Selecting the most appropriate time points to profile in high-throughput studies

被引：20

作者：

Kleyman, Michael ^{[1
]}

Sefer, Emre ^{[1
]}

Nicola, Teodora ^{[2
]}

Espinoza, Celia ^{[3
,4
]}

Chhabra, Divya ^{[3
,4
]}

Hagood, James S. ^{[3
,4
]}

Kaminski, Naftali ^{[5
]}

Ambalavanam, Namasivayam ^{[2
]}

Bar-Joseph, Ziv ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Sch Comp Sci, Machine Learning & Computat Biol, Pittsburgh, PA 15213 USA

[2] Univ Alabama Birmingham, Dept Pediat, Div Neonatol, Birmingham, AL USA

[3] Univ Calif San Diego, Dept Pediat, Div Resp Med, San Diego, CA 92103 USA

[4] CARady Childrens Hosp San Diego, San Diego, CA USA

[5] Yale Univ, Sch Med, Sect Pulm Crit Care & Sleep Med, New Haven, CT USA

来源：

ELIFE | 2017年 / 6卷

基金：

美国国家卫生研究院;

关键词：

EMBRYONIC STEM-CELLS; GENE-EXPRESSION; LUNG DEVELOPMENT; DNA METHYLATION; GROWTH-FACTOR; MOUSE LUNG; MURINE; PATTERNS; IDENTIFICATION; SIGNATURE;

D O I：

10.7554/eLife.18541

中图分类号：

Q [生物科学];

学科分类号：

090105 [作物生产系统与生态工程];

摘要：

Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments.

引用

页数：30

共 64 条

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