Tools,Codes,Publication,Application ,iST_sST,pub(1pub2pre),Categories,Demos,Journal,Published_Date,Cite_Number,License,Repository_Description
AdRoit,https://zenodo.org/record/5272308#.Y-OeZi9BxD8,https://doi.org/10.1038/s42003-021-02739-1,R,sST,1,spatial decomposition,https://zenodo.org/record/5272308#.Y-OeZi9BxD8,Communications Biology,2021/10/22,9,other,Accurate and robust method to infer complex transcriptome composition
Antisplodge,https://github.com/HealthML/AntiSplodge/,https://doi.org/10.1093/nargab/lqac073,python,sST,1,spatial decomposition,https://github.com/HealthML/AntiSplodge/blob/main/AntiSplodge_minimal_example.ipynb,NAR Genomics and Bioinformatics,2022/10/10,1,mit,Spatial Transcriptomics deconvolution pipeline
ATHENA,https://github.com/AI4SCR/ATHENA,https://doi.org/10.1093/bioinformatics/btac303,python,sST&iST,1,spatial heterogeneity,https://github.com/AI4SCR/ATHENA,Bioinformatics,2022/4/26,13,bsd-3-clause,"ATHENA is an open-source computational framework written in Python that facilitates the visualization, processing and analysis of (spatial) heterogeneity from spatial omics data."
AtlasXbrowser,https://github.com/atlasxomics/AtlasXbrowser,https://doi.org/10.1101/2022.05.11.491526,webtools,sST,1,visualization;interactive,https://github.com/atlasxomics/AtlasXbrowser,bioRxiv,2022/5/20,2,mit,Interactive browser for Atlas Data
banyan,https://github.com/carter-allen/banyan,https://doi.org/10.1101/2022.04.11.487907,R,sST,2,spatial domain;cell-cell/gene-gene interaction,https://carter-allen.github.io/banyan_breast_cancer.html,bioRxiv,2022/4/12,1,other,Bayesian network models for community detection in spatially resolved single cell data
BarDensr,https://github.com/jacksonloper/bardensr,https://doi.org/10.1371/journal.pcbi.1008256,python,iST,1,imputation and denoise,https://github.com/jacksonloper/bardensr,PLOS Computational Biology,2021/3/8,13,mit,barcode demixing through nonnegative spatial regression
BASS,https://github.com/zhengli09/BASS,https://doi.org/10.1186/s13059-022-02734-7,R,sST&iST,1,spatial domain;integration,https://zhengli09.github.io/BASS-Analysis/,Genome Biology,2022/8/4,25,gpl-3.0,Bayesian Analytics for Spatial Segmentation
BayesEATS,https://github.com/jingeyu/BayesEATS,https://doi.org/10.1007/s12561-021-09308-4,R,sST,1,spatial decomposition;spatial domain,https://github.com/jingeyu/BayesEATS,Statistics in Biosciences,2021/4/12,1,other,BayesEATS: an R package for Bayesian joint modeling of single-cell expression data and bulk spatial transcriptomic data
BayesSpace,https://github.com/edward130603/BayesSpace,https://doi.org/10.1038/s41587-021-00935-2,R,sST,1,super resolution;spatial domain,https://github.com/edward130603/BayesSpace,Nature Biotechnology,2021/6/3,303,other,Bayesian model for clustering and enhancing the resolution of spatial gene expression experiments.
Baysor,https://github.com/kharchenkolab/Baysor,https://doi.org/10.1038/s41587-021-01044-w,command line,iST,1,cell segmentation,https://vimeo.com/558564804,Nature Biotechnology,2021/10/14,109,mit,Bayesian Segmentation of Spatial Transcriptomics Data
Belayer,https://github.com/raphael-group/belayer,https://doi.org/10.1016/j.cels.2022.09.002,command line,iST,1,spatial domain;spatial variable genes;spatial expression modules,https://github.com/raphael-group/belayer,Cell Systems,2022/10/19,5,other,Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries.
Bento,https://github.com/ckmah/bento-tools,https://doi.org/10.1101/2022.06.10.495510,python,iST,2,subcellular analysis;visualization,https://bento-tools.readthedocs.io/en/latest/tutorials.html,bioRxiv,2022/6/13,7,bsd-2-clause,A Python toolkit for subcellular analysis of spatial transcriptomics data
BOOST-GP,https://github.com/Minzhe/BOOST-GP,https://doi.org/10.1093/bioinformatics/btab455,R,sST&iST,1,spatial variable genes,https://github.com/Minzhe/BOOST-GP,Bioinformatics,2021/6/17,30,other,Bayesian mOdeling Of Spatial Transcriptomics via Gaussian Process
BOOST-MI,https://github.com/Xijiang1997/BOOST-MI,https://doi.org/10.1002/sim.9530,R,sST&iST,1,spatial variable genes,https://github.com/Xijiang1997/BOOST-MI,Statistics in Medicine,2022/7/25,10,apache-2.0,BOOST-MI (BOOST-Ising) is a method to detect spatially variable (SV) genes in spatial transcriptomics (ST) datasets. It is a Bayesian modeling framework for the analysis of gene expression count data on a lattice defined by a number of array spots.
Bulk2Space,https://github.com/ZJUFanLab/bulk2space,https://doi.org/10.1038/s41467-022-34271-z,python,sST,1,spatial reconstruction of sc,https://github.com/ZJUFanLab/bulk2space,Nature Communications,2022/10/30,15,gpl-3.0,"a spatial deconvolution method based on deep learning frameworks, which converts bulk transcriptomes into spatially resolved single-cell expression profiles"
BulkSingalR,https://github.com/jcolinge/BulkSignalR,https://doi.org/10.1101/2022.11.17.516911,R,sST,2,cell-cell/gene-gene interaction,https://github.com/jcolinge/BulkSignalR,bioRxiv,2022/11/18,0,other,"BulkSignalR is used to infer ligand-receptor (L-R) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics."
CARD,https://github.com/YingMa0107/CARD/,https://doi.org/10.1038/s41587-022-01273-7,R,sST,1,spatial decomposition,https://yingma0107.github.io/CARD/,Nature Biotechnology,2022/5/2,115,gpl-3.0,"CARD could accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. "
CCST,https://github.com/xiaoyeye/CCST,https://doi.org/10.1038/s43588-022-00266-5,command line,sST&iST,1,spatial domain,https://github.com/xiaoyeye/CCST,Nature Computational Science,2022/6/27,47,other,Cell clustering for spatial transcriptomics data with graph neural network
CeLEry ,https://github.com/QihuangZhang/CeLEry ,https://doi.org/10.1038/s41467-023-39895-3,python,sST&iST,1,spatial reconstruction of sc,https://github.com/QihuangZhang/CeLEry,Nature Communications,2023/7/8,7,mit,Leveraging spatial transcriptomics data to recover cell locationsin single-cell RNA-seq with CeLEry
cell2location,https://github.com/BayraktarLab/cell2location/,https://doi.org/10.1038/s41587-021-01139-4,python,sST,1,spatial decomposition,https://cell2location.readthedocs.io/en/latest/,Nature Biotechnology,2022/1/13,324,apache-2.0,Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics (cell2location model)
CellCharter,https://github.com/CSOgroup/cellcharter,https://doi.org/10.1101/2023.01.10.523386,python,sST&iST,2,spatial domain,https://cellcharter.readthedocs.io/en/latest/notebooks/codex_mouse_spleen.html,bioRxiv,2023/1/12,3,bsd-3-clause,"A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data."
CellComm,https://github.com/edroaldo/fusca,https://doi.org/10.21203/rs.3.pex-1843/v1,R,sST,2,cell-cell/gene-gene interaction,https://github.com/edroaldo/fusca/blob/main/tutorial/CellComm_ST_tutorial.ipynb,Research Square,2022/4/13,1,mit,"Framework for Unified Single-Cell Analysis (FUSCA) is a software package for single-cell data analysis developed in R. At the moment, it contains two modules: 1) CellRouter, for reconstruction of complex single-cell trajectories, and 2) CellComm, to infer intercellular communication networks from scRNA-seq data."
CellContact,https://github.com/hkim240/CellContact,https://doi.org/10.1101/2022.02.16.480673,MATLAB,sST,2,spatial variable genes,https://github.com/hkim240/CellContact,bioRxiv,2022/2/19,4,mit,CellContact: Detecting cell contact-dependent gene expression from spatial transcriptomics data
CellDART,https://github.com/mexchy1000/CellDART,https://doi.org/10.1093/nar/gkac084,R,sST,1,spatial decomposition,https://github.com/mexchy1000/CellDART,Nucleic Acids Research,2022/1/26,31,other,domain adaptation of spatial and single-cell transcriptome
Celloscope,https://github.com/szczurek-lab/Celloscope,https://doi.org/10.1101/2022.05.24.493193,command line,sST,2,spatial decomposition,https://github.com/szczurek-lab/Celloscope,bioRxiv,2022/5/26,2,gpl-3.0,Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data 
cellphoneDB,https://github.com/ventolab/CellphoneDB,https://doi.org/10.1038/s41586-022-04918-4,command line,sST&iST,2,cell-cell/gene-gene interaction,https://github.com/ventolab/CellphoneDB,Nature,2022/7/6,118,mit,"CellPhoneDB can be used to search for a particular ligand/receptor, or interrogate your own HUMAN single-cell transcriptomics data."
