基于python的空间转录组资料大全

最近我们的python单细胞暑期班开课了，👉趁暑假学会Python，解锁单细胞数据的无限可能

有很多同学在问空转的资料，但是课程里暂时还没有，所以先给大家收集点好的资料解馋吧~

1.Squidpy 是Python空间组学数据分析的主流工具之一

他的官方网站是：

https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/index.html

一个比较完整的小鼠肝脏空间转录组数据处理教程

https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_vizgen_mouse_liver.html

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Squidpy 基于 AnnData 数据结构，和 Scanpy 完全兼容。这套代码是基于 Vizgen MERSCOPE 平台的，但同样适用于其他平台，唯一区别在于数据读入时的格式和参数设置，后续分析代码几乎通用。

10X数据专门的教程在：

https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_visium_hne.html

2.scverse空间组学数据教程集合

初级、中级、高级、特定技术的都有啦

https://spatialdata.scverse.org/en/latest/tutorials/notebooks/notebooks.html

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3.scverse的学习页面

https://scverse.org/learn/

这里有22个教程，其中有3个是+空转的，但是其他的也有用，也可以学一下。

Data structures

数据结构

1. Working with scverse objects in backed mode 在后端模式下操作 scverse 对象 原文：In this tutorial, we demonstrate working with scverse data objects without loading full datasets. (AnnData and MuData are saved as .h5ad and .h5mu files) 翻译：在本教程中，我们演示了如何在不加载完整数据集的情况下操作 scverse 数据对象。（AnnData 和 MuData 被保存为 .h5ad 和 .h5mu 文件）
2. Concatenation of multimodal data 多组学数据的拼接 原文：This tutorial shows how you can concatenate 2 MuData objects that may represent complementary slices of the same dataset or 2 modalities into one AnnData. 翻译：本教程展示了如何将两个 MuData 对象（可能代表同一数据集的互补切片或两种组学）拼接为一个 AnnData。
3. Getting started with AnnData AnnData 入门 原文：This tutorial helps you to explore the structure and content of single-cell data analysis results in a *.h5ad file using AnnData, Scanpy, and Python. 翻译：本教程帮助你使用 AnnData、Scanpy 和 Python 探索 *.h5ad 文件中单细胞数据分析结果的结构和内容。
4. Concatenation 拼接 原文：In this notebook we showcase how to perform concatenation, meaning to keep all sub elements of each object, and stack these elements in an ordered way. 翻译：在本教程中，我们展示了如何进行拼接，即保留每个对象的所有子元素，并以有序方式堆叠这些元素。
5. Axes in AnnData and MuData AnnData 与 MuData 的坐标轴 原文：In this tutorial we showcase operations on independent AnnData objects (scRNAseq matrix + metadata), demonstrating how various processing workflows can be stored in one MuData object. 翻译：在本教程中，我们展示了对独立 AnnData 对象（scRNAseq 矩阵 + 元数据）的操作，说明了如何将多种处理流程存储在一个 MuData 对象中。

