Abstract Number:
3451
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Chang Yu (1), Zhijin Wu (2)
Institutions:
(1) N/A, N/A, (2) Brown University, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
MicroRNAs (miRNAs) are promising biomarker candidates for their association with a wide range of diseases and their presence in easy-to-obtain biofluids. Since many extracellular miRNAs have concentrations that are often below or near the limit of detection, it is more appropriate to evaluate them as binary biomarkers than as continuous or count variables. Similar to other technologies, the binary detection of a miRNA molecule is influenced by technical variations, which we refer to as the sample-specific sensitivity. We propose a new likelihood ratio test that accounts for the sample-specific sensitivity and compare it to a binomial test which assumes all samples having the same sensitivity equals one. We focus on the NanoString nCounter data as an example for estimating the sample-specific sensitivities by pooling information across all features. With simulations, we demonstrate that, when the sample qualities are not balanced between comparison groups, the proposed test remains valid with stronger statistical power and controlled false discovery rate. Additionally, we provide applications of the new test procedure to publicly available nCounter data sets from the GEO database.
Keywords:
Sample-Specific Sensitivity|Binary Biomarker|MicroRNA|NanoString nCounter|Statistical Test| Biomarker Identification
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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