Introduction Results so far Hypothesis about CFS Approach Results Detecting Pathological Pathways of the Chronic Fatigue Syndrome by the Comparison of Networks Frank Emmert-Streib 1 Earl F. Glynn 1 Christopher Seidel 1 Christoph L. Bausch 1 Arcady Mushegian 1 , 2 1 Stowers Institute for Medical Research 2 University of Kansas School of Medicine 7th June 2006 Frank Emmert-Streib Detecting pathological Pathways of the CFS
Introduction Results so far Hypothesis about CFS Approach Results Outline Introduction 1 Properties of CFS Results so far 2 Hypothesis about CFS 3 Pragmatic definitions Approach 4 Quasi-pathway Quasi-pathway Classify patients Classify genes Inferring causality Network Comparison Results 5 Biological processes used in our analysis Frank Emmert-Streib Detecting pathological Pathways of the CFS Network comparison
Introduction Results so far Hypothesis about CFS Properties of CFS Approach Results CFS has no diagnostic clinical signs or laboratory abnormalities CFS is defined by symptoms and disability It is unclear if CFS represents single disease Frank Emmert-Streib Detecting pathological Pathways of the CFS
Introduction Results so far Hypothesis about CFS Properties of CFS Approach Results CFS has no diagnostic clinical signs or laboratory abnormalities CFS is defined by symptoms and disability It is unclear if CFS represents single disease Frank Emmert-Streib Detecting pathological Pathways of the CFS
Introduction Results so far Hypothesis about CFS Properties of CFS Approach Results CFS has no diagnostic clinical signs or laboratory abnormalities CFS is defined by symptoms and disability It is unclear if CFS represents single disease Frank Emmert-Streib Detecting pathological Pathways of the CFS
Introduction Results so far Hypothesis about CFS Approach Results Characterize (define) CFS by clinical data + questionnaire microarray + clinical data = ⇒ (classify patients by clinical data, clustering, differentially expressed genes) heterogeneous illness & fundamental metabolic perturbations W HISTLER et al. 2003 Frank Emmert-Streib Detecting pathological Pathways of the CFS
Introduction Results so far Hypothesis about CFS Pragmatic definitions Approach Results Hypothesis Pathways are important rather than ’genes’ = ⇒ differentially expressed pathways, M. X IONG 2004 Questions How to define pathways? 1 How to identify pathways? 2 How to compare pathways? 3 Frank Emmert-Streib Detecting pathological Pathways of the CFS
Introduction Results so far Hypothesis about CFS Pragmatic definitions Approach Results Definition A pathway (directed graph) is an interconnected group of genes (variables) that regulates a biological process Definition A biological process is (hierarchically) defined by GO (gene ontology) terms Frank Emmert-Streib Detecting pathological Pathways of the CFS
Introduction Results so far Hypothesis about CFS Pragmatic definitions Approach Results Definition A pathway (directed graph) is an interconnected group of genes (variables) that regulates a biological process Definition A biological process is (hierarchically) defined by GO (gene ontology) terms Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Used data Clinical Data (questionnaire + blood) = ⇒ classify patients Gene Expression (peripheral blood mononuclear cells) GO database = ⇒ classify genes = ⇒ reconstruct quasi-pathways (biological subprocesses) Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Why quasi-pathways? Central Dogma of Molecular Biology DNA - CHIP-chip RNA - microarray Protein - proteomics Only partial information is used (available) to reconstruct the network Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Why quasi-pathways? Central Dogma of Molecular Biology DNA - CHIP-chip RNA - microarray Protein - proteomics Only partial information is used (available) to reconstruct the network Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Assumption Patients participating are ’fair’ Result Two groups of patients (classification) non-sick 1 sick (chronic fatigue syndrome) 2 Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Assumption GO database is correct (mega experiment) Result N groups of genes for N different biological processes (classification) Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison GO is a hierarchical database molecular function (7460) cellular component (1533) biological process (9384) 18377 GO terms Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Examples of biological (sub)processes: regulation of cell cycle, GO:0000074 DNA repair, GO:0006281 circadian rhythm, GO:0007623 endocytosis, GO:0006897 ATP metabolism, GO:0046034 Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Examples of biological (sub)processes: regulation of cell cycle, GO:0000074, 791 DNA repair, GO:0006281, 538 circadian rhythm, GO:0007623, 44 endocytosis, GO:0006897, 225 ATP metabolism, GO:0046034, 14 Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison Expected disorder in biological processes immune cell activation , GO:0045321, 36 positive regulation of apoptosis, GO:0043065, 42 positive regulation of transcription, GO:0045941, 101 circadian rhythm, GO:0007623, 44 Expected order in biological processes housekeeping pathways, ??? Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison correlation ρ AC ↑ = ⇒ edge between A and B temporal ordering = ⇒ direction Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison correlation ρ AC ↑ = ⇒ edge between A and B temporal ordering = ⇒ direction Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison correlation does not imply causality: ρ AC ↑ partial correlation of first order: ρ AC . B ↓ Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison correlation does not imply causality: ρ AC ↑ partial correlation of first order: ρ AC . B i ↑ partial correlation of higher order: ρ AC . { B i } ↓ (parallel pathways) Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison correlation does not imply causality: ρ AC ↑ partial correlation of first order: ρ AC . B i ↑ partial correlation of higher order: ρ AC . { B i } ↓ Example N = 50, n = |{ B i }| = 8 � N � ∼ 10 8 n Frank Emmert-Streib Detecting pathological Pathways of the CFS
Quasi-pathway Introduction Quasi-pathway Results so far Classify patients Hypothesis about CFS Classify genes Approach Inferring causality Results Network Comparison d-separation x � _ y |{ B i } ⇐ ⇒ ρ xy . { B i } = 0 (1) _ _ _ _ V ERMA et al. 1988, P EARL 1988, G EIGER et al. 1990, S PIRTES et al. 1998 Frank Emmert-Streib Detecting pathological Pathways of the CFS
Recommend
More recommend
