eBook: Multiplex Technology Guide - 26

Tips for Improving Your
Proteomic Assay Data Analysis
Regardless of the analytical method, there are a number of
strategies to ensure data quality.
Heather Darby
To begin an assay development project, it often
helps to first define the desired end-product and
then work backwards to determine the best path to
your specific goal. Likewise, choosing the appropriate type of data analysis for your custom immunoassay is best done with an eye on the final product.

is present or absent. Qualitative tests include pregnancy tests or pathogen screening panels.
To build an algorithm that delivers qualitative results,
it's essential to have well-characterized samples
for evaluating and optimizing assay performance.
Results from known samples are collated and used
to identify a clear threshold that distinguishes samples into their appropriate groups. For example, in
an xMAP®-based assay, a threshold median fluorescence intensity (MFI) value is selected when it
appropriately discriminates between positive and
negative samples; all samples with an MFI below
the threshold value are then 'known negatives' and
all samples with MFIs above it are 'known positives.'
This threshold is then confirmed with blind samples
to ensure that the algorithm is functioning appropriately. This is the simplest data analysis option for
immunoassays on beads.

Broadly speaking, immunoassay results fall into
three categories: qualitative, semi-quantitative,
and quantitative. Qualitative analysis gives a simple binary result-positive or negative, for example. Semi-quantitative analysis categorizes the
data, but with more than two distinct options.
Quantitative analysis assigns a numerical value to
a sample based on the range of a standard material
of known concentration.

Qualitative analysis
Qualitative analysis delivers easily interpreted answers by qualifying results as yes/no or positive/
negative, for example. It is the preferred method in
many circumstances that don't require in-depth investigation, such as whether an analyte of interest

Semi-quantitative analysis
Semi-quantitative analysis is more accurately considered an expansion of qualitative analysis. Rather
than producing binary results, semi-quantitative

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