Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S

By Hyungwon Choi, Damian Fermin, Alexey I. Nesvizhskii. Molecular & Cellular Proteomics, 2008

380 citations recorded by the source corpus.

Spectral counting has become a commonly used approach for measuring protein abundance in label-free shotgun proteomics. At the same time, the development of data analysis methods has lagged behind. Currently most studies utilizing spectral counts rely on simple data transforms and posthoc corrections of conventional signal-to-noise ratio statistics. However, these adjustments can neither handle the bias toward high abundance proteins nor deal with the drawbacks due to the limited number of replicates. We present a novel statistical framework (QSpec) for the significance analysis of differential expression with extensions to a variety of experimental design factors and adjustments for protein properties. Using synthetic and real experimental data sets, we show that the proposed method outperforms conventional statistical methods that search for differential expression for individual proteins. We illustrate the flexibility of the model by analyzing a data set with a complicated experimental design involving cellular localization and time course.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/458e3399947df1b468bdd6f7116fb8a9c664c42a

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Citations in the corpus

Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “Another method proposed for spectral counting was to weight the abundances of shared peptides based on the total SpC per protein [62], or alternatively distribute shared peptides based on the abundance of unique peptides for proteins with shared sequences [63, 64].”
A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (2010, neutral): “[226]).”