Salīdzināt metodes

	Daudzomu mikrobioma daudzveidības analīze	Analīze tīklos balstītai mikrobiomu daudzveidībai
Nozare	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2010s–present	2012
Autors	Developed collectively; key frameworks by Le Cao et al. (mixOmics, 2017) and Argelaguet et al. (MOFA, 2018)	Faust, Raes, Friedman, Alm and colleagues
Tips	Integrative computational pipeline	Integrative bioinformatics pipeline
Pirmavots	Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for 'omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752.	Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687.
Citi nosaukumi	multi-omics microbiome profiling, integrated microbiome omics, multi-modal microbiome analysis, microbiome multi-omics integration	microbial co-occurrence network analysis, microbiome network ecology, ecological network-based diversity, NBMDA
Saistītās	5	5
Kopsavilkums	Multi-omics microbiome diversity analysis integrates two or more omic data layers — such as metagenomics, metatranscriptomics, metabolomics, and metaproteomics — to characterise both the composition and functional activity of microbial communities. By linking taxonomic diversity metrics with molecular phenotype data, the approach uncovers how community structure translates into ecological and host-relevant functions that no single omic layer can reveal alone.	Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associations as edges in a network, enabling identification of keystone taxa, community modules, and ecological interaction patterns that simple diversity indices cannot detect.