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Updated: Jul 11, 2025

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Automated single-cell omics end-to-end framework with data-driven batch inference.

Yuan Wang^1,2,3, William Thistlethwaite^2,3, Alicja Tadych²

¹Department of Computer Science, Princeton University, Princeton, NJ, USA.

Biorxiv : the Preprint Server for Biology

|November 14, 2023

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This summary is machine-generated.

SPEEDI automates single-cell multi-omics analysis, integrating diverse datasets for improved reproducibility. This end-to-end pipeline enhances biological insight discovery from complex single-cell data.

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell multi-omics analysis generates complex, heterogeneous datasets.
Reproducibility in single-cell data analysis remains a significant challenge.
Manual parameter selection hinders efficient data integration and cell type labeling.

Purpose of the Study:

To present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated framework for single-cell multi-omics data analysis.
To improve the reproducibility and accessibility of single-cell data integration and cell type labeling.
To enable data-driven batch inference and downstream analyses without user input.

Main Methods:

Developed SPEEDI, an end-to-end automated framework for batch inference, data integration, and cell type labeling.

Keywords:

Single-cell genomics batch identification cell type mapping information theory integration scATAC-seq scRNA-seq

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Implemented a data-driven batch inference method compatible with existing integration and cell-typing tools.

Automated pre-processing, sample integration, and cell type mapping, eliminating manual parameter selection.

Main Results:

SPEEDI transforms heterogeneous single-cell data matrices into uniformly annotated and integrated datasets.
The framework successfully performs automated pre-processing, integration, and cell type mapping.
Downstream analyses of differential signals and gene functional modules are supported.

Conclusions:

SPEEDI significantly enhances reproducibility in single-cell multi-omics analysis.
The automated, data-driven approach lowers the barrier for biological insight extraction.
SPEEDI provides a valuable tool for researchers working with complex single-cell datasets.