4 research outputs found
DOT: A flexible multi-objective optimization framework for transferring features across single-cell and spatial omics
Single-cell RNA sequencing (scRNA-seq) and spatially-resolved
imaging/sequencing technologies have revolutionized biomedical research. On one
hand, scRNA-seq provides information about a large portion of the transcriptome
for individual cells, but lacks the spatial context. On the other hand,
spatially-resolved measurements come with a trade-off between resolution and
gene coverage. Combining scRNA-seq with different spatially-resolved
technologies can thus provide a more complete map of tissues with enhanced
cellular resolution and gene coverage. Here, we propose DOT, a novel
multi-objective optimization framework for transferring cellular features
across these data modalities. DOT is flexible and can be used to infer
categorical (cell type or cell state) or continuous features (gene expression)
in different types of spatial omics. Our optimization model combines practical
aspects related to tissue composition, technical effects, and integration of
prior knowledge, thereby providing flexibility to combine scRNA-seq and both
low- and high-resolution spatial data. Our fast implementation based on the
Frank-Wolfe algorithm achieves state-of-the-art or improved performance in
localizing cell features in high- and low-resolution spatial data and
estimating the expression of unmeasured genes in low-coverage spatial data
across different tissues. DOT is freely available and can be deployed
efficiently without large computational resources; typical cases-studies can be
run on a laptop, facilitating its use.Comment: 36 pages, 6 figure