Redko, I. | Search bioRxiv

bioRxiv2025

SCOT+: A Comprehensive Software Suite for Single-Cell alignment Using Optimal Transport

SummaryNew advances in single-cell multi-omics experiments have allowed biologists to examine how various biological factors regulate processes in concert on the cellular level. However, measuring multiple cellular features for a single cell can be quite resource-intensive or impossible with the current technology. By using optimal transport (OT) to align cells and features across disparate datasets produced by separate assays, Single Cell alignment using Optimal Transport+ (SCOT+), our unsupervised single-cell alignment software suite, allows biologists to align their data without the need for any correspondence. SCOT+ has a generic optimal transport solution that can be reduced to multiple different OT optimization procedures, each of which provide state-of-the-art single-cell alignment performance. With our user-friendly website and tutorials, this new package will help improve biological analyses by allowing for more accurate downstream analyses on multi-omics single-cell measurements. Implementation and AvailabilityOur algorithm is implemented in Pytorch and available on PyPI and GitHub (https://github.com/scotplus/scotplus). Additionally, we have many tutorials available in a separate GitHub repository (https://github.com/scotplus/book_source) and on our website (https://scotplus.github.io/).

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bioRxiv2022

Jointly aligning cells and genomic features of single-cell multi-omics data with co-optimal transport

The availability of various single-cell sequencing technologies allows one to jointly study multiple genomic features and understand how they interact to regulate cells. Although there are experimental challenges to simultaneously profile multiple features on the same single cell, recent computational methods can align the cells from unpaired multi-omic datasets. However, studying regulation also requires us to map the genomic features across different measurements. Unfortunately, most single-cell multi-omic alignment tools cannot perform these alignments or need prior knowledge. We introduce O_SCPLOWSCOOTRC_SCPLOW, a co-optimal transport-based method, which jointly aligns both cells and genomic features of unpaired single-cell multi-omic datasets. We apply O_SCPLOWSCOOTRC_SCPLOW to various single-cell multi-omic datasets with different types of measurements. Our results show that O_SCPLOWSCOOTRC_SCPLOW provides quality alignments for unsupervised cell-level and feature-level integration of datasets with sparse feature correspondences (e.g., one-to-one mappings). For datasets with dense feature correspondences (e.g., many-to-many mappings), our joint framework allows us to provide supervision on one level (e.g., cell types), thus improving alignment performance on the other (e.g., genomic features) or vice-versa. The unique joint alignment framework makes O_SCPLOWSCOOTRC_SCPLOW a helpful hypothesis-generation tool for the integrative study of unpaired single-cell multi-omic datasets. Available at: https://github.com/rsinghlab/SCOOTR.

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