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.
Inhaltsverzeichnis (Table of Contents)
- THEORY.
- Normalization.
- Comparison of two groups of samples.
- Signal Log Ratio Algorithm.
- Correlation (r).
- Log2-transformation.
- Intensity ratio.
- Hypothesis pair.
- Threshold for p-value.
- Fold change
- Time series.
- Microarray preparation
- Probe preparation, hybridization and imaging.
- Low level information analysis.
- INTRODUCTION.
- SIRS, SEPSIS AND SEPTIC SHOCK.
- Related Background.
- .CEL File Description.
- Gene Expression Omnibus (GEO).
- KEGG.
- MATERIALS & METHODS.
- Data.
- Data Analysis.
- Clustering.
- Enrichment tests.
- Lagged Correlation.
- Additional Information.
- RESULTS.
- Differentially Expressed Genes.
- Clustering.
- Regulation of some important genes.
- HLA-DMB & LCN2.
- Correlation of LCN 2and LTF.
- SLC4A1 & IL5RA.
- Gender Linked Genes.
- Gene Set Enrichment Analysis (GSEA).
- Kegg Mapper.
- Glycolysis Gluconeogenesis.
- Ribosome.
- Toll Like Receptors Signaling Pathway and Heatmap.
- DISCUSSION.
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis aimed to identify trauma-specific transcriptomic signatures for septic sub-cohorts by analyzing a large-scale dataset of trauma patients. The analysis integrated clinical variables and utilized both established and novel methods, including lagged correlation between transcripts and clinical subtype counts.
- Transcriptomic signatures in sepsis.
- Impact of trauma on gene expression.
- Correlation analysis of gene expression and clinical variables.
- Novel pathways and correlation methods.
- Metabolic syndrome and insulin signaling.
Zusammenfassung der Kapitel (Chapter Summaries)
The "THEORY" chapter provides an overview of the theoretical concepts used in the analysis, including normalization methods, correlation analysis, and time series data. "INTRODUCTION" introduces the concepts of SIRS, sepsis, and septic shock, along with relevant background information, data file descriptions, and databases used, like Gene Expression Omnibus (GEO) and KEGG. The "MATERIALS & METHODS" chapter outlines the data used, data analysis techniques, clustering methods, enrichment tests, lagged correlation, and additional information. "RESULTS" presents the findings, including differentially expressed genes, clustering patterns, regulation of specific genes, gender-linked genes, and gene set enrichment analysis. The "DISCUSSION" chapter interprets the results and discusses their implications.
Schlüsselwörter (Keywords)
Sepsis, trauma, transcriptomics, gene expression, correlation analysis, clinical variables, metabolic syndrome, insulin signaling, ribosomal genes, KEGG, Gene Expression Omnibus (GEO), lagged correlation.
- Quote paper
- Deepak Tanwar (Author), 2014, Comprehensive Reanalysis of Genomic Storm (Transcriptomic) Data, Integrating Clinical Varibles and Utilizing New and Old Approaches, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/284986