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scRNA-seq

单细胞RNA测序

1. RNA velocity RNA 动力学分析 原文：This tutorial guides you through how RNA velocity can be inferred from single cell RNA-seq data using scVelo. 翻译：本教程指导你如何利用 scVelo 从单细胞 RNA-seq 数据推断 RNA velocity。
2. Preprocessing, clustering and cell-type annotation 预处理、聚类与细胞类型注释 原文：This fundamental tutorial covers common analysis steps: quality control, normalization, feature selection, dimensionality reduction, clustering, and cell-type annotation. 翻译：本基础教程涵盖常见分析步骤：质控、归一化、特征选择、降维、聚类和细胞类型注释。
3. Compositional analysis 组成分析 原文：This tutorial introduces compositional analysis at cell identity cluster level, based on known cell types or states affected by perturbations. 翻译：本教程介绍了基于已知细胞类型或受扰动影响的状态，在细胞身份聚类层面进行组成分析。
4. Pseudotemporal ordering 拟时序排序 原文：This tutorial show how a pseudotime can be constructed and compares different pseudotimes. 翻译：本教程展示了如何构建拟时序，并比较不同的拟时序。
5. Batch-effect removal with scvi-tools 用 scvi-tools 去除批次效应 原文：In this tutorial, we demonstrate how to use scvi-tools to fit a model to single-cell count data, correct batch effects, and perform differential gene expression analysis. 翻译：在本教程中，我们演示了如何使用 scvi-tools 对单细胞计数数据拟合模型、校正批次效应，并进行差异基因表达分析。
6. Pseudo-bulk differential expression and functional analysis 伪bulk差异表达与功能分析 原文：This notebook showcases decoupler for pathway and TF enrichment on ~5k Blood myeloid cells from healthy and COVID-19 infected patients. 翻译：本教程展示了如何使用 decoupler 对来自健康和 COVID-19 感染患者的约 5000 个血液髓系细胞进行通路和转录因子富集分析。
7. Perturbation modeling 扰动建模 原文：This tutorial covers 3 approaches using single-cell perturbation data: Augur (identify affected cell types), scGen (predict transcriptional response), Mixscape (quantify CRISPR sensitivity). 翻译：本教程涵盖了三种利用单细胞干扰数据的方法：Augur（识别受影响的细胞类型）、scGen（预测转录响应）、Mixscape（量化 CRISPR 敏感性）。

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Spatial

空间组学

1. Spatial analysis at subcellular resolution 亚细胞分辨率的空间分析 原文：This tutorial shows how to use bentotools to study gene expression at subcellular resolution. 翻译：本教程展示了如何使用 bentotools 研究亚细胞分辨率下的基因表达。
2. Spatial analysis with squidpy 使用 squidpy 进行空间分析 原文：This tutorial demonstrate how to use squidpy to analyse transcriptomics data with spatial resolution. 翻译：本教程演示了如何使用 squidpy 分析具有空间分辨率的转录组数据。
3. Spatial clustering of spacial transcriptomics data with CellCharter 用 CellCharter 对空间转录组数据进行聚类 原文：This tutorial demonstrate how to use CellCharter to cluster spatial transcriptomics data, obtained with the CosMx technology from Nanostring. This method can also be used for spatial proteomics data. 翻译：本教程演示了如何使用 CellCharter 对利用 Nanostring 的 CosMx 技术获得的空间转录组数据进行聚类。该方法也可用于空间蛋白质组数据。

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Adaptive immune cell receptor

适应性免疫细胞受体

1. Single-cell T-cell receptor analysis with scirpy 使用 scirpy 进行单细胞 T 细胞受体分析 原文：In this tutorial, we show how to perfrom QC on scTCR-seq data, define clonotype, cluster receptors by their sequence similarity and compute repertoire overlaps between patients. 翻译：在本教程中，我们展示了如何对 scTCR-seq 数据进行质控、定义克隆型、根据序列相似性对受体进行聚类，并计算患者之间的受体库重叠。

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Surface proteins

表面蛋白

1. CITE-seq integration CITE-seq 整合分析 原文：These notebooks showcase CITE-seq analysis of PBMCs with dsb normalization, MOFA+ data integration, and weighted nearest neighbors handling multimodal embeddings. 翻译：这些教程展示了 PBMC 的 CITE-seq 分析，包括 dsb 归一化、MOFA+ 数据整合以及加权最近邻处理多模态嵌入。

ATAC-seq

染色质可及性分析

1. Processing chromatin accessibility 染色质可及性数据处理 原文：This chapter shows multimodal single-cell gene expression and chromatin accessibility analysis. In this notebook, scATAC-seq data processing is described. 翻译：本章节展示了多组学单细胞基因表达和染色质可及性分析。在本教程中，介绍了 scATAC-seq 数据处理。
2. Joint analysis of paired and unpaired multiomic data with MultiVI 用 MultiVI 联合分析配对与非配对多组学数据 原文：This tutorial shows how to read multiomic data, create a joint object with paired/unpaired data, train MultiVI model, visualize latent space, and run differential analyses. 翻译：本教程展示了如何读取多组学数据、创建包含配对/未配对数据的联合对象、训练 MultiVI 模型、可视化潜在空间并进行差异分析。

