Accurate Detection of MicroRNAs from NanoString nCounter with a Latent Mixture Model

MicroRNAs (miRNA) are promising biomarker candidates for diagnosing neurodegenerative diseases due to their presence in easy-to-obtain biofluids. The NanoString nCounter is a popular platform measuring miRNA for it avoids amplification bias. Existing methods for nCounter data processing and analysis rely heavily on the handful of control probes and housekeeping genes for background estimation and/or normalization. Motivated by the observations from hundreds of samples compiled from multiple studies, we propose a multi-study joint processing method, multi-study miRNA detection (MMD). MMD is based on a latent mixture model that accounts for both probe-specific and sample-specific effects. The probe effects are estimated jointly from samples across studies. Sample-specific background and normalization factors are estimated from all probes instead of relying on a few controls. We demonstrate that MMD outperforms the built-in method from Nanostring in signal detection and has greater power in identifying differentially present miRNAs which are largely overlooked by alternative methods, in both simulation and real data comparison.