ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.
ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.
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DOI：
10.1016/j.isci.2023.106792
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发表时间：
2023-06-16
期刊：
影响因子：
5.8
通讯作者：
Zhou, Xin Maizie
中科院分区：
文献类型：
--
作者：
Hu, Yunfei;Zhao, Yuying;Schunk, Curtis T.;Ma, Yingxiang;Derr, Tyler;Zhou, Xin Maizie
空间转录组学（ST）的进步，使复杂的组织，通过量化基因表达在空间定位点的深入了解。几个显着的聚类方法已被引入利用空间和转录信息在ST数据集的分析。然而，不同ST测序技术和数据集类型的数据质量会影响不同方法和基准的性能。为了利用ST数据中的空间背景和转录谱，我们开发了一个基于图的多阶段框架，用于鲁棒聚类，称为ADEPT。为了控制和稳定数据质量，ADEPT依赖于图形自动编码器主干，并对估算的差异表达基因矩阵进行迭代聚类，以最大限度地减少聚类结果的方差。ADEPT在分析不同平台生成的ST数据时优于其他流行方法，如空间域识别、可视化、空间轨迹推断和数据去噪。我们开发了一个基于图的空间转录组学数据聚类工具，ADEPT ADEPT选择差异表达基因进行插补ADEPT实现了稳健的聚类结果，具有低方差ADEPT提高了一系列现有方法的准确性生物信息学自动化;系统生物学数据处理;转录组学
Advancements in spatial transcriptomics (ST) have enabled an in-depth understanding of complex tissues by quantifying gene expression at spatially localized spots. Several notable clustering methods have been introduced to utilize both spatial and transcriptional information in the analysis of ST datasets. However, data quality across different ST sequencing techniques and types of datasets influence the performance of different methods and benchmarks. To harness spatial context and transcriptional profile in ST data, we developed a graph-based, multi-stage framework for robust clustering, called ADEPT. To control and stabilize data quality, ADEPT relies on a graph autoencoder backbone and performs an iterative clustering on imputed, differentially expressed genes-based matrices to minimize the variance of clustering results. ADEPT outperformed other popular methods on ST data generated by different platforms across analyses such as spatial domain identification, visualization, spatial trajectory inference, and data denoising. We developed a graph-based clustering tool for spatial transcriptomics data, ADEPT ADEPT selects differentially expressed genes to perform imputation ADEPT achieved robust clustering results with low variance ADEPT improved accuracy over a range of existing methods Automation in bioinformatics; Data processing in systems biology; Transcriptomics
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影响因子：
6
作者：
Hu J;Schroeder A;Coleman K;Chen C;Auerbach BJ;Li M
通讯作者：
Li M
影响因子：
25
作者：
Maynard KR;Collado-Torres L;Weber LM;Uytingco C;Barry BK;Williams SR;Catallini JL 2nd;Tran MN;Besich Z;Tippani M;Chew J;Yin Y;Kleinman JE;Hyde TM;Rao N;Hicks SC;Martinowich K;Jaffe AE
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Jaffe AE
影响因子：
48
作者：
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Scadden, David T.
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne
影响因子：
56.9
作者：
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通讯作者：
Frisen, Jonas