Single-Cell Multi-Modal GAN (scMMGAN) reveals spatial patterns in single-cell data from triple negative breast cancer
Single-Cell Multi-Modal GAN (scMMGAN) reveals spatial patterns in single-cell data from triple negative breast cancer
复制标题
单细胞多模态 GAN (scMMGAN) 揭示三阴性乳腺癌单细胞数据的空间模式
DOI：
10.1101/2022.07.04.498732
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发表时间：
2022
期刊：
影响因子：
6.5
通讯作者：
Matthew Amodio, Scott E
中科院分区：
文献类型：
--
作者：
Matthew Amodio, Scott E
测量生物系统的技术取得了令人兴奋的进展，目前处于研究的前沿。收集沿着增加的组学维度的数据的能力产生了对工具的需求，以一起分析所有这些信息，而不是将每种技术孤立到单独的分析管道中。为了推进这一目标，我们引入了一个称为单细胞多模态GAN（scMMGAN）的框架，该框架将来自多种模态的数据集成到环境数据空间中的统一表示中，以便使用对抗学习和数据几何技术的组合进行下游分析。该框架的关键改进是一个额外的扩散几何损失与一个新的内核，约束否则过参数化的GAN网络。我们证明了scMMGAN在各种数据模式上产生比替代方法更有意义的对齐的能力，并且其输出可用于从真实世界的生物实验数据中得出结论。我们强调了来自研究三阴性乳腺癌发展的实验的数据，在该实验中，我们展示了scMMGAN如何用于识别新的基因关联，并且我们证明了仅在scRNAseq数据上识别的细胞簇发生在局部空间模式中，这些模式揭示了对空间转录组图像的见解。
Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information together, rather than siloing each technology into separate analysis pipelines. To advance this goal, we introduce a framework called the Single-Cell Multi-Modal GAN (scMMGAN) that integrates data from multiple modalities into a unified representation in the ambient data space for downstream analysis using a combination of adversarial learning and data geometry techniques. The framework’s key improvement is an additional diffusion geometry loss with a new kernel that constrains the otherwise over-parameterized GAN network. We demonstrate scMMGAN’s ability to produce more meaningful alignments than alternative methods on a wide variety of data modalities, and that its output can be used to draw conclusions from real-world biological experimental data. We highlight data from an experiment studying the development of triple negative breast cancer, where we show how scMMGAN can be used to identify novel gene associations and we demonstrate that cell clusters identified only on the scRNAseq data occur in localized spatial patterns that reveal insights on the spatial transcriptomic images.
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DOI：
10.1073/pnas.0500334102
发表时间：
2005-05-24
影响因子：
11.1
作者：
Coifman, RR;Lafon, S;Zucker, SW
通讯作者：
Zucker, SW
影响因子：
16.6
作者：
Yang KD;Belyaeva A;Venkatachalapathy S;Damodaran K;Katcoff A;Radhakrishnan A;Shivashankar GV;Uhler C
通讯作者：
Uhler C
DOI：
10.1109/mlsp52302.2021.9596214
发表时间：
2021-10
期刊：
IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing
影响因子：
--
作者：
Kuchroo M;Godavarthi A;Tong A;Wolf G;Krishnaswamy S
通讯作者：
Krishnaswamy S
影响因子：
5
作者：
Hussein, Yaser R.;Bandyopadhyay, Sudeshna;Ali-Fehmi, Rouba
通讯作者：
Ali-Fehmi, Rouba