Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
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基于基因共表达网络推理 (ICAnet) 的独立成分分析可破译功能模块，以实现更好的单细胞聚类和批量集成。
DOI：
10.1093/nar/gkab089
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发表时间：
2021-05-21
影响因子：
14.9
通讯作者：
Ni T
中科院分区：
文献类型：
--
作者：
Wang W;Tan H;Sun M;Han Y;Chen W;Qiu S;Zheng K;Wei G;Ni T
摘要随着可公开获取的单细胞RNA测序数据集的大量增加，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、提高细胞类型预测准确性、促进生物发现的有效工具。然而，目前的方法主要基于整体共表达相关性，而忽略了仅存在于细胞亚群中的共表达，从而无法发现某些罕见的细胞类型，且对批次效应敏感。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理（ICAnet），将scRNA-seq数据分解为一系列独立的基因表达成分并推断出共表达模块，从而改善了细胞聚类和罕见细胞类型发现。ICAnet显示了使用跨越多种细胞/组织/供体/文库类型的scRNA-seq数据集进行细胞聚类和批量整合的有效性能。它可以在不同文库构建策略以及不同测序深度和细胞数量的数据集上稳定工作。我们证明了ICAnet在来自不同来源的多个独立scRNA-seq数据集中发现罕见细胞类型的能力。重要的是，在急性髓性白血病scRNA-seq数据集中激活的识别模块有可能作为新的诊断标志物。因此，ICAnet是细胞聚类和单细胞RNA-seq数据分析的生物学解释的竞争工具。
Abstract 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.
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影响因子：
9.3
作者：
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者：
Yanai I
影响因子：
64.5
作者：
Goolam M;Scialdone A;Graham SJL;Macaulay IC;Jedrusik A;Hupalowska A;Voet T;Marioni JC;Zernicka-Goetz M
通讯作者：
Zernicka-Goetz M
影响因子：
4.3
作者：
Hedblom, Andreas;Laursen, Kristian B.;Persson, Jenny L.
通讯作者：
Persson, Jenny L.
影响因子：
82.9
作者：
Eppert, Kolja;Takenaka, Katsuto;Dick, John E.
通讯作者：
Dick, John E.
影响因子：
7.7
作者：
Gomez, Javier A.;Rutkowski, D. Thomas
通讯作者：
Rutkowski, D. Thomas