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 ## Description
 
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-Single-cell RNA sequencing (scRNA-seq) is a well-established technique in the era of next-generation sequencing. From identifying novel cell types and characterizing responses to treatment, its widespread applications are undeniably valuable to the field of molecular and cell biology. The reliability of its application, however, depends entirely on the quality of upstream pre-processing, with cell-level filtering being an important component.
+Single-cell RNA sequencing (scRNA-seq) is a well-established technique in the era of next-generation sequencing. From identifying novel cell types and characterizing responses to treatment, its widespread applications are undeniably valuable to the field of molecular and cell biology. The reliability of these applications, however, depend entirely on the quality of upstream pre-processing, with cell-level filtering being an important component.
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 For droplet-based protocols, _low quality_ cells are those that originate from droplets that contain more than one cell (doublet), no cells (empty droplet), or a damaged cell. While there are many scRNA-seq quality control tools available to identify doublets and empty droplets, few are specialised in identifing damaged cells. Damaged cell detection is more often achieved by setting thresholds for metrics such as average mitochondrial gene expression or feature counts per droplets. These thresholds, even when dynamically calculated, vary across cell types, tissues, treatment conditions, and species, with no established ground truth to define them precisely in any context. As a result, applying them in isolation can easily disrupt the delicate balance to become too stringent, excluding many true cells from downstream analysis, or too lenient, letting many contaminating damaged cells remain. But searching for true damaged droplets in a sample-specific manner is tedious and not always intuitive, leading to user-defined filtering that lacks reproducibility.