ArticleFrontiers in genetics2016
ROMA: Representation and Quantification of Module Activity from Target Expression Data.
Article in Frontiers in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Severe inflammation and lineage skewing are associated with poor engraftment of engineered hematopoietic stem cells in patients with sickle cell disease.Nature communications · 2025Trial
- Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy.Cell reports. Medicine · 2023Trial
- Pathway and Kinase Analysis in Proteomics and Phosphoproteomics with rROMA.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Comparative transcriptomics in serial organs uncovers early and pan-organ developmental changes associated with organ-specific morphological adaptation.Nature communications · 2025Article
- Analytical approach of synchronous and asynchronous update schemes applied to solving biological Boolean networks.PloS one · 2025Article
- From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis.BMC genomics · 2024Article
- AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction.Communications biology · 2024Article
- Representation and quantification of module activity from omics data with rROMA.NPJ systems biology and applications · 2024Article
- Global proteomic identifies multiple cancer-related signaling pathways altered by a gut pathobiont associated with colorectal cancer.Scientific reports · 2023Article
- DNA hypermethylation driven by DNMT1 and DNMT3A favors tumor immune escape contributing to the aggressiveness of adrenocortical carcinoma.Clinical epigenetics · 2023Article
- Multi-modal quantification of pathway activity with MAYA.Nature communications · 2023Article
- Review
- Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps.NPJ systems biology and applications · 2022Article
- Article
- Exploring Pathway-Based Group Lasso for Cancer Survival Analysis: A Special Case of Multi-Task Learning.Frontiers in genetics · 2021Article
- DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data.GigaScience · 2020Article
- Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases.Cancers · 2020Article
- Interpreting pathways to discover cancer driver genes with Moonlight.Nature communications · 2020Article
- Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data.Computational and structural biotechnology journal · 2020Article
- Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In many analyses of high-throughput data in systems biology, there is a need to quantify the activity of a set of genes in individual samples. A typical example is the case where it is necessary to estimate the activity of a transcription factor (which is often not directly measurable) from the expression of its target genes. We present here ROMA (Representation and quantification Of Module Activities) Java software, designed for fast and robust computation of the activity of gene sets (or modules) with coordinated expression. ROMA activity quantification is based on the simplest uni-factor linear model of gene regulation that approximates the expression data of a gene set by its first principal component. The proposed algorithm implements novel functionalities: it provides several method modifications for principal components computation, including weighted, robust and centered methods; it distinguishes overdispersed modules (based on the variance explained by the first principal component) and coordinated modules (based on the significance of the spectral gap); finally, it computes statistical significance of the estimated module overdispersion or coordination. ROMA can be applied in many contexts, from estimating differential activities of transcriptional factors to finding overdispersed pathways in single-cell transcriptomics data. We describe here the principles of ROMA providing several practical examples of its use. ROMA source code is available at https://github.com/sysbio-curie/Roma.
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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.