sparseQuant implements a novel statistical approach for design and analysis of label-sparse selected reaction monitoring (SRM)-based targeted proteomic experiments. Label-sparse experiments strike a middle ground between the label-free and fully label-based approaches, by utilizing a reduced set of labeled reference transitions and accurately quantifying an expanded list of proteins.

sparseQuant contains tools for designing label-sparse experiments, which allow the researchers to find a compromise between the number of quantified proteins, the number of reference proteins and transitions and the number of replicates in the future experiment, to achieve the desired sensitivity and optimize the cost.

sparseQuant also contains tools for analyzing the acquired data at the protein level, and find proteins that change in abundance between conditions.

The tools is implemented as an open-source R package and can be used by researchers with a limited statistics background.

This work is supported by the NSF CAREER Award 1054826 to Dr. Olga Vitek.


Targeted protein quantification using sparse reference labeling



Ching-Yun (Veavi) Chang and Olga Vitek


C.-Y. Chang, E. Sabidó, R. Aebersold, O. Vitek. “Targeted protein quantification using sparse reference lebeling”. Submitted. 2013.