CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication.
CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication.
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CellCall：整合配对配体-受体和转录因子活性以进行细胞-细胞通讯
DOI：
10.1093/nar/gkab638
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发表时间：
2021-09-07
影响因子：
14.9
通讯作者：
Zhao X
中科院分区：
文献类型：
--
作者：
Zhang Y;Liu T;Hu X;Wang M;Wang J;Zou B;Tan P;Cui T;Dou Y;Ning L;Huang Y;Rao S;Wang D;Zhao X
摘要随着单细胞RNA测序（scRNA-seq）技术的迅速发展，细胞间通讯的系统解码引起了人们极大的研究兴趣。到目前为止，已经开发了几种计算机方法，但大多数方法都缺乏预测连接细胞内外的通信途径的能力。在这里，我们开发了CellCall，这是一个通过整合配对配体-受体和转录因子（TF）活性来推断细胞间和细胞内通信途径的工具包。此外，CellCall使用嵌入式通路活性分析方法来识别参与某些细胞类型之间细胞间串扰的显著激活的通路。此外，CellCall提供了一套丰富的可视化选项（Circos图，Sankey图，气泡图，山脊图等）。来展示分析结果。对人类睾丸细胞和肿瘤免疫微环境的scRNA-seq数据集的案例研究表明，CellCall在细胞间通讯分析和内部TF活性探索中具有可靠和独特的功能，并得到了进一步的实验验证。通过对CellCall与其他工具的对比分析，发现CellCall具有更高的准确性和更丰富的功能。总之，CellCall提供了一个复杂而实用的工具，使研究人员能够根据scRNA-seq数据破译细胞间通讯和相关的内部调控信号。CellCall可在https://github.com/ShellyCoder/cellcall上免费获得。
Abstract With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.
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影响因子：
4.6
作者：
Han H;Shim H;Shin D;Shim JE;Ko Y;Shin J;Kim H;Cho A;Kim E;Lee T;Kim H;Kim K;Yang S;Bae D;Yun A;Kim S;Kim CY;Cho HJ;Kang B;Shin S;Lee I
通讯作者：
Lee I
影响因子：
4.4
作者：
Bovolenta LA;Acencio ML;Lemke N
通讯作者：
Lemke N
影响因子：
14.9
作者：
Griffon A;Barbier Q;Dalino J;van Helden J;Spicuglia S;Ballester B
通讯作者：
Ballester B
影响因子：
48
作者：
Browaeys, Robin;Saelens, Wouter;Saeys, Yvan
通讯作者：
Saeys, Yvan
影响因子：
5.6
作者：
Handly LN;Yao J;Wollman R
通讯作者：
Wollman R