空间转录组学#
涵盖空间数据预处理、去卷积和下游分析的教程。
预处理#
- 裁剪和旋转空间转录组学数据
- 数据预处理
- 定义裁剪区域
- 旋转 90 度
- 细胞分割（10x HD）
- 分析 NanoString 数据
- Analyze 10x Atera (WTA Preview) FFPE breast cancer data
- 分析 Visium HD 数据
- 空间聚类和降噪表达
- 空间集成和聚类
- 预处理数据
- Training STAligner modelHere, we used
ov.space.pySTAligner到 construct a STAGATE object 到 train 该 model.We are using 该train_STAligner_subgraph函数 从 STAligner 到 reduce GPU memory usage, each slice is considered 作为 a subgraph 对于 training. - 聚类分析 该 spaceWe can use
GMM,leiden或louvain到 聚类 该 space.ov.utils.cluster(adata,use_rep='STAligner',method='GMM',n_components=7,covariance_type='full', tol=1e-9, max_iter=1000, random_state=3607orsc.pp.neighbors(adata, use_rep='STAligner', random_state=666)ov.utils.cluster(adata,use_rSTAlignerGATE',method='leiden',resolution=1)
去卷积#
下游#
- 单细胞空间转移张量
- Spatial CommunicationSpatial communication is a point of interest for us for the 空间转录组学 Society, and we would like to find the conduction process of spatial communication.Here, we introduce two method integrated in OmicVerse named
COMMOTandflowsig.We made three improvements in integrating theCOMMOTandflowsigalgorithm in OmicVerse:- We reduced the installation conflict ofCOMMOTandflowsig, user only need to update OmicVerse to the latest version.- We optimized the 可视化 ofCOMMOTandflowsigand unified the data 预处理 process so that users don’t need to struggle with different data processing flows.- We have fixed some bugs that could occur during function.If you found this tutorial helpful, please citeCOMMOT,flowsigand OmicVerse:- Cang, Z., Zhao, Y., Almet, A.A. et al. Screening cell–cell communication in 空间转录组学 via collective optimal transport. Nat Methods 20, 218–228 (2023). https://doi.org/10.1038/s41592-022-01728-4- Almet, A.A., Tsai, YC., Watanabe, M. et al. Inferring pattern-driving intercellular flows from 单细胞 and 空间转录组学. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02380-w - 空间同深度计算
- 单细胞空间对齐工具