Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations
Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations
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揭示单细胞和群体中具有异质遗传和环境背景的基因组轨迹
DOI：
10.1101/159913
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Campbell K
中科院分区：
文献类型：
--
作者：
Campbell K
伪时间算法可以用来从横截面数据集中提取潜在的时间信息，从而可以在收集真实时间序列数据具有挑战性或令人望而却步的情况下研究动态生物过程。计算技术出现在单细胞组学和癌症建模等领域，在这些领域中，伪时间可以用来了解细胞分化或肿瘤进展。然而，迄今为止的方法通常假定同质的遗传和环境背景，随着数据集的大小和复杂性的增长，这变得特别有限。作为对这一问题的解决方案，我们描述了一种新的统计框架，该框架在存在非均匀遗传、表型或环境背景的情况下学习伪时间轨迹。我们证明，这使我们能够确定这些因素和潜在的基因组轨迹之间的相互作用。通过将该模型应用于单细胞基因表达数据和群体水平的癌症研究，我们发现它揭示了遗传和环境因素以及基因在通路中的表达之间已知的和新的交互作用。我们提供了我们的方法PhenoPaThat https://github.com/kieranrcampbell/phenopath的R实现
Pseudotime algorithms can be employed to extract latent temporal information from crosssectional data sets allowing dynamic biological processes to be studied in situations where the collection of genuine time series data is challenging or prohibitive. Computational techniques have arisen from areas such as single-cell ‘omics and in cancer modelling where pseudotime can be used to learn about cellular differentiation or tumour progression. However, methods to date typically assume homogenous genetic and environmental backgrounds, which becomes particularly limiting as datasets grow in size and complexity. As a solution to this we describe a novel statistical framework that learns pseudotime trajectories in the presence of non-homogeneous genetic, phenotypic, or environmental backgrounds. We demonstrate that this enables us to identify interactions between such factors and the underlying genomic trajectory. By applying this model to both single-cell gene expression data and population level cancer studies we show that it uncovers known and novel interaction effects between genetic and enironmental factors and the expression of genes in pathways. We provide an R implementation of our methodPhenoPathat https://github.com/kieranrcampbell/phenopath
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DOI：
--
发表时间：
2015
期刊：
Foundations of Biomedical Knowledge Representation
影响因子：
--
作者：
A. Tucker;Yuanxi Li;S. Ceccon;S. Swift
通讯作者：
S. Swift
影响因子：
11.2
作者：
Facciabene A;Motz GT;Coukos G
通讯作者：
Coukos G
影响因子：
48
作者：
Jean Fan;N. Salathia;R. Liu;Gwendolyn E. Kaeser;Y. Yung;Joseph L. Herman;F. Kaper;Jian-Bing Fan
通讯作者：
Jean Fan;N. Salathia;R. Liu;Gwendolyn E. Kaeser;Y. Yung;Joseph L. Herman;F. Kaper;Jian-Bing Fan
影响因子：
50.3
作者：
Dong C;Yuan T;Wu Y;Wang Y;Fan TW;Miriyala S;Lin Y;Yao J;Shi J;Kang T;Lorkiewicz P;St Clair D;Hung MC;Evers BM;Zhou BP
通讯作者：
Zhou BP
更新随机网络以整合横截面和纵向研究
DOI：
--
发表时间：
2015
期刊：
Conference on Artificial Intelligence in Medicine in Europe
影响因子：
--
作者：
A. Tucker;Yuanxi Li
通讯作者：
Yuanxi Li