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Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell
Shaoheng Liang1,2, Jinzhuang Dou1, Ramiz Iqbal1
1Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center.
Biorxiv : the Preprint Server for Biology
|October 14, 2020
Summary
Batch-Corrected Distance (BCD) improves single-cell gene expression analysis by addressing batch effects. This new metric enhances clustering and visualization accuracy for longitudinal datasets.
Area of Science:
- Computational Biology
- Genomics
- Data Science
Background:
- Single-cell gene expression analysis relies on clustering and visualization.
- Euclidean distance is suboptimal for handling batch effects in biological data.
- Batch effects introduce variability, obscuring true cell identities.
Approach:
- Introduced Batch-Corrected Distance (BCD), a novel metric.
- BCD utilizes temporal/spatial locality to control for batch effects.
- Validated BCD on simulated and real-world datasets (mouse retina, lung).
Key Points:
- BCD improves clustering accuracy and visualization quality.
- Demonstrated utility in understanding Coronavirus Disease 2019 (COVID-19) progression.
- Outperforms state-of-the-art batch correction methods on longitudinal data.
Conclusions:
- BCD offers a robust solution for batch effect correction in single-cell data.
- The metric can be integrated with existing clustering and visualization tools.
- Enables more accurate scientific discoveries from complex biological datasets.
