PCA vs PLS-DA vs OPLS-DA: Which One to Choose for Omics Data Analysis?

MetwareBio data analysis blog series

• How to understand the WGCNA analysis in publications? (1/2)
• Understanding WGCNA Analysis in Publications
• Harnessing the Power of WGCNA Analysis in Multi-Omics Data
• WGCNA Explained: Everything You Need to Know
• Omics Data Analysis Series

In numerous studies utilizing metabolomics and other omics approaches for biological discovery, multivariate analyses such as PCA, PLS-DA, and OPLS-DA are frequently employed to extract meaningful patterns from complex datasets. This raises an important question: What distinguishes PCA, PLS-DA, and OPLS-DA, and how do they influence the interpretation of biological data?

This article provides a comprehensive comparison of PCA vs PLS-DA vs OPLS-DA—each a type of multivariate analysis—highlighting their respective principles, advantages, and typical applications in metabolomics, proteomics, and other omics fields.

What is PCA analysis?

Principal Component Analysis (PCA), an unsupervised multivariate statistical analysis method, strategically employs orthogonal transformations. This approach transforms potentially correlated variables into linearly uncorrelated variables known as principal components. In essence, PCA compresses raw data into principal components to vividly describe the characteristics of the original dataset. PC1 embodies the most salient feature in a multidimensional data matrix, with PC2 capturing the next most significant feature, and so forth (Eriksson et al., 2006).

What is PLS-DA analysis?

Partial Least-Squares Discriminant Analysis (PLS-DA), a multivariate dimensionality reduction tool prevalent in chemometrics for over two decades, is recommended for omics data analysis. PLS-DA can be considered a "supervised" version of PCA, combining dimensionality reduction with group information consideration. As a result, it not only serves for dimensionality reduction but also facilitates feature selection and classification.

What is OPLS-DA analysis?

Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), as the name suggests, seamlessly integrates orthogonal signal correction (OSC) and PLS-DA methods. It adeptly decomposes the X matrix into Y-related and unrelated information, streamlining the selection of differential variables. Unlike PCA, OPLS-DA stands as a supervised discriminant analysis statistical method with a focus on the predictive component. You can quickly generate OPLS-DA plot for free with our Metware Cloud Platform. Watch this video tutorial on the right.

PLS-DA vs PCA: Key Differences and Use Cases in Omics Analysis

PCA vs PLS-DA vs OPLS-DA: Method Comparison Table

What is PCA analysis used for?

Beyond the mathematical basis, PCA has practical roles in ensuring data quality and exploring meaningful patterns in omics datasets.

Identifying Outliers and Biological Repeats

PCA is commonly used as a quality control tool in omics workflows.
By visualizing biological replicates in a PCA score plot, researchers can assess whether samples cluster tightly—indicating good repeatability—or show unwanted dispersion or outliers.
Outlier detection is critical in preventing false positives or negatives in downstream statistical analysis.
Samples that fall far from their group cluster should be excluded before performing differential analysis or pathway enrichment.

PCA score plots for biological replicates

For instance, Figure 1's left graph exhibits well-distributed biological replicates, making it conducive for subsequent differential metabolite screening. Conversely, the right graph showcases outlier samples, prompting the recommendation to eliminate such samples to circumvent false positives or negatives in subsequent differential metabolite selection.
You can quickly generate PCA plots for free using our Metware Cloud Platform. To see how it works, check out this video tutorial and start exploring today!

Discovering Primary Variation Trends

Another key function of PCA is to uncover the major sources of variation in the dataset.
Principal components are ordered by how much variance they explain, with PC1 accounting for the greatest difference among samples.
In a study involving two variables, such as breed and treatment temperature, resulting in four sample groups, PCA may reveal that breed contributes the most significant difference along PC1, followed by treatment temperature along PC2.
This insight allows researchers to understand which biological factors are most responsible for group separation before applying more complex supervised methods like PLS-DA.

PLS-DA vs OPLS-DA: What Are These Analyses Used For?

PLS-DA: PLS-DA builds upon PCA by incorporating group information, enabling the forcible grouping of data. This feature facilitates an intuitive examination of differences between various groups, making PLS-DA a crucial tool for screening differential metabolites. Through PLS-DA analysis, metabolites demanding focused attention—acting as major contributors to differences between treatments or groups—are pinpointed. 

OPLS-DA: Both PLS-DA and OPLS-DA can be utilized for the selection of differential metabolites. The key distinction lies in the inclusion of orthogonal correction signals in OPLS-DA, aiding in the filtration of errors introduced by non-experimental factors. Each OPLS-DA model is built with a single predictive component to ensure sufficient model performance. In a study involving drought-treated plants, for instance, slight differences in light intensity among treated plants could introduce metabolite variations. OPLS-DA efficiently filters out such false positives, directing attention to metabolites of genuine interest.OPLS-DA is particularly useful in analyzing spectral data to identify significant variables.

How These Methods Apply to Different Omics Fields

In metabolomics, PCA is often used for exploratory analysis, while PLS-DA and OPLS-DA help identify significant metabolite changes between groups.
In proteomics, OPLS-DA is especially useful for identifying protein biomarkers due to its improved interpretability.
In spatial metabolomics and multi-omics, these tools are used to distinguish tissue-specific patterns or integrate omics layers.

How to Choose the Right Multivariate Method

Choosing the right multivariate analysis method depends on your study objective and the nature of your data.

PCA is best suited for exploratory analysis. It helps visualize overall data structure, detect outliers, and evaluate biological replicates without relying on prior group labels.

PLS-DA is ideal for supervised analysis when groups are known. It enables effective classification and identification of differential features, making it useful for biomarker discovery and group separation.

OPLS-DA enhances PLS-DA by removing variations unrelated to class separation. This makes the model more interpretable and robust, especially when dealing with complex biological data with noise or batch effects.

In practice, a typical workflow might begin with PCA for quality control, followed by PLS-DA or OPLS-DA for refined classification and mechanistic insights.

FAQs – PCA, PLS-DA, and OPLS-DA

Q1: What is the main difference between PCA and PLS-DA?
PCA is an unsupervised method used to explore patterns in the data without considering group labels. PLS-DA, on the other hand, is a supervised technique that incorporates group information to achieve classification and identify significant variables. 

PLS-DA vs PCA: Key Differences and Use Cases in Omics Analysis

Q2: Why is OPLS-DA considered more interpretable than PLS-DA?
OPLS-DA separates the predictive variation (related to group separation) from orthogonal variation (unrelated noise), resulting in simplified models with clearer group distinctions and easier interpretation.

Q3: Can PCA and PLS-DA be used in the same workflow?
Yes. PCA is often used first for data quality assessment and outlier detection, followed by PLS-DA or OPLS-DA for deeper classification and differential analysis.

Q4: Which method is best for identifying key metabolites?
PLS-DA and OPLS-DA are both effective for identifying metabolites contributing to group differences. OPLS-DA is particularly useful when data contains noise, as it improves model robustness and clarity.

Summary

PCA, PLS-DA, and OPLS-DA analyses are commonly used statistical analysis methods in omics research. The choice of method depends on the research purpose and data characteristics. At MetwareBio's Boston laboratory, we offer extensive proteomics, metabolomics and multi-omics testing services, alongside comprehensive data analysis services. Access our free and user-friendly Metware Cloud Platform for seamless analysis of your multi-omics data. Have questions? We're here to offer guidance and support every step of the way!