An Alternating Direction Minimization based denoising method for extracted ion chromatogram

Accurate extracted ion chromatograms (XIC or EIC) is of great importance for Liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, current preprocessing methods for XIC mainly focus on removing the peaks of low quality. Such operations are helpful for quantitation, but also contain two potential disadvantages: one is that some valuable information may be lost after the peak removal, and the other is that the retained peak lists are still contain noise. Both of the potential disadvantages may bias the final quantitative results. To solve these problems, we proposed an Alternating Direction Minimization (ADM) based denoising framework for XIC. This framework splits the observed XIC signal into baseline (background), noise and true signal, and the true signal is extracted for further analysis. The advantage of this framework is that the inner relationships over each XIC are considered, for which means that the XIC data is handled in a global way. Also, this framework is not sensitive to the noise type, so that it can be applied to wider applications. We adopted the framework for some sample data for quantitation. The quantitative results are employed to show the performance of XIC denoising. Experimental results confirm that the proposed method provides better and more reliable quantitations.