Cellpose,https://github.com/mouseland/cellpose,https://doi.org/10.1038/s41592-022-01663-4,GUI,iST,1,nuclei segmentation;cell segmentation,https://cellpose.readthedocs.io/en/latest/,Nature Methods,2022/11/7,199,bsd-3-clause,a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
CellProfiler,https://github.com/CellProfiler/CellProfiler,https://doi.org/10.1186/s12859-021-04344-9,GUI,iST,1,nuclei segmentation;cell segmentation,https://tutorials.cellprofiler.org/,BMC Bioinformatics,2021/9/10,557,other,An open-source application for biological image analysis
CellTrek,https://github.com/navinlabcode/CellTrek,https://doi.org/10.1038/s41587-022-01233-1,R,sST,1,spatial reconstruction of sc;cell co-location;,https://github.com/navinlabcode/CellTrek,Nature Biotechnology,2022/3/21,71,other,CellTrek is a computational framework that can directly map single cells back to their spatial coordinates in tissue sections based on scRNA-seq and ST data. 
CLARIFY,https://github.com/MihirBafna/CLARIFY,https://doi.org/10.1093/bioinformatics/btad269,python,sST&iST,1,cell-cell/gene-gene interaction,https://github.com/MihirBafna/CLARIFY,Bioinformatics,2023/6/30,5,other,CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics
ClusterMap,https://github.com/wanglab-broad/ClusterMap,https://doi.org/10.1038/s41467-021-26044-x,python,iST,1,cell segmentation;spatial domain,https://github.com/wanglab-broad/ClusterMap/blob/main/Tutorial.md,Nature Communications,2021/10/9,42,gpl-3.0,ClusterMap for multi-scale clustering analysis of spatial gene expression
CNN-Preg,https://github.com/kuanglab/CNN-PReg,https://doi.org/10.1093/bioinformatics/btab812,command line,sST,1,spatial expression modules,https://github.com/kuanglab/CNN-PReg,Bioinformatics,2021/11/30,1,other,Detecting Spatially Co-expressed Gene Clusters with Functional Coherence by Graph-regularized Convolutional Neural Network
CNTools,https://github.com/yctao7/CNTools,https://doi.org/10.21203/rs.3.rs-2212129/v1,command line,iST,2,spatial domain,https://github.com/yctao7/CNTools,Research Square,2022/11/4,0,mit,CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images
COMMOT,https://github.com/zcang/COMMOT,https://doi.org/10.1101/2022.08.24.505185,python,sST&iST,1,cell-cell/gene-gene interaction;visualization,https://commot.readthedocs.io/en/latest/index.html,bioRxiv,2022/8/26,3,mit,Screening cell-cell communication in spatial transcriptomics via collective optimal transport
ConGI,https://github.com/biomed-AI/ConGI,https://doi.org/10.1101/2022.09.30.510297,command line,sST,2,spatial dimensionality reduction,https://github.com/biomed-AI/ConGI/blob/main/tutorial.ipynb,bioRxiv,2022/10/4,0,other,Identifying Spatial Domain by Adapting Transcriptomics with Histology through Contrastive Learning
conST,https://github.com/ys-zong/conST,https://doi.org/10.1101/2022.01.14.476408,python,sST&iST,2,spatial dimensionality reduction,https://github.com/ys-zong/conST,bioRxiv,2022/1/17,16,other,conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics
CoSTA,https://github.com/rpmccordlab/CoSTA,https://doi.org/10.1186/s12859-021-04314-1,python,sST&iST,1,spatial expression modules,https://github.com/rpmccordlab/CoSTA,BMC Bioinformatics,2021/8/9,14,mit,CoSTA: Unsupervised Convolutional Neural Network Learning for Spatial Transcriptomics Analysis
Cottrazm,https://github.com/Yelab2020/Cottrazm,https://doi.org/10.1038/s41467-023-36560-7,R,sST,1,spatial heterogeneity,https://github.com/Yelab2020/Cottrazm,Nature Communications,2023/2/20,18,other,"Cottrazm (Construct Tumor Transition Zone (Boundary) Microenvironment based on spatial transcriptomics) aims to construct the microenvironment of tumor boundary based on spatial transcriptomics, single-cell transcriptomics and HE-stained histological images."
C-SIDE,https://github.com/dmcable/spacexr,https://doi.org/10.1038/s41592-022-01575-3,R,sST&iST,1,spatial variable genes,https://raw.githack.com/dmcable/spacexr/master/vignettes/differential-expression.html,Nature Methods,2022/9/1,38,gpl-3.0,Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics
CTSV,https://github.com/jingeyu/CTSV,https://doi.org/10.1093/bioinformatics/btac457,R,sST,1,spatial variable genes,https://bioconductor.org/packages/devel/bioc/html/CTSV.html,Bioinformatics,2022/7/6,4,other,Identification of cell-type-specific spatially variable genes accounting for excess zeros
CVAM,https://github.com/JMnotDGR/CVAM,https://doi.org/10.3390/biom13050767,python,sST,1,cnv detection,https://github.com/JMnotDGR/CVAM,Biomolecules,2023/4/25,0,other,CVAM is a tool to infer the CNV profile from spatial transcriptome data based on variational graph autoencoder (VGAE) and HMM under multi-level filtering.
CytoCommunity,NA,https://doi.org/10.1101/2022.11.06.515344,NA,iST,2,cell-cell/gene-gene interaction;spatial domain,NA,bioRxiv,2022/11/7,3,NA,NA
CytoSPACE,https://github.com/digitalcytometry/cytospace,https://doi.org/10.1101/2022.05.20.488356,command line,sST,1,spatial reconstruction of sc,https://github.com/digitalcytometry/cytospace,bioRxiv,2022/5/23,3,gpl-3.0,CytoSPACE: Optimal mapping of scRNA-seq data to spatial transcriptomics data
Cytosplore,https://viewer.cytosplore.org/,https://doi.org/10.1038/s41586-021-03465-8,GUI,iST,1,visualization,https://viewer.cytosplore.org/documentation/generaloverview,Nature,2021/10/7,346,NA,NA
deepBlink,https://github.com/bbquercus/deepblink/,https://doi.org/10.1093/nar/gkab546,command line,iST,1,spot detection,https://github.com/bbquercus/deepblink/,Nucleic Acids Research,2021/6/24,16,other,Threshold independent detection and localization of diffraction-limited spots.
DeepCCI,https://github.com/JiangBioLab/DeepCCI,https://doi.org/10.1093/bioinformatics/btad596,python,sST,1,cell-cell/gene-gene interaction,https://github.com/JiangBioLab/DeepCCI,Bioinformatics,2023/9/23,2,mit,DeepCCI is a graph convolutional network (GCN)-based deep learning framework for Cell-Cell Interactions inference from scRNA-seq data.
DeepLinc,https://github.com/xryanglab/DeepLinc,https://doi.org/10.1186/s13059-022-02692-0,command line,sST&iST,1,cell-cell/gene-gene interaction;spatial domain,https://github.com/xryanglab/DeepLinc,Genome Biology,2022/6/3,10,mit,De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data
DEEPsc,https://github.com/fmaseda/DEEPsc,https://doi.org/10.3389/fgene.2021.636743,MATLAB,sST,1,spatial reconstruction of sc,https://github.com/fmaseda/DEEPsc,Frontiers in Genetics,2021/3/23,21,other,"DEEPsc is a system-adaptive, deep learning-based method to impute spatial information onto a scRNA-seq dataset from a given spatial reference atlas. "
DeepSpaCE,https://github.com/tmonjo/DeepSpaCE,https://doi.org/10.1038/s41598-022-07685-4,command line,sST,1,imputation and denoise,https://github.com/tmonjo/DeepSpaCE,Scientific Reports,2022/3/8,29,gpl-3.0,The Deep learning model for Spatial gene Clusters and Expression (DeepSpaCE) is a method that predicts spatial gene-expression levels and transcriptomic cluster types from tissue section images using deep learning.
DeepST,https://github.com/JiangBioLab/DeepST,https://doi.org/10.1093/nar/gkac901,python,sST&iST,1,spatial domain,https://github.com/JiangBioLab/DeepST,Nucleic Acids Research,2022/10/4,33,mit,Identify spatial domain
DestVI,https://github.com/scverse/scvi-tools,https://doi.org/10.1038/s41587-022-01272-8,python,sST,1,spatial decomposition,https://docs.scvi-tools.org/en/stable/tutorials/notebooks/DestVI_tutorial.html,Nature Biotechnology,2022/4/21,71,bsd-3-clause,Deep probabilistic analysis of single-cell omics data
DIALOGUE,https://github.com/livnatje/DIALOGUE,https://doi.org/10.1038/s41587-022-01288-0,R,sST&iST,1,spatial domain;multicellular programs;Spatial reconstruction of sc-RNA data,https://github.com/livnatje/DIALOGUE/wiki/Tutorial,Nature Biotechnology,2022/5/5,39,other,DIALOGUE is a dimensionality reduction method that uses cross-cell-type associations to identify multicellular programs (MCPs) and map the cell transcriptome as a function of its environment. 
DIST,https://github.com/zhaoyp1997/DIST,https://doi.org/10.1093/bib/bbad013,python,sST,1,imputation and denoise;super resolution,https://github.com/zhaoyp1997/DIST/blob/main/a_self-supervised_learning_example_for_ST_human_melanoma_(mel1_rep1)_data.ipynb,Briefings in Bioinformatics,2023/1/4,5,apache-2.0,Python package for denoise and imputation of spatial transcriptomics using deep learning.
DistMap,https://github.com/rajewsky-lab/distmap,https://doi.org/10.1126/science.aan3235,R,sST&iST,1,spatial reconstruction of sc,https://github.com/rajewsky-lab/distmap,Science,2017/9/1,341,mit, DistMap can be used to spatially map single cell RNA sequencing data by using an existing reference database of in situs.
