Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences

Abstract

Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.

ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

“…It has previously been used to estimate protein abundance, based on peptide concentrations in the protein summarisation process. It has proven useful in detecting outliers in peptide expression profiles and limiting their influence on protein quantitation [22]. In ComplexBrowser, we employed our own implementation of the FARMS algorithm for performing weighted summarisation of log-transformed expression changes of protein complex subunits.…”

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

“…The algorithm will in effect down-weight any of a protein's constituent peptides with a quantification pattern that contradicts its other peptides. In this aspect, Triqler resembles our previously published method, Diffacto [47], which uses factor analysis to obtain a similar effect. However, Triqler expands on this idea with the integration of identification errors, a more intuitive way to impute missing values and posterior probabilities that facilitate better interpretation of the results.…”

Integrated identification and quantification error probabilities for shotgun proteomics

“…Slowly but steadily the softwares for protein identification are getting their error rates under better control, though much work is still left [27,32]. However, error rates in protein quantification have been mostly limited to setting intermediate false discovery rate (FDR) thresholds for the identifications or other heuristic cutoffs, such as requiring at least a certain number of peptides [8,3] or a certain correlation between peptide quantifications [41,40]. This gives no direct control of the errors in the reported lists of differential proteins and also discards potentially valuable information for proteins that just missed one of the thresholds.…”

“…Thirdly, we generally rely on the fact that by averaging the peptide quantities a reliable protein quantity estimate will be obtained. However, a single misidentification, quantification error or poorly imputed value can dramatically change the results [40]. Finally, t-tests or ANOVAs are typically employed to search for differentially expressed proteins.…”