MSImpute: Imputation of label-free mass spectrometry peptides by low-rank approximation
Soroor Hediyeh-zadeh,Andrew Webb,Melissa Davis Department of Medical Biology, The University of Melbourne; Colonial Foundation Healthy Ageing Centre, WEHI.
Abstract
Recent developments in mass spectrometry (MS) instruments and data acquisition modes have aided multiplexed, fast, reproducible and quantitative analysis of proteome profiles, yet missing values remain a formidable challenge for proteomics data analysis. The stochastic nature of sampling in Data Dependent Acquisition (DDA), suboptimal preprocessing of Data Independent Acquisition (DIA) runs and dynamic range limitation of MS instruments impedes the reproducibility and accuracy of peptide quantification and can introduce systematic patterns of missingness that impact downstream analyses. Thus, imputation of missing values becomes an important element of data analysis. In this talk, we discuss low-rank models for imputation of peptides Missing At Random (MAR) in label-free quantification, and present our latest research on combining low-rank models with shape-based data analysis techniques for imputation of Missing Not At Random (MNAR) peptide measurements. These models are implemented in R language in package msImpute, available on the Bioconductor repository.
Keywords: mass spectrometry,label-free proteomics,imputation