ArticleNucleic acids research2020
Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- A skin-kidney neutrophil axis links ultraviolet exposure to renal angiogenesis and tissue remodeling in healthy and lupus kidneys.Journal of immunology (Baltimore, Md. : 1950) · 2026Article
- Functional Depth Biomarkers Distinguish Lung Squamous Cell Carcinoma from Lung Adenocarcinoma.Research square · 2026Article
- PI3Kδ inhibition alters CD8 T cell differentiation and reprograms the tumor microenvironment following adoptive immunotherapy.Journal of immunology (Baltimore, Md. : 1950) · 2026Article
- DeCAF defines clinical fibroblast subtypes and multidimensional tumor-stroma crosstalk shaping prognosis and immunotherapy response.Cell reports. Medicine · 2026Article
- Multimodal analyses of early, untreated systemic sclerosis skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling.JCI insight · 2026Article
- Integrating polygenic signals and single-cell multiomics identifies cell-type-specific regulomes critical for immune- and aging-related diseases.Nature aging · 2026Article
- Inferring pathway activity from single-cell and spatial transcriptomics data with PaaSc.PLoS computational biology · 2025Article
- Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP.Genome research · 2025Article
- Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE).PLoS computational biology · 2025Article
- Integrating microbial GWAS and single-cell transcriptomics reveals associations between host cell populations and the gut microbiome.Nature microbiology · 2025Article
- SC-VAR: a computational tool for interpreting polygenic disease risks using single-cell epigenomic data.Briefings in bioinformatics · 2025Article
- Revealing tissue architecture through the hypercomplex Fourier analysis of spatial transcriptomics data.Bioinformatics advances · 2025Article
- Gene set optimization for single cell transcriptomics.Computational intelligence methods for bioinformatics and biostatistics : ... international meeting, CIBB ... : revised selected papers. CIBB (Meeting) · 2025Article
- Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA.Communications biology · 2024Article
- Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Accurate estimation of pathway activity in single cells for clustering and differential analysis.Genome research · 2024Article
- Article
- Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.Genomics, proteomics & bioinformatics · 2024Article
- CAraCAl: CAMML with the integration of chromatin accessibility.BMC bioinformatics · 2024Article
- irGSEA: the integration of single-cell rank-based gene set enrichment analysis.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Statistical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis, which can mitigate sparsity and noise, and improve interpretation and power, by aggregating expression data to the pathway level. Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific techniques has been limited. Importantly, no existing methods support cell-level gene set inference. To address this challenge, we developed a new gene set testing method, Variance-adjusted Mahalanobis (VAM), that integrates with the Seurat framework and can accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both data visualization and statistical enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, both population and cell-level inference is supported. As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.