TY - JOUR
T1 - NanoStringDiff
T2 - A novel statistical method for differential expression analysis based on NanoString nCounter data
AU - Wang, Hong
AU - Horbinski, Craig
AU - Wu, Hao
AU - Liu, Yinxing
AU - Sheng, Shaoyi
AU - Liu, Jinpeng
AU - Weiss, Heidi
AU - Stromberg, Arnold J.
AU - Wang, Chi
N1 - Publisher Copyright:
© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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U2 - 10.1093/nar/gkw677
DO - 10.1093/nar/gkw677
M3 - Article
C2 - 27471031
AN - SCOPUS:85016102098
SN - 0305-1048
VL - 44
SP - e151
JO - Nucleic Acids Research
JF - Nucleic Acids Research
IS - 20
ER -