A Strategy to Compare Single-Cell RNA Sequencing Data Sets Provides Phenotypic Insight into Cellular Heterogeneity Underlying Biological Similarities and Differences Between Samples.

IF 2.3 Q3 BIOCHEMICAL RESEARCH METHODS
Bioinformatics and Biology Insights Pub Date : 2024-10-02 eCollection Date: 2024-01-01 DOI:10.1177/11779322241280866
Dan C Wilkinson, Elizabeth Tallman, Mishal Ashraf, Tatiana Gelaf Romer, Jeehoon Lee, Benjamin Burnett, Pierre R Bushel
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引用次数: 0

Abstract

Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets.

比较单细胞 RNA 测序数据集的策略可从表型上洞察样本间生物学相似性和差异性背后的细胞异质性。
单细胞 RNA 测序(scRNA-seq)可通过提取转录组数据对细胞表型进行无偏见的评估。下游分析的一个重要问题是如何评估高维空间样本之间的生物学相似性和差异性。当样本中存在细胞异质性时,这个问题就变得尤为复杂。在这里,我们介绍 scCompare,这是一个用于比较 scRNA-seq 数据集的计算管道。利用基于相关性的映射,将已知数据集的表型特征转移到另一个数据集上,以平均每个细胞群注释表型的转录组特征。从统计学角度推导出的表型包容性下限允许细胞在与已知表型不同的情况下不被映射,从而促进了潜在新型细胞类型的检测。我们使用人外周血单核细胞(PBMCs)的 scRNA-seq 数据集对我们的工具进行了比较,结果表明,在大多数细胞类型中,scCompare 的精确度和灵敏度都优于单细胞变异推理(scVI)。在细胞图谱数据集上进一步使用 scCompare,可以深入了解样本间生物多样性的细胞异质性。此外,我们还利用细胞图谱更好地了解了 scCompare 管道中使用的关键参数的影响。我们认为,scCompare 对研究界比较大型 scRNA-seq 数据集很有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioinformatics and Biology Insights
Bioinformatics and Biology Insights BIOCHEMICAL RESEARCH METHODS-
CiteScore
6.80
自引率
1.70%
发文量
36
审稿时长
8 weeks
期刊介绍: Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on bioinformatics methods and their applications which must pertain to biological insights. All papers should be easily amenable to biologists and as such help bridge the gap between theories and applications.
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