DOT,https://github.com/saezlab/dot,https://doi.org/10.48550/arXiv.2301.01682,R,sST,2,cell type identifiation,https://github.com/saezlab/dot,arXiv,2023/1/4,0,gpl-3.0,DOT is a method for transferring cell features from a reference single-cell RNA-seq data to spots/cells in spatial omics.
DR-SC,https://github.com/feiyoung/DR.SC,https://doi.org/10.1093/nar/gkac219,R,sST&iST,1,spatial dimensionality reduction;spatial domain,https://feiyoung.github.io/DR.SC/index.html,Nucleic Acids Research,2022/3/22,21,other,DR.SC: Joint dimension reduction and spatial clustering for single-cell/spatial transcriptomics data
DSSC,https://github.com/xianglin226/DSSC,https://doi.org/10.1101/gr.276477.121,command line,sST&iST,1,spatial domain,https://github.com/xianglin226/DSSC,Genome Research,2022/10/5,0,apache-2.0,A model-based constrained deep learning clustering approach for spatial-resolved single-cell data
DSTG,https://github.com/Su-informatics-lab/DSTG,https://doi.org/10.1093/bib/bbaa414,command line,sST,1,spatial decomposition,https://github.com/Su-informatics-lab/DSTG,Briefings in Bioinformatics,2020/12/18,98,mit,Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence 
eggplant,https://github.com/almaan/eggplant,https://doi.org/10.1101/2021.11.11.468178,python,sST,2,integration,https://spatial-eggplant.readthedocs.io/en/latest/notebooks/mouse-olfactory-bulb-analysis.html,bioRxiv,2021/11/13,15,mit,eggplant is a method designed to transfer information from multiple spatial-transcriptomics data sets to a single reference representing a Common Coordinate Framework (CCF).
EGN,https://github.com/Yan98/EGN,https://doi.org/10.1109/WACV56688.2023.00501,command line,sST,1,imputation and denoise,https://github.com/Yan98/EGN,IEEE Xplore,2023/2/6,6,other,Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction
EnDecon,https://github.com/Zhangxf-ccnu/EnDecon,https://doi.org/10.1093/bioinformatics/btac825,R,iST,1,spatial decomposition,https://github.com/Zhangxf-ccnu/EnDecon,Bioinformatics,2022/12/22,9,other,EnDecon: cell type deconvolution of spatiallyresolved transcriptomics data via ensemble learning
FICT,https://github.com/haotianteng/FICT,https://doi.org/10.1093/bioinformatics/btab704,command line,iST,1,spatial domain,https://github.com/haotianteng/FICT-SAMPLE,Bioinformatics,2021/10/7,24,other,FISH Iterative Cell Typing
FISHFactor,https://github.com/bioFAM/FISHFactor,https://doi.org/10.1101/2021.11.04.467354,python,iST,2,subcellular analysis,https://github.com/bioFAM/FISHFactor/blob/master/fishfactor_demo.ipynb,bioRxiv,2021/11/5,3,lgpl-3.0,A probabilistic factor model for spatial transcriptomics data with subcellular resolution
FISH-quant,https://github.com/FISH-quant,https://doi.org/10.1261/rna.079073.121,python,iST,1,nuclei segmentation;cell segmentation;spot detection;transcript assignment;visualization;subcellular analysis,https://github.com/FISH-quant,RNA,2022/3/28,39,bsd-3-clause,FISH-quant v2: a scalable and modular tool for smFISH image analysis
FIST,https://github.com/kuanglab/FIST,https://doi.org/10.1371/journal.pcbi.1008218,MATLAB,sST,1,imputation and denoise,https://github.com/kuanglab/FIST,PLOS Computational Biology,2021/4/7,22,other,FIST: Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion
FIST-nD,https://github.com/kuanglab/FIST-nD,https://doi.org/10.1101/2022.10.12.511928,command line,sST,2,imputation and denoise,https://github.com/kuanglab/FIST-nD/blob/master/example/walkthrough.md,bioRxiv,2022/10/17,0,other,"FIST-nD, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion in n-Dimensions imputes 3D as well as 2D spatial transcriptomics data."
GCNG,https://github.com/xiaoyeye/GCNG,https://doi.org/10.1186/s13059-020-02214-w,command line,iST,1,cell-cell/gene-gene interaction,https://github.com/xiaoyeye/GCNG,Genome Biology,2020/12/10,77,other,using graph convolutional neural network and spaital transcriptomics data to infer cell-cell interactions
GeneSegNet,https://github.com/BoomStarcuc/GeneSegNet,https://doi.org/10.1101/2022.12.13.520283,command line,iST,2,cell segmentation,https://github.com/BoomStarcuc/GeneSegNet,bioRxiv,2022/12/15,3,mit,GeneSegNet: A Deep Learning Framework for Cell Segmentation by Integrating Gene Expression and Imaging Information.
GeomxTools,https://github.com/Nanostring-Biostats/GeomxTools,https://doi.org/10.18129/B9.bioc.GeomxTools,R,iST,2,raw data processing pipeline,https://bioconductor.org/packages/release/bioc/vignettes/GeomxTools/inst/doc/GeomxSet_coercions.html,NA,NA,0,mit,"Tools for NanoString GeoMx Digital Spatial Profiler data for reading DCC and PKC files to NanoStringGeomxSet class, Normalization and QC. "
gimVI,https://github.com/scverse/scvi-tools,https://doi.org/10.48550/arXiv.1905.02269,R,sST&iST,1,imputation and denoise,https://github.com/scverse/scvi-tools,arXiv,2019/5/6,49,bsd-3-clause,Deep probabilistic analysis of single-cell omics data
Giotto,https://github.com/RubD/Giotto/,https://doi.org/10.1101/701680,R,sST&iST,1,quality control;image&spot alignment;normalization;dimensionality reduction;visualization;spatial domain;spatial variable genes;interactive;gene co-expression;cell-cell/gene-gene interaction;differential expression;clustering;toolboxes,www.giottosuite.com,bioRxiv,2019/7/13,41,mit,Spatial omics analysis toolbox
GIST,https://github.com/asifzubair/GIST,https://doi.org/10.1093/nar/gkac320,R,sST,1,spatial decomposition,https://github.com/asifzubair/GIST/tree/main/vignettes,Nucleic Acids Research,2022/4/21,5,gpl-3.0,Guiding-Image Spatial Transcriptomics 
GLISS,https://github.com/junjiezhujason/gliss,https://doi.org/10.1101/2020.08.12.248971,R,sST&iST,1,spatial reconstruction of sc;spatial variable genes,https://github.com/junjiezhujason/gliss,bioRxiv,2020/8/13,8,other," GLISS can discover new spatial genes and recover cell locations in scRNA-seq data from landmark genes determined from SGE data and offer a new dimension reduction technique to cluster the genes, while accounting for the inferred spatial structure of the cells."
GLUER,https://github.com/tanlabcode/GLUER,https://doi.org/10.1101/2021.01.25.427845,python,iST,2,integration;dimensionality reduction,https://github.com/tanlabcode/GLUER,bioRxiv,2021/1/27,16,mit,GLUER: integrative analysis of multi-omics and imaging data at single-cell resolution by deep neural networks
GPcounts,https://github.com/ManchesterBioinference/GPcounts,https://doi.org/10.1093/bioinformatics/btab486,python,sST,1,spatial variable genes,https://github.com/ManchesterBioinference/GPcounts/blob/master/demo_notebooks/GPcounts_spatial.ipynb,Bioinformatics,2021/6/30,25,apache-2.0,GPcounts is Gaussian process regression package for counts data with negative binomial and zero-inflated negative binomial likelihoods
GraphST,https://github.com/JinmiaoChenLab/GraphST,https://doi.org/10.1101/2022.08.02.502407,python,sST,2,integration;spatial domain;spatial decomposition,https://github.com/JinmiaoChenLab/GraphST,bioRxiv,2022/8/3,1,agpl-3.0,"GraphST is a versatile graph self-supervised contrastive learning model that incorporates spatial location information and gene expression profiles to accomplish three key tasks, spatial clustering, spatial transcriptomics (ST) data integration, and single-cell RNA-seq (scRNA-seq) transfer onto ST."
GSDensity ,https://github.com/qingnanl/gsdensity,https://doi.org/10.21203/rs.3.rs-2235639/v1,R,sST,2,spatial expression modules,http://htmlpreview.github.io/?https://github.com/qingnanl/gsdensity/blob/master/vignette/spatial_example_10x_visium.html,Research Square,2022/11/9,0,other,"gsdensity, a computational tool dedicated for 'pathway centric' analysis of single-cell data."
hexCNN,https://github.com/ehoogeboom/hexaconv,https://doi.org/10.1109/BIBM55620.2022.9995701,command line,sST,1,spatial domain,https://github.com/ehoogeboom/hexaconv,2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM),2023/1/2,3,other,"G-HexaConv, a group equivariant convolutional neural network on hexagonal lattices."
Hist2ST,https://github.com/biomed-AI/Hist2ST,https://doi.org/10.1093/bib/bbac297,python,sST,1,imputation and denoise,https://github.com/biomed-AI/Hist2ST/blob/main/tutorial.ipynb,Briefings in Bioinformatics,2022/7/18,27,other," Hist2ST, a deep learning-based model using histology images to predict RNA-seq expression."
