Protocol for achieving enhanced snRNA-seq data quality using Quality Clustering
Year: 2025
Type: article
Abstract: Single-nucleus RNA sequencing (snRNA-seq) data analysis presents a challenge in samples that have high levels of ambient RNA contamination. Quality Clustering (QClus) removes empty and highly contaminated droplets by utilizing multiple contamination metrics. Here, we present the steps for snRNA-seq data preprocessing using the QClus algorithm. First, we describe how to set up a computational environment. Next, we demonstrate how to use QClus to remove highly contaminated droplets, and finally, we show how to visualize and evaluate the results. For complete details on the use and execution of this protocol, please refer to Schmauch et al. 1 • Instructions for QClus setup for preprocessing of snRNA-seq samples • Application of unspliced RNA counts for improved data quality • Steps to run QClus to filter out contamination • Guidance on QC step visualization for protocol evaluation Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Single-nucleus RNA sequencing (snRNA-seq) data analysis presents a challenge in samples that have high levels of ambient RNA contamination. Quality Clustering (QClus) removes empty and highly contaminated droplets by utilizing multiple contamination metrics. Here, we present the steps for snRNA-seq data preprocessing using the QClus algorithm. First, we describe how to set up a computational environment. Next, we demonstrate how to use QClus to remove highly contaminated droplets, and finally, we show how to visualize and evaluate the results.
Source: STAR Protocols
Institution: University of Eastern Finland
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,550
OpenAlex ID: W4409019265