SPEEDI: A fully automated computational framework for inferring and correcting batch effects in single-cell omics data

Background

Single-cell omics technologies, such as single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq), have revolutionized the study of cellular heterogeneity by enabling high-resolution profiling of gene expression and chromatin accessibility at the level of individual cells. These approaches are now widely used to investigate complex tissues, developmental processes, and disease states. However, as the field has matured, researchers increasingly seek to integrate data from multiple samples, experiments, or even modalities to achieve broader biological insights. This integration is essential for comparing conditions, increasing statistical power, and building comprehensive cell atlases. Yet, the process is complicated by technical variability, known as batch effects, which can obscure true biological signals and hinder meaningful interpretation of the data.

Current approaches to batch correction in single-cell omics data typically rely on the availability of accurate batch labels and require manual parameter tuning by experienced analysts. In practice, batch labels are often incomplete, missing, or inaccurately recorded, especially in large collaborative projects or legacy datasets. Furthermore, existing methods may not adequately detect or correct for subtle or unknown sources of technical variation, leading to either overcorrection (removal of true biological differences) or undercorrection (retention of technical artifacts). Manual intervention and expert knowledge are frequently needed to optimize these methods, which introduces subjectivity and limits reproducibility. As a result, there is a significant need for robust, automated solutions that can infer and correct batch effects without prior knowledge of batch structure, ensuring reliable integration and downstream analysis of heterogeneous single-cell datasets.

Technology

This technology is an automated, data-driven system for batch effect inference and correction in single-cell omics data, designed to streamline the integration and analysis of heterogeneous datasets such as scRNA-seq, scATAC-seq, and multi-modal single-cell profiles. The system begins by acquiring and quality-controlling measurement values from multiple samples, filtering out low-quality or outlier cells based on metrics like UMI counts and mitochondrial content. It then normalizes and reduces the dimensionality of the data using methods such as PCA or LSI, followed by clustering cells into putative types. Batch effects are inferred by calculating Local Inverse Simpson Index (LISI) scores for each cell’s cluster and sample identity, which quantify the diversity of samples within each cluster. Statistically significant gaps in these scores, identified using Dixon’s Q-test, allow the system to group samples into distinct batches. These inferred batch assignments are then used as covariates in standard batch correction methods, enabling robust data integration, cell-type annotation, and downstream analyses—all without requiring prior batch labels or manual parameter tuning.

What differentiates this technology is its fully automated, end-to-end approach that eliminates the need for manual batch annotation or expert intervention, a common limitation in existing single-cell data integration pipelines. By leveraging data-driven batch inference, the system can identify and correct for unknown or incomplete batch effects, ensuring that biological signals are preserved while technical artifacts are minimized. Its modular workflow integrates quality control, normalization, clustering, batch inference, and correction into a single pipeline, and is validated across multiple real-world datasets and modalities. The inclusion of a web-based interface further democratizes access, allowing researchers without bioinformatics expertise to perform sophisticated single-cell analyses. Benchmarking results demonstrate superior integration quality, improved cell-type annotation, and greater concordance with gold-standard references compared to traditional methods, making this solution highly robust, reproducible, and broadly applicable across diverse experimental conditions.

Benefits

• Fully automated batch effect inference and correction without requiring prior batch labels or manual tuning
• Improves integration and harmonization of heterogeneous single-cell omics data across multiple modalities (scRNA-seq, scATAC-seq, multiome)
• Enhances data quality through rigorous quality control and normalization steps tailored per sample
• Enables accurate cell-type clustering and annotation by removing technical artifacts while preserving biological variance
• Facilitates robust and reproducible downstream analyses such as differential expression and functional module discovery
• Accessible to non-expert users via a web-based interface, democratizing single-cell data analysis
• Validated on diverse real-world datasets demonstrating superior performance compared to existing methods using supplied batch labels
• Scalable to datasets with thousands of cells and multiple samples, supporting complex experimental designs

Applications

• Automated single-cell data integration
• Batch effect correction for omics
• Web-based single-cell analysis platform
• Cell-type annotation across multi-modal data
• Quality control for single-cell experiments

Additional info

This automated system processes single-cell omics data by performing quality control, normalization, and dimension reduction. It clusters cells, then infers technical variations (batches) by analyzing Local Inverse Simpson Index scores and applying Dixon’s Q-test to group samples. These data-driven batch assignments are subsequently used for robust data integration, enabling accurate downstream biological analyses across diverse single-cell datasets without requiring prior labels or manual tuning.