The motivation behind DISCO
Last updated
Last updated
Five years ago, before I started single-cell research, I was skeptical about single-cell techniques. At that time, the quality of single-cell data was limited, with low UMIs per cell and smaller cell counts per library. However, the field has advanced tremendously over the last five years. Improvements in single-cell RNA-seq have elevated data quality, offering higher UMI counts and larger cell libraries (see figure below). More importantly, single-cell approaches now reveal insights that bulk RNA-seq simply cannot capture, reshaping our understanding by uncovering previously hidden biological nuances. Many findings once established by bulk RNA-seq have been redefined through single-cell analyses.
In 2020, I joined Dr. Jinmiao Chen's lab. In the beginning, I worked on many projects involving single-cell data analysis. Every day, I had conversations with my supervisor, Dr. Jinmiao, as follows:
I soon grew tired of these repetitive tasks and began searching for ways to automate them. As single-cell techniques grew in popularity, these tasks became essential not only for my work but for others in the field as well. This realization became my initial motivation to develop DISCO
My initial motivation for creating the DISCO database was to streamline my own workflow and automate single-cell analysis, allowing me to dedicate more time to learning and exploring new areas. In fact, many of the functions in DISCO were originally designed for my own projects and have become essential tools in my work. However, I’m confident that many researchers in this field face similar challenges, and DISCO can be just as beneficial for them.
As DISCO’s user base has grown, I’ve also incorporated feedback from our users, including colleagues in my lab and my wife, who is an enthusiastic user of DISCO! Some functions, like scEnrichment—which enables users to input DEG lists to infer cell types or phenotypes—were developed in response to their valuable suggestions.
Repository
Sample data and metadata
Atlas
Integrated data with manual annotation
Cell Type
Markers of cell types and their ontology