HisToGene,https://github.com/maxpmx/HisToGene,https://doi.org/10.1101/2021.11.28.470212,python,sST&iST,2,super resolution;imputation and denoise,https://github.com/maxpmx/HisToGene/blob/main/tutorial.ipynb,bioRxiv,2021/11/28,17,mit,PyTorch implementation of HisToGene
HoloNet,https://github.com/lhc17/HoloNet,https://doi.org/10.1101/2022.06.22.496105,python,sST,2,cell-cell/gene-gene interaction,https://holonet-doc.readthedocs.io,bioRxiv,2022/6/27,2,mit,HoloNet. Reveal the holograph of functional communication events in spatial transcriptomics. Help understand how microenvironments shaping cellular phenotypes
HTA,https://github.com/alonalj/hta,https://doi.org/10.1093/bioinformatics/btab569,python,sST&iST,1,spatial heterogeneity,https://github.com/alonalj/hta,Bioinformatics,2021/8/4,6,mit,"The HTA package can statistically assess the level of both spatial, and global, heterogeneity within a spatial sample."
Ilastik,https://github.com/ilastik/ilastik,https://doi.org/10.1038/s41592-019-0582-9,GUI,iST,1,cell segmentation,https://github.com/ilastik/ilastik.github.io,Nature Methods,2019/9/30,1738,lgpl-3.0,The interactive learning and segmentation toolkit
ImSpiRE,https://github.com/TongjiZhanglab/ImSpiRE,https://doi.org/10.1101/2023.05.04.539342,python,sST,2,super resolution,https://github.com/TongjiZhanglab/ImSpiRE,BioRxiv,2023/5/5,0,mit,ImSpiRE is an image feature-aided spatial resolution enhancement method for the ISC spatial transcriptome.
InSituType,https://github.com/Nanostring-Biostats/InSituType,https://doi.org/10.1101/2022.10.19.512902,R,iST,2,spatial domain,https://github.com/Nanostring-Biostats/InSituType,bioRxiv,2022/10/25,7,other,An R package for performing cell typing in SMI and other single cell data
iSpatial,https://github.com/YiZhang-lab/iSpatial,https://doi.org/10.1126/sciadv.abq0990,R,iST,1,imputation and denoise,http://htmlpreview.github.io/?https://github.com/Czh3/iSpatial/blob/master/vignettes/introduction.html,Science Advances,2022/8/26,5,other,infer whole genome spatial transcriptome by integrating single cell RNAseq and spatial transcriptome
istar,https://github.com/daviddaiweizhang/istar,https://doi.org/10.1038/s41587-023-02019-9,python,sST,1,super resolution,https://github.com/daviddaiweizhang/istar,Nature Biotechnology,2024/1/2,1,gpl-3.0,"This software package implements iStar (Inferring Supper-resolution Tissue ARchitecture), which enhances the spatial resolution of spatial transcriptomic data from a spot-level to a near-single-cell level."
ISTDECO,https://github.com/axanderssonuu/istdeco,https://doi.org/10.1101/2021.03.01.433040,python,iST,2,transcript decoder;spot detection,https://github.com/axanderssonuu/istdeco,bioRxiv,2021/3/2,6,mit,ISTDECO is a simple tool for deconvolving combinatorial image data generated by *FISH techniques into series of non-combinatorial images.
JSIT,https://github.com/jpbryan13/JSIT,https://doi.org/10.1101/2022.11.22.517523,MATLAB,iST,2,transcript decoder,https://github.com/jpbryan13/JSIT,bioRxiv,2022/11/24,0,mit,Decoding pipeline for imaging transcriptomics data
JSTA,https://github.com/wollmanlab/JSTA,https://doi.org/10.15252/msb.202010108,python,iST,1,spatial decomposition;cell segmentation,https://github.com/wollmanlab/JSTA/tree/master/tutorials,Molecular Systems Biology,2021/5/31,45,other,JSTA: joint cell segmentation and cell type annotation for spatial transcriptomics
LANTSA,https://github.com/zccqq/LANTSA,https://doi.org/10.1101/2022.03.13.484116,python,sST,2,spatial decomposition;visualization;integration,https://lantsa.readthedocs.io/en/stable/index.html,bioRxiv,2022/3/14,0,bsd-3-clause,Landmark-based transferable subspace analysis for single-cell and spatial transcriptomics
Liger,https://github.com/welch-lab/liger,https://doi.org/10.1016/j.cell.2019.05.006,R,sST&iST,1,integration,https://github.com/welch-lab/liger,Cell,2019/6/6,812,gpl-3.0,R package for integrating and analyzing multiple single-cell datasets
maple,https://github.com/carter-allen/maple,https://doi.org/10.1101/2022.02.28.482296,R,sST,2,integration,https://carter-allen.github.io/stxBrain_multi_maple.html,bioRxiv,2022/3/3,9,other,Bayesian Analysis of Multi-Sample Spatial Transcriptomics Data
Matisse,https://github.com/Moldia/Matisse,https://doi.org/10.1186/s12859-021-04302-5,MATLAB,iST,1,dimensionality reduction;clustering;cell segmentation;visualization;gene co-expression;cell type identifiation;cell-cell/gene-gene interaction;differential expression;toolboxes,https://github.com/Moldia/Matisse/blob/master/Examples/html/MATISSE_EXAMPLE_mouseBrain.html,BMC Bioinformatics,2021/7/31,3,other,"The Matlab Analysis Toolbox for Spatially resolved transcriptomics aims to provide an easy to use tool for performing a initial analyis for spot-based technologies in the field of Spatially-resolved transcriptomics, with focus on In Situ Sequencing experiments. "
MaxFuse,https://github.com/shuxiaoc/maxfuse,https://doi.org/10.1038/s41587-023-01935-0,python,sST,1,integration,https://github.com/shuxiaoc/maxfuse,Nature Biotechnology,2023/9/7,4,other,MaxFuse is a Python package for integrating single-cell datasets from different modalities with no overlapping features and/or under low signal-to-noise ratio regimes.
Maxspin,https://github.com/dcjones/maxspin,https://doi.org/10.1101/2022.11.02.514777,python,sST&iST,2,spatial variable genes,https://github.com/dcjones/maxspin,bioRxiv,2022/11/4,1,mit,Detecting spatially varying genes using mutual information
MEFISTO,https://github.com/bioFAM/MOFA2,https://doi.org/10.1038/s41592-021-01343-9,R,sST&iST,1,spatial dimensionality reduction,https://biofam.github.io/MOFA2/MEFISTO,Nature Methods,2022/1/13,59,lgpl-3.0,MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion.
MERFISH_analysis,https://github.com/ZhuangLab/MERFISH_analysis,https://doi.org/10.1126/science.aaa6090,MATLAB,iST,2,raw data processing pipeline,http://zhuang.harvard.edu/merfish.html,Science,2015/4/10,1598,other,Matlab software for the analysis of MERFISH data and the construction of MERFISH probes.
MERINGUE,https://github.com/JEFworks-Lab/MERINGUE,https://doi.org/10.1101/gr.271288.120,R,sST&iST,1,spatial variable genes;spatial expression modules,https://jef.works/MERINGUE/,Genome Research,2021/5/25,73,gpl-3.0,characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities
mesmer,https://github.com/vanvalenlab/intro-to-deepcell,https://doi.org/10.1038/s41587-021-01094-0,python,iST,1,cell segmentation,https://www.deepcell.org/about,Nature Biotechnology,2021/11/18,274,other,An introduction to deepcell and deep learning
MESSI,https://github.com/doraadong/MESSI,https://doi.org/10.1093/bioinformatics/btaa769,command line,iST,1,cell-cell/gene-gene interaction;spatial variable genes,https://github.com/doraadong/MESSI/tree/master/tutorials,Bioinformatics,2020/8/26,28,mit,A predictive framework to identify signaling genes active in cell-cell interaction 
MIST,https://github.com/linhuawang/MIST,https://doi.org/10.1101/2021.05.14.443446,python,sST,2,imputation and denoise;spatial domain,https://github.com/linhuawang/MIST,bioRxiv,2021/5/17,0,gpl-3.0,Missing value Imputation for Spatially resolved Transcriptomics (MIST)
MISTy,https://github.com/saezlab/mistyR/,https://doi.org/10.1186/s13059-022-02663-5,R/python,sST&iST,1,cell-cell/gene-gene interaction,https://saezlab.github.io/mistyR/,Genome Biology,2022/4/14,37,gpl-3.0,Multiview Intercellular SpaTial modeling framework
MP-MIM,https://github.com/YuLin-code/MP-MIM,https://doi.org/10.3389/fgene.2022.912813,command line,sST,1,spatial dimensionality reduction,https://github.com/YuLin-code/MP-MIM/blob/master/tutorial/Tutorial.md,Frontiers in Genetics,2022/8/12,1,other,MP-MIM is a novel embedding evaluation method that employs a graph convolution way for message passing and spatial autocorrelation index to evaluate embeddings from deep learning models used for spatial transcriptome analysis.
MULTILAYER,https://github.com/SysFate/MULTILAYER,https://doi.org/10.1016/j.cels.2021.04.008,command line,sST,1,spatial domain;spatial variable genes;slices alignment,https://github.com/SysFate/MULTILAYER,Cell Systems,2021/5/7,17,other,"MULTILAYER applies agglomerative clustering within contiguous locally-defined transcriptomes (herein defined as gene expression elements or gexels), combined with community detection methods for graph partitioning."
MUSE,https://github.com/AltschulerWu-Lab/MUSE,https://doi.org/10.1038/s41587-022-01251-z,python,sST&iST,1,integration;spatial domain,https://github.com/AltschulerWu-Lab/MUSE,Nature Biotechnology,2022/3/28,34,mit,MUSE is a deep learning approach characterizing tissue composition through combined analysis of morphologies and transcriptional states for spatially resolved transcriptomics data.
