Biomarker discovery in amenably sampled body fluids has the potential to empower clinical screening programs for the early detection of disease. Liquid Chromatography interfaced to Mass Spectrometry (LC-MS) has emerged as a central technique for sensitive and automated analysis of proteins and metabolites from these clinical samples. However, the potential of LC-MS as a precise and reliable platform for discovery and screening is dependent on robust, sensitive and specific signal extraction and interpretation. The output of LC-MS is formed as a set of quantifiable images containing thousands of biochemical signals regulated in disease and treatment. We propose to tackle this problem for the first time with a biomedical image analysis paradigm. A novel workflow of image reconstruction, groupwise image registration and Bayesian functional mixed-effects modeling is presented. Poisson counting noise and lognormal biological variation are modeled in the raw image domain, resulting in markedly improved detection limit for differential analysis.
|Conference||IEEE 11th International Symposium on Biomedical Imaging (ISBI)|
|Period||1/04/14 → 2/05/14|
- image registration
- functional mixed model
- mass spectrometry