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Fig. 1 | Microbiome

Fig. 1

From: Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome

Fig. 1

Topological data analysis (TDA) reveals altered metagenomic profiles in ME/CFS. Metagenomic data including bacterial composition predicted bacterial metabolic pathways, plasma immune profiles and symptom severity scores were analyzed by using TDA (AYASDI software) to define multidimensional subgroups. TDA of variance-normalized Euclidean distance metric with four lenses [neighborhood lenses (NL1 and NL2), ME/CFS, and IBS diagnosis] revealed that ME/CFS samples formed distinct networks separately from controls. The controls grouped more tightly than the ME/CFS patients. IBS co-morbidity was identified as the strongest driving factor in the separation of metagenomic and immune profile of ME/CFS individuals. Dots that are not connected to the networks represent outliers. Metagenomic data including bacterial composition and inferred metabolic pathways, plasma immune profiles, and health symptom severity scores were integrated for topological data analysis (TDA) using the AYASDI platform (Ayasdi, Menlo Park, California). AYASDI represents high-dimensional, complex biological data sets as a structured 3-dimensional network [56]. Each node in the network comprises one or more subject(s) who share variables in multiple dimensions. Lines connect network nodes that contain shared data points. Unlike traditional network models where a single sample makes a single node, the size of a node in the topological network was proportional to the number of variables with a similar profile. We built a network comprised of 100 samples and 1358 variables (574 variables representing bacterial relative abundance at different taxonomic levels, 61 variables reflecting levels of each immune molecule in the assay, 586 variables representing metabolic pathways, 80 variables representing different ME/CFS fatigue, and other symptom score/health questionnaire items and information on co-morbidities, and demographic variables). All variables were weighted equally. Variance-normalized Euclidean distance method was used as the distance metric; a range of filter lenses (neighborhood lens 1 and 2, ME/CFS, and IBS diagnosis) was used to identify networks

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