Guide: Performing Principal Component Analysis (PCA) in Omics Studio

Introduction to PCA

Principal component analysis (PCA) is a dimensionality-reduction technique used to simplify complex datasets by transforming correlated variables into a smaller number of orthogonal components (principal components). The transformation ensures that the first principal component captures the largest variance in the data, the second component captures the next largest variance, and so on . By emphasising variation and bringing out strong patterns in the dataset , PCA helps researchers explore high-dimensional omics data and visualise relationships between samples.

Omics Studio integrates PCA into its Explorer interface, allowing users to quickly examine overall patterns across samples and to identify outliers or batch effects. The following sections explain how to log in, open a study, and perform PCA in Omics Studio.

Navigating to PCA in the Explorer

1. Open the Expression View.
   In the Explorer’s left-hand navigation, expand Expression View. Several analysis tools become visible, including Principal Component Analysis, Total Identification Overview, Summed Abundance Calculation and Relative Expression.

2. Select Principal Component Analysis.
   Click Principal Component Analysis. The main pane displays a brief description of PCA and a button labeled Start your expression analysis. The description notes that PCA is used to simplify complex datasets by transforming them into orthogonal principal components so that patterns are easier to visualize.

Running a PCA

Step 1 – Select expression data

1. Start the analysis.
   Click Start your expression analysis. A multi-step wizard appears. The first step prompts you to Select expression data. You must choose one or more expression datasets from the study to serve as the basis for the PCA; these determine which samples are included in the analysis. If you have created sample lists (My Lists), you can alternatively select from those.

2. Choose datasets.
   Open the drop-down under Select expression datasets and choose the dataset(s) you wish to analyze (e.g., an abundance matrix or normalized expression values). A dataset must be assigned to the study for it to appear here. Selected datasets appear in the list below the drop-down.

3. Continue.
   Once at least one dataset is selected, click Continue to proceed to the next step.

Step 2 – Set grouping and preferences

1. Group samples (optional).
   PCA visualisations often use colours or shapes to represent experimental groups. In the second step, select a Sample grouping variable from the sample metadata (e.g., Treatment, Time point, Batch) to colour-code samples in the PCA plot. You may optionally select a second grouping variable to differentiate by shapes.

2. Variance scaling and normalization.
   Omics Studio may offer options to scale variables (e.g., mean-center and unit-variance scaling) before PCA. Scaling ensures variables contribute equally to the analysis and is important when variables differ in magnitude. Choose the appropriate scaling method if prompted.

3. Review and run.
   After setting grouping and scaling preferences, click Run PCA (or Continue) to compute the principal components. Omics Studio calculates the covariance matrix, performs an orthogonal transformation and produces principal components in which the greatest variance lies on the first component and successive components capture decreasing variance .

Step 3 – Explore the PCA results

1. Interpret the scatter plot.
   Omics Studio plots the first two (or three) principal components in a scatter plot. Each point represents a sample; proximity between points indicates similarity. Samples belonging to the same group should cluster together if the grouping variable captures meaningful variation.

2. Change axes.
   Use drop-down menus to select which principal components are displayed on the x-, y- (and z-) axes. While PC1 and PC2 often capture the majority of variation, examining PC3 or PC4 may reveal additional patterns or outliers.

3. Colour and shape options.
   You can change the colour palette or shape scheme associated with your grouping variables to improve visual contrast. Hover over a sample point to view sample metadata.

4. Download results.
   Buttons in the top right allow you to download the PCA scores and loadings tables as CSV files or export the PCA plot as an image (PNG/SVG) for inclusion in reports.

Interpreting PCA results

PCA helps identify patterns by projecting high-dimensional data into a smaller number of principal components. Key points to remember when interpreting the PCA output:

• Variance explained: The amount of variance captured by each component is displayed (e.g., PC1 = 45 %, PC2 = 25 %). Components with higher variance contribute more to overall differences among samples. When the first two or three PCs account for most of the variance, a low-dimensional visualization captures the major patterns.
• Clustering: Samples that cluster together in the PCA plot are similar in terms of the measured variables. Distant points or outliers may represent different conditions, batch effects or quality issues.
• Orthogonality: Each principal component is orthogonal (statistically uncorrelated) to the others ; therefore, patterns observed along one component are independent of those captured by other components.
• Caveats: PCA is sensitive to the scaling of variables and only captures linear relationships. Non-linear relationships may require other methods (e.g., t-SNE or UMAP).

Best practices and tips

• Pre-processing: Before running PCA, normalize or transform your expression data (e.g., log2 transformation) and handle missing values. Standardizing variables to unit variance prevents features with large variances from dominating the analysis.
• Filter low-variance features: Removing features (genes/proteins) with low variance across samples can reduce noise and improve interpretability.
• Inspect loadings: PCA loadings indicate how strongly each variable contributes to a principal component. Highly positive or negative loadings identify variables driving differences between samples. Omics Studio may provide a loadings table alongside the plot.
• Combine with statistical tests: PCA provides a global view; follow up with differential expression or other statistical analyses to confirm which variables drive the observed patterns.

Conclusion

Principal component analysis is a powerful exploratory tool for high-dimensional omics data. In Omics Studio, PCA can be run directly from the Explorer’s Expression View, allowing users to interactively select datasets, group samples, and visualise principal components. The orthogonal transformation underlying PCA ensures that each component captures maximal variance, making it easier to uncover strong patterns and identify outliers. By following the steps outlined in this guide, researchers can efficiently perform PCA and interpret the results to inform downstream analyses and experimental decisions.