How to Analyze Proteomics Data Using Omics Playground
Steps involved in the analysis, statistical methods applied and suggested analyses to visually interpret your proteomics data.
Published on December 23rd, 2024
⏱ 12 min read
Introduction
Proteomics is revolutionizing our understanding of biological systems, uncovering insights into disease mechanisms, biomarker discovery, and drug development. However, analyzing proteomics data effectively can feel overwhelming, especially with the wide range of tools and methods available.
Omics Playground was designed to support researchers by providing an intuitive, powerful platform tailored for proteomics, among other types of data.
In this post, we’ll guide you step-by-step on how to leverage Omics Playground for proteomics data analysis. You’ll learn how to prepare and upload your data, and use a comprehensive suite of analyses to extract meaningful biological insights.
Here’s what we’ll cover:
- Preparing Your Proteomics Data for Upload. Tips to ensure your data is in the correct format and ready for analysis.
- Uploading Your Data to Omics Playground. A walkthrough of the upload process, including the normalization and batch correction options available, along with information about the default settings Omics Playground applies for proteomics data.
- Analyzing Your Proteomics Data. Discover the analyses available in Omics Playground, from basic clustering and differential expression to advanced analyses like biomarker analysis and drug connectivity.
By the end of this guide, you’ll have a clear roadmap to confidently analyze your proteomics data on Omics Playground, transforming complex datasets into actionable insights. Let’s dive in!
How to prepare your proteomics data for the upload
To analyze your proteomics data using Omics Playground, your input files must be in CSV (comma-separated values) format. The platform primarily requires two key files, with an optional third file for advanced comparisons:
- Abundance Table: Rows represent proteins, and columns represent samples. This file contains the quantitative abundance levels of each protein across all samples.
- Samples File: Rows represent samples, and columns represent phenotypes or experimental conditions. This file provides metadata about your samples, such as treatment groups or biological replicates.
- (Optional) Comparisons File: Rows represent samples, and columns represent specific conditions for comparisons. If you plan to perform multiple comparisons, preparing this file beforehand can save time. However, for a smaller number of comparisons, you can easily define them interactively within the platform’s interface.
For detailed instructions on how to prepare each file, check out our data preparation video tutorial. If you encounter issues during file preparation, be sure to review our common formatting errors guide for troubleshooting tips.
How to upload your proteomics data to Omics Playground
Once your input files are ready, logging into Omics Playground and uploading your data is a straightforward process. Here’s how to get started:
1. Initiate the upload
Log into your Omics Playground account and click “Upload New Data” on the platform’s welcome page. You’ll be prompted to specify the type of data you’re analyzing and the organism.
2. Follow the guided steps
The upload process consists of five easy-to-follow steps:
Steps 1 & 2: Upload input files
Upload your abundance table and samples file, as discussed in the previous section.
Step 3: Select comparisons
Define your comparisons by either uploading a prepared comparisons file or using the platform’s drag-and-drop interface to select them interactively.
Step 4: Quality Control/Batch Correction (QC/BC)
Customize your data processing by selecting normalization and batch correction methods, or simply use the default settings, which are optimized for proteomics data:
- Normalization: maxMedian.
- Missing value imputation: SVDimpute
- Outlier removal: None
- Batch correction: None
For more details about these methods, check out our in-depth data upload guide.
Step 5: Final Details
Provide a name and a short description for your dataset. You can also choose additional computation options. If you’re unsure which options to select, the platform will apply recommended defaults. For more information, refer to the computation options guide.
3. Run the Computation
Once all steps are completed, click “Compute” to initiate the data processing. The platform will handle everything in the background. For new datasets, computations typically take 10–30 minutes, depending on dataset size. You’ll receive an email notification when the process is complete and your dataset is ready for exploration.
For subsequent logins, reloading an existing dataset is much faster and usually takes just a few minutes.
How to analyze your proteomics data with Omics Playground
From clustering to advanced network and biomarker discovery methods, the platform is designed to address a wide range of research needs.
