Selected region after second step of analysis for chromosome 4 (a), chromosome 8 (b), chromosome 16 (c), and chromosome 20 (d)

<p><b>Copyright information:</b></p><p>Taken from "Haplotype-sharing analysis for alcohol dependence based on quantitative traits and the Mantel statistic"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S75-S75.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866706.</p><p></p

Equilateral triangle as illustration of the metric space of IBD distributions

<p><b>Copyright information:</b></p><p>Taken from "Haseman-Elston weighted by marker informativity"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S50-S50.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866733.</p><p></p

Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics

As mass spectrometry-based metabolomics becomes more widely used in biomedical research, it is important to revisit existing data analysis paradigms. Existing data preprocessing efforts have largely focused on methods which start by extracting features separately from each sample, followed by a subsequent attempt to group features across samples to facilitate comparisons. We show that this preprocessing approach leads to unnecessary variability in peak quantifications that adversely impacts downstream analysis. We present a new method, bakedpi, for the preprocessing of both centroid and profile mode metabolomics data that relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to detect peaks. This new method reduces this unnecessary quantification variability and increases power in downstream differential analysis