NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome.
NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome.
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NonLoss：一种从单细胞转录组中识别差异生物模块的新分析方法
DOI：
10.21037/atm-21-6401
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发表时间：
2021-12
影响因子：
--
通讯作者：
Jin Y
中科院分区：
文献类型：
--
作者：
Zhao H;Guo Y;Ma Y;Chen Y;Sun H;Sun D;Wu N;Jin Y
疾病相关生物模块的识别对我们理解疾病的发生过程起着重要的作用。尽管单细胞RNA测序（scRNA-seq）提供了高分辨率的转录组数据，可以潜在地表征细胞内细微的基因表达变化，但基因表达信息对单个基因影响的易感性也使其难以区分生物模块。方法采用基于Shannon’s熵和Spearman秩相关分析的方法对生物功能模块的基因表达信息进行量化。这两种方法的巧妙结合，使前者的变异分析和后者的一致性分析成为更加稳健的生物功能分析工具。我们开发了一种名为NonLoss的计算分析方法和桌面应用程序，以更稳健地分析scRNA-seq数据，并提取细胞群体之间的真实生物学差异。该方法通过处理所有基因的表达水平数据来标注特定的功能模块，既降低了维数，又提高了功能识别的可靠性，避免了单个基因的随机干扰。非损失原则可以用于评估功能模块的变化，同时识别重要功能。此外，可以识别出对重要功能有贡献的特定基因，甚至是那些表达发生细微变化的基因。结果表明，NonLoss在3种不同的应用中产生了重要的生物学见解。结论NonLoss具有友好的图形用户界面，能够在单细胞分辨率下识别生物学相关表达变化模块。
Background The identification of disease-related biological modules plays an important role in our understanding of the process of diseases. Although single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data that can potentially characterize subtle gene expression changes within cells, the susceptibility of the gene expression information to the influence of individual genes also makes it difficult to distinguish the biological module. Methods To quantify gene expression information for biological function modules, we adopted the method based on Shannon’s entropy and Spearman rank correlation analysis. The ingenious combination of these two methods enables the variation analysis of the former and the consistency analysis of the latter to make a more robust biological function analysis tool. Results We developed a computational analytical method and desktop application called NonLoss to analyze scRNA-seq data more robustly and to extract real biological differences between cell populations. The method derives its power by handling expression level data from all genes annotated to a specific function module, both for dimensionality reduction and reliability of function identification, avoiding random disturbance of individual genes. NonLoss can in principle be used to assess changes of function modules and identify vital functions simultaneously. Furthermore, specific genes contributing to important functions, even those with subtle expression changes, can be identified. The results demonstrated that NonLoss yields biologically significant insights into 3 different applications. Conclusions NonLoss was developed with a user-friendly graphical user interface, and it could identify the module of biologically relevant expression changes at a single-cell resolution.
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影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
基因表达数据分析的匹配对特征选择方法综述
影响因子：
6
作者：
Liang S;Ma A;Yang S;Wang Y;Ma Q
通讯作者：
Ma Q
影响因子：
30.8
作者：
Li, Huipeng;Courtois, Elise T.;Prabhakar, Shyam
通讯作者：
Prabhakar, Shyam
影响因子：
3
作者：
Wang, Tianyu;Li, Boyang;Nabavi, Sheida
通讯作者：
Nabavi, Sheida
一种识别单细胞RNA-seq实验中差异分布的统计方法。
影响因子：
12.3
作者：
Korthauer KD;Chu LF;Newton MA;Li Y;Thomson J;Stewart R;Kendziorski C
通讯作者：
Kendziorski C