Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use spacepartitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graphtheoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.
|Number of pages
|Proceedings of the National Academy of Sciences of the United States of America
|Published - Sep 15 2009
- Orthogonal range query
- Quantitative proteomics
- Space partitioning data structures
- Tandem mass spectrometry