Phenotype prediction from single-cell RNA-seq data using attention-based neural networks.

MOTIVATION: A patient's disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models. RESULTS: Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies. AVAILABILITY AND IMPLEMENTATION: The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT.

Lens centering error measurement based on subwavelength grating with power analysis.

This study presents a technique for measuring the centering error of a lens. The technique uses power analysis and is highly accurate. The module is designed by a prism that is patterned using a subwavelength grating combined with a reflection-centering system by using the -1st-order rays at 5 mW and 650 nm. The optical axis error is tested and analyzed by the powermeter by using Snell's law and diffractive characteristics. The resolution of the optical-centering error in the system is amplified by a factor of 4 compared with the centering error measuring system based on autocollimation. The technique proposed in this study improves the sensitivity of the instrument and reduces the requirements of the sensor at the end side based on the direction or energy variation of the light.