Meta-analysis of peptides to detect protein significance

Abstract

Shotgun assays are widely used in biotechnologies to characterize large molecules, which are hard to be measured as a whole directly. For instance, in Liquid Chromatography - Mass Spectrometry (LC-MS) shotgun experiments, proteins in biological samples are digested into peptides, and then peptides are separated and measured. However, in proteomics study, investigators are usually interested in the performance of the whole proteins instead of those peptide fragments. In light of meta-analysis, we propose an adaptive thresholding method to select informative peptides, and combine peptide-level models to protein-level analysis. The meta-analysis procedure and modeling rationale can be adapted to data analysis of other types of shotgun assays.

Keywords: Adaptive thresholding; Meta-analysis; Shotgun technology.

Figure 1.

Intensities of peptides from Adolase A in spike-in data. The x-axis indicates samples. There are 18 samples from 6 conditions in total. Each condition has three samples. The samples are ordered by their conditions. Different condition has different spike-in protein concentrations, which are 25, 50, 100, 200, 400, and 800 (fmol), ordered from left to right in the figure. The y-axis indicates the log2 scaled peptide intensities. Different lines with different colors and types indicate different peptides.

Figure 2.

Random missing probability estimation. The x-axis indicates the average intensity of each peptide. The y-axis indicates the missing rate of each peptide. The solid curve is fitted by the cubic spline regression. The value on the y-axis for the dotted line indicates the estimated completely random missing probability $\widehat{\pi }$.

Figure 3.

The Receiver Operating Characteristic (ROC) plots for six simulation studies. The x-axes indicate false positive rate, and the y-axes indicate true positive rate. The curves with different colors and line types show the average true positive rates across 10 times replicates for each method respectively. The corresponding shadows show the standard errors. Black solid lines indicate PEAT results; Red dashed lines indicate SFPQ results; Orange longdash lines indicate RRollup results; Blue dotdash lines indicate ZRollup results; Green twodash lines indicate MSstats results.

Figure 4.

Heatmap of the estimated protein expression for the burn injury study. Each row indicates one protein, each column indicates one sample. The samples are from three categories – healthy controls, burn patients from the first time point, burn patients from the second time point. We add a white line among different biological conditions. The white cells in the heatmap indicate that no protein expression values were estimated due to peptide observations were missing for the entire group.