转录组
计算机科学
管道(软件)
人工智能
图像处理
计算生物学
模式识别(心理学)
计算机视觉
图像(数学)
生物
基因
基因表达
遗传学
程序设计语言
作者
Thomas Stoeger,Nico Battich,Markus D. Herrmann,Yauhen Yakimovich,Lucas Pelkmans
出处
期刊:Methods
[Elsevier]
日期:2015-09-01
卷期号:85: 44-53
被引量:36
标识
DOI:10.1016/j.ymeth.2015.05.016
摘要
Single-cell transcriptomics has recently emerged as one of the most promising tools for understanding the diversity of the transcriptome among single cells. Image-based transcriptomics is unique compared to other methods as it does not require conversion of RNA to cDNA prior to signal amplification and transcript quantification. Thus, its efficiency in transcript detection is unmatched by other methods. In addition, image-based transcriptomics allows the study of the spatial organization of the transcriptome in single cells at single-molecule, and, when combined with superresolution microscopy, nanometer resolution. However, in order to unlock the full power of image-based transcriptomics, robust computer vision of single molecules and cells is required. Here, we shortly discuss the setup of the experimental pipeline for image-based transcriptomics, and then describe in detail the algorithms that we developed to extract, at high-throughput, robust multivariate feature sets of transcript molecule abundance, localization and patterning in tens of thousands of single cells across the transcriptome. These computer vision algorithms and pipelines can be downloaded from: https://github.com/pelkmanslab/ImageBasedTranscriptomics.
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