QClus: a droplet filtering algorithm for enhanced snRNA-seq data quality in challenging samples - UTU Research Portal

A1 Refereed original research article in a scientific journal

QClus: a droplet filtering algorithm for enhanced snRNA-seq data quality in challenging samples

Authors: Schmauch, Eloi; Ojanen, Johannes; Galani, Kyriakitsa; Jalkanen, Juho; Harju, Kristiina; Hollmén, Maija; Kokki, Hannu; Gunn, Jarmo; Halonen, Jari; Hartikainen, Juha; Kiviniemi, Tuomas; Tavi, Pasi; Kaikkonen, Minna U; Kellis, Manolis; Linna-Kuosmanen, Suvi

Publisher: OXFORD UNIV PRESS

Publishing place: OXFORD

Publication year: 2025

Journal: Nucleic Acids Research

Journal name in source: NUCLEIC ACIDS RESEARCH

Journal acronym: NUCLEIC ACIDS RES

Volume: 53

Issue: 1

Number of pages: 13

ISSN: 0305-1048

eISSN: 1362-4962

DOI: https://doi.org/10.1093/nar/gkae1145

Web address : https://doi.org/10.1093%2Fnar%2Fgkae1145

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/477906384

Abstract

Single-nuclei RNA sequencing remains a challenge for many human tissues, as incomplete removal of background signal masks cell-type-specific signals and interferes with downstream analyses. Here, we present Quality Clustering (QClus), a droplet filtering algorithm targeted toward challenging samples. QClus uses additional metrics, such as cell-type-specific marker gene expression, to cluster nuclei and filter empty and highly contaminated droplets, providing reliable filtering of samples with varying number of nuclei and contamination levels. In a benchmarking analysis against seven alternative methods across six datasets, consisting of 252 samples and over 1.9 million nuclei, QClus achieved the highest quality in the greatest number of samples over all evaluated quality metrics and recorded no processing failures, while robustly retaining numbers of nuclei within the expected range. QClus combines high quality, automation and robustness with flexibility and user-adjustability, catering to diverse experimental needs and datasets.

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Funding information in the publication:
Aarne Koskelo Foundation [to E.S. and S.L.K.]; Academy of Finland [333021 to M.U.K and 342074 to S.L.K.]; Antti and Tyyne Soininen Foundation [to E.S.]; University of Eastern Finland [to E.S.]; Finnish Cultural Foundation [to E.S.] Finnish Foundation for Cardiovascular Research [to M.U.K. and S.L.K.]; Maud Kuistila Memorial Foundation [to S.L.K.]; Orion Research Foundation [to E.S. and S.L.K.]; Saastamoinen Foundation [to E.S.]; Sigrid Juselius Foundation [to M.U.K. and S.L.K.]; Väisälä Fund [to E.S.]; Yrjö Jahnsson Foundation [to E.S. and S.L.K.]. Funding for open access charge: The Academy of Finland.