ArticleGenome biology2024
MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Single-nucleus and machine-learning integration identifies HLA-DRA and FTL as immune-metabolic axes and traditional Chinese medicine-targetable hubs in calcific aortic valve disease.Journal of computer-aided molecular design · 2026Article
- Expression Atlas in 2026: enabling FAIR and open expression data through community collaboration and integration.Nucleic acids research · 2026Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem.Nature methods · 2025Article
- CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology.International journal of molecular sciences · 2025Review
- Article
- MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data.Genome biology · 2024Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.
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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.