NanoStringDiff: a novel statistical method for differential expression analysis based on NanoString nCounter data

The advanced medium-throughput NanoString nCounter technology has been increasingly used for mRNA or miRNA differential expression (DE) studies due to its advantages including direct measurement of molecule expression levels without amplification, digital readout and superior applicability to formal...

Full description

Saved in:
Bibliographic Details
Published in:Nucleic acids research 2016-11, Vol.44 (20), p.e151-e151
Main Authors: Wang, Hong, Horbinski, Craig, Wu, Hao, Liu, Yinxing, Sheng, Shaoyi, Liu, Jinpeng, Weiss, Heidi, Stromberg, Arnold J, Wang, Chi
Format: Article
Language:English
Subjects:
Algorithms
Computational Biology - methods
Computer Simulation
Datasets as Topic
Gene Expression Profiling - methods
Gene Expression Regulation
Methods Online
MicroRNAs - genetics
Models, Statistical
Reproducibility of Results
RNA, Messenger - genetics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The advanced medium-throughput NanoString nCounter technology has been increasingly used for mRNA or miRNA differential expression (DE) studies due to its advantages including direct measurement of molecule expression levels without amplification, digital readout and superior applicability to formalin fixed paraffin embedded samples. However, the analysis of nCounter data is hampered because most methods developed are based on t-tests, which do not fit the count data generated by the NanoString nCounter system. Furthermore, data normalization procedures of current methods are either not suitable for counts or not specific for NanoString nCounter data. We develop a novel DE detection method based on NanoString nCounter data. The method, named NanoStringDiff, considers a generalized linear model of the negative binomial family to characterize count data and allows for multifactor design. Data normalization is incorporated in the model framework through data normalization parameters, which are estimated from positive controls, negative controls and housekeeping genes embedded in the nCounter system. We propose an empirical Bayes shrinkage approach to estimate the dispersion parameter in the model and a likelihood ratio test to identify differentially expressed genes. Simulations and real data analysis demonstrate that the proposed method performs better than existing methods.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkw677