Optimizing Proteomics Data Differential Expression Analysis via High-Performing Rules and Ensemble Inference

Question

1. What is the impact of data preprocessing steps on differential expression analysis (DEA) performance in proteomics data?

2. What are the performance metrics used to evaluate candidate workflows for proteomic datasets?

3. What are the common frequent selection patterns identified for 'H' workflows across three quantification platforms?

4. What are the preferred expression matrix types and normalization methods for high-performing workflows in DEA?

Accepted Answer

The impact of data preprocessing steps on DEA performance in proteomics data is poorly understood. However, studies have shown that the choice of normalization methods and DEA statistical methods exert greater influence on performance over other steps. High-performing workflows prefer no normalization and incline MinProb for imputation while eschewing simple statistical tools for DEA. By studying the impact of choices at each step of a workflow on DEA performance, a unique resource to guide workflow selection on new datasets can be provided. Additionally, an ensemble inference strategy that integrates DEA results from individual top-performing workflows can further improve DEA performance by 1~5% for FragPipe-based or 1~4% for maxquant-based DDA data, and 2~4% for DIA-NN-based DIA data. The combination of multiple proteomic data layers provides complementary information that enhances DEA outcomes.

Accepted Answer

The performance metrics used to evaluate candidate workflows for proteomic datasets include partial area under receiver operator characteristic curves (pAUC) with false-positive rate (FPR) thresholds of 0.01, 0.05, 0.1, normalized Matthew's correlation coefficient (nMCC), and geometric mean of specificity and recall (G-mean). These metrics are used to assess the performance of workflows in identifying differentially expressed proteins. Additionally, the mean or median performance of a workflow across multiple datasets is used to establish its overall performance ranking. Leave-one-project-out cross-validation (LOPOCV) is also employed to test the consistency between workflow ranks obtained by benchmarking and the true performance of new datasets.

Accepted Answer

The common frequent selection patterns identified for 'H' workflows across three quantification platforms are normalization methods 'center.mean','center.median' and 'None' with support ratio bigger than 10%, and MVI algorithms 'MinProb' and 'MinDet' for imputing missing values. Additionally, protein MaxLFQ intensity is frequently coupled with limma in FragPipe and DIA-NN 'H' workflows, and protein peak intensity is coupled with limma and ROTS in DIA-NN 'H' workflows. These patterns are platform-specific and highly recommended for improving workflow performance.

Accepted Answer

For high-performing workflows in DEA, the preferred expression matrix types are MaxLFQ intensity at the protein level and peptide MaxLFQ intensity for DIA data. At the peptide level, peak intensities work better in maxquant-related workflows. For normalization methods, no normalization (F:None), center.mean, and center.median are frequently found in high-performing workflows. These methods are recommended based on benchmark results, but data normalization should be carefully customized to the data and research question at hand. It is possible that there are situations where normalization techniques are warranted, but they should be tailored to the specific data and research question.

Accepted Answer

Ensemble inference can improve DEA performance by integrating results from multiple high-performing workflows, providing a more comprehensive view of the differential expression landscape and increasing the confidence of the results. By combining outcomes from different workflows, false positives and negatives can be reduced, and the robustness of the results can be increased. Ensemble inference strategies, such as ens_3inp, ens_2inp, and ens_TK, have been shown to improve DEA performance by providing comprehensive and accurate estimation of differential expressions. These strategies integrate the outcomes of top-ranking workflows, using methods like hurdle or minimum p-values, to calculate integrated p-values and improve performance metrics such as pAUC(0.01) and Gmean. Ensemble inference is particularly recommended for DDA data processed by FragPipe and maxquant, as well as DIA data processed by DIA-NN, where it consistently outperforms single workflows and other integration strategies.

Accepted Answer

For DEA with FragPipe's quantification outputs, the recommended workflow is a combination of protein MaxLFQ intensity, no normalization, and limma at first, followed by a MVI algorithm of MinProb or MinDect. This combination has been shown to provide high performance in exhaustive testing of workflow combinations.

Accepted Answer

Ranked workflows offer a knowledge resource for users to identify optimal tools. They test an extensive set of steps and combinations, including expression matrix types, data preprocessing methods, and DEA tools, resulting in 10,808 workflows. These workflows provide guidelines for optimal workflow selection. The evaluation was conducted across 5 performance indicators and 3 quantification platforms, ensuring balanced perspectives. The study emphasizes the significant impact of missing data on DEA performance and the importance of selecting the right MVI algorithm. It also investigates the impact of tool selections in each important step of a complex workflow. The findings show that expression matrix types have minor effects on DEA performance, while normalization may worsen it. However, selecting a good MVI algorithm and a compatible DEA tool is highly beneficial.

Accepted Answer

Default parameters ensure a stable and general evaluation of tools working on randomly chosen datasets. They are optimized by developers and produce commonly accepted performance. By using default parameters, we ensure a reliable and consistent evaluation of the tools. However, adjusting these parameters can affect the final DEA performance, increase time cost, and may require additional labelled golden standard data for validation, which is not feasible for analysing real-life data. Therefore, default parameters are considered the best-practice values for benchmarking tools.

Accepted Answer

The key factors affecting DEA performance include the selection of a good MVI algorithm, normalization method, and DEA tool. Our benchmarking results indicate that these choices are more crucial for DEA performance compared to the choice of expression matrix type. Additionally, ensemble inference strategies using top-ranking workflows can significantly improve DEA performance. The benchmarking results provide a stable and general evaluation of the tools' performance on randomly chosen datasets, and can be used to guide optimization of DEA workflows.

Optimizing Proteomics Data Differential Expression Analysis via High-Performing Rules and Ensemble Inference

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Most frequently asked questions

1. What is the impact of data preprocessing steps on differential expression analysis (DEA) performance in proteomics data?

2. What are the performance metrics used to evaluate candidate workflows for proteomic datasets?

3. What are the common frequent selection patterns identified for 'H' workflows across three quantification platforms?

4. What are the preferred expression matrix types and normalization methods for high-performing workflows in DEA?

5. How can ensemble inference improve DEA performance?

6. What workflows are recommended for DEA with FragPipe's quantification outputs?

7. What are the optimal workflow selection guidelines provided by ranked workflows?

8. How do default parameters affect tool evaluation?

9. What are the key factors affecting DEA performance?

Citations

einprot: flexible, easy-to-use, reproducible workflows for statistical analysis of quantitative proteomics data

einprot: flexible, easy-to-use, reproducible workflows for statistical analysis of quantitative proteomics data

References

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Bioconductor: open software development for computational biology and bioinformatics

Significance analysis of microarrays applied to the ionizing radiation response

Multiple imputation using chained equations: Issues and guidance for practice

limma: Linear Models for Microarray Data

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