Olink Data Analysis Tutorial: Common Tools, Software, and R Package Recommendations

After conducting proteomics research using the Olink Explore or Explore HT platform, researchers ultimately obtain the core results in the form of an NPX (Normalized Protein eXpression) matrix. However, NPX data is not plug-and-play; reliable differential protein, pathway information, and integration results require systematic data processing and analysis workflows. This article systematically introduces the basic steps of Olink data analysis and recommends commonly used software and R packages to help research teams efficiently transform raw matrices into publishable results.

 

I. Basic Workflow of Olink Data Analysis

1. Preprocessing of NPX Data

(1) Quality control: Remove proteins with a high proportion (>20%) below the LOD (Limit of Detection);

(2) Missing value imputation: Common methods include median imputation or KNN imputation;

(3) Batch correction: Control cross-plate effects through Olink's internal standardization or R packages like limma's removeBatchEffect.

 

2. Differential Analysis and Statistical Methods

(1) Inter-group differential detection: Use common t-tests, ANOVA, or non-parametric tests, combined with FDR adjustment;

(2) Fold change conversion: ΔNPX can be converted to fold expression using the formula Fold Change = 2^(ΔNPX);

(3) Result presentation: Commonly use Volcano Plot to visually display significance and fold changes.

 

3. Biological Interpretation and Downstream Analysis

(1) Functional annotation: Identify relevant pathways using databases like KEGG, Reactome;

(2) Clustering and pattern analysis: Use heatmaps, PCA, UMAP, etc., to reveal sample grouping characteristics;

(3) Multi-omics integration: Combine with transcriptomics and metabolomics to enhance biological interpretation depth.

 

II. Commonly Used Olink Analysis Tools and Software

1. Graphical Tools (Suitable for Beginners)

（1）Olink® Insights

① Olink's official online visualization platform can quickly generate Volcano Plots, heatmaps, and differential analysis results;

② Suitable for users without programming experience for preliminary exploration.

 

（2）Perseus

① Free proteomics data processing software that supports differential analysis, clustering, and pathway annotation;

② User-friendly interface, suitable for interactive analysis of small to medium-sized projects.

 

2. Commonly Used Olink Statistical Software

① GraphPad Prism: Suitable for univariate statistical analysis and simple visualization;

② SPSS or R language: Suitable for complex multi-factor analysis and modeling.

 

III. Recommended R Packages for Olink Data Analysis

1. Preprocessing and Differential Analysis

（1）limma

① Efficient batch effect removal, linear model differential analysis;

② Suitable for standardized analysis workflows of medium to large datasets.

 

（2）edgeR / DESeq2

① Originally used for RNA-seq but also applicable to NPX matrices, supports normalization and differential detection;

② Particularly suitable for large cohort studies with replicate samples.

 

2. Visualization and Multidimensional Analysis

(1) ggplot2: Create Volcano Plots, box plots, trend charts, etc.;

(2) ComplexHeatmap: Generate high-quality heatmaps and group clustering;

(3) FactoMineR and factoextra: PCA, clustering analysis, and sample distribution visualization.

 

3. Biological Interpretation and Pathway Analysis

（1）clusterProfiler

① Support for KEGG, GO, Reactome pathway enrichment analysis;

② Can generate high-quality visualizations like bubble charts, bar charts, etc.

 

（2）fgsea

① Quickly perform GSEA (Gene Set Enrichment Analysis), suitable for exploratory projects;

② Directly applicable to NPX differential expression ranking data.

 

IV. Common Issues and Optimization Suggestions

1. Can NPX results be directly compared across projects?

(1) Not recommended, as there are differences in NPX normalization across different batches;

(2) If integrating multiple batches, additional cross-batch standardization is required.

 

2. How to handle low-abundance proteins below the LOD?

(1) If the LOD proportion is low, use half-LOD values to avoid statistical bias;

(2) If the LOD proportion is high, it is advisable to exclude the protein or only use for qualitative reference.

 

3. Common thresholds for differential screening

(1) Fold Change ≥2 and FDR <0.05 are common standards;

(2) Thresholds can be adjusted based on project nature (exploratory vs. validation).

 

The Olink proteomics platform provides researchers with high-throughput, low-sample detection capabilities, but real value lies in high-quality data analysis and interpretation.Through appropriate tools, R packages, and statistical strategies, the NPX matrix can be transformed into conclusions with clinical and scientific value. Biotage Parker Biotech leverages a professional analysis team and multi-platform integration capabilities to help research teams efficiently transition from raw data to results, achieving effective implementation of proteomics research.

 

Biotech by Biotai Park - Characterization of Biological Products, Premier Mass Spectrometry Services for Multi-Omics

 

Related Services:

Olink Proteomics