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Updated: Dec 12, 2025
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Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization
Published on: October 29, 2019
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Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell
Shuyi Zhang1,2, Jacob R Leistico1,2, Christopher Cook3
1Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61820, USA.
Physical Review. E
|August 16, 2020
Summary
New computational tools using Riemannian geometry help analyze single-cell CITE-seq data. This approach effectively removes batch effects and distinguishes true signals from noise in cell surface protein detection.
Area of Science:
- Single-cell multi-omics analysis
- Computational biology
- Biostatistics
Background:
- Next-generation sequencing enables simultaneous transcriptome and cell surface protein detection in single cells.
- Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) generates complex count data.
- Existing analytical tools for CITE-seq data are limited, posing computational challenges.
Purpose of the Study:
- To develop novel mathematical and statistical frameworks for analyzing CITE-seq data.
- To address the challenge of batch effect removal in high-throughput single-cell protein detection.
- To establish a robust method for distinguishing biological signals from background noise in CITE-seq experiments.
Main Methods:
- Application of Riemannian geometry concepts for batch effect correction.
- Development of a statistical framework for signal-to-noise ratio enhancement.
- Validation using two independent CITE-seq datasets from mouse and human samples.
Main Results:
- Successful removal of batch effects in CITE-seq data using Riemannian geometry.
- Effective differentiation of positive signals from background noise.
- Demonstrated robustness and applicability on diverse biological datasets.
Conclusions:
- The proposed Riemannian geometry-based and statistical methods provide powerful tools for CITE-seq data analysis.
- These advancements enhance the understanding of cellular heterogeneity in various biological contexts.
- The developed framework offers a rigorous approach to interpreting single-cell surface protein data.
