Improving the default data analysis workflow for large autoimmune biomarker discovery studies with ProtoArrays

Abstract

Contemporary protein microarrays such as the ProtoArray® are used for autoimmune antibody screening studies to discover biomarker panels. For ProtoArray data analysis, the software Prospector and a default workflow are suggested by the manufacturer. While analyzing a large data set of a discovery study for diagnostic biomarkers of the Parkinson’s disease (ParkCHIP), we have revealed the need for distinct improvements of the suggested workflow concerning raw data acquisition, normalization and preselection method availability, batch effects, feature selection, and feature validation. In this work, appropriate improvements of the default workflow are proposed. It is shown that completely automatic data acquisition as a batch, a re-implementation of Prospector’s pre-selection method, multivariate or hybrid feature selection, and validation of the selected protein panel using an independent test set define in combination an improved workflow for large studies.

Primary Central Nervous System (CNS) Lymphoma B Cell Receptors Recognize CNS Proteins

“…Although none of the PCNSL Igs was reactive with common Ags, the large-scale protein microarray, successfully used for autoimmune profiling in neurodegenerative and inflammatory disorders (13)(14)(15), identified 1547 proteins that were recognized by the 23 recAbs. Interestingly, recAbs frequently recognized proteins physiologically expressed by CNS neurons.…”

Protein microarray technology: Assisting personalized medicine in oncology (Review)

“…In addition, bioinformatics specific for protein microarray needs specific development in order to process large amounts of data arising. Analyzing protein microarray data implies the following steps: Data acquisition, data pre-processing, visualization, differential analysis, computational annotation and network analysis (52,53).…”

“…Protein concentrations in the samples, whether biological fluids, cell/tissue lysates, can have hundreds of fold different scale. Therefore, detection needs to cover concentrations over many orders of magnitude (53,54). An overview of major advantages and disadvantages for the most used protein microarrays variants (55-57) is presented in Table II.…”

Quality Control of Expression Profiling Data

“…Even where an attempt has been made to summarise gene expression data, it was only applied to microarrays [121,122] and not transferred to other types of expression data. What is also needed is a method which, once devised, does not require adjustment of sample data for it to work, as has been the case previously [123][124][125]. Moreover, even unique "tissue specific genes" might be of little practical use if they are expressed at low levels and would therefore be absent in many smaller libraries or not detected in smaller size samples, after all, the general tendency is to miniaturise the assays and samples with the ultimate goal of single cell analysis [126][127][128].…”