Below you’ll find a selection of the analyses available guiding you through a typical proteomics data analysis workflow. Specifically:
Summary of steps involved in proteomics data analysis:
- Identify patterns and groups with Clustering Analysis.
- Identify key proteins across conditions with Differential Expression Analysis.
- Visualize protein set activity and identify pathways with protein set enrichment and pathway analysis.
- Identify correlations between proteomics and transcriptomics datasets.
- Identify potential protein biomarkers for your disease of interest or drug response.
- Identify compounds that produce proteomic signatures similar to the observed data.
- Understand functional relationships and interactions of proteins with protein network analysis.
1. Identify Patterns and Groups in Your Proteomics Data with Clustering Analysis
Most researchers start their analyses performing clustering analysis which offers valuable insights into the underlying structure of your data, such as identifying groups or patterns based on phenotypes or protein expression variations.
You can explore your proteomics data using visualization options like heatmap, UMAP, PCA, and t-SNE plots, and dynamically adjust settings to tailor the analysis. Omics Playground provides a few personalization options as well, such as modifying plot colors and labels, enabling users to effectively interpret clusters.
A unique feature of Omics Playground is that it includes the ability to analyze data at both the protein and pathway levels, leveraging extensive databases for functional annotations, which enhances the biological interpretation of clusters.
2. Identify Key Proteins Across Conditions with Differential Expression Analysis
Users can then continue their analysis by performing Differential Expression Analysis to identify proteins or protein sets that are significantly different between conditions of interest. This analysis highlights candidates for further functional evaluation.
With the platform, you can leverage tools like volcano plots and MA plots to visualize differential expression. Selecting specific proteins highlights them in the plot, displays their expression in the selected pairwise comparison, and provides an overview across all comparisons in your dataset. Thresholds for FDR and logFC values can be adjusted to refine significance, and sortable tables allow you to explore results efficiently.
The platform’s multi-method statistical analysis adds robustness by marking proteins consistently identified across methods. For proteomics data, the statistical tests available are ttest, ttest.welch, trend.limma and notrend.limma.
Additional options include summaries of top up- and down-regulated proteins, comparative volcano plots across multiple conditions, and detailed statistics about protein expression and regulation across methods.
3. Visualize Protein Set Activity and Identify Pathways with Protein Set Enrichment and Pathway Analysis
Users can extend their analysis by performing Protein Set Enrichment Analysis (PSEA) to identify biological processes or pathways significantly enriched in their proteomics data. PSEA plots allow users to visualize protein set activity, such as the upregulation of specific drug responses. Thresholds for FDR and logFC values can be adjusted, and data can be filtered by pairwise comparisons, with results highlighted through a robust star system indicating consensus across multiple peer-reviewed methods, such as ssgsea, gsva and fgsea. PSEA is based on a collection of more than 30 public databases, such as Hallmark, GO terms and Msig, for a total of more than 50,000 gene sets. GO terms are also displayed as manipulable graphs, offering an additional layer of biological insight.
Pathway analysis builds on PSEA by offering a detailed view of pathway-level changes. The platform supports two well-known collections: WikiPathways and Reactome. Interactive visualizations allow users to explore pathways, highlighting specific proteins and their roles in affected components.
These features combine to provide a versatile and interactive environment for exploring and sharing enrichment and pathway analysis results in the context of proteomics.
4. Identify Correlations Public Between Proteomics and Transcriptomics Datasets
A follow up step could be that of comparing pairwise comparisons from your current dataset with previously uploaded datasets or leverage a curated database of over 6,000 experiments from the GEO repository, encompassing more than 10,000 pairwise comparisons. Although these datasets are mostly transcriptomics datasets, they can still be used for comparative analysis with proteomics datasets. The module can thus also be used to identify similar datasets from the public database that can be used for further comparative analysis.
Once a relevant dataset is selected, the platform supports one-to-one comparisons, such as aligning pairwise comparisons from two proteomics datasets with each other or directly comparing proteomic data with transcriptomic data. For instance, correlations can be identified between transcriptomic and proteomic data from the same patients in multiomic studies, revealing trends like consistent up- and down-regulation patterns.