Tips & Tricks

技巧与提示

1. Interoperability 兼容性 原文：This document lists resources for conversion to other data formats and programming languages, e.g. R, Julia, ... 翻译：本文档列出了转换为其他数据格式和编程语言（如 R、Julia 等）的资源。
2. Advanced plotting 高级绘图 原文：This tutorial explains how to customize matplotlib plots generated by scanpy or other scverse libraries. 翻译：本教程解释了如何自定义由 scanpy 或其他 scverse 库生成的 matplotlib 图。
3. Plotting in scanpy scanpy 绘图 原文：This tutorial explores the visualization possibilities of scanpy, including embeddings and the visualization of marker genes and differentially expressed genes. 翻译：本教程探索了 scanpy 的可视化功能，包括嵌入图、marker 基因和差异表达基因的可视化。

4.SpatialData

2024年发在natrue methods上

https://www.nature.com/articles/s41592-024-02212-x

5.Cell2location

官方教程在：

https://cell2location.readthedocs.io/en/latest/notebooks/cell2location_tutorial.html

Loading packages 加载包

Loading Visium and single cell data 加载Visium和单细胞数据

1.Estimating cell type signatures (NB regression) 1.估计细胞类型特征（负二项回归）

2.Cell2location: spatial mapping 2.Cell2location：空间映射

3.Visualising cell abundance in spatial coordinates 3.在空间坐标中可视化细胞丰度

4.Downstream analysis 4.下游分析

Leiden clustering of cell abundance 细胞丰度的Leiden聚类

Identifying cellular compartments / tissue zones using matrix factorisation (NMF) 使用矩阵分解（NMF）识别细胞区室/组织区域

Estimate cell-type specific expression of every gene in the spatial data (needed for NCEM) 估计空间数据中每个基因的细胞类型特异性表达（NCEM所需）

5.Advanced use 5.高级用法

Working with the posterior distribution and computing arbitrary quantiles 处理后验分布并计算任意分位数

6.Tangram

https://github.com/broadinstitute/Tangram#tutorials

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7.stlearn

https://stlearn.readthedocs.io/en/latest/

8.A practical guide to spatial transcriptomics

2024年6月发表在Molecular Aspects of Medicine上，10.3分的文章

https://doi.org/10.1016/j.mam.2024.101276                                                                                                                                                                                                            IF: 10.3 Q1 

9.tommy整理的资料大全

https://github.com/crazyhottommy/awesome_spatial_omics

这里是无法显示链接的，但是进入上面的github页面就可以显示啦。

在这里面是有基于python的，也有基于R的。

Review papers

Spatial landscapes of cancers: insights and opportunities 癌症的空间景观：洞察与机遇

The emerging landscape of spatial profiling technologies 空间分析技术的新兴景观

The expanding vistas of spatial transcriptomics 空间转录组学的拓展视野

Exploring tissue architecture using spatial transcriptomics 利用空间转录组学探索组织结构

Statistical and machine learning methods for spatially resolved transcriptomics data analysis. first author Zexian was my colleague when I was at DFCI. 空间分辨转录组学数据分析的统计学和机器学习方法。第一作者Zexian是我在DFCI时的同事。

Spatial omics and multiplexed imaging to explore cancer biology 空间组学和多重成像探索癌症生物学

Method of the Year: spatially resolved transcriptomics 年度方法：空间分辨转录组学

Computational challenges and opportunities in spatially resolved transcriptomic data analysis by Jean Fan. Jean Fan撰写的空间分辨转录组数据分析中的计算挑战与机遇

Spatial components of molecular tissue biology 分子组织生物学的空间组分

Methods and applications for single-cell and spatial multi-omics 单细胞和空间多组学的方法与应用

The dawn of spatial omics 空间组学的黎明

Tutorials

Orchestrating Spatially-Resolved Transcriptomics Analysis with Bioconductor 使用Bioconductor编排空间分辨转录组学分析

