使用MMIDAS共同推断单细胞数据集中的离散细胞类型和持续的特定类型变异性
Yeganeh Marghi1, Rohan Gala1, Fahimeh Baftizadeh1
1Allen Institute, 615 Westlake Ave N, Seattle, WA, USA.
bioRxiv : the preprint server for biology
|October 24, 2023
概括
我们开发了MMIDAS,这是一种结合混合模型和深度神经网络的无监督方法,可以准确识别数千种细胞类型及其特定变异性. 这种方法改善了用于生物研究的细胞类型定义.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 识别可复制细胞类型对于理解生物功能,发育,疾病和进化至关重要.
- 现有的细胞类型分析方法经常与大型数据集扎,导致许多细胞类型的稳定识别不准确.
研究的目的:
- 提出一种新的无监督方法,MMIDAS,用于对离散细胞类型和连续类型特定变异性的联合推断.
- 解决当前集群和潜伏因子建模方法在处理高维单细胞数据方面的局限性.
主要方法:
- 开发了MMIDAS,将通用混合模型与多臂深度神经网络集成在一起.
- 将MMIDAS应用于四个不同的脑细胞数据集,包括不同的技术,物种和实验条件.
主要成果:
- MMIDAS成功地识别了可复制的细胞类型,即使是处理数百或数千个不同的细胞群.
- 该方法准确地从单模和多模数据集中推断出细胞类型依赖的连续变异性.
结论:
- 在复杂的生物系统中,MMIDAS为细胞类型定义和可变性分析提供了强大的,可扩展的解决方案.
- 这一进步有助于更深入地了解细胞异质性及其在生物过程中的作用.
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