Please read Dr. Wei’s article in Methods titled, “Model-based autoencoders for imputing discrete single-cell RNA-seq data.“
A cell is a fundamental unit in biology. Recent revolutionary biotechnology, single-cell RNA sequencing (scRNA-seq), has made it possible to profile all gene expression activities (transcriptome) at the single cell level. This new technology is so powerful that it has helped researchers to understand complex biological questions in many applications better. As a result, the past few years have witnessed a surging number of studies based on scRNA-seq. Despite the advances in measuring technologies, the analysis of scRNA-seq data remains a statistical and computational challenge.The scRNA-seq generates discrete count data, which takes the form of n×p matrices, representing the read counts mapped to the p genes across n cells. The read count values represent the relative expression levels of each gene in a cell. Higher count value means higher relative expression levels. To compare gene expression levels across samples/cells, the read counts need to be normalized by the library size and other biases. To read the full article.
Model-based autoencoders for imputing discrete single-cell RNA-seq data. Tian T, Min MR, Wei Z. 2021 Aug;192:112-119. PMID: 32971193 DOI: 10.1016/j.ymeth.2020.09.010. Epub 2020 Sep 22.