NCEM,https://github.com/theislab/ncem,https://doi.org/10.1038/s41587-022-01467-z,python,sST&iST,1,cell-cell/gene-gene interaction,https://ncem.readthedocs.io/en/latest/index.html,Nature Biotechnology,2022/10/27,49,bsd-3-clause,Learning cell communication from spatial graphs of cells
NEUROeSTIMator,https://research-git.uiowa.edu/michaelson-lab-public/neuroestimator/,https://doi.org/10.1101/2022.04.08.487573,R,sST,1,neuronal activity quantifation,https://research-git.uiowa.edu/michaelson-lab-public/neuroestimator/,bioRxiv,2022/4/10,2,gpl-3.0,"NEUROeSTIMator is a tool to quantify neuronal activity from single cell, bulk tissue, or spatial RNA-sequencing data. It integrates signals of activation distributed throughout the broader transcriptome to estimate neuronal activation in a way that is robust against differences in species, neuron type, and brain region."
NICHES,http://github.com/msraredon/NICHES,https://doi.org/10.1093/bioinformatics/btac775,R,sST,1,cell-cell/gene-gene interaction,http://github.com/msraredon/NICHES,Bioinformatics,2022/12/2,17,gpl-3.0,NICHES: Niche Interactions and Cellular Heterogeneity in Extracellular Signaling
NMFreg,https://github.com/tudaga/NMFreg_tutorial,https://doi.org/10.1126/science.aaw1219,R,sST,1,spatial dimensionality reduction,https://github.com/tudaga/NMFreg_tutorial,Science,2019/3/28,1339,mit,A tutorial on NMFreg applied to cerebellum data.
nnSVG,https://github.com/lmweber/nnSVG,https://doi.org/10.1101/2022.05.16.492124,R,sST&iST,2,spatial variable genes,https://github.com/lmweber/nnSVG,bioRxiv,2022/5/19,10,mit,nnSVG: scalable method to identify spatially variable genes (SVGs) in spatially-resolved transcriptomics data based on nearest-neighbor Gaussian processes (NNGPs)
NoVaTeST,https://github.com/abidabrar-bracu/NoVaTeST,https://doi.org/10.1101/2022.09.25.509381,python,sST,2,spatial variable genes,https://github.com/abidabrar-bracu/NoVaTeST,bioRxiv,2022/9/27,0,mit,"NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data."
NovoSpaRc,https://github.com/rajewsky-lab/novosparc,https://doi.org/10.1038/s41596-021-00573-7,python,sST&iST,1,spatial reconstruction of sc,https://github.com/rajewsky-lab/novosparc/blob/master/reconstruct_drosophila_embryo_tutorial.ipynb,Nature Protocols,2021/8/4,51,bsd-3-clause,"novoSpaRc, de novo Spatial Reconstruction of Single-Cell Gene Expression"
Optocoder,https://github.com/rajewsky-lab/optocoder,https://doi.org/10.1093/nargab/lqac042,command line,sST,1,bead barcode decoder,https://github.com/rajewsky-lab/optocoder,NAR Genomics and Bioinformatics,2022/6/7,1,gpl-3.0,Optocoder is a computational framework that processes microscopy images to decode bead barcodes in space.
OrderShapeEM,https://github.com/jchen1981/OrderShapeEM,https://doi.org/10.1093/genetics/iyac095,R,sST&iST,1,spatial expression modules,https://github.com/jchen1981/OrderShapeEM,Genetics,2022/6/22,0,other,Optimal false discovery rate control using auxiliary (order) information based on a shape-constrained EM algorithm
Paella,https://github.com/Winnie09/Paella,https://doi.org/10.1101/2022.09.05.506682,R,sST,2,spatial trajectory;spatial variable genes,https://winnie09.github.io/Wenpin_Hou/pages/Paella.html,bioRxiv,2022/9/6,0,mit,"Paella, a computational method to decompose a cell trajectory into multiple spatial sub-trajectories and identify genes with differential temporal patterns across spatial sub-trajectories."
PAST,https://github.com/lizhen18THU/PAST,https://doi.org/10.1101/2022.11.09.515447,python,sST&iST,2,slices alignment;visualization;integration;spatial domain,https://past.readthedocs.io/en/latest/,bioRxiv,2022/11/10,1,mit,PAST: a Prior-based self-Attention method for Spatial Transcriptomics
paste,https://github.com/raphael-group/paste,https://doi.org/10.1038/s41592-022-01459-6,python,sST,1,slices alignment;3D reconstruction;visualization;integration,https://github.com/raphael-group/paste/blob/main/docs/source/notebooks/getting-started.ipynb,Nature Methods,2022/5/16,66,bsd-3-clause,Probabilistic Alignment of Spatial Transcriptomics Experiments
PathoMCH,https://github.com/alonalj/PathoMCH,https://doi.org/10.1038/s41598-020-75708-z,command line,sST,1,imputation and denoise,https://github.com/alonalj/PathoMCH,Scientific Reports,2020/11/2,74,mit,Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer
pciseq,https://github.com/acycliq/pciSeq,https://doi.org/10.1038/s41592-019-0631-4,python,iST,1,cell type identifiation,https://github.com/acycliq/pciSeq,Nature Methods,2019/11/18,179,mit,A probabilistic cell typing algorithm for spatial transcriptomics.
PERSIST,https://github.com/iancovert/persist/,https://doi.org/10.1101/2022.05.13.491738,python,iST,2,gene panel selection,https://github.com/iancovert/persist/,bioRxiv,2022/5/19,1,mit,Predictive and robust gene selection for spatial transcriptomics (PERSIST) is a computational approach to select target genes for FISH studies. 
Points2regions,https://tissuumaps.github.io/TissUUmaps/plugins/-Points2Regions,https://doi.org/10.1101/2022.12.07.519086,plugins,iST,2,spatial domain;interactive,https://tissuumaps.github.io/TissUUmaps/plugins/-Points2Regions,bioRxiv,2022/12/10,2,NA,Points2Regions allows the user to automatically identify regions of similar gene expression
POLARIS,https://github.com/JiawenChenn/POLARIS,https://doi.org/10.1101/2022.12.08.519631,command line,sST&iST,2,spatial decomposition;spatial variable genes,https://github.com/JiawenChenn/POLARIS,bioRxiv,2022/12/13,0,mit,POLARIS is a versatile and generally applicable method for the analysis of spatial transcriptomics (ST) data.
PoSTcode,https://github.com/gerstung-lab/postcode,https://doi.org/10.1101/2021.10.12.464086,python,iST,2,spot detection;transcript decoder,https://github.com/gerstung-lab/postcode,bioRxiv,2021/10/14,9,mit,Decoding of image-based spatial transcriptomics
PRECAST,https://github.com/feiyoung/PRECAST,https://doi.org/10.1101/2022.06.26.497672,R,sST,1,slices alignment;visualization;integration;spatial domain,https://feiyoung.github.io/PRECAST/index.html,bioRxiv,2022/6/30,2,gpl-3.0,an efficient data integration method for multiple spatial transcriptomics data with non- cluster-relevant e铿€ects such as the complex batch e铿€ects. 
RCTD,https://github.com/dmcable/spacexr,https://doi.org/10.1038/s41587-021-00830-w,R,sST,1,spatial decomposition,https://github.com/dmcable/spacexr/tree/master/vignettes,Nature Biotechnology,2021/2/20,337,gpl-3.0,Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics
Redeconve,https://github.com/ZxZhou4150/Redeconve,https://doi.org/10.1101/2022.12.22.521551,R,sST,2,spatial decomposition,https://github.com/ZxZhou4150/Redeconve,bioRxiv,2022/12/22,1,mit,Deconvolution of spatial transcriptomics at single-cell resolution
RESEPT,https://github.com/OSU-BMBL/RESEPT,https://doi.org/10.1016/j.csbj.2022.08.029,command line,sST,1,spatial domain,https://github.com/OSU-BMBL/RESEPT,Computational and Structural Biotechnology Journal,2022/8/24,12,mit,A deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics
ReSort,https://github.com/LiuzLab/RESORT,https://doi.org/10.1101/2022.12.15.520612,command line,sST,2,spatial decomposition,https://github.com/LiuzLab/RESORT,bioRxiv,2022/12/20,1,other,Accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy
RS-FISH,https://github.com/PreibischLab/RS-FISH,https://doi.org/10.1038/s41592-022-01669-y,GUI,iST,1,spot detection;visualization;interactive,https://github.com/PreibischLab/RS-FISH,Nature Methods,2022/11/17,15,gpl-2.0,"Tool for precise, interactive, fast and scalable FISH spot detection"
SageNet,https://github.com/MarioniLab/SageNet,https://doi.org/10.1101/2022.04.14.488419,python,sST&iST,2,spatial reconstruction of sc,https://github.com/MarioniLab/SageNet,bioRxiv,2022/4/18,0,mit,Spatial reconstruction of dissociated single-cell data
saSpatial,https://www.frontiersin.org/articles/10.3389/fnins.2022.1086168/full#supplementary-material,https://doi.org/10.3389/fnins.2022.1086168,R,sST,1,spatial variable genes,https://www.frontiersin.org/articles/10.3389/fnins.2022.1086168/full#supplementary-material,Frontiers in Neuroscience,2022/11/29,4,other,Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics
SAW,https://github.com/BGIResearch/SAW,https://doi.org/10.1038/s41592-021-01203-6,command line,sST,1,raw data processing pipeline,https://github.com/BGIResearch/SAW,Nature Methods,2021/8/2,274,other,SAW: Stereo-seq Analysis Workflow
SCAN-IT,https://github.com/zcang/SCAN-IT,https://www.bmvc2021-virtualconference.com/conference/papers/paper_1139.html,python,iST,1,spatial domain,https://github.com/zcang/SCAN-IT,British Machine Vision Conference,2021/11/22,2,mit,A deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes.
