ArticlebioRxiv : the preprint server for biology2025
Semi-parametric Empirical Bayes Method for Multiplet Detection in snATAC-seq with Probabilistic Multi-omic Integration.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Updated by
Authors and funding
6 authors.
Funding
Abstract
Multiplets, formed when multiple cells are captured in a droplet, produce hybrid molecular profiles that confound single-cell analyses. Detecting multiplets in single-nucleus ATAC-seq (snATAC-seq) data is particularly challenging due to sparsity and overdispersion of chromatin accessibility measurements. We introduce SEBULA, a semi-parametric empirical Bayes model that yields well-calibrated posterior probabilities for multiplet detection, enabling principled false discovery rate control. SEBULA also integrates probabilistic evidence with complementary signals from other modalities, such as scRNA-seq. Benchmarking on simulations and seven annotated trimodal DOGMA-seq datasets demonstrates SEBULA's superior performance. The open-source software is computationally efficient.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.