Author:
Mowoe Metoboroghene Oluwaseyi,Garnett Shaun,Lennard Katherine,Talbot Jade,Townsend Paul,Jonas Eduard,Blackburn Jonathan Michael
Abstract
Abstract
Background
The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity.
Results
Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively.
Conclusions
Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application (https://metaomics.uct.ac.za/shinyapps/Pro-MAP/.) for those not well versed in the R programming language.
Funder
Andrea Fine Foundation
National Research Foundation (NRF) South African Research Chair (SARChI) grant
Medical Research Council grant
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
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