What does the Olink NPX value represent? How to correctly interpret protein expression data
In Olink's proteomics analysis based on the Proximity Extension Assay (PEA), the main outcome measure obtained by researchers is the NPX (Normalized Protein eXpression) value. NPX is a unique data unit of Olink, reflecting the relative abundance of proteins. It is rigorously standardized, background-subtracted, and log-transformed to enable comparison across samples and batches. Correctly understanding the meaning and interpretation of NPX is crucial to ensuring the reliability of biomarker screening and subsequent research.
1. Basic Concept of NPX Values
1. Definition of NPX
(1) NPX is a proprietary data unit of Olink used to represent the relative abundance of target proteins;
(2) It is derived from the original signals (Cq values from qPCR or tag counts from NGS) through normalization, background subtraction, and log2 transformation;
(3) The unit is typically on a 'log2 scale,' with higher values indicating higher relative protein abundance.
2. NPX is not an Absolute Concentration
(1) NPX reflects relative changes between samples but cannot directly correspond to specific concentrations in pg/mL or ng/mL;
(2) For absolute quantification, external standards or other detection methods (such as ELISA or mass spectrometry) are needed for calibration.
2. Calculation Process of NPX Values
1. Collection of Raw Signals
(1) Record the cycle threshold (Cq) in the qPCR method and the sequence tag count in the NGS method;
(2) Remove blank wells and outliers of reference genes to ensure the quality of basic data.
2. Standardization and Logarithmic Transformation
(1) Correct batch effects using Olink internal controls (reference proteins, amplification controls);
(2) After normalization, perform a log2 transformation to output stable NPX values, commonly ranging from -5 to 15.
3. Quality Control Steps
(1) Signals below the LOD (limit of detection) are marked as missing or low confidence;
(2) Use standards or replicate samples to check batch-to-batch consistency, with CV usually controlled within 10%.
3. How to Correctly Interpret NPX Data
1. Analysis of Relative Changes
(1) Focus on the differences in NPX between groups rather than the absolute values;
(2) Common statistical methods include t-test, ANOVA, or non-parametric tests, combined with FDR correction to screen for differential proteins.
2. Biological Interpretation
(1) It is recommended to select candidate proteins by combining Fold Change (2 to the power of difference) with p-values;
(2) Functional analysis can be performed by combining pathway enrichment (KEGG/Reactome) and multi-omics data (transcriptomics, metabolomics).
3. Precautions
(1) NPX values from different projects cannot be directly compared horizontally due to differences in normalization and batch effects;
(2) For critical expression or low-abundance proteins, pay attention to LOD markings to avoid over-interpretation.
4. Support from Biotech Pioneer
1. Data Processing and Analysis Services
(1) Provide NPX standardization, differential analysis, pathway annotation, and visualization reports;
(2) Assist researchers in selecting reliable candidate biomarkers based on NPX results.
2. Integrated Validation Solutions
(1) Provide ELISA or mass spectrometry validation for key proteins, converting NPX to absolute concentration;
(2) Deliver complete data packages that meet publication and clinical research standards.
NPX is the core indicator for interpreting Olink's proteomics results, representing the standardized relative protein abundance.Scientifically understanding the statistical and biological significance of NPX helps researchers more accurately identify candidate markers and conduct subsequent research. Biotech Pioneer, with its extensive project experience and dual-platform validation capabilities, helps research teams from NPX data interpretation to results transformation, facilitating the entire process of proteomics research.
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