ArticlePLoS computational biology2020
Predicting cell lineages using autoencoders and optimal transport.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 62 citations in OpenAlex.
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- Cell context-dependent in silico organelle localization in label-free microscopy images.Nature methods · 2026Article
- MultistageOT: Multistage optimal transport infers trajectories from a snapshot of single-cell data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer's disease pathology.bioRxiv : the preprint server for biology · 2025Article
- Mapping cells through time and space with moscot.Nature · 2025Article
- Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns.Nature communications · 2025Article
- Debiasing Sinkhorn divergence in optimal transport of cellular dynamics.bioRxiv : the preprint server for biology · 2025Article
- scEGOT: single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport.BMC bioinformatics · 2024Article
- Visual interpretability of bioimaging deep learning models.Nature methods · 2024Article
- Article
- Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion.Molecular systems biology · 2024Article
- Elucidating dynamic cell lineages and gene networks in time-course single cell differentiation.Artificial intelligence in the life sciences · 2023Article
- Learning single-cell perturbation responses using neural optimal transport.Nature methods · 2023Article
- A genome-wide optical pooled screen reveals regulators of cellular antiviral responses.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer's disease.Nature communications · 2022Article
- Temporal modelling using single-cell transcriptomics.Nature reviews. Genetics · 2022Review
- SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- A deep generative model of 3D single-cell organization.PLoS computational biology · 2022Article
- Optimal transport analysis reveals trajectories in steady-state systems.PLoS computational biology · 2021Article
- Data science in cell imaging.Journal of cell science · 2021Review
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Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lineage tracing involves the identification of all ancestors and descendants of a given cell, and is an important tool for studying biological processes such as development and disease progression. However, in many settings, controlled time-course experiments are not feasible, for example when working with tissue samples from patients. Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for predicting the lineages of cells using time-labeled datasets from different stages of a cellular process. Given a single-cell image from one of the stages, ImageAEOT generates an artificial lineage of this cell based on the population characteristics of the other stages. These lineages can be used to connect subpopulations of cells through the different stages and identify image-based features and biomarkers underlying the biological process. To validate our method, we apply ImageAEOT to a benchmark task based on nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Our results demonstrate the promise of computational methods based on autoencoding and optimal transport principles for lineage tracing in settings where existing experimental strategies cannot be used.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.