Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.

ABSTRACT:

Motivation

LC-MS allows for the identification and quantification of proteins from biological samples. As with any high-throughput technology, systematic biases are often observed in LC-MS data, making normalization an important preprocessing step. Normalization models need to be flexible enough to capture biases of arbitrary complexity, while avoiding overfitting that would invalidate downstream statistical inference. Careful normalization of MS peak intensities would enable greater accuracy and precision in quantitative comparisons of protein abundance levels.

Results

We propose an algorithm, called EigenMS, that uses singular value decomposition to capture and remove biases from LC-MS peak intensity measurements. EigenMS is an adaptation of the surrogate variable analysis

SUBMITTER: Karpievitch YV 

PROVIDER: S-EPMC2752608 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature