How to Choose a Normalization Strategy for miRNA Quantitative Real-Time (qPCR) Arrays

Deo, AmeyaHögskolan i Skövde,Institutionen för vård och natur,Forskningscentrum för Systembiologi,Högskolan i Skövde, Skövde, Sweden (författare)

Low-density arrays for quantitative real-time PCR (qPCR) are increasingly being used as an experimental technique for miRNA expression profiling. As with gene expression profiling using microarrays, data from such experiments needs effective analysis methods to produce reliable and high-quality results. In the pre-processing of the data, one crucial analysis step is normalization, which aims to reduce measurement errors and technical variability among arrays that might have arisen during the execution of the experiments. However, there are currently a number of different approaches to choose among and an unsuitable applied method may induce misleading effects, which could affect the subsequent analysis steps and thereby any conclusions drawn from the results. The choice of normalization method is hence an important issue to consider. In this study we present the comparison of a number of data-driven normalization methods for TaqMan low-density arrays for qPCR and different descriptive statistical techniques that can facilitate the choice of normalization method. The performance of the normalization methods was assessed and compared against each other as well as against standard normalization using endogenous controls. The results clearly show that the data-driven methods reduce variation and represent robust alternatives to using endogenous controls.

NATURVETENSKAP Biologi Biokemi och molekylärbiologi hsv//swe
NATURAL SCIENCES Biological Sciences Biochemistry and Molecular Biology hsv//eng
miRNA
qPCR array normalization
quantitative real-time PCR
Natural sciences
Naturvetenskap
Medicine

Carlsson, JessicaÖrebro universitet,Högskolan i Skövde,Institutionen för vård och natur,Forskningscentrum för Systembiologi,Hälsoakademin,Tumörbiologi, Bioinformatik(Swepub:oru)jccn (författare)
Lindlöf, AngelicaHögskolan i Skövde,Institutionen för vård och natur,Forskningscentrum för Systembiologi,Högskolan i Skövde, Skövde, Sweden(Swepub:his)ling (författare)
Högskolan i SkövdeInstitutionen för vård och natur (creator_code:org_t)

Ingår i:Journal of Bioinformatics and Computational Biology: Imperial College Press9:6, s. 795-8120219-72001757-6334

Av författaren/redakt...: Deo, Ameya; Carlsson, Jessic ...; Lindlöf, Angelic ...

Om ämnet

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.