Additional file 11 of The fusion of multi-omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders
Creators: name: Han, Yan
name: Zeng, Xinglin
name: Hua, Lin
name: Quan, Xingping
name: Chen, Ying
name: Zhou, Manfei
name: Chuang, Yaochen
name: Li, Yang
name: Wang, Shengpeng
name: Shen, Xu
name: Wei, Lai
name: Yuan, Zhen
name: Zhao, Yonghua
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Neuroscience
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Cancer

description: Additional file 11. Supplementary Results. Supplementary Figure S1. Gut microbiota diversity analysis in normal aging and neurocognitive disorders (NCDs) groups. Alpha diversity analysis were measured by Chao1 index, Shannon index, and Simpson index at the species level. Beta diversity analysis was measured by Bray-Curtis dissimilarity, with P values computed using a two-sided Wilcoxon test. Supplementary Figure S2. Identification and characterization of urinary exosomes. (A) Morphological characteristics of exosomes observed by transmission electron microscopy (TEM). (B) The size of the exosomes was determined using nanoparticle tracking analysis (NTA). (C) Marker protein expression of exosomes detected by Western blot. Supplementary Figure S3. The contrast ROC curves of different omics discriminated NA and NCDs via leave one out cross validation. The contrast ROC of single omics (Panel A). The contrast ROC of double omics (Panel B). The ROC of multi-omics (Panel C). The EEG related ROC (Panel D). The gut microbiota related ROC (Panel E). The metabolomic related ROC (Panel F). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S4. The contrast PR-ROC curves of different omics discriminated NA and NCDs via leave one out cross validation. The contrast ROC of single omics (Panel A). The contrast ROC of double omics (Panel B). The ROC of multi-omics (Panel C). The EEG related ROC (Panel D). The gut microbiota related ROC (Panel E). The metabolomic related ROC (Panel F). Meta, metabolomic; GM, gut microbiota. Supplementary Table S1. The machine learning performance of different omics via 10-fold cross-validation. The values were presented as mean (standard deviation). Supplementary Figure S5. The ROC curves of different omics via 10-fold cross-validation. The single omics discriminated the normal aging (NA) and neurocognitive disorders (NCDs) group in blue (Panel A-C). The double omics discriminated the NA and NCDs group in green (Panel D-F). The classification of multi-omics (Panel G). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S6. The contrast ROC curves of different omics discriminated NA and NCDs via 10-fold cross-validation. The contrast ROC of single omics (Panel A). The contrast ROC of double omics (Panel B). The ROC of multi-omics (Panel C). The EEG related ROC (Panel D). The gut microbiota related ROC (Panel E). The metabolomic related ROC (Panel F). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S7. The PR-ROC of different omics via 10-fold cross-validation. The single omics discriminated the NA and NCDs group in blue (Panel A-C). The double omics discriminated the NA and NCDs group in green (Panel D-F). The classification of multi-omics (Panel G). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S8. The contrast PR-ROC of different omics discriminated NA and NCDs via 10-fold cross-validation. The contrast PR-ROC of single omics (Panel A). The contrast PR-ROC of double omics (Panel B). The PR-ROC of multi-omics (Panel C). The EEG related PR-ROC (Panel D). The gut microbiota related PR-ROC (Panel E). The metabolomic related PR-ROC (Panel F). Meta, metabolomic; GM, gut microbiota. Supplementary Table S2. The machine learning performance of different omics via 5-fold cross-validation. Supplementary Figure S9. The ROC of different omics via 5-fold cross-validation. The single omics discriminated the NA and NCDs group in blue (Panel A-C). The double omics discriminated the NA and NCDs group in green (Panel D-F). The classification of multi-omics (Panel G). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S10. The contrast ROC of different omics discriminated NA and NCDs via 5-fold cross-validation. The contrast ROC of single omics (Panel A). The contrast ROC of double omics (Panel B). The ROC of multi-omics (Panel C). The EEG related ROC (Panel D). The gut microbiota related ROC (Panel E). The metabolomic related ROC (Panel F). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S11. The PR-ROC of different omics via 5-fold cross-validation. The single omics discriminated the NA and NCDs group in blue (Panel A-C). The double omics discriminated the NA and NCDs group in green (Panel D-F). The classification of multi-omics (Panel G). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S12. The contrast PR-ROC of different omics discriminated NA and NCDs via 5-fold cross-validation. The contrast PR-ROC of single omics (Panel A). The contrast PR-ROC of double omics (Panel B). The PR-ROC of multi-omics (Panel C). The EEG related PR-ROC (Panel D). The gut microbiota related PR-ROC (Panel E). The metabolomic related PR-ROC (Panel F). Meta, metabolomic; GM, gut microbiota. Supplementary Figure S13. The selected features. Panel A) The left is bar plot of selected features and their contributions of EEG, metabolomic and gut microbiota model (weight in SVM) in descending order. The right is a bee swarm plot in which each point represents a participant (n = 41). Panel B) Feature category contribution calculated by summing the weight in EEG, metabolomic and gut microbiota model. Panel C and panel D represented the gut microbiota and metabolomics respectively. In bar plot, blue bar represented higher value of the feature for association with NCDs while the red bar presented higher association with normal aging. In bee swarm plot, color indicates the value of the feature, with red higher and blue lower. Negative contribution indicates the feature attribution for prediction of NCDs while Positive contribution indicates the feature attribution of normal aging. Meta, metabolomic; GM, gut microbiota.