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Related Experiment Video

Updated: Jun 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on

H Robert Frost¹

¹Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.

Plos Computational Biology

|April 29, 2024

Summary

This summary is machine-generated.

We introduce the Reconstruction Set Test (RESET), a novel method for single sample gene set testing. RESET efficiently identifies gene set importance and detects differential patterns in single-cell RNA sequencing data with superior performance.

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Last Updated: Jun 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single sample gene set testing is crucial for analyzing high-dimensional biological data.
Existing methods may lack efficiency or comprehensive pattern detection capabilities.

Purpose of the Study:

To introduce a novel, analytically unique single sample gene set testing method named Reconstruction Set Test (RESET).
To quantify gene set importance by assessing the reconstructive capacity of gene sets for all measured genes.

Main Methods:

RESET employs a computationally efficient randomized reduced rank reconstruction algorithm.
The method is implemented in the RESET R package, available on CRAN.
It effectively detects differential abundance and differential correlation patterns.

Main Results:

RESET demonstrates superior performance in analyzing real and simulated single-cell RNA sequencing (scRNA-seq) data.
The method achieves higher accuracy compared to other single sample approaches.
RESET offers a lower computational cost, enhancing efficiency.

Conclusions:

RESET is a powerful and efficient tool for single sample gene set testing in scRNA-seq analysis.
The method provides a novel approach to quantifying gene set importance.
RESET outperforms existing methods in both performance and computational cost.