Comprehensive Reanalysis of Genomic Storm (Transcriptomic) Data Integrating Clinical Varibles and Utilizing New and Old Approaches

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.