Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics

• URL: http://arxiv.org/abs/2601.00277v1
• Date: Thu, 01 Jan 2026 09:28:56 GMT
• Title: Benchmarking Preprocessing and Integration Methods in Single-Cell Genomics
• Authors: Ali Anaissi, Seid Miad Zandavi, Weidong Huang, Junaid Akram, Basem Suleiman, Ali Braytee, Jie Hua,
• Abstract summary: This study examines a general pipeline for single-cell data analysis, which includes normalization, data integration, and dimensionality reduction.<n>We evaluate six datasets across diverse modalities, tissues, and organisms using three metrics: Silhouette Coefficient Score, Adjusted Rand Index, and Calinski-Harabasz Index.<n>Results show that Seurat and Harmony excel in data integration, with Harmony being more time-efficient, especially for large datasets.
• Score: 2.410981712001601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration [53.683726781791385]
  We introduce a scalable and flexible generative framework called single-cell Multi-omics Regularized Disentangled Representations (scMRDR) for unpaired multi-omics integration.<n>Our method achieves excellent performance on benchmark datasets in terms of batch correction, modality alignment, and biological signal preservation.
  arXiv  Detail & Related papers  (2025-10-28T21:28:39Z)
• scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis [23.973638982075016]
  We present scUnified, an AI-ready standardized resource for single-cell RNA sequencing data.<n> scUnified consolidates 13 high-quality datasets spanning two species and nine tissue types.
  arXiv  Detail & Related papers  (2025-09-30T07:23:01Z)
• CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data [10.429856767305687]
  We propose a novel probabilistic learning framework that explicitly incorporates conditional independence relationships between multi-modal data. We demonstrate the versatility of our framework across various applications pertinent to single-cell multi-omics data integration.
  arXiv  Detail & Related papers  (2024-05-28T23:44:09Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters [64.31109141089598]
  We introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data. scUNC seamlessly integrates information from different views without the need for a predefined number of clusters. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-28T08:34:58Z)
• Regression-Based Analysis of Multimodal Single-Cell Data Integration Strategies [0.0]
  Multimodal single-cell technologies enable the simultaneous collection of diverse data types from individual cells. This study highlights the exceptional performance of Echo State Networks, boasting a remarkable correlation score of 0.94. These findings hold promise for advancing comprehension of cellular differentiation and function, leveraging the potential of Machine Learning.
  arXiv  Detail & Related papers  (2023-11-21T16:31:27Z)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Is your data alignable? Principled and interpretable alignability testing and integration of single-cell data [24.457344926393397]
  Single-cell data integration can provide a comprehensive molecular view of cells. Existing methods suffer from several fundamental limitations. We present a spectral manifold alignment and inference framework.
  arXiv  Detail & Related papers  (2023-08-03T16:04:14Z)
• Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics [44.97217246897902]
  We address the challenge of using energy-based models to produce high-quality, label-specific data in structured datasets. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing. We use a novel training algorithm that exploits non-equilibrium effects.
  arXiv  Detail & Related papers  (2023-07-13T15:08:44Z)
• Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration [0.0]
  We propose a novel framework to align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Compared with the other state-of-the-art methods, our method performs better in both simulated and real single-cell data.
  arXiv  Detail & Related papers  (2021-12-05T13:00:58Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.