The acquisition of actionable, meaningful insights from multiplex immunoassays requires a robust, accurate pipeline for data analysis and interpretation. This is also key to gaining, identifying, and validating the protein signatures required for vital clinical advancements.
As multiplex protein analysis can generate large datasets, the management, quality control, analysis, and interpretation of acquired data necessitate a uniform process. This process should be optimized with software and used alongside a set of tools able to ensure robust data analysis and high data consistency.
Four key steps should be considered when developing a statistical pipeline for multiplex immunoassays.
1. Data acquisition and cleanup: Gathering the data
Data must be initially combined with its clinical and/or experimental annotations, for example, severe versus mild infection or cases versus controls.
A normalization step is required to bridge projects when multiple datasets are involved. This involves adjusting medians from overlapping/bridging samples to ensure that the datasets are comparable.
The data must then be cleaned up by addressing potential misformatted datasets, missing titles, QC warnings, and removing samples found to be nonrepresentative in later steps of analysis.
2. Quality control and exploratory data analysis: Confirming that the data looks as expected
The overall shape of the data and specific data points must then be evaluated. The goal of this step is to determine whether the data is suitable for use as is or requires editing before use.
Common means of confirming data quality include looking for outliers, odd or non-normally distributed data, or other unusual configurations; using principal component analysis plots; and identifying any samples that fall outside standard ranges.
3. Statistical analysis: Using the data to answer the selected biological question
It is important that the data corresponds to the researcher’s basic assumptions after the acquisition, cleanup, and QC steps. This involves selecting a statistical test that matches the study and is most fit to answer the biological question.
Annotation and visualization are also implemented to present the data in a form that conveys the key results in an accessible and authentic fashion: for example, using volcano plots and box plots, aligning annotations with existing knowledge of key proteins or pathways of interest, or highlighting statistically significant proteins after adjusting for multiple testing.
4. Biological interpretation: Understand how these proteins relate to the biological question
Further research insights can be obtained by applying additional biological context once the data analysis has been finalized. For example, it may be prudent to assess how the differentially expressed proteins are related to the pathway or disease of interest.
It is possible to guide this process using a comprehensive database of annotations, providing literature- and data-derived information on disease-related biomarkers and pathway coverage, alongside scores and rankings of connections.
Pathway enrichments can also be looked at in a more quantitative way. This alternative approach is useful when attempting to identify and generate new hypotheses and new pathways of interest.
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