Single-cell RNA sequencing (scRNA-seq) is an exciting tool in biology that lets researchers dive deep into the activities of individual cells. Imagine being able to see what each cell is doing in a tissue sample—what genes are active, and how those activities differ between cell types. This level of detail helps scientists understand complex biological processes and identify special “marker genes” that are unique to specific cell types.
However, scRNA-seq becomes trickier when samples come from multiple donors. Each person’s genetic background introduces variation, meaning that a gene active in a specific cell type in one donor may not show the same behavior in another. This makes it challenging to consistently detect cell-type-specific genes across a population.
The new statistical tool scCTS, developed by researchers at Emory University is designed to help researchers identify cell type-specific genes from large scRNA-seq datasets, even when data is collected from multiple individuals. Traditional methods often miss important genes due to the variation between donors, but scCTS accounts for this complexity by using a statistical approach that works across population-level data.
By identifying more consistent and biologically meaningful cell-type-specific genes, scCTS offers insights that traditional methods might overlook. This is especially useful for studying complex diseases, immune responses, or development, where small differences in gene activity can have major biological consequences.
Identifying unique marker genes for specific cell types is essential for understanding health and disease. With more reliable methods like scCTS, researchers can better pinpoint which genes are associated with certain cell types, leading to improved diagnostics, personalized medicine, and new therapeutic targets.
For example, in diseases like cancer or autoimmune disorders, understanding how gene activity changes within individual cell types across a population could reveal new pathways for treatment. The scCTS tool offers a way to uncover these crucial details.
In summary, scCTS makes it easier to extract meaningful insights from complex scRNA-seq datasets by addressing the variability between donors. With better tools for identifying cell type-specific genes, researchers can accelerate their understanding of biology and bring us closer to new medical breakthroughs.
Availability – The proposed method is implemented in an R package scCST, which is freely available on GitHub at https://github.com/ToryDeng/scCTS,
scCTS – identifying the cell type-specific marker genes from population-level single-cell RNA-seq
