New Algorithm Improves Handling of Missing Data in Single-Cell Sequencing
Researchers have developed CROT (Cluster-Regularized Optimal Transport), an algorithm designed to impute missing data in single-cell sequencing datasets, particularly when data is absent in large contiguous patches rather than scattered randomly. Existing imputation methods typically assume data is missing uniformly and struggle with structured, large-scale gaps common in biological and clinical datasets. CROT addresses this gap by combining optimal transport with cluster regularization, achieving higher accuracy and faster runtimes on high-dimensional tabular data.
Single-cell sequencing technologies generate high-dimensional biological data, but missing values—especially in large structured patches—pose serious obstacles to downstream analysis. Current imputation methods generally assume uniform or random missingness and perform poorly when entire blocks of data are absent. CROT (Cluster-Regularized Optimal Transport) introduces a novel approach that leverages optimal transport theory, regularized by cluster structure, to better capture the underlying heterogeneity of the data even under significant missingness. The authors report that CROT achieves superior imputation accuracy compared to existing methods while also reducing computational runtime, making it scalable to large datasets. The work targets both biological research contexts—such as genomics—and clinical data analysis where structured data absence is common. The paper has been accepted to ACM-BCB 2026, and the implementation is publicly available on GitHub.
What's missing
The abstract does not specify which benchmark datasets or competing methods were used for comparison, nor does it quantify the magnitude of accuracy improvements or runtime reductions. It is also unclear how CROT performs when missingness patterns deviate from the patch-based assumption it is designed for, or whether it has been validated on clinical datasets beyond genomics. The study's own limitations, such as sensitivity to cluster number selection and behavior under extreme missingness rates, are not discussed in the available abstract.
What different sources said
- arXiv cs.LGCenter
Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport
Related
Gut Bacteria Enzyme Found to Break Down Heat-Processed Food Compounds, Producing Novel Biogenic Amines
Researchers have discovered that an enzyme in common gut bacteria can degrade N-epsilon-carboxymethyllysine (CML), a compound formed during thermal food processing, producing previously unknown biogenic amines. The enzyme, ornithine decarboxylase SpeC from enterobacteria, acts on CML and related modified lysine derivatives through a low-level 'underground' catalytic activity. This finding suggests a previously unrecognized communication axis between thermally processed dietary compounds and gut microbial physiology, with potential implications for host health.
Full-Length Gene Sequencing Reveals Two Distinct Bacterial Communities in Black-Legged Ticks Expanding Into Canada
Researchers used Oxford Nanopore full-length 16S rRNA gene sequencing to characterize the microbiome of Ixodes scapularis black-legged ticks collected in Nova Scotia, Canada, distinguishing between tick-adapted bacteria and environmentally acquired bacteria. The study comes as I. scapularis — the primary vector of Lyme disease — is rapidly expanding northward into Canada due to climate change. The findings suggest that environmentally derived bacteria in tick microbiomes are not mere contamination, which has implications for how tick microbiome data is collected and interpreted across surveillance studies.
Study Identifies Metabolic Link Between Cell Envelope Stress and Biofilm Formation in Bacteria
Researchers have discovered that the metabolite acetyl-CoA directly inhibits enzymes that degrade the bacterial signaling molecule c-di-GMP, connecting cell envelope biosynthesis stress to biofilm formation in Pseudomonas aeruginosa. The study found that sub-inhibitory concentrations of antibiotics targeting early peptidoglycan biosynthesis — but not other antibiotic classes — elevate c-di-GMP levels by reducing phosphodiesterase activity, with acetyl-CoA competing for the enzyme active site. Because the relevant enzyme domain is broadly conserved across bacterial species, this checkpoint mechanism may be widespread and could have implications for understanding antibiotic-induced biofilm responses.