ArticleNature communications2023
Multi-modal quantification of pathway activity with MAYA.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 10 citations in OpenAlex.
- GOLDEN fusion: a graph-oriented learning with domain-embedding network fusion for generating super gene sets in functional genomics.Briefings in bioinformatics · 2026Article
- Astrocytic Neuroligin-3 influences gene expression and social behavior, but is dispensable for synapse number.Molecular psychiatry · 2025Article
- Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA.Communications biology · 2024Article
- From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis.BMC genomics · 2024Article
- Representation and quantification of module activity from omics data with rROMA.NPJ systems biology and applications · 2024Article
- Multi-modal quantification of pathway activity with MAYA.Nature communications · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.
Identifiers
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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.