Integrating Modalities Benchmarking Single-Cell Genomics Methods

📝 Original Paper Info

- Title: Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics
- ArXiv ID: 2601.00277
- Date: 2026-01-01
- Authors: Ali Anaissi, Seid Miad Zandavi, Weidong Huang, Junaid Akram, Basem Suleiman, Ali Braytee, Jie Hua

📝 Abstract

Single-cell data analysis has the potential to revolutionize personalized medicine by characterizing disease-associated molecular changes at the single-cell level. Advanced single-cell multimodal assays can now simultaneously measure various molecules (e.g., DNA, RNA, Protein) across hundreds of thousands of individual cells, providing a comprehensive molecular readout. A significant analytical challenge is integrating single-cell measurements across different modalities. Various methods have been developed to address this challenge, but there has been no systematic evaluation of these techniques with different preprocessing strategies. This study examines a general pipeline for single-cell data analysis, which includes normalization, data integration, and dimensionality reduction. The performance of different algorithm combinations often depends on the dataset sizes and characteristics. We evaluate six datasets across diverse modalities, tissues, and organisms using three metrics: Silhouette Coefficient Score, Adjusted Rand Index, and Calinski-Harabasz Index. Our experiments involve combinations of seven normalization methods, four dimensional reduction methods, and five integration methods. The results show that Seurat and Harmony excel in data integration, with Harmony being more time-efficient, especially for large datasets. UMAP is the most compatible dimensionality reduction method with the integration techniques, and the choice of normalization method varies depending on the integration method used.

💡 Summary & Analysis

1. **New Approach** - This study offers a more accurate diagnostic system than traditional methods. Imagine it as finding the best convenience store with GPS. 2. **Data Usage Methods** - The team used various datasets to train their models, similar to preparing food using different recipes. 3. **Technical Advancements** - Advances in AI technology have made disease diagnosis more precise, akin to cars becoming faster and safer.

📄 Full Paper Content (ArXiv Source)

📊 논문 시각자료 (Figures)

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