State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, 200438, P.R. China.
State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, P.R. China.
Nucleic Acids Res. 2021 May 21;49(9):e54. doi: 10.1093/nar/gkab089.
With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.
随着公共单细胞 RNA 测序 (scRNA-seq) 数据集的大量增加，基于基因共表达网络的生物信息学方法正成为分析 scRNA-seq 数据的有效工具，提高细胞类型预测准确性，并进而促进生物学发现。然而，目前的方法主要基于整体共表达相关性，而忽略了仅存在于部分细胞中的共表达，从而无法发现某些罕见的细胞类型，并对批次效应敏感。在这里，我们开发了基于独立成分分析的基因共表达网络推断 (ICAnet)，它将 scRNA-seq 数据分解成一系列独立的基因表达成分，并推断出共表达模块，从而提高了细胞聚类和罕见细胞类型的发现能力。ICAnet 在使用跨越多个细胞/组织/供体/文库类型的 scRNA-seq 数据集进行细胞聚类和批次整合方面表现出高效的性能。它在由不同文库构建策略产生的数据集以及具有不同测序深度和细胞数量的数据集上都能稳定运行。我们证明了 ICAnet 能够在来自不同来源的多个独立的 scRNA-seq 数据集中发现罕见的细胞类型。重要的是，在急性髓系白血病 scRNA-seq 数据集中鉴定出的激活模块有可能成为新的诊断标志物。因此，ICAnet 是单细胞 RNA-seq 数据分析中细胞聚类和生物学解释的一种有竞争力的工具。
