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cytoKernel:强大的内核嵌入式用于评估单细胞数据的差异表达

Tusharkanti Ghosh¹, Ryan M Baxter², Souvik Seal³

¹Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

bioRxiv : the preprint server for biology

|September 4, 2024

概括

我们开发了cytoKernel,这是一种基于内核的新型得分测试,用于分析单细胞RNA测序和细胞测量数据. 它检测了传统方法错过的微妙表达差异,改进了差异表达分析.

科学领域:

一个单细胞的奥米克.
计算生物学是一种计算生物学.
生物统计学生物统计学

背景情况:

现有的差分表达方法通常集中在总量测量上,缺少单细胞数据中的复杂模式.
高通量测序和细胞测量产生复杂的多模式数据分布.
检测微妙的,非全球表达变异对于理解细胞特征至关重要.

研究的目的:

引入cytoKernel,一个基于内核的强大得分测试,用于单细胞数据的微分表达式分析.
为了能够检测到总和微妙的差异表达模式.
提供一种利用单细胞数据的全部概率分布的方法.

主要方法:

内核嵌入被用来计算对象的概率分布之间的对分歧.
应用基于内核的分数测试来评估微分表达式.
该方法在模拟和真实单细胞RNA测序和质细胞计数据集上进行基准测试.

主要成果:

cytoKernel有效地控制了错误发现率 (FDR).
该方法在识别差异化模式方面,与现有方法相比,表现优越.
cytoKernel成功地检测出通常被传统方法忽视的微妙变异.

结论:

cytoKernel为单细胞基因组学和细胞计量中的差异表达分析提供了一种强大的新方法.
该方法提高了从高维单细胞数据中识别复杂生物变异的能力.
该cytoKernel R包可用于更广泛的科学应用.

关键词:

质量细胞计量 (Mass Cytometry) 是一种测量质量细胞的方法.差异化模式的差异化模式非参数方法非参数方法我们的 scRNAseqq.

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DNA Microarrays

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