Deep Phenotyping of the Lipidomic Response in COVID and non-COVID Sepsis

This is the support site for the paper Deep Phenotyping of the Lipidomic Response in COVID and non-COVID Sepsis. The primary multi-omics data set presented and examined in the study aggregates, among others, proteomics, lipidomics, flow cytometry, and mass cytometry data. Several approaches were applied in the analysis of the measured variables, including projecting their correlation structure in a 2D plane using UMAP embedding algorithm. The two panels below facilitate visualization and exploration of the correlation structure of the data, for the purposes of testing hypotheses and generating new ones:

• Correlation structure of COVID-19 panel¹
• Differential correlation structure of COVID-19 vs. non-COVID sepsis panel²

Please allow 30-60 seconds for the panels to load. Note that:

• The samples used were collected at hospital enrollment day.
• Differential correlation corresponds to the change of correlation of a pair of variables in control vs. condition cohorts, e.g., 12-HETE, a lipoxygenase product of arachidonic acid, is correlated with C-C motif chemokine ligand 5 in our non-COVID sepsis cohort, but not in COVID-19 cohort, and this represents change (decrease or loss in this case) of correlation:
  

  Please find more information here³ or here⁴.

¹Corresponds to Figure 7 in the paper
²Corresponds to Supplementary Figure 15 in the paper
³Leann Myers, Maria J. Sirois. (2014). Spearman correlation coefficients, differences between. Wiley Statistics Reference Online.
⁴Zimin Li, Yuxi Zhang, et al. (2018). Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females. PLOS One.