A multi-model statistical approach for proteomic spectral count quantitation

Abstract

Summary The rapid development of mass spectrometry (MS) technologies has solidified shotgun proteomics as the most powerful analytical platform for large-scale proteome interrogation. The ability to map and determine differential expression profiles of the entire proteome is the ultimate goal of shotgun proteomics. Label-free quantitation has proven to be a valid approach for discovery shotgun proteomics, especially when sample is limited. Label-free spectral count quantitation is an approach analogous to RNA sequencing whereby count data is used to determine differential expression. Here we show that statistical approaches developed to evaluate differential expression in RNA sequencing experiments can be applied to detect differential protein expression in label-free discovery proteomics. This approach, termed MultiSpec, utilizes open-source statistical platforms; namely edgeR, DESeq and baySeq, to statistically select protein candidates for further investigation. Furthermore, to remove bias associated with a single statistical approach a single ranked list of differentially expressed proteins is assembled by comparing edgeR and DESeq q-values directly with the false discovery rate (FDR) calculated by baySeq. This statistical approach is then extended when applied to spectral count data derived from multiple proteomic pipelines. The individual statistical results from multiple proteomic pipelines are integrated and cross-validated by means of collapsing protein groups.

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