Reply to "Issues arising from benchmarking single-cell RNA sequencing imputation methods"

Issues arising from benchmarking single-cell RNA sequencing imputation methods

On June 25th, 2018, Huang et al. published a computational method SAVER on Nature Methods for imputing dropout gene expression levels in single cell RNA sequencing (scRNA-seq) data. Huang et al. performed a set of comprehensive benchmarking analyses, including comparison with the data from RNA fluorescence in situ hybridization, to demonstrate that SAVER outperformed two existing scRNA-seq imputation methods, scImpute and MAGIC. However, their computational analyses were based on semi-synthetic data that the authors had generated following the Poisson-Gamma model used in the SAVER method. We h…

用于非靶向代谢组学缺失值插补的多视图变分自编码器

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

针对非靶向代谢组学数据缺失问题，提出基于多视图变分自编码器的方法，整合全基因组测序数据与参考代谢物信息实现缺失值插补，性能优于传统方法，为精准医学中的多组学整合研究提供支撑。

High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis

In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. In this article, we introduce a surprisingly simple, interpretable, and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional lo…