Deconvolution vs Clustering Analysis for Multi-cellular Pixel-Resolution Spatially Resolved Transcriptomics Data A blog post by Jean Fan. 多细胞像素分辨率空间分辨转录组学数据的解卷积与聚类分析对比 Jean Fan的博客文章

Analysis, visualization, and integration of spatial datasets with Seurat 使用Seurat进行空间数据集的分析、可视化和整合

Benchmarking

Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. 空间和单细胞转录组学整合方法在转录本分布预测和细胞类型解卷积方面的基准测试 我们发现Tangram、gimVI和SpaGE在预测RNA转录本空间分布方面优于其他整合方法，而Cell2location、SpatialDWLS和RCTD是斑点细胞类型解卷积的顶级方法。

Robust alignment of single-cell and spatial transcriptomes with CytoSPACE 使用CytoSPACE进行单细胞和空间转录组的稳健比对

A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics 空间转录组学细胞解卷积的综合基准测试与实用指南

Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing MERFISH空间转录组学与bulk和单细胞RNA测序的比较分析

Databases

SODB facilitates comprehensive exploration of spatial omics data [website] SODB促进空间组学数据的全面探索 [网站]

Museum of Spatial Transcriptomics 空间转录组学博物馆

Methods

Ex situ sequencing-based 基于外部测序的方法

Spatial Transcriptomics - Visualization and analysis of gene expression in tissue sections by spatial transcriptomics (Note now commercialized as Visium, 10x Genomics) 空间转录组学 - 通过空间转录组学对组织切片中基因表达的可视化和分析（注：现已商业化为Visium，10x Genomics）

HDST - High-definition spatial transcriptomics for in situ tissue profiling HDST - 用于原位组织分析的高清空间转录组学

STRS - In situ polyadenylation enables spatial mapping of the total transcriptome [code] STRS - 原位多腺苷酸化实现全转录组的空间图谱绘制 [代码]

Expansion Spatial Transcritptomics - Expansion spatial transcriptomics 扩展空间转录组学 - 扩展空间转录组学

SlideSeq - Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution SlideSeq - Slide-seq：高空间分辨率下测量全基因组表达的可扩展技术

SlideSeq2 - Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Note now commercialized as Seeker, Curio Bioscience) SlideSeq2 - 使用Slide-seqV2实现近细胞分辨率的高敏感性空间转录组学（注：现已商业化为Seeker，Curio Bioscience）

SlideTags - Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics SlideTags - Slide-tags：多模态空间基因组学的可扩展单核条码技术

StereoSeq - Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays (Note now commercialized as STOmics, MGI/BGI/Complete Genomics) StereoSeq - 使用DNA纳米球图案阵列构建小鼠器官发生的时空转录组图谱（注：现已商业化为STOmics，MGI/BGI/Complete Genomics）

DNA Microscopy - DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction DNA显微镜 - DNA显微镜：通过独立化学反应实现无光学时空遗传成像

Volumetric DNA Microscopy - Volumetric imaging of an intact organism by a distributed molecular network 体积DNA显微镜 - 通过分布式分子网络对完整生物体进行体积成像

In situ sequencing-based 基于原位测序的方法

STARmap - Three-dimensional intact-tissue sequencing of single-cell transcriptional states STARmap - 单细胞转录状态的三维完整组织测序

STARmap PLUS - Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer's disease STARmap PLUS - 阿尔茨海默病小鼠模型中单细胞转录状态和组织病理学的整合原位图谱

IGS - In situ genome sequencing resolves DNA sequence and structure in intact biological samples IGS - 原位基因组测序解析完整生物样本中的DNA序列和结构

Imaging/hybridization-based 基于成像/杂交的方法

MERFISH - Spatially resolved, highly multiplexed RNA profiling in single cells MERFISH - 单细胞中空间分辨的高度多重RNA分析

