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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Jun 12, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

从多模式测序数据中推断细胞类型的生物物理解释性推断.

Tara Chari¹, Gennady Gorin², Lior Pachter^3,4

¹Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.

Nature computational science

|September 24, 2024

概括

这项研究引入了机械的K-means (meK-means),一种用于分析多式单细胞基因组学数据的新方法. 它集成测量以揭示共享的生物物理状态,并改善细胞聚类以获得机械洞察力.

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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Last Updated: Jun 12, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Published on: May 22, 2017

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

科学领域:

单细胞基因组学单细胞基因组学
计算生物学是一种计算生物学.
系统生物学系统生物学

背景情况:

多模式单细胞基因组学可以同时测量多个细胞过程.
目前用于多式联网数据的细胞聚类方法通常使用临时方法,忽视数据属性.
准确的细胞类型确定对于理解细胞异质性和生物机制至关重要.

研究的目的:

在多式单细胞基因组学数据中开发一种可解释和一致的细胞群确定方法.
通过建模潜在的生物物理状态来整合不同的分子测量.
为定义基于共享细胞过程的细胞群提供一种机制框架.

主要方法:

开发了机械式K-means (meK-means),这是一个用于多式联络数据的新型集群算法.
综合新生的和成熟的mRNA测量使用转录的统一模型.
利用分子模式之间的因果关系和物理关系来定义集群.

主要成果:

通过整合多种数据模式,meK-means有效地对细胞进行聚类.
该方法确定了控制观察到的分子数量的共享转录动态.
集群是由底层细胞过程的参数定义的,提供机械解释性.

结论:

meK-means为多式联网单细胞数据集成和集群提供了一个原则性的方法.
这种方法可以对细胞异质性和动态进行更深入的机制研究.
它提供了一个新的范式来定义基于生物物理状态的细胞群,而不仅仅是分子形状.