Protocol for achieving enhanced snRNA-seq data quality using Quality Clustering

Protocol for achieving enhanced snRNA-seq data quality using Quality Clustering

Summary: 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 s...

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Bibliographic Details
Main Authors: Paavo J. Tavi, Johannes Ojanen, Pia Laitinen, Suvi Linna-Kuosmanen
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:STAR Protocols
Subjects:
Bioinformatics
Single Cell
RNA-seq
Computer sciences
Online Access:http://www.sciencedirect.com/science/article/pii/S2666166725001236
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Summary:Summary: 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 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
ISSN:2666-1667

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