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The Perseus computational platform for comprehensive analysis of (prote)omics data

Nature Methods volume 13, pages 731–740 (2016)Cite this article

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Abstract

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

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Figure 1: The Perseus data analysis platform.
Figure 2: Post-translational modifications.
Figure 3: Interaction proteomics.
Figure 4: Time-series analysis.
Figure 5: Cross-omics data comparison by 2D annotation enrichment analysis.
Figure 6: Machine learning for clinical proteomics and biomarker discovery.

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Acknowledgements

This project has received funding from the European Union′s Horizon 2020 research and innovation programme under grant agreement no. 686547 (J.C.) and from the FP7 grant agreement GA ERC-2012-SyG_318987–ToPAG (J.C.).

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Authors and Affiliations

  1. Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany

    Stefka Tyanova, Tikira Temu, Pavel Sinitcyn, Arthur Carlson & Jürgen Cox

  2. Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California, USA.,

    Marco Y Hein

  3. Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Tamar Geiger

  4. Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

    Matthias Mann

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  1. Stefka Tyanova
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Integrated supplementary information

Supplementary Figure 1 Schematic representation of the Workflow window in Perseus

All data matrices uploaded in the running session of Perseus and all processing steps are displayed in the order of execution. The workflow allows the users to keep track of all steps in the analysis and to navigate through data matrices and visualization components just by clicking on the respective node in the diagram. The nodes can be modified to contain description and additional information for clarity. If a data matrix node is selected, information about the number of samples and data points is displayed in the most right panel of Perseus. Moreover, if an analysis node is selected, all parameters that were used in that step can be reviewed. Each data matrix, as well as all visualization windows can be exported in publication ready formats. The workflow scheme can be conveniently saved as a pdf file and used as a documentation of all steps of the analysis.

Supplementary Figure 2 Plug-in architecture of Perseus

The current structure of Perseus relies on a data matrix type and various functions for accessing and transforming the matrix are developed. The base code implementing these operations is open source and can be downloaded from GitHub (github.com/JurgenCox/perseus-plugins). The rest of the functionality is organized in two main interfaces: ‘Processing’ and ‘Analysis’ and the resulting module are added to the software core as plug-ins. Developers wishing to extend the software can build upon the main source code and contribute the new plug-ins to our online plug-in store.

Supplementary Figure 3 Missing value imputation

Perseus offers several imputation techniques including a method that draws random values from a distribution meant to simulate expression below the detection limit. The width and the down shift of the distribution can be set to closely represent the missing population. When missing values occur randomly, a distribution similar to that of the measured data is normally used for imputation. In contrast, a frequently used assumption in proteomics experiments is that low expression proteins give rise to missing values, therefore a Gaussian distribution with a median shifted from the measured data distribution median towards low expression should result in accurate imputation of such values. The mode parameter defines the measured data distribution to be used in the calculation of the random distribution. When the samples do not differ largely in their overall distribution, the use of the complete dataset is recommended. The measured distribution is shown in blue and the imputed values in orange. (a) No down-shift and distribution width of 0.5 do not simulate low abundant missing values. (b) Down-shift of 1.8 and distribution width of 0.5 simulate the assumption of low abundant proteins giving rise to missing values. (c) Down-shift of 3.6 and width of 0.5 result in an undesirable bi-modal distribution.

Supplementary Figure 4 Density-enhanced scatterplots between proteome, transcriptome and translatome levels produced by the upload plug-in

Short read NGS data as for instance produced by the Illumina platform can be imported for further analysis in the Perseus workflow. In the example we calculate RPKM values for each gene (Ingolia N. T. et al., Science, 2009) and compare these with iBAQ values calculated by MaxQuant from proteomics data derived from yeast (Kulak N. A. et al., Nature methods, 2014).

Supplementary Figure 5 Augmented data matrix

In addition to the main data matrix, Perseus can make use of background information complementary to the expression columns. (a) Often one of the first processing steps in data analysis is filtering for a minimum number of valid values. As some statistical methods require all values to be present (e.g. PCA) data imputation may be necessary. Upon imputation a second matrix is created in the background storing information of which values were measured and which – imputed and can later be used to highlight or remove the imputed values. (b) In a more advanced filtering option, first a ‘Quality matrix’ is created, which contains additional information about each expression value in the main matrix and which is used for filtering. For example, the number of peptides used for protein quantification can be used to filter proteins, which were identified with less than 2 peptides.

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Tyanova, S., Temu, T., Sinitcyn, P. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13, 731–740 (2016). https://doi.org/10.1038/nmeth.3901

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