Fig. 2
From: Machine learning for precision diagnostics of autoimmunity
Analysis of integrated clinical, laboratory and omics data. (A) Summary statistics of clinical data showed the distribution of clinical data types and identifies non-informative data objects for reduction of data. (B) PCA of cytokine concentrations in AID and non-AID patients. Laboratory data differentiated autoimmune and non-autoimmune patients. However, cytokine concentrations overlapped when subdividing AID into disease types. (C) Immunomics germline gene analysis revealed high frequency of certain combinations of V and J genes across cohorts, where red indicates a high frequency and light blue a low frequency. (D) Top panel: The cumulative degree frequency (CDF) distributions of CDR3 (a.a.) similarities in B-cell repertoires of representative samples of AID patients showed a mixed power-law (orange) and Poisson (gray) distribution in SLE. Bottom panel: power-law and exponential (red) degree distribution in RA. (E) Complexity of genomics data for diagnosis was largely reduced by applying preprocessing additional filtering procedures (see Methods). (F) Concentration of altered metabolites in AID comparing HC (green bar), and arthritis cohorts (red and blue bars). Dark blue indicates a high concentration of metabolites and light blue indicates a low concentration of metabolites. The clustering resulted in distinctive clusters. HC cohort cluster was clearly separated from the remaining cohorts.