The module offers different representations of the two datasets, such as Protein UMAPs, volcano plots and scatter plots. Fold-change correlation plots further highlight the relationship between datasets, allowing users to identify strong correlations in gene or protein regulation trends. Specific proteins can be analyzed in detail, revealing correlations or cases where two datasets diverge, like signaling proteins showing opposite expression patterns. These capabilities make the platform a robust tool for comparative analysis, enabling comprehensive exploration of proteomics and multiomic data.
5. Identify Potential Protein Biomarkers for Your Disease of Interest or Drug Response
Depending on your research focus, a next step could be that of performing biomarker analysis to identify potential protein biomarkers for your disease of interest or drug response. This analysis is typically informed by earlier results from differential expression and pathway enrichment.
Using Omics Playground, you can identify potential biomarkers for phenotypes using a combination of six machine learning algorithms and two statistical tests. This feature supports focused biomarker research by enabling analyses on specific groups, protein families, or user-defined collections, making it a powerful tool for advancing targeted research.
6. Identify Compounds that Produce Proteomic Signatures Similar to the Observed Data
You can use the drug connectivity tab to identify drugs or compounds that produce proteomic signatures similar to (or that counteract) the observed data. Using Omics Playground, you can identify:
- Drug modes of action
- Repurposable drugs for treating specific conditions
- Target proteins for therapeutic intervention
- Synergistic partner drugs using sensitivity data.
The module employs GSEA-like meta-analysis to enhance confidence in data interpretation by summarizing results across multiple experiments. Further analysis clusters drugs by shared modes of action, identifying those with the highest correlation or inhibitory potential relative to the dataset.
The DataView tab complements this by providing detailed insights into target proteins, showing their up- or down-regulation in a specific disease. It also includes data on phenotypic associations, expression rankings, correlated proteins, tissue-specific expression, and overall data quality, making it an essential tool for proteomic and phenotypic exploration.
7. Understand Functional Relationships and Interactions of Proteins with Protein Network Analysis
Users can perform protein network analysis via two approaches: Weighted Gene Co-expression Network Analysis (WGCNA) and the Prize-Collecting Steiner Forest (PCSF) approach.
Weighted Gene Co-expression Network Analysis (WGCNA)
WGCNA is a method used to find groups of genes (called modules) that show similar expression patterns across samples. These modules often represent genes, or in this case, proteins involved in related biological processes or pathways.
While originally developed for the analysis of transcriptomics datasets, its principles are also well-suited for the analysis of proteomics datasets.
WGCNA builds a network where proteins are connected based on how strongly their expression is correlated, grouping highly connected proteins into modules. Each module is summarized by a representative expression profile (eigengene), which can be linked to traits like disease status or experimental conditions. The method also identifies hub proteins within modules, which are key players that may regulate module activity.
WGCNA is widely used to simplify complex protein expression data, uncover biomarkers, and explore protein function.
Prize-Collecting Steiner Forest (PCSF) approach.
The PCSF approach is a computational method used to identify biologically relevant subnetworks from complex interaction data, such as protein-protein interaction networks. It integrates diverse -omics data (e.g., gene expression, proteomics) by assigning prizes to nodes (biomolecules) based on their biological importance and costs to edges (interactions) based on their reliability. The goal is to extract subnetworks that maximize node prizes while minimizing edge costs.
PCSF allows for multiple disconnected subnetworks (a “forest”), making it ideal for modeling independent pathways or processes. This is particularly useful in diseases like cancer, where multiple signaling pathways may be active.
Conclusion
Analyzing proteomics data can be a complex journey, but Omics Playground was designed to support you by providing intuitive tools for every step: from data preparation to meaningful biological interpretation. By following this guide, you’ve seen how to upload your data and explore it through the different analysis modules available in the platform.
The analysis workflow presented in this guide serves as a useful example, but one of the platform’s most powerful features is its flexibility. It allows you to tailor your analysis based on your specific findings and research goals.
Explore your proteomics data with ease and confidence, try Omics Playground now!