Exploratory analysis of multi-omics datasets: A handy guide for biomedical researchers

Multi-omics data integrates information from multiple biological domains, such as genomics, transcriptomics, proteomics, and metabolomics. These omics datasets provide information about different molecular components in biological systems, and integrating them can reveal complex relationships and help identify biomarkers, pathways, and potential therapeutic targets. Exploratory analysis of multi-omics datasets is a critical process for biomedical researchers aiming to gain insights from various types of high-dimensional biological data. In this blogpost, we’ll take a look at how such analyses can be conducted, with a step-by-step explanation of the process.

Exploring Multi-Omics Data: The Basic Process

Here are the key steps to be followed when conducting an exploratory data analysis.

Data Collection:

• Gather omics datasets from various sources, including genotyping, gene expression profiling, protein quantification, metabolite measurements, and epigenetic modifications.
• These datasets may come from experiments like microarrays, RNA-seq, mass spectrometry, or DNA sequencing.

Data Preprocessing:

• Clean the data to remove noise, missing values, and outliers.
• Normalize and standardize the data to ensure that different omics datasets are on the same scale for meaningful comparisons.

Data Integration:

• Integrate the omics datasets to create a comprehensive multi-omics dataset. Integration methods include data fusion, data alignment, and dimensionality reduction techniques.

Data Exploration and Visualization:

• Generate various types of plots and visualizations to explore the integrated data, such as scatter plots, heatmaps, hierarchical clustering, and network diagrams.
• Use dimensionality reduction techniques to visualize high-dimensional data in two or three dimensions.
• Identify patterns, correlations, and potential outliers in the data.

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Statistical Methods Used for Data Exploration

While there are a number of statistical methods you can use to explore your multi-omics data, here are the three most popular ones:

Principal Component Analysis (PCA):

PCA is a dimensionality reduction technique that transforms high-dimensional data into a set of linearly uncorrelated variables called principal components, allowing for a simplified representation of the data while retaining its major sources of variation.

Pros: Reduces dimensionality, highlights major sources of variation, and aids visualization.

Cons: Assumes linear relationships, may not capture non-linear patterns, and might not be suitable for datasets with complex structures.

Hierarchical Clustering:

Hierarchical clustering is a method that groups similar elements in a dataset into nested clusters, creating a tree-like structure (dendrogram) to represent relationships and similarities between samples or features.

Pros: Groups similar samples or features into clusters, revealing structure in the data.

Cons: Sensitive to outliers, computationally intensive for large datasets, and choice of distance metric impacts results.

Heatmaps:

Heatmaps visually represent data in a matrix format, where colors indicate the magnitude of values.

Pros: Provides a visual representation of data patterns, useful for identifying clusters and trends.

Cons: Interpretation can be subjective, and the choice of color scale may influence perception.

Conclusion

From deciphering disease mechanisms to identifying potential therapeutic targets, the comprehensive insights derived from multi-omics analyses propel precision medicine forward. Exploratory data analysis of multi-omics data stands as a powerful compass guiding biomedical researchers through the complex landscape of biology and medicine.

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Looking for support when working with multi-omics data? Get advice from experts, through Editage’s Statistical Analysis & Review Services.