scanpy,https://github.com/scverse/scanpy,https://doi.org/10.1186/s13059-017-1382-0,python,sST,1,quality control;normalization;dimensionality reduction;visualization;spatial variable genes;integration;differential expression;clustering;toolboxes,https://scanpy.readthedocs.io/en/stable/,Genome Biology,2018/2/6,3717,bsd-3-clause,Single-cell analysis in Python. Scales to >1M cells.
scGCO,https://github.com/WangPeng-Lab/scGCO,https://doi.org/10.1038/s41467-022-33182-3,python,sST&iST,1,spatial variable genes,https://github.com/WangPeng-Lab/scGCO/blob/master/code/Tutorial/scGCO_tutorial.ipynb,Nature Communications,2022/9/19,10,mit,Single-cell Graph Cuts Optimization
scGIST,https://github.com/yafi38/scGIST,https://doi.org/10.1101/2022.09.25.509418,python,iST,1,gene panel selection,https://github.com/yafi38/scGIST,bioRxiv,2022/9/27,0,other,scGIST is a deep neural network that designs sc-ST gene panel through constrained feature selection. 
scMAGS,https://github.com/doganlab/scmags,https://doi.org/10.1101/2022.03.22.485261,python,sST&iST,2,gene panel selection,https://scmags.readthedocs.io/en/latest/select.html,bioRxiv,2022/3/24,0,apache-2.0,Marker Gene Selection Tool For Spatial Transcriptomics
SC-MEB,https://github.com/Shufeyangyi2015310117/SC.MEB,https://doi.org/10.1093/bib/bbab466,R,sST,1,spatial domain,https://shufeyangyi2015310117.github.io/SC.MEB/index.html,Briefings in Bioinformatics,2021/10/13,38,other,SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes
scSpace,https://github.com/ZJUFanLab/scSpace,https://doi.org/10.1101/2022.05.07.491043,python,sST&iST,2,spatial reconstruction of sc,https://github.com/ZJUFanLab/scSpace,bioRxiv,2022/5/20,2,gpl-3.0,an integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo space with spatial transcriptome references
scSpatial Explorer,https://github.com/FredPont/spatial,https://doi.org/10.1186/s12859-023-05150-1,GUI,sST&iST,1,visualization;interactive,https://github.com/FredPont/spatial,BMC Bioinformatics,2023/1/27,2,other,Single-Cell Spatial Explorer
SD2,https://github.com/leihouyeung/SD2,https://doi.org/10.1093/bioinformatics/btac605,R,sST&iST,1,spatial decomposition,https://github.com/leihouyeung/SD2,Bioinformatics,2022/9/4,7,other,SD2: Spatially resolved transcriptomics deconvolution through integration of spatial and dropout information
SEDR,https://github.com/JinmiaoChenLab/SEDR/,https://doi.org/10.1101/2021.06.15.448542,python,sST,2,spatial dimensionality reduction,https://github.com/JinmiaoChenLab/SEDR_analyses/,bioRxiv,2021/7/2,49,mit,SEDR (spatial embedded deep representation) learns a low-dimensional latent representation of gene expression embedded with spatial information for spatial transcriptomics analysis.
Semla,https://github.com/ludvigla/semla,https://doi.org/10.1093/bioinformatics/btad626,R,sST,1,toolboxes,https://github.com/ludvigla/semla,Bioinformatics,2023/10/16,4,mit,semla is an R package that collects useful tools for Spatially Resolved Transcriptomics data analysis and visualization.
sepal,https://github.com/almaan/sepal,https://doi.org/10.1093/bioinformatics/btab164,command line,sST&iST,1,spatial expression modules,https://github.com/almaan/sepal,Bioinformatics,2021/3/8,26,mit,sepal : Spatial Expression PAttern Locator
seqFISH-PLUS,https://github.com/CaiGroup/seqFISH-PLUS,https://doi.org/10.1038/s41586-019-1049-y,MATLAB,iST,1,raw data processing pipeline,https://github.com/CaiGroup/seqFISH-PLUS,Nature,2019/3/25,974,other,seqFISH+ Analysis Workflow
Seurat,https://github.com/satijalab/seurat,https://doi.org/10.1016/j.cell.2021.04.048,R,sST,1,quality control;normalization;dimensionality reduction;visualization;interactive;spatial variable genes;spatial decomposition;integration;differential expression;clustering;toolboxes,https://satijalab.org/seurat/,Cell,2021/5/31,5599,other,R toolkit for single cell genomics
SiGra,https://github.com/QSong-github/SiGra,https://doi.org/10.1101/2022.08.18.504464,command line,iST,1,spatial domain;imputation and denoise,https://github.com/QSong-github/SiGra/blob/main/Tutorials/SiGra_train.ipynb,bioRxiv,2022/8/19,1,mit,Single-cell spatial elucidation through image-augmented graph transformer
SIRV,https://github.com/tabdelaal/SIRV,https://doi.org/10.1101/2021.07.26.453774,python,sST,2,spatial trajectory,https://github.com/tabdelaal/SIRV,bioRxiv,2021/7/27,7,mit,Spatial RNA velocity
SlideCNA,https://github.com/dkzhang777/SlideCNA,https://doi.org/10.1101/2022.11.25.517982,R,sST,2,cnv detection,https://github.com/dkzhang777/SlideCNA,bioRxiv,2022/11/27,0,other,SlideCNA is a method to call copy number alterations (CNA) from spatial transcriptomics data (specifically adapted for Slide-seq data).
slideseq-tools,https://github.com/MacoskoLab/slideseq-tools,https://doi.org/10.1038/s41587-020-0739-1,command line,sST,1,raw data processing pipeline,https://github.com/MacoskoLab/slideseq-tools,Nature Biotechnology,2020/12/7,549,other,Tools for analyzing Slide-seq data
SlowMoMan,https://github.com/yunwilliamyu/SlowMoMan,https://doi.org/10.1101/2022.08.23.505019,webtools,sST,2,spatial trajectory,https://yunwilliamyu.github.io/SlowMoMan/,bioRxiv,2022/8/25,0,mit,Feature selection on 2D embeddings of manifolds using slow vibrational modes
SMART-Q,https://github.com/shenlab-ucsf/SMART-Q,https://doi.org/10.1371/journal.pone.0228760,command line,iST,1,transcript decoder;transcript assignment,https://github.com/shenlab-ucsf/SMART-Q,PLOS ONE,2020/4/29,4,other,SMART-Q is a modular and customizable pipeline processing single molecule FISH data with much-improved efficiency and accuracy compared to the existing methods.
SMfishhmrf,https://bitbucket.org/qzhudfci/smfishhmrf-py/src/master/,https://doi.org/10.1038/nbt.4260,python,sST&iST,1,spatial domain,http://spatial.rc.fas.harvard.edu/install.html,Nature Biotechnology,2018/10/29,173,other,This tool infers spatial domain-like patterns from spatial gene expression data that are generated by multiplexed single molecule FISH or by other spatial profiling technologies
SOMDE,https://github.com/XuegongLab/somde,https://doi.org/10.1093/bioinformatics/btab471,python,sST&iST,1,spatial variable genes,https://github.com/WhirlFirst/somde/blob/master/slide_seq0819_11_SOM.ipynb,Bioinformatics,2021/6/23,28,mit,Algorithm for finding gene spatial pattern based on Gaussian process accelerated by SOM
SONAR,https://github.com/lzygenomics/SONAR,https://github.com/lzygenomics/SONAR,R,sST,1,spatial decomposition,https://github.com/lzygenomics/SONAR,Nature Communications,2023/8/7,3,gpl-3.0,SONAR is the algorithm of cell-type deconvolution for spatial transcriptomics
SOTIP,https://github.com/TencentAILabHealthcare/SOTIP,https://doi.org/10.1038/s41467-022-34867-5,command line,sST&iST,1,spatial heterogeneity;spatial domain,https://github.com/TencentAILabHealthcare/SOTIP/tree/master/SOTIP_analysis/tutorial/,Nature Communications,2022/11/28,10,other,SOTIP is a Versatile Method for Microenvironment Modelling with Spatial Omics Data
SpaceClones3,https://github.com/phillipnicol/SpaceClones3,https://doi.org/10.1007/978-3-030-64511-3_10,R,sST&iST,1,clone inference,https://github.com/phillipnicol/SpaceClones3,Mathematical and Computational Oncology,2020/12/7,0,other,"This is an R package which implements a statistical procedure for finding tumor subclones from spatially resolved RNA-seq data (i.e., spatial transcriptomics, Slide-seq, seq-FISH)."
SpaceFlow,https://github.com/hongleir/SpaceFlow,https://doi.org/10.1038/s41467-022-31739-w,python,sST&iST,1,spatial trajectory;spatial domain;spatial dimensionality reduction,https://github.com/hongleir/SpaceFlow/blob/master/tutorials/seqfish_mouse_embryogenesis.ipynb,Nature Communications,2022/7/14,29,mit,SpaceFlow is Python package for identifying spatiotemporal patterns and spatial domains from Spatial Transcriptomic (ST) Data.
SPACEL,https://github.com/QuKunLab/SPACEL,https://doi.org/10.21203/rs.3.rs-2411530/v1,python,sST&iST,2,spatial domain;spatial decomposition;3D reconstruction,https://github.com/QuKunLab/SPACEL,Research Square,2023/1/5,1,other,SPACEL (SPatial Architecture Characterization by dEep Learning) is a Python package of deep-learning-based methods for ST data analysis.