HiPR-FISH - Highly Multiplexed Spatial Mapping of Microbial Communities HiPR-FISH - 微生物群落的高度多重空间图谱

EEL-FISH - Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH EEL-FISH - 使用EEL FISH通过电泳捕获mRNA实现可扩展的原位单细胞分析

Normalization

Gene count normalization in single-cell imaging-based spatially resolved transcriptomic 基于单细胞成像的空间分辨转录组学中的基因计数标准化

Spatial omics data analysts sometimes use the “log1p” (y=log[1+x’]) transform incorrectly. Key fact: x’ represents normalized, not raw, umis/spot counts. And it really matters how you normalize! 空间组学数据分析师有时错误使用“log1p”（y=log[1+x']）变换。关键事实：x'代表标准化后的，而非原始的umis/spot计数。标准化方法真的很重要！

Computational Tools

Sopa enables processing and analyses of image-based spatial-omics using a standard data structure and output. We currently support the following technologies: Xenium, MERSCOPE, CosMX, PhenoCycler, MACSIMA, Hyperion. Sopa was designed for generability and low-memory consumption on large images (scales to 1TB+ images). Sopa使用标准数据结构和输出实现基于图像的空间组学处理和分析。我们目前支持以下技术：Xenium、MERSCOPE、CosMX、PhenoCycler、MACSIMA、Hyperion。Sopa设计用于大图像的通用性和低内存消耗（可扩展至1TB+图像）。

Monkeybread A python package developed at Immunitas to do spatial analysis for Merfish data. Monkeybread 由Immunitas开发的用于Merfish数据空间分析的python包

Giotto a toolbox for integrative analysis and visualization of spatial expression data Giotto 空间表达数据整合分析和可视化工具箱

Voyager is a package that facilitates exploratory spatial data analysis and visualization for spatial genomics data represented by SpatialFeatureExperiment objects. Voyager是一个促进SpatialFeatureExperiment对象表示的空间基因组数据探索性空间数据分析和可视化的软件包

nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes nnSVG：使用最近邻高斯过程的可扩展空间变异基因识别

DestVI identifies continuums of cell types in spatial transcriptomics data. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org). DestVI识别空间转录组学数据中细胞类型的连续谱。DestVI作为开源软件包scvi-tools的一部分提供

Here we present spateo, a open source framework that welcomes community contributions for quantitative spatiotemporal modeling of spatial transcriptomics. 在此我们介绍spateo，一个欢迎社区贡献的开源框架，用于空间转录组学的定量时空建模

SpaGene: Scalable and model-free detection of spatial patterns and colocalization SpaGene：可扩展且无模型的空间模式和共定位检测

Palo: Spatially-aware color palette optimization for single-cell and spatial data Palo：单细胞和空间数据的空间感知调色板优化

squidpy - paper - code: Squidpy: a scalable framework for spatial omics analysis squidpy - 论文 - 代码：Squidpy：空间组学分析的可扩展框架

ncem - paper - code: Learning cell communication from spatial graphs of cells ncem - 论文 - 代码：从细胞空间图学习细胞通讯

Spatially informed cell-type deconvolution for spatial transcriptomics Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. 空间转录组学的空间信息细胞类型解卷积 在此，我们介绍一种解卷积方法，基于条件自回归的解卷积（CARD），它将单细胞RNA测序（scRNA-seq）的细胞类型特异性表达信息与组织位置间细胞类型组成的相关性相结合

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace 使用scSpace从单细胞RNA测序数据重建细胞伪空间

SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure SpatialCorr：识别具有空间变异相关结构的基因集

RCTD: Robust decomposition of cell type mixtures in spatial transcriptomics RCTD：空间转录组学中细胞类型混合物的稳健分解

Supervised spatial inference of dissociated single-cell data with SageNet: a graph neural network approach that spatially reconstructs dissociated single cell data using one or more spatial references. 使用SageNet对解离单细胞数据进行监督空间推断：一种图神经网络方法，使用一个或多个空间参考对解离的单细胞数据进行空间重建

