Statistical Applications in Genetics and Molecular Biology

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for sensitive detection and quantification of proteins and peptides in complex biological fluids like serum. LC-MS produces complex data sets, consisting of some hundreds of millions of data points per sample at a resolution of 0.1 amu in the m/z domain and 7000 data points in the time domain. However, the detection of the lower abundance proteins from this data is hampered by the presence of artefacts, such as high frequency noise and spikes. Moreover, not all of the tens of thousands of the chromatograms produced per sample are relevant for the pursuit of the biomarkers. Thus in analysing the LC-MS data, two critical pre-processing issues arise. Which of the thousands of the: 1. chromatograms per sample are relevant for the detection of the biomarkers?, and 2. signals per chromatogram are truly compound-related? Each of these issues involves assessing the significance (deviation from noise) of multiple observations and the issue of multiple comparisons arises. Current methods disregard the multiplicity and provide no concrete threshold for significance. However, with such procedures, the probability of one or more false-positives is high as the number of tests to be performed is large, and must be controlled. Realizing that the cut-offs for declaring a chromatogram (or a signal) to be compound-related can hugely influence which proteins are detected, it seems natural to define thresholds that are neither arbitrary nor subjective. We suggest the choice of thresholds guided by the critical aim of controlling the False Discovery Rate (FDR) in multiple hypotheses testing for significance over a large set of features produced per sample. This involves the use of the regression diagnostics to characterize the signals of a chromatogram (e.g. as outliers or influential) and to suggest suitable tests statistics for the multiple testing procedures (MTP) for discriminating noise and spikes from true signals. The role of ∗This work was funded by the BioRange program of the Netherlands Bioinformatics Centre (NBIC), which is supported by a BSIK grant through the NGI. We gratefully acknowledge support from two Groningen Bioinformatics Centre funds, the Bolgewasen project (grant number SENTER-TSGE3043) and the Tropisch Fruit project (grant number SENTER-TSOM3005), and also the funding by MRC UK (grant No. RRAK11686). RB was funded by the Dutch Cancer Fund (KWF grant number RUG 2004-3165). the Generalized Linear Models (GLM) in this MTP is investigated. The method is applied to LCMS datasets from trypsin-digested serum spiked with varying levels of horse heart cytochrome C (cytoc).