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Related Concept Videos

¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the others.
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Semi-parametric empirical bayes method for multiplet detection in snATAC-seq with probabilistic multi-omic

Yuntian Wu1, Haoran Hu2, Wei Chen2,3,4

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.

Plos Computational Biology
|April 29, 2026
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Summary

SEBULA accurately detects multiplets in single-nucleus ATAC-seq data by modeling singlets directly from chromatin accessibility signals. This method improves accuracy and integrates multiomic data for robust multiplet identification.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Multiplets, arising from multiple cells in single-cell sequencing droplets, create distorted molecular profiles.
  • Detecting multiplets in single-nucleus ATAC-seq (snATAC-seq) data is difficult due to sparse and overdispersed chromatin accessibility data.
  • Computational methods integrating multi-feature and multi-modal evidence are needed for accurate multiplet detection.

Purpose of the Study:

  • Introduce SEBULA, a novel semi-parametric empirical Bayes framework for multiplet detection in snATAC-seq data.
  • Develop a method that models the singlet background directly from observed chromatin accessibility signals, avoiding synthetic doublets.
  • Extend SEBULA to integrate complementary evidence from additional features and modalities, such as gene expression.

Main Methods:

  • SEBULA models the singlet background using fragment-level chromatin accessibility signals from snATAC-seq data.
  • The framework generates classification probabilities for direct false discovery rate control.
  • SEBULA integrates evidence from multiple features and modalities, including gene expression profiles.

Main Results:

  • SEBULA demonstrates improved sensitivity and specificity compared to existing snATAC-seq methods across simulations and seven multimodal datasets.
  • The evidence integration framework achieves performance comparable or superior to state-of-the-art multiomic approaches.
  • SEBULA maintains computational efficiency while providing robust multiplet detection.

Conclusions:

  • SEBULA offers a powerful and accurate approach for multiplet detection in snATAC-seq data.
  • The framework's ability to integrate multiomic data enhances multiplet identification accuracy.
  • SEBULA provides a reliable tool for improving downstream analyses in single-cell genomics.