SatuRn: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications
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SatuRn : Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications. / Gilis, Jeroen; Vitting-Seerup, Kristoffer; Van den Berge, Koen; Clement, Lieven.
In: F1000Research, Vol. 10, 374, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - SatuRn
T2 - Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications
AU - Gilis, Jeroen
AU - Vitting-Seerup, Kristoffer
AU - Van den Berge, Koen
AU - Clement, Lieven
N1 - Publisher Copyright: © 2022 Gilis J et al.
PY - 2022
Y1 - 2022
N2 - Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive single-cell transcriptome sequencing (scRNA-seq) datasets. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs, and scaling to scRNA-seq applications.
AB - Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive single-cell transcriptome sequencing (scRNA-seq) datasets. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs, and scaling to scRNA-seq applications.
KW - Differential transcript usage
KW - RNA-seq
KW - SatuRn
KW - Single-cell transcriptomics
KW - Splicing
KW - Statistical framework
U2 - 10.12688/f1000research.51749.2
DO - 10.12688/f1000research.51749.2
M3 - Journal article
C2 - 36762203
AN - SCOPUS:85147555385
VL - 10
JO - F1000Research
JF - F1000Research
SN - 2046-1402
M1 - 374
ER -
ID: 343299032