Optimization of Statistical Methods Impact on Quantitative Proteomics Data

Pursiheimo, Anna; Vehmas, Anni P.; Afzal, Saira; Suomi, Tomi; Chand, Thaman; Strauss, Leena; Poutanen, Matti; Rokka, Anne; Corthals, Garry L.; Elo, Laura L.

doi:10.1021/acs.jproteome.5b00183

Cited by 66 publications

(67 citation statements)

References 32 publications

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“…2C), further demonstrating its effectiveness over the other methods. Comparison to our previously introduced probe-level method PECA 13 and our protein-level reproducibility-optimization method ROTS 12 showed the combined benefits of ROPECA with increased power to detect true positives like PECA while simultaneously reporting less false positives like ROTS (Supplementary Fig. 2).…”

Section: Resultsmentioning

confidence: 95%

“…1. ROPECA first optimizes the reproducibility of statistical testing for each data separately by maximizing the overlap of top-ranked features over group-preserving bootstrap datasets to enable robust analysis 12 and then combines all available data from the peptide-level to improve the accuracy of the results by estimating the significance of median peptide-level p -values using the beta distribution 13 . This approach is different from tools, such as InfernoRDN (formerly known as DAnTE), which roll up already the peptide-level abundances before applying any statistical testing 14 .…”

Section: Introductionmentioning

confidence: 99%

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Enhanced differential expression statistics for data-independent acquisition proteomics

Suomi

Elo

2017

Sci Rep

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We describe a new reproducibility-optimization method ROPECA for statistical analysis of proteomics data with a specific focus on the emerging data-independent acquisition (DIA) mass spectrometry technology. ROPECA optimizes the reproducibility of statistical testing on peptide-level and aggregates the peptide-level changes to determine differential protein-level expression. Using a ‘gold standard’ spike-in data and a hybrid proteome benchmark data we show the competitive performance of ROPECA over conventional protein-based analysis as well as state-of-the-art peptide-based tools especially in DIA data with consistent peptide measurements. Furthermore, we also demonstrate the improved accuracy of our method in clinical studies using proteomics data from a longitudinal human twin study.

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Section: Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Enhanced differential expression statistics for data-independent acquisition proteomics

Suomi

Elo

2017

Sci Rep

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“…The ROTS method has previously been benchmarked in label-free shotgun proteomics using spike-in mixtures and complex mouse liver samples, where it has shown competitive performance against other state-of-the-art methods [11]. Here, the performance of ROTS with quantitative mass spectrometry based proteomics data is illustrated in another published benchmark spike-in study, where the truly differentially expressed proteins are known.…”

Section: Resultsmentioning

confidence: 99%

“…The ROTS method has already been used in various applications, such as microarrays [10], mass spectrometry proteomics [11] as well as bulk and single-cell RNA-seq [9, 12], and its competitive performance has been shown against other tools for differential expression analysis. Here we introduce a Bioconductor R package ROTS for performing differential expression analysis using the ROTS method and demonstrate the applicability of the method in three diverse case studies.…”

Section: Introductionmentioning

confidence: 99%

ROTS: An R package for reproducibility-optimized statistical testing

et al. 2017

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Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

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“…Pursiheimo et al . 5 evaluated several commonly used statistical methods for DEP detection, but neglecting the influence of MVs in the proteomics data. The primary reasons why a peptide is typically not observed in mass spectrometry (MS) analyses include low abundance, poor ionization, and random sampling in shotgun proteomics.…”

Section: Introductionmentioning

confidence: 99%

In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values

Wang

Chen

et al. 2017

Sci Rep

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Considering as one of the major goals in quantitative proteomics, detection of the differentially expressed proteins (DEPs) plays an important role in biomarker selection and clinical diagnostics. There have been plenty of algorithms and tools focusing on DEP detection in proteomics research. However, due to the different application scopes of these methods, and various kinds of experiment designs, it is not very apparent about the best choice for large-scale proteomics data analyses. Moreover, given the fact that proteomics data usually contain high percentage of missing values (MVs), but few replicates, a systematic evaluation of the DEP detection methods combined with the MV imputation methods is essential and urgent. Here, we analyzed a total of four representative imputation methods and five DEP methods on different experimental and simulated datasets. The results showed that (i) MV imputation could not always improve the performances of DEP detection methods and the imputation effects differed in the missing value percentages; (ii) the DEP detection methods had different statistical powers affected by the percentage of MVs. Two statistical methods (i.e. the empirical Bayesian random censoring threshold model, and the significance analysis of microarray) performed better than the other evaluated methods in terms of accuracy and sensitivity.

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Optimization of Statistical Methods Impact on Quantitative Proteomics Data

Cited by 66 publications

References 32 publications

Enhanced differential expression statistics for data-independent acquisition proteomics

Enhanced differential expression statistics for data-independent acquisition proteomics

ROTS: An R package for reproducibility-optimized statistical testing

In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values

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