Cargando…
The covariance environment defines cellular niches for spatial inference
The tsunami of new multiplexed spatial profiling technologies has opened a range of computational challenges focused on leveraging these powerful data for biological discovery. A key challenge underlying computation is a suitable representation for features of cellular niches. Here, we develop the c...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153165/ https://www.ncbi.nlm.nih.gov/pubmed/37131616 http://dx.doi.org/10.1101/2023.04.18.537375 |
_version_ | 1785035881579544576 |
---|---|
author | Haviv, Doron Gatie, Mohamed Hadjantonakis, Anna-Katerina Nawy, Tal Pe’er, Dana |
author_facet | Haviv, Doron Gatie, Mohamed Hadjantonakis, Anna-Katerina Nawy, Tal Pe’er, Dana |
author_sort | Haviv, Doron |
collection | PubMed |
description | The tsunami of new multiplexed spatial profiling technologies has opened a range of computational challenges focused on leveraging these powerful data for biological discovery. A key challenge underlying computation is a suitable representation for features of cellular niches. Here, we develop the covariance environment (COVET), a representation that can capture the rich, continuous multivariate nature of cellular niches by capturing the gene-gene covariate structure across cells in the niche, which can reflect the cell-cell communication between them. We define a principled optimal transport-based distance metric between COVET niches and develop a computationally efficient approximation to this metric that can scale to millions of cells. Using COVET to encode spatial context, we develop environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA-seq data into a latent space. Two distinct decoders either impute gene expression across spatial modality, or project spatial information onto dissociated single-cell data. We show that ENVI is not only superior in the imputation of gene expression but is also able to infer spatial context to disassociated single-cell genomics data. |
format | Online Article Text |
id | pubmed-10153165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-101531652023-05-03 The covariance environment defines cellular niches for spatial inference Haviv, Doron Gatie, Mohamed Hadjantonakis, Anna-Katerina Nawy, Tal Pe’er, Dana bioRxiv Article The tsunami of new multiplexed spatial profiling technologies has opened a range of computational challenges focused on leveraging these powerful data for biological discovery. A key challenge underlying computation is a suitable representation for features of cellular niches. Here, we develop the covariance environment (COVET), a representation that can capture the rich, continuous multivariate nature of cellular niches by capturing the gene-gene covariate structure across cells in the niche, which can reflect the cell-cell communication between them. We define a principled optimal transport-based distance metric between COVET niches and develop a computationally efficient approximation to this metric that can scale to millions of cells. Using COVET to encode spatial context, we develop environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA-seq data into a latent space. Two distinct decoders either impute gene expression across spatial modality, or project spatial information onto dissociated single-cell data. We show that ENVI is not only superior in the imputation of gene expression but is also able to infer spatial context to disassociated single-cell genomics data. Cold Spring Harbor Laboratory 2023-04-20 /pmc/articles/PMC10153165/ /pubmed/37131616 http://dx.doi.org/10.1101/2023.04.18.537375 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Haviv, Doron Gatie, Mohamed Hadjantonakis, Anna-Katerina Nawy, Tal Pe’er, Dana The covariance environment defines cellular niches for spatial inference |
title | The covariance environment defines cellular niches for spatial inference |
title_full | The covariance environment defines cellular niches for spatial inference |
title_fullStr | The covariance environment defines cellular niches for spatial inference |
title_full_unstemmed | The covariance environment defines cellular niches for spatial inference |
title_short | The covariance environment defines cellular niches for spatial inference |
title_sort | covariance environment defines cellular niches for spatial inference |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153165/ https://www.ncbi.nlm.nih.gov/pubmed/37131616 http://dx.doi.org/10.1101/2023.04.18.537375 |
work_keys_str_mv | AT havivdoron thecovarianceenvironmentdefinescellularnichesforspatialinference AT gatiemohamed thecovarianceenvironmentdefinescellularnichesforspatialinference AT hadjantonakisannakaterina thecovarianceenvironmentdefinescellularnichesforspatialinference AT nawytal thecovarianceenvironmentdefinescellularnichesforspatialinference AT peerdana thecovarianceenvironmentdefinescellularnichesforspatialinference AT havivdoron covarianceenvironmentdefinescellularnichesforspatialinference AT gatiemohamed covarianceenvironmentdefinescellularnichesforspatialinference AT hadjantonakisannakaterina covarianceenvironmentdefinescellularnichesforspatialinference AT nawytal covarianceenvironmentdefinescellularnichesforspatialinference AT peerdana covarianceenvironmentdefinescellularnichesforspatialinference |