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Naslov:CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues
Avtorji:ID Vathrakokoili Pournara, Anna (Avtor)
ID Miao, Zhichao (Avtor)
ID Yilimaz Beker, Ozgur (Avtor)
ID Nolte, Nadja Franziska (Avtor)
ID Brazma, Alvis (Avtor)
ID Papatheodorou, Irene (Avtor)
Datoteke:URL URL - Izvorni URL, za dostop obiščite https://doi.org/10.1093/bioadv/vbae048
 
Jezik:Angleški jezik
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:Logo NIB - Nacionalni inštitut za biologijo
Povzetek:Motivation Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods. Results In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods.
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Datum objave:23.03.2024
Leto izida:2024
Št. strani:str. 1-18
Številčenje:Vol. 4, issue 1, [article no.] vbae048
PID:20.500.12556/DiRROS-28668 Novo okno
UDK:575.111
ISSN pri članku:2635-0041
DOI:10.1093/bioadv/vbae048 Novo okno
COBISS.SI-ID:273250563 Novo okno
Opomba:Soavtorji: Zhichao Miao, Ozgur Yilimaz Beker, Nadja Nolte, Alvis Brazma, Irene Papatheodorou; Nasl. z nasl. zaslona; Opis vira z dne 26. 3. 2026;
Datum objave v DiRROS:27.03.2026
Število ogledov:47
Število prenosov:8
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del revije

Naslov:Bioinformatics advances
Skrajšan naslov:Bioinform. adv.
Založnik:Oxford University Press
ISSN:2635-0041
COBISS.SI-ID:103549955 Novo okno

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:RNA-Seq posamezne celice, dekonvolucija celičnih tipov, ocena


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