With the advancement of sequencing technology, cell separation, and whole-genome amplification techniques, single cell technology for genome sequencing is emerging gradually. In comparison to traditional genome sequencing at the multi-cellular level, single-cell sequencing can not only measure the gene expression level more accurately but also can detect a small amount of gene expression or rare noncoding RNA. This technology has garnered increasing interest among researchers engaged in single-cell studies in recent years.
Researchers from the Chinese University of Hong Kong have developed a reproducible computational workflow for scRNA-seq data analysis which including tasks like quality control, normalization, data correction, pseudotime analysis, copy number analysis, etc. The researchers illustrate the application of these steps using publicly available datasets and provide practical recommendations for their implementation. This study serves as a comprehensive tutorial for researchers keen on single-cell data analysis, aiding users in constructing and refining their own analysis pipelines.
Availability – The code used in this paper is publicly available in GitHub (https://github.com/OpenGene/scrnapip).