CAMML: Multi-Label Immune Cell-Typing and Stemness Analysis for Single-Cell RNA-sequencing
CAMML: Multi-Label Immune Cell-Typing and Stemness Analysis for Single-Cell RNA-sequencing
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DOI：
10.1142/9789811250477_0019
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发表时间：
2021-11
影响因子：
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通讯作者：
Courtney Schiebout;H. R. Frost
中科院分区：
文献类型：
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作者：
Courtney Schiebout;H. R. Frost
在单细胞RNA测序（scRNA-seq）数据中推断细胞类型对于理解复杂组织（如肿瘤免疫微环境（TME））中发生的潜在细胞机制和表型特别重要。scRNA-seq数据的稀疏性和噪声，再加上免疫细胞类型经常连续出现的事实，使得TME scRNA-seq数据的细胞分型成为一个重大挑战。已经提出了几种单标记细胞分型方法来解决噪声和稀疏性的限制，但是考虑免疫TME中细胞类型的经常重叠的谱仍然是一个障碍。为了解决这个问题，我们开发了一种新的scRNA-seq细胞分型方法，使用方差调整的马氏距离与多标记（CAMML）的细胞分型。CAMML利用细胞类型特异性加权基因集为数据集中的每个细胞对每个潜在细胞类型进行评分。这允许细胞通过它们的最高得分细胞类型标记为单个标记分类或基于得分截止值以给出多标记分类。对于单标记细胞分型，CAMML的性能与现有的细胞分型方法SingleR和Garnett相当。对于小区可能表现出多种小区类型的特征的情况（例如，未分化的细胞），CAMML支持的多标记分类相对于当前最先进的方法提供了重要的益处。通过整合跨研究、组学平台和物种的数据，CAMML作为一种强大且适应性强的方法，可以克服scRNA-seq分析的挑战。
Inferring the cell types in single-cell RNA-sequencing (scRNA-seq) data is of particular importance for understanding the potential cellular mechanisms and phenotypes occurring in complex tissues, such as the tumor-immune microenvironment (TME). The sparsity and noise of scRNA-seq data, combined with the fact that immune cell types often occur on a continuum, make cell typing of TME scRNA-seq data a significant challenge. Several single-label cell typing methods have been put forth to address the limitations of noise and sparsity, but accounting for the often overlapped spectrum of cell types in the immune TME remains an obstacle. To address this, we developed a new scRNA-seq cell-typing method, Cell-typing using variance Adjusted Mahalanobis distances with Multi-Labeling (CAMML). CAMML leverages cell type-specific weighted gene sets to score every cell in a dataset for every potential cell type. This allows cells to be labelled either by their highest scoring cell type as a single label classification or based on a score cut-off to give multi-label classification. For single-label cell typing, CAMML performance is comparable to existing cell typing methods, SingleR and Garnett. For scenarios where cells may exhibit features of multiple cell types (e.g., undifferentiated cells), the multi-label classification supported by CAMML offers important benefits relative to the current state-of-the-art methods. By integrating data across studies, omics platforms, and species, CAMML serves as a robust and adaptable method for overcoming the challenges of scRNA-seq analysis.