A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.
A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.
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DOI：
10.1101/gr.275569.121
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发表时间：
2021-10
期刊：
影响因子：
7
通讯作者：
Scheuermann RH
中科院分区：
文献类型：
--
作者：
Aevermann B;Zhang Y;Novotny M;Keshk M;Bakken T;Miller J;Hodge R;Lelieveldt B;Lein E;Scheuermann RH
单细胞基因组学正在迅速推进我们对细胞表型多样性的认识，包括细胞类型和细胞状态。在单细胞/细胞核RNA测序（scRNA-seq）的推动下，广泛的生物和组织特征的综合细胞图谱项目正在进行中。因此，发现的转录表型以一致和简洁的方式定义和传播是至关重要的。分子生物标志物历来在生物学研究中发挥着重要作用，从通过表面蛋白表达确定免疫细胞类型到通过其分子驱动因素确定疾病。在这里，我们描述了一个基于机器学习的标记基因选择算法，NS-Forest 2.0版本，它利用随机森林特征选择的非线性属性和二进制表达评分方法来发现最小的标记基因表达组合，以最佳地捕获完整scRNA-seq转录谱中所代表的细胞类型身份。所选择的标记基因提供了一个表达条形码，既是下游生物学研究的有用工具，也是语义细胞类型定义的必要和充分特征。利用NS-Forest识别人脑中颞回细胞类型的标记基因，揭示了细胞信号和非编码rna在神经元细胞类型识别中的重要性。
Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.
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影响因子：
64.5
作者：
Enge M;Arda HE;Mignardi M;Beausang J;Bottino R;Kim SK;Quake SR
通讯作者：
Quake SR
影响因子：
14.8
作者：
Krishnaswami SR;Grindberg RV;Novotny M;Venepally P;Lacar B;Bhutani K;Linker SB;Pham S;Erwin JA;Miller JA;Hodge R;McCarthy JK;Kelder M;McCorrison J;Aevermann BD;Fuertes FD;Scheuermann RH;Lee J;Lein ES;Schork N;McConnell MJ;Gage FH;Lasken RS
通讯作者：
Lasken RS
影响因子：
18.4
作者：
Levitin HM;Yuan J;Sims PA
通讯作者：
Sims PA
DOI：
10.1073/pnas.1507125112
发表时间：
2015-06-09
影响因子：
11.1
作者：
Darmanis S;Sloan SA;Zhang Y;Enge M;Caneda C;Shuer LM;Hayden Gephart MG;Barres BA;Quake SR
通讯作者：
Quake SR
影响因子：
64.5
作者：
Asp, Michaela;Giacomello, Stefania;Lundeberg, Joakim
通讯作者：
Lundeberg, Joakim