SpaCell,https://github.com/BiomedicalMachineLearning/SpaCell,https://doi.org/10.1093/bioinformatics/btz914,command line,sST,1,cell type identifiation,https://github.com/BiomedicalMachineLearning/SpaCell,Bioinformatics,2019/12/4,44,mit,SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells
Spacemake,https://github.com/rajewsky-lab/spacemake,https://doi.org/10.1093/gigascience/giac064,command line,sST,1,quality control;image&spot alignment;spatial domain;integration;imputation and denoise,https://spacemake.readthedocs.io/en/latest/,GigaScience,2022/7/19,13,other,spacemake: processing and analysis of large-scale spatial transcriptomics data
SpaceMarker,https://github.com/FertigLab/SpaceMarkers,https://doi.org/10.1101/2022.06.02.490672,R,sST,2,cell-cell/gene-gene interaction,https://github.com/FertigLab/SpaceMarkers,bioRxiv,2022/6/3,2,mit,An R/Bioconductor software tool to identify genes associated with latent space interactions in spatial transcriptomics.
Spaceranger,https://github.com/sbooeshaghi/spaceranger,https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/installation,command line,sST,2,raw data processing pipeline,https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/installation,NA,NA,0,other,10x genomics spatial genomics software
SpaCET,https://github.com/data2intelligence/SpaCET,https://doi.org/10.1158/1538-7445.AM2021-2795,R,sST,1,clone inference;cell-cell/gene-gene interaction,https://data2intelligence.github.io/SpaCET/articles/,Cancer Research,2021/7/1,0,gpl-3.0,Spatial Cellular Estimator
SpaceX,https://github.com/bayesrx/SpaceX,https://doi.org/10.1093/bioinformatics/btac645,R,sST,1,gene co-expression,https://satwikach.github.io/SpaceX.github.io/,Bioinformatics,2022/9/30,4,other,SpaceX: Gene Co-expression Network Estimation for Spatial Transcriptomics
spaCI,https://github.com/QSong-github/spaCI,https://doi.org/10.1093/bib/bbac563,python,iST,1,cell-cell/gene-gene interaction,https://github.com/QSong-github/spaCI/blob/main/tutorials/spaCI_manual.ipynb,Briefings in Bioinformatics,2022/11/21,28,mit,Deciphering spatial cellular communications through adaptive graph model
SpaDAC,https://github.com/huoyuying/SpaDAC,https://doi.org/10.1016/j.jgg.2023.06.005,python,sST,1,spatial domain,https://github.com/huoyuying/SpaDAC,Journal of Genetics and Genomics,2023/9/26,3,other,SpaDAC: SPAtially embedded Deep Attentional graph Clustering
SPADE,https://github.com/mexchy1000/spade,https://doi.org/10.1093/nar/gkab095,command line,sST,1,spatial variable genes;spatial domain,https://github.com/mexchy1000/spade,Nucleic Acids Research,2021/2/4,24,other,SPADE: Integrating spatial transcriptomic data with deep features of tissue image
SpaDecon,https://github.com/kpcoleman/SpaDecon,https://doi.org/10.1038/s42003-023-04761-x,python,sST,1,spatial decomposition,https://github.com/kpcoleman/SpaDecon,Communications Biology,2023/4/7,5,mit,SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning
SpaGCN,https://github.com/jianhuupenn/SpaGCN,https://doi.org/10.1101/2020.11.30.405118,python,sST&iST,1,spatial domain;spatial variable genes,https://github.com/jianhuupenn/SpaGCN/blob/master/tutorial/tutorial.md,bioRxiv,2020/12/3,15,mit,"SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network"
SpaGE,https://github.com/tabdelaal/SpaGE,https://doi.org/10.1093/nar/gkaa740,python,iST,1,imputation and denoise,https://github.com/tabdelaal/SpaGE/blob/master/SpaGE_Tutorial.ipynb,Nucleic Acids Research,2020/8/25,89,mit,Enhancing spatial transcriptomics data by predicting the expression of unmeasured genes from a dissociated scRNA-seq data
spage2vec,https://github.com/wahlby-lab/spage2vec,https://doi.org/10.1111/febs.15572,python,iST,1,spatial dimensionality reduction;spatial domain,https://github.com/wahlby-lab/spage2vec,The FEBS Journal,2020/9/25,28,apache-2.0,Spage2vec: Unsupervised representation of localized spatial gene expression signatures
SpaGene,https://github.com/liuqivandy/SpaGene,https://doi.org/10.1101/gr.276851.122,R,sST,1,spatial variable genes;gene co-expression,https://htmlpreview.github.io/?https://github.com/liuqivandy/SpaGene/blob/master/Tutorial/,Genome Research,2022/9/9,10,other,SpaGene is a R package to find sptially variable genes and colocalized gene pairs from spatial omics data.
spaGFT,https://github.com/jxLiu-bio/SpaGFT,https://doi.org/10.1101/2022.12.10.519929,python,sST,2,spatial variable genes;spatial expression modules,https://github.com/jxLiu-bio/SpaGFT,bioRxiv,2022/12/14,1,mit,"SpaGFT: Graph fourier transformer for representation, analysis, and interpretation of spatially variable genes"
span,https://github.com/tare/span,https://doi.org/10.1126/science.aav9776,webtools,sST,1,visualization;interactive,https://github.com/tare/span,Science,2019/4/4,286,bsd-3-clause,A simple js tool to annotate Spatial Transcriptomics (ST) data
Spaniel,https://github.com/RachelQueen1/Spaniel,https://doi.org/10.1101/619197,R,sST,2,quality control;normalization;visualization,https://github.com/RachelQueen1/Spaniel,bioRxiv,2019/5/6,9,mit,Spatial Transcriptomics Analysis
SpaOTsc,https://github.com/zcang/SpaOTsc,https://doi.org/10.1038/s41467-020-15968-5,python,sST&iST,1,spatial reconstruction of sc,https://github.com/zcang/SpaOTsc,Nature Communications,2020/4/29,173,mit,SpaOTsc: spatial optimal transport for single-cell transcriptomics data
SPAPROS,https://github.com/theislab/spapros,https://doi.org/10.1101/2022.08.16.504115,python,iST,2,gene panel selection,https://spapros.readthedocs.io/en/latest/_tutorials/spapros_tutorial_advanced_selection.html,bioRxiv,2022/8/17,5,mit,Probe set selection for targeted spatial transcriptomics.
spARC,https://github.com/KrishnaswamyLab/sparc,https://doi.org/10.1101/2022.08.24.505139,python,sST,2,imputation and denoise;cell-cell/gene-gene interaction,https://github.com/KrishnaswamyLab/spARC/blob/main/tutorial/SPARC-tutorial.ipynb,bioRxiv,2022/8/26,2,gpl-3.0,Denoising spatial transcriptomics data
Sparcle,https://github.com/sandhya212/Sparcle_for_spot_reassignments,https://doi.org/10.1093/bioadv/vbac048,python,iST,1,transcript assignment;cell segmentation,https://github.com/sandhya212/Sparcle_for_spot_reassignments,Bioinformatics Advances,2022/6/17,8,mit,Code for Sparcle: Spatial reassignment of spots to cells via profile likelihood estimation in transcriptomic images
SPARK,https://github.com/xzhoulab/SPARK,https://doi.org/10.1186/s13059-021-02404-0,R,sST&iST,1,spatial variable genes,https://xzhoulab.github.io/SPARK/,Genome Biology,2021/6/21,80,gpl-3.0,Spatially resolved transcriptomic analysis
SpaSE,https://github.com/lulizou/spase,https://doi.org/10.1101/2021.12.01.470861,R,iST,2,allelic,https://github.com/lulizou/spase,bioRxiv,2021/12/3,1,mit,detecting spatial patterns of allele-specific expression 
SpaSEG,https://github.com/y-bai/SpaSEG,https://doi.org/10.1101/2022.11.16.516728,command line,sST&iST,2,spatial domain;cell-cell/gene-gene interaction;spatial variable genes,https://github.com/y-bai/SpaSEG,bioRxiv,2022/11/17,1,mit,unified framework for analysis of spatial transcriptomics data
SPATA2,https://github.com/theMILOlab/SPATA2,https://doi.org/10.1016/j.ccell.2022.05.009,R,sST,1,quality control;normalization;dimensionality reduction;spatial domain;visualization;interactive;spatial trajectory;cnv detection;differential expression;toolboxes,https://themilolab.github.io/SPATA2/index.html,Cancer Cell,2022/6/13,160,other,A Toolbox for Spatial Gene Expression Analysis
SpaTalk,https://github.com/ZJUFanLab/SpaTalk,https://doi.org/10.1038/s41467-022-32111-8,R,sST&iST,1,cell-cell/gene-gene interaction,https://github.com/ZJUFanLab/SpaTalk,Nature Communications,2022/7/30,41,gpl-3.0,Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data
Spateo,https://github.com/aristoteleo/spateo-release,https://doi.org/10.1101/2022.12.07.519417,python,sST,2,cell segmentation;spatial domain;visualization;cell co-location;spatial variable genes;cell-cell/gene-gene interaction;interactive;slices alignment;3D reconstruction;spatial trajectory;integration;toolboxes,https://spateo-release.readthedocs.io/en/latest/index.html,bioRxiv,2022/12/11,11,bsd-2-clause,Spatiotemporal modeling of spatial transcriptomics 
spatial factorization,https://github.com/willtownes/spatial-factorization-py,https://doi.org/10.1038/s41592-022-01687-w,python,sST&iST,1,spatial dimensionality reduction,https://github.com/willtownes/spatial-factorization-py,Nature Methods,2022/12/31,21,lgpl-3.0,Probabilistic factor models for spatially correlated data. Includes nonnegative and real-valued versions.
