小柯机器人

科学家在单细胞水平量化了实验扰动的影响
2021-02-10 13:53

美国耶鲁大学Smita Krishnaswamy和David van Dijk小组合作在研究中取得进展。他们在单细胞水平量化了实验扰动的影响。这一研究成果发表在2021年2月8日出版的国际学术期刊《自然-生物技术》上。

研究人员使用跨时空转录组在单细胞水平连续测量和量化了扰动效应的影响。研究人员将该时空描述为流形,并开发了使用图表信号处理的方法来观察和估计每个实验条件下的每个单细胞。该可能性估计可以用于鉴别扰动效应特别影响的细胞群。

研究人员还开发了顶点频率聚类,以在粒度级别提取与干扰响应匹配的受影响细胞群。该算法在识别每种条件下富集或耗竭细胞簇时的准确度比测试的次佳表现算法高57%。在实况比较中,从这些聚类鉴定的特异基因比其它六种替代算法更准确。

据悉,目前用于比较不同条件单细胞RNA测序数据集的方法着眼于转录状态空间的离散区域,例如细胞簇。

附:英文原文

Title: Quantifying the effect of experimental perturbations at single-cell resolution

Author: Daniel B. Burkhardt, Jay S. Stanley, Alexander Tong, Ana Luisa Perdigoto, Scott A. Gigante, Kevan C. Herold, Guy Wolf, Antonio J. Giraldez, David van Dijk, Smita Krishnaswamy

Issue&Volume: 2021-02-08

Abstract: Current methods for comparing single-cell RNA sequencing datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm at identifying clusters of cells that are enriched or depleted in each condition is, on average, 57% higher than the next-best-performing algorithm tested. Gene signatures derived from these clusters are more accurate than those of six alternative algorithms in ground truth comparisons. Matched treatment and control single-cell RNA sequencing samples are more accurately compared at the single-cell level.

DOI: 10.1038/s41587-020-00803-5

Source: https://www.nature.com/articles/s41587-020-00803-5

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:68.164
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex


本期文章:《自然—生物技术》:Online/在线发表

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