SpotClean adjusts for spot swapping in spatial transcriptomics data: A quality issue in spatial transcriptomics data, and a statistical method to adjust for it. SpotClean调整空间转录组学数据中的斑点交换：空间转录组学数据中的质量问题及其统计调整方法

Nonnegative spatial factorization 非负空间因式分解

SPICEMIX: Integrative single-cell spatial modeling of cell identity SPICEMIX：细胞身份的整合单细胞空间建模

De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc 使用DeepLinc从单细胞空间转录组数据从头重建细胞相互作用景观

Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process 通过高斯过程进行空间分子分析数据的贝叶斯建模

Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics 通过空间转录组学的多视图图学习解码功能性细胞-细胞通讯事件

BANKSY unifies cell-type clustering and domain segmentation by constructing a product space of cells' own and microenvironment transcriptomes. BANKSY通过构建细胞自身和微环境转录组的乘积空间统一细胞类型聚类和域分割

StereoCell - StereoCell enables high accuracy single cell segmentation for spatial transcriptomic dataset StereoCell - StereoCell实现空间转录组数据集的高精度单细胞分割

cell2location cell2location

STcEM - Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury STcEM - 空间转录组学相关电子显微镜图谱脑损伤的转录和超微结构反应

Deconvolution

spacedeconv is a unified interface to 31 deconvolution tools with a focus on spatial transcriptomics datasets. The package is able to directly estimate cell type proportions of immune cells and can deconvolute any cell type if an annotation single-cell reference dataset is available spacedeconv是31种解卷积工具的统一接口，专注于空间转录组学数据集。该软件包能够直接估计免疫细胞的细胞类型比例，如果有注释的单细胞参考数据集，可以解卷积任何细胞类型

Differential expression

A statistical method to uncover gene expression changes in spatial transcriptomics Cell type-specific inference of differential expression (C-SIDE) is a statistical model that identifies which genes (within a determined cell type) are differentially expressed on the basis of spatial position, pathological changes or cell–cell interactions. 揭示空间转录组学中基因表达变化的统计方法 细胞类型特异性差异表达推断（C-SIDE）是一个统计模型，识别哪些基因（在确定的细胞类型内）基于空间位置、病理变化或细胞-细胞相互作用而差异表达

Niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions 空间转录组学数据中的生态位差异基因表达分析识别上下文依赖的细胞-细胞相互作用

Spatial domain

BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis BANKSY统一细胞分型和组织域分割，实现可扩展的空间组学数据分析

Integration

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST 使用PRECAST进行概率嵌入、聚类和比对以整合空间转录组学数据

High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE 使用CytoSPACE进行单细胞和空间转录组的高分辨率比对

Search and Match across Spatial Omics Samples at Single-cell Resolution 单细胞分辨率下跨空间组学样本的搜索和匹配

Alignment of spatial genomics data using deep Gaussian processes 使用深度高斯过程进行空间基因组数据比对

Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs Starfysh整合空间转录组和组织学数据以揭示异质性肿瘤-免疫枢纽

3D reconstruction

Alignment and integration of spatial transcriptomics data 空间转录组学数据的比对和整合

Clustering

BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies BASS：多尺度和多样本分析实现空间转录组学研究中准确的细胞类型聚类和空间域检测

DeepST: A versatile graph contrastive learning framework for spatially informed clustering, integration, and deconvolution of spatial transcriptomics DeepST：用于空间转录组学空间信息聚类、整合和解卷积的多功能图对比学习框架

Cell-cell interaction

De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc 使用DeepLinc从单细胞空间转录组数据从头重建细胞相互作用景观

Modeling intercellular communication in tissues using spatial graphs of cells 使用细胞空间图建模组织中的细胞间通讯

Imputation

Accurate inference of genome-wide spatial expression with iSpatial 使用iSpatial进行全基因组空间表达的准确推断

Interactive tool

VITESSCE Visual Integration Tool for Exploration of Spatial Single-Cell Experiments VITESSCE 空间单细胞实验探索的可视化整合工具

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