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Updated: May 6, 2026

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

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

Yuan Wang¹, William Thistlethwaite², Alicja Tadych²

¹Department of Computer Science, Princeton University, Princeton, NJ 08540, USA; Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.

|October 4, 2024

Summary

This summary is machine-generated.

We developed SPEEDI, an automated pipeline for single-cell multi-omics analysis. This framework enhances data integration and cell-type labeling, improving reproducibility for complex biological datasets.

Keywords:

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

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Last Updated: May 6, 2026

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

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Published on: May 23, 2018

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Published on: January 10, 2019

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell multi-omics analysis generates complex, heterogeneous datasets.
Reproducibility in single-cell data analysis is a significant challenge.
Current methods often require extensive parameter tuning and manual intervention.

Purpose of the Study:

To present SPEEDI (single-cell pipeline for end-to-end data integration), a fully automated framework.
To improve reproducibility and accessibility of single-cell multi-omics data analysis.
To facilitate batch inference, data integration, and cell-type labeling.

Main Methods:

Developed a data-driven batch inference method.
Implemented a fully automated end-to-end workflow for pre-processing, integration, and cell-type mapping.
Ensured compatibility with existing integration and cell-typing tools.

Main Results:

SPEEDI transforms heterogeneous data matrices into a uniformly annotated and integrated dataset.
The framework automates parameter selection and execution without user input.
Enables downstream analyses, including differential signal analysis and gene functional module identification.

Conclusions:

SPEEDI significantly improves reproducibility in single-cell multi-omics analysis.
The automated nature of SPEEDI lowers the barrier for biological insight extraction.
An interactive web application is available at https://speedi.princeton.edu/.