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Updated: Jul 19, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell

Shaoheng Liang^1,2,3, Jinzhuang Dou¹, Ramiz Iqbal¹

¹Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center.

Research Square

|August 7, 2023

Summary

Batch-Corrected Distance (BCD) improves single-cell gene expression analysis by addressing batch effects. This new metric enhances clustering and visualization accuracy for biological datasets.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell gene expression data analysis relies heavily on clustering and visualization techniques.
Traditional methods often use Euclidean distance, which is suboptimal for complex biological data.
Batch effects, arising from variations in sample collection and processing, introduce noise and obscure true biological signals.

Conclusions:

Batch-Corrected Distance (BCD) offers a robust solution for handling batch effects in single-cell gene expression analysis.
BCD enables more reliable clustering and visualization, leading to enhanced biological insights.
The metric's direct integration capability with existing tools facilitates broader adoption and scientific discovery.