Bayesian Normalization Model for Label-Free Quantitative Analysis by LC-MS

Abstract

We introduce a new method for normalization of data acquired by liquid chromatography coupled with mass spectrometry (LC-MS) in label-free differential expression analysis. Normalization of LC-MS data is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences but experimental bias. There are different sources of bias including variabilities during sample collection and sample storage, poor experimental design, noise, etc. In addition, instrument variability in experiments involving a large number of LC-MS runs leads to a significant drift in intensity measurements. Although various methods have been proposed for normalization of LC-MS data, there is no universally applicable approach. In this paper, we propose a Bayesian normalization model (BNM) that utilizes scan-level information from LC-MS data. Specifically, the proposed method uses peak shapes to model the scan-level data acquired from extracted ion chromatograms (EIC) with parameters considered as a linear mixed effects model. We extended the model into BNM with drift (BNMD) to compensate for the variability in intensity measurements due to long LC-MS runs. We evaluated the performance of our method using synthetic and experimental data. In comparison with several existing methods, the proposed BNM and BNMD yielded significant improvement.

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

“…To remove specific types of unwanted variations, the signal drift correction (when quality control samples are available), the batch effect removal (when internal standards or quality control samples are available), and the scaling (not suitable when the self-averaging property does not hold) are adopted13. These commonly used strategies are generally grouped into two categories: (1) method-driven normalization approaches extrapolating external model that is based upon internal standards or quality control samples and (2) data-driven normalization approaches scaling or transforming metabolomics data151617181920. As reported in Ejigu’s work, the method-driven strategies may not be practical due to several reasons, especially their unsuitability for treating untargeted metabolomics data, while data-driven ones are better choices for untargeted LC/MS based metabolomics data15.…”

Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies

“…where, x d,n is the ion abundance for n th compound of d th sample; δ d,n ( s ) is a latent indicator to model the missing scans; the chromatographic peak shape is characterized by the exponentially modified Gaussian (EMG) function [17] parameterized by ϕ , as described in Eq. (9), and e d,n ( s ) is the random noise.…”

“…The peak shape parameters ϕ are considered to have a normal distribution and its detailed priors are described in [17]. The extended model contains variables that are mutually coupled, providing no analytical form for the posterior distribution in calculation.…”

“…We adopt a two-phase approach to iteratively update the latent variables and estimate the parameters between the two parts. Specifically, we use a Markov chain Monte Carlo (MCMC) sampling method [17] to infer the peak shape model parameters of the second part (i.e., ion abundance x t , x β , and shape function parameters ϕ ). We then treat x t , x β as observed variables to implement the inference on the first part using the same algorithm [8] employed in the intensity-level purification.…”

Normalizing Item‐Based Collaborative Filter Using Context‐Aware Scaled Baseline Predictor

“…Even if an explicit definition of each of the possible rating is supplied, some users might be reluctant to give high/low scores to items they liked/disliked. There are some different rating normalization schemes which are designed for different reasons [17][18][19]. Also, many of the observed rating values are due to effects associated with either users or items, independently of their interaction.…”