Bachelor Thesis from the year 2014 in the subject Computer Science – Bioinformatics, grade: 165/200 (A+), , language: English, abstract: Aim: I sought to determine trauma-specific transcriptomic signatures for septic sub-cohorts.
Methods: In retrospective large-scale data analysis, I applied (old and new methods), including lagged correlation between transcripts and clinical subtype counts (by integrating over 800 samples from trauma patients).
Results: Focussing on novel pathways and correlation methods we revealed (persistently down-regulated) ribosomal genes and changed time profiles of metabolic enzyme precursors /transcripts. Candidates associated to insulin signalling, including HK3, hinted towards “metabolic syndrome”. Correlation analysis yielded robust results for LCN2 and LTF (r>0.9), but only moderate associations to subtype counts (e.g. top-performing r (Eosinophil, IL5RA)>0.6).
Discussion: Gene Centred Normalisation Reduces Ambiguity and Improves Interpretation.
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