Normalization and missing value imputation for label-free LC-MS analysis

Abstract

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

Figure 1

Examples of missing data. Intensities for a peptide with two treatment groups with (A) no missing values, (B) MCAR missing values, (C) censored missing values, and (D) censored missing values imputed as a minimum observed value.

Figure 2

Percent coverage for nominal 95% confidence intervals of protein-level differences.

Figure 3

Histograms of the null p-values for normalized (left) and raw (right) peptide abundances.

Figure 4

Top three eigentrends identified in raw (left), imputed (middle); and normalized after imputation data (right). X-axis is the sample index, y-axis are values in eigentrends.

Figure 5

Top three eigentrends identified in raw (left), normalized (middle); and imputed after normalization data (right). X-axis is the sample index, y-axis are values in eigentrends.