Mozaffari, S. | Search bioRxiv

bioRxiv2018

Versatile Multi-Detector Scheme for Adaptive Optics Scanning Laser Ophthalmoscopy

Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) is a powerful tool for imaging the retina at high spatial and temporal resolution. In this paper, we present a multi-detector scheme for AOSLO which has two main configurations: pixel reassignment and offset aperture imaging. In this detection scheme, the single element detector of the standard AOSLO is replaced by a fiber bundle which couples the detected light into multiple detectors. The pixel reassignment configuration allows for more light throughput while maintaining optimal confocal resolution. The increase in signal-to-noise ratio (SNR) from this configuration can improve the accuracy of motion registration techniques. The offset aperture imaging configuration enhances the detection of multiply scattered light, which improves the contrast of retinal vasculature and inner retinal layers similar to methods such as nonconfocal split-detector imaging and multi-offset aperture imaging.

bioengineering↗

bioRxiv2018

Heritability Estimation and Differential Analysis with Generalized Linear Mixed Models in Genomic Sequencing Studies

MotivationGenomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large data sets.\n\nResultsHere, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites.\n\nAvailability and implementationPQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html.\n\nContactXZ (xzhousph@umich.edu)\n\nSupplementary informationSupplementary data are available online.

genomics↗