J., Black, S., Crenshaw, E. C., Koron, A. N., Streett, S. E., Gambhir, S. S., Sandborn, W. J., Boland, B. S., Hastie, T., Tibshirani, R., Chang, J. T., Nolan, G. P., … Rogalla, S.
(
2023
).
A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy.
Science Advances
,
9
(
3
).
https://doi.org/10.1126/sciadv.add1166Bhate, S. S., Seigal, A., & Caicedo, J.
(
2022
).
Deciphering causal genomic templates of complex molecular phenotypes.
bioRxiv.
,
(
).
https://doi.org/10.1101/2022.08.15.503769Lee, M. Y., Bedia, J. S., Bhate, S. S., Barlow, G. L., Phillips, D., Fantl, W. J., Nolan, G. P., & Schürch, C. M.
(
2022
).
CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images.
BMC Bioinformatics, 23(1).
,
(
).
https://doi.org/10.1186/s12859-022-04570-9Zhang, X., Wang, X., Shivashankar, G. V., & Uhler, C.
(
2022
).
Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease.
Nature Communications
,
13
(
1
).
https://doi.org/10.1038/s41467-022-35233-1
