scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies

dc.authoridDogan, Berat/0000-0003-4810-1970
dc.authoridBaran, Yusuf/0000-0003-3423-0771
dc.authorwosidDogan, Berat/AAJ-7288-2020
dc.contributor.authorBaran, Yusuf
dc.contributor.authorDogan, Berat
dc.date.accessioned2024-08-04T20:53:24Z
dc.date.available2024-08-04T20:53:24Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractSingle-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expres-sion and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preser-ving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUEBITAK) [120C152]en_US
dc.description.sponsorshipThis study was supported by The Scientific and Technological Research Council of Turkey (TUEBITAK) [120C152 to B.D.].en_US
dc.identifier.doi10.1016/j.compbiomed.2023.106634
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid36774895en_US
dc.identifier.scopus2-s2.0-85147661684en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2023.106634
dc.identifier.urihttps://hdl.handle.net/11616/101164
dc.identifier.volume155en_US
dc.identifier.wosWOS:000946722500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMarker gene selectionen_US
dc.subjectscRNA-seqen_US
dc.subjectSpatial transcriptomicsen_US
dc.titlescMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studiesen_US
dc.typeArticleen_US

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