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CosGeneGate 为单细胞分析选择多功能且可靠的生物标志物。

CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis.

机构信息

Department of Biostatistics, Yale University, New Haven, CT, 06520, United States.

Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06520, United States.

出版信息

Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae626.

Abstract

MOTIVATION

Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types.

RESULTS

Here, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.

摘要

动机

选择具有代表性的基因或标记基因来区分细胞类型是单细胞测序分析中的一项重要任务。尽管已经提出了许多方法来选择标记基因,但所选的基因可能存在冗余性和/或不表现出细胞类型特异性表达模式来区分细胞类型。

结果

在这里,我们提出了一个新的模型,称为 CosGeneGate,用于选择标记基因以进行更有效的标记选择。CosGeneGate 的灵感来自于结合基于细胞类型分类准确性和标记基因特异性表达模式选择标记基因的优势。我们证明了 CosGeneGate 选择的标记基因在各种下游分析中的性能优于现有方法,包括公共数据集和新测序数据集。可以在以下网站上找到 CosGeneGate 为人类主要细胞类型和组织鉴定的非冗余标记基因:https://github.com/VivLon/CosGeneGate/blob/main/marker%20gene%20list.xlsx。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ff/11596696/31b7a9e7827a/bbae626f1.jpg

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