计算机科学
代谢组学
可视化
软件
数据类型
数据库
基因组学
计算生物学
蛋白质组学
数据挖掘
工作流程
生物信息学
生物
基因组
生物化学
基因
程序设计语言
出处
期刊:Methods in molecular biology
日期:2019-09-30
卷期号:: 191-217
被引量:11
标识
DOI:10.1007/978-1-4939-9831-9_15
摘要
In this age of –omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.
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