Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/17190
Title: PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization.
Authors: Da Gama Duarte, Jessica;Goosen, Ryan W;Lawry, Peter J;Blackburn, Jonathan M
Affiliation: Department of Integrative Biomedical Sciences & Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa. jessica.duarte@onjcri.org.au.. Tumour Immunology Laboratory, Olivia Newton-John Cancer Research Institute/School of Cancer Medicine, La Trobe University, Level 5, ONJCWC, 145 Studley Road, Heidelberg, VIC, 3084, Australia. jessica.duarte@onjcri.org.au..
Department of Integrative Biomedical Sciences & Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa..
Olivia Newton-John Cancer Research Institute/School of Cancer Medicine, La Trobe University, Level 5, ONJCWC, 145 Studley Road, Heidelberg, VIC, 3084, Australia..
Department of Integrative Biomedical Sciences & Institute for Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.. Blackburn Laboratory, N3.03, Wernher & Beit Building North, Institute of Infectious Disease & Molecular Medicine, UCT Faculty of Health Sciences, Observatory, Cape Town, 7925, South Africa..
Issue Date: 27-Feb-2018
EDate: 2018
Citation: BMC research notes 2018-02-27; 11(1): 156
Abstract: Protein microarrays provide a high-throughput platform to measure protein interactions and associated functions, and can aid in the discovery of cancer biomarkers. The resulting protein microarray data can however be subject to systematic bias and noise, thus requiring a robust data processing, normalization and analysis pipeline to ensure high quality and robust results. To date, a comprehensive data processing pipeline is yet to be developed. Furthermore, a lack of analysis consistency is evident amongst different research groups, thereby impeding collaborative data consolidation and comparison. Thus, we sought to develop an accessible data processing tool using methods that are generalizable to the protein microarray field and which can be adapted to individual array layouts with minimal software engineering expertise. We developed an improved version of a previously developed pipeline of protein microarray data processing and implemented it as an open source software tool, with particular focus on widening its use and applicability. The Protein Microarray Analyser software presented here includes the following tools: (1) neighbourhood background correction, (2) net intensity correction, (3) user-defined noise threshold, (4) user-defined CV threshold amongst replicates and (5) assay controls, (6) composite 'pin-to-pin' normalization amongst sub-arrays, and (7) 'array-to-array' normalization amongst whole arrays.
URI: http://ahro.austin.org.au/austinjspui/handle/1/17190
DOI: 10.1186/s13104-018-3266-0
ORCID: http://orcid.org/0000-0003-4289-5204
PubMed URL: 29482592
Type: Journal Article
Subjects: PMA
Protein Microarray Analyser
Protein microarrays
Appears in Collections:Journal articles

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