Discriminative feature of cells characterizes cell populations of interest by a small subset of genes.
Discriminative feature of cells characterizes cell populations of interest by a small subset of genes.
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DOI：
10.1371/journal.pcbi.1009579
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发表时间：
2021-11
影响因子：
4.3
通讯作者：
Ohkawa Y
中科院分区：
文献类型：
--
作者：
Fujii T;Maehara K;Fujita M;Ohkawa Y
生物体是由具有特定状态的各种细胞类型组成的。为了全面了解器官和组织的功能，人们通过识别特定的标记基因来对细胞类型进行分类和定义。统计检验对于识别标记基因至关重要，这通常涉及评估基因平均表达水平的差异。基于差异表达基因(DEG)的分析是这类方法中最常用的方法。然而，随着样本量的增加，例如在单细胞分析中，基于DEG的分析面临着与P值膨胀相关的困难。在这里，我们提出了细胞区分性特征(DFC)的概念，这是一种替代基于DEG的方法。我们使用Logistic回归和自适应套索惩罚来实现DFC，以执行二进制分类来区分感兴趣的群体和变量选择以获得定义基因的一小部分。我们使用人工数据证明了DFC优先考虑非独立表达的基因对，并且DFC能够表征肌肉卫星/祖细胞群体。结果表明，DFC很好地捕捉了该细胞群体的细胞类型特异性标记、特定基因表达模式和亚类。DFC可以补充基于DEG的方法来解释大数据集。基于DG的分析使用不同组之间表达差异的基因列表，而DFC可以被称为区别性方法，在细胞表征任务中具有潜在的应用。随着单细胞高通量分析的最新进展，诸如scRNA-seq的细胞表征方法可以有效地服从于判别性方法。检测单个基因表达差异的统计方法对于理解细胞类型是不可或缺的。然而，传统的统计方法，如基于差异表达基因(DEG)的分析，由于样本量大和单细胞转录组学等探索性数据分析引入的选择偏差，面临着与P值膨胀相关的困难。在这里，我们提出了细胞区分性特征(DFC)的概念，这是一种替代基于DEG的方法。我们使用Logistic回归和自适应套索惩罚来实现DFC，以执行二进制分类以区分感兴趣的群体和变量选择，以获得定义基因的一小部分。我们使用人工数据证明了DFC优先考虑非独立表达的基因对，并且它能够表征肌肉卫星/祖细胞群体。结果表明，DFC很好地捕捉了该细胞群体的细胞类型特异性标记、特定基因表达模式和亚类。DFC可以补充基于差异表达基因的方法来解释大数据集。
Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods. Statistical methods for detecting differences in individual gene expression are indispensable for understanding cell types. However, conventional statistical methods, such as differentially expressed gene (DEG)-based analysis, have faced difficulties associated with the inflation of P-values because of both the large sample size and selection bias introduced by exploratory data analysis such as single-cell transcriptomics. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for the discrimination of a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data, and that it enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement differentially expressed gene-based methods for interpreting large data sets.
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DOI：
10.1214/10-aoas388
发表时间：
2011-01-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Breheny P;Huang J
通讯作者：
Huang J
影响因子：
7.7
作者：
Barruet, Emilie;Garcia, Steven M.;Pomerantz, Jason H.
通讯作者：
Pomerantz, Jason H.
DOI：
10.1073/pnas.1418845112
发表时间：
2015-02-17
影响因子：
11.1
作者：
Fortier, Simon;MacRae, Tara;Sauvageau, Guy
通讯作者：
Sauvageau, Guy
影响因子：
8
作者：
Deng, Houtao;Runger, George
通讯作者：
Runger, George
影响因子：
64.5
作者：
Khajuria RK;Munschauer M;Ulirsch JC;Fiorini C;Ludwig LS;McFarland SK;Abdulhay NJ;Specht H;Keshishian H;Mani DR;Jovanovic M;Ellis SR;Fulco CP;Engreitz JM;Schütz S;Lian J;Gripp KW;Weinberg OK;Pinkus GS;Gehrke L;Regev A;Lander ES;Gazda HT;Lee WY;Panse VG;Carr SA;Sankaran VG
通讯作者：
Sankaran VG