Jayoung Ryu, Romain Lopez, Charlotte Bunne, Aviv Regev
{"title":"Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport","authors":"Jayoung Ryu, Romain Lopez, Charlotte Bunne, Aviv Regev","doi":"arxiv-2405.00838","DOIUrl":null,"url":null,"abstract":"It is now possible to conduct large scale perturbation screens with complex\nreadout modalities, such as different molecular profiles or high content cell\nimages. While these open the way for systematic dissection of causal cell\ncircuits, integrated such data across screens to maximize our ability to\npredict circuits poses substantial computational challenges, which have not\nbeen addressed. Here, we extend two Gromov-Wasserstein Optimal Transport\nmethods to incorporate the perturbation label for cross-modality alignment. The\nobtained alignment is then employed to train a predictive model that estimates\ncellular responses to perturbations observed with only one measurement\nmodality. We validate our method for the tasks of cross-modality alignment and\ncross-modality prediction in a recent multi-modal single-cell perturbation\ndataset. Our approach opens the way to unified causal models of cell biology.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"44 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.00838","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
It is now possible to conduct large scale perturbation screens with complex
readout modalities, such as different molecular profiles or high content cell
images. While these open the way for systematic dissection of causal cell
circuits, integrated such data across screens to maximize our ability to
predict circuits poses substantial computational challenges, which have not
been addressed. Here, we extend two Gromov-Wasserstein Optimal Transport
methods to incorporate the perturbation label for cross-modality alignment. The
obtained alignment is then employed to train a predictive model that estimates
cellular responses to perturbations observed with only one measurement
modality. We validate our method for the tasks of cross-modality alignment and
cross-modality prediction in a recent multi-modal single-cell perturbation
dataset. Our approach opens the way to unified causal models of cell biology.