Related Experiment Video
Updated: Aug 5, 2026
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
Protocol for automated optimization of feature selection and clustering parameters in single-cell RNA-seq using
1Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Harvard Medical School, Boston, MA, USA.
Abstract:
Single-cell RNA sequencing (RNA-seq) analysis requires dataset-specific tuning of feature selection and clustering parameters. Here, we present a protocol for automated optimization of feature selection and clustering parameters in single-cell RNA-seq using scAutoTune. We describe steps for installing scAutoTune, preparing Seurat objects from public single-cell RNA-seq datasets, and performing grid-based parameter sweeping with optional Harmony batch correction. We then detail procedures for computing silhouette-based performance metrics, visualizing generalized additive model (GAM)-smoothed landscapes, and applying optimal settings to generate uniform manifold approximation and projection (UMAP) embeddings and interpretable clusters. For complete details on the use and execution of this protocol, please refer to Sun et al.1.
