ArticleFrontiers in microbiology2018
Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.
Article in Frontiers in microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Microbial Diversity and Composition Is Associated with Patient-Reported Toxicity during Chemoradiation Therapy for Cervical Cancer.International journal of radiation oncology, biology, physics · 2020Trial
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- Detoxifying and depolymerizing microorganisms reveal intertwined guild collaborations in the gut microbiome of a generalist macro-algivorous fish.bioRxiv : the preprint server for biology · 2025Article
- Immune environment and antigen specificity of the T cell receptor repertoire of malignant ascites in ovarian cancer.PloS one · 2023Article
- Fusobacterium is enriched in oral cancer and promotes induction of programmed death-ligand 1 (PD-L1).Neoplasia (New York, N.Y.) · 2022Article
- Bacterial low-abundant taxa are key determinants of a healthy airway metagenome in the early years of human life.Computational and structural biotechnology journal · 2022Article
- Compositional zero-inflated network estimation for microbiome data.BMC bioinformatics · 2020Article
- Effect of Antibiotics on Gut and Vaginal Microbiomes Associated with Cervical Cancer Development in Mice.Cancer prevention research (Philadelphia, Pa.) · 2020Article
- Understanding the Mechanisms Behind the Response to Environmental Perturbation in Microbial Mats: A Metagenomic-Network Based Approach.Frontiers in microbiology · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the
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