ArticlePLoS computational biology2024
Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- PI3Kδ inhibition alters CD8 T cell differentiation and reprograms the tumor microenvironment following adoptive immunotherapy.Journal of immunology (Baltimore, Md. : 1950) · 2026Article
- GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Gene set optimization for single cell transcriptomics.Computational intelligence methods for bioinformatics and biostatistics : ... international meeting, CIBB ... : revised selected papers. CIBB (Meeting) · 2025Article
- Revealing tissue architecture through the hypercomplex Fourier analysis of spatial transcriptomics data.Bioinformatics advances · 2025Article
- Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
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Abstract
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.
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