DIMA: Data-driven selection of a suitable imputation algorithm

MotivationImputation is a prominent strategy when dealing with missing values (MVs) in proteomics data analysis pipelines. However, the performance of different imputation methods is difficult to assess and varies strongly depending on data characteristics. To overcome this issue, we present the concept of a data-driven selection of a suitable imputation algorithm (DIMA).ResultsThe performance and broad applicability of DIMA is demonstrated on 121 quantitative proteomics data sets from the PRIDE database and on simulated data consisting of 5 – 50% MVs with different proportions of missing not at random and missing completely at random values. DIMA reliably suggests a high-performing imputation algorithm which is always among the three best algorithms and results in a root mean square error difference (ΔRMSE) ≤ 10% in 84% of the cases.Availability and ImplementationSource code is freely available for download at github.com/clemenskreutz/OmicsData.

PeptideMind – applying machine learning algorithms to assess replicate quality in shotgun proteomic data

Assessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states.

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.