SpatialCorr,https://github.com/mbernste/spatialcorr,https://doi.org/10.1101/2022.02.04.479191,python,sST,2,spatial variable genes;spatial expression modules,https://github.com/mbernste/spatialcorr/blob/main/tutorial/SpatialCorr_tutorial.ipynb,bioRxiv,2022/2/9,4,mit,SpatialCorr: Identify gene sets with spatially varying correlation
SpatialCPie,https://github.com/jbergenstrahle/SpatialCPie,https://doi.org/10.1186/s12859-020-3489-7,R,sST,1,visualization;spatial domain,https://bioconductor.org/packages/devel/bioc/vignettes/SpatialCPie/inst/doc/SpatialCPie.html,BMC Bioinformatics,2020/4/29,11,mit,"SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationship between clusters in order to guide the user during cluster identification, selection and further downstream applications."
SpatialDE,https://github.com/PMBio/SpatialDE,https://doi.org/10.1101/2021.10.27.466045,R,sST&iST,1,spatial variable genes,https://github.com/PMBio/SpatialDE,bioRxiv,2021/10/28,18,mit,Test genes for Spatial Variation
SpatialDecon,https://github.com/Nanostring-Biostats/SpatialDecon,https://doi.org/10.1038/s41467-022-28020-5,R,iST,1,spatial decomposition,https://bioconductor.org/packages/release/bioc/vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html,Nature Communications,2022/1/19,93,mit,The SpatialDecon library implements the SpatialDecon algorithm for mixed cell deconvolution in spatial gene expression datasets. (This algorithm also works in bulk expression profiling data.)
SpatialDM,https://github.com/StatBiomed/SpatialDM,https://doi.org/10.1101/2022.08.19.504616,python,sST,2,cell-cell/gene-gene interaction,https://github.com/StatBiomed/SpatialDM,bioRxiv,2022/8/22,8,apache-2.0,Spatial direct messaging detected by bivariate Moran
SpatialDWLS,https://github.com/RubD/Giotto,https://doi.org/10.1186/s13059-021-02362-7,R,sST,1,spatial decomposition,www.giottosuite.com,Genome Biology,2021/5/10,131,mit,Spatial omics analysis toolbox
SpatialExperiment,https://github.com/drighelli/SpatialExperiment,https://doi.org/10.1093/bioinformatics/btac299,R,sST&iST,1,visualization,https://github.com/drighelli/SpatialExperiment,Bioinformatics,2022/4/25,45,other,SpatialExperiment is an R/Bioconductor S4 class for storing data from spatial -omics experiments. 
SpatialGE,https://github.com/FridleyLab/spatialGE,https://doi.org/10.1093/bioinformatics/btac145,R,sST,1,quality control;normalization;spatial domain;spatial decomposition,https://fridleylab.github.io/spatialGE/,Bioinformatics,2022/3/7,10,other,An R package for visualization and analysis of spatially-resolved gene expression
Spatial-ID,https://github.com/TencentAILabHealthcare/spatialID,https://doi.org/10.1038/s41467-022-35288-0,python,sST,1,cell type identifiation,https://github.com/TencentAILabHealthcare/spatialID,Nature Communications,2022/12/10,17,other,Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding
SpatialInferCNV,https://github.com/aerickso/SpatialInferCNV,https://doi.org/10.1038/s41586-022-05023-2,R,sST,1,cnv detection;clone inference,https://aerickso.github.io/SpatialInferCNV/,Nature,2022/8/10,88,other,Clone Calling from Visium Spatial Transcriptomics of Cancer samples
Spatial-LDA,https://github.com/calico/spatial_lda,https://doi.org/10.1089/cmb.2019.0340,python,iST,1,spatial domain,https://github.com/calico/spatial_lda,Journal of Computational Biology,2020/4/3,41,mit,Probabilistic topic model for identifying cellular micro-environments. 
spatialLIBD,https://github.com/LieberInstitute/spatialLIBD,https://doi.org/10.1186/s12864-022-08601-w,R,sST,1,visualization,http://spatial.libd.org/spatialLIBD,BMC Genomics,2022/6/10,44,other,Code for the spatialLIBD R/Bioconductor package and shiny app
SpatialMap,http://github.com/JingNing329/SpatialMap,https://doi.org/10.3389/fgene.2022.893522,NA,iST,1,imputation and denoise,https://sqsun.github.io/software.html,Frontiers in Genetics,2022/5/26,1,gpl-3.0,"SpatialMap is a computational method, which primarily facilitates spatial mapping of unmeasured gene profiles in spatial transcriptomic data via integrating with scRNA-seq data from the same tissue. "
SpatialPCA,https://github.com/shangll123/SpatialPCA,https://doi.org/10.1038/s41467-022-34879-1,R,sST&iST,1,spatial dimensionality reduction,https://lulushang.org/SpatialPCA_Tutorial/,Nature Communications,2022/11/24,39,other,Spatially aware dimension reduction for spatial transcriptomics.
SpatialScope,https://spatialscope-tutorial.readthedocs.io/en/latest/,https://doi.org/10.1101/2023.03.14.532529,python,sST,2,integration,https://github.com/YangLabHKUST/SpatialScope,BioRxiv,2023/3/15,1,gpl-3.0,SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data by leveraging deep generative models
SPCS,https://github.com/Usos/SPCS,https://doi.org/10.1093/bib/bbac116,R,sST,1,imputation and denoise,https://github.com/Usos/SPCS/blob/main/tutorial.md,Briefings in Bioinformatics,2022/3/10,4,mit,A Spatial and Pattern Combined Smoothing Method of Spatial Transcriptomic Expression
SPICEMIX,https://github.com/ma-compbio/SpiceMIx,https://doi.org/10.1038/s41588-022-01256-z,command line,sST&iST,1,spatial domain;spatial expression modules,https://github.com/ma-compbio/SpiceMIx,Nature Genetics,2023/1/9,20,mit,"spatial transcriptome, single cell"
Spider,https://github.com/YANG-ERA/Artist,https://www.biorxiv.org/content/10.1101/2023.05.21.541605v1.full,python,sST&iST,2,simulation;interactive,https://github.com/YANG-ERA/Artist,bioRxiv,2023/3/22,0,other,Spider: a flexible and unified framework for simulating spatial transcriptomics data
SPIRAL,https://github.com/guott15/SPIRAL,https://doi.org/10.1186/s13059-023-03078-6,python,sST,1,slices alignment;integration,https://github.com/guott15/SPIRAL,Genome Biology,2023/10/20,3,other,SPIRAL is a method for batch effect removing and coordinationg alignment for spatially resolved transcriptomes.
Splotch,https://github.com/tare/Splotch,https://doi.org/10.1101/757096,command line,sST,2,slices alignment;integration;imputation and denoise,https://github.com/tare/Splotch/blob/master/Tutorial.ipynb,bioRxiv,2019/9/5,15,bsd-3-clause,Splotch is a hierarchical generative probabilistic model for analyzing Spatial Transcriptomics (ST) data.
SPOTclean,https://github.com/zijianni/SpotClean,https://doi.org/10.1038/s41467-022-30587-y,R,sST,1,imputation and denoise,https://github.com/zijianni/SpotClean/tree/master/vignettes,Nature Communications,2022/5/27,16,other,R package for decontaminating the spot swapping effect and recovering true expression in spatial transcriptomics data
SPOTlight,https://github.com/MarcElosua/SPOTlight,https://doi.org/10.1093/nar/gkab043,R,sST,1,spatial decomposition;visualization,https://github.com/MarcElosua/SPOTlight/blob/main/vignettes/SPOTlight_kidney.Rmd,Nucleic Acids Research,2021/1/15,324,gpl-3.0,Spatial Transcriptomics Capture Location Deconvolution 
SPRI,https://github.com/xiaoyeye/SPRI,https://doi.org/10.1101/2022.02.09.479510,R,sST,2,spatial variable genes,https://github.com/xiaoyeye/SPRI/tree/main/docs,bioRxiv,2022/2/10,0,other,SPRI: Spatial Pattern Recognition using Information based method for spatial gene expression data
SPROD,https://github.com/yunguan-wang/SPROD,https://doi.org/10.1038/s41592-022-01560-w,command line,sST,1,imputation and denoise,https://github.com/yunguan-wang/SPROD,Nature Methods,2022/8/4,27,other,SPROD: Spatial expression profiling de-noising
spruce,https://github.com/carter-allen/spruce,https://doi.org/10.1101/2021.06.23.449615,R,sST,2,spatial domain,https://github.com/carter-allen/spruce,bioRxiv,2021/6/24,6,other,Bayesian mutivariate models for clustering spatial transcriptomics data
spSeudoMap,https://github.com/bsungwoo/spSeudoMap,https://doi.org/10.1186/s13073-023-01168-5,R,sST,1,spatial domain;cell type identifiation,https://github.com/bsungwoo/spSeudoMap,Genome Medicine,2023/3/17,3,other,spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data
Squidpy,https://github.com/scverse/squidpy,https://doi.org/10.1038/s41592-021-01358-2,python,sST&iST,1,visualization;spatial variable genes;cell-cell/gene-gene interaction;interactive;slices alignment;integration;nuclei segmentation;differential expression;clustering;toolboxes,https://squidpy.readthedocs.io/en/latest/index.html,Nature Methods,2022/1/31,288,bsd-3-clause,Spatial Single Cell Analysis in Python
SRTsim,https://github.com/xzhoulab/SRTsim,https://doi.org/10.1186/s13059-023-02879-z,R,sST,1,simulation,https://github.com/xzhoulab/SRTsim,Genome Biology,2023/3/3,14,gpl-3.0,"SRTsim is an independent, reproducible, and flexible Spatially Resolved Transcriptomics (SRT) simulation framework that can be used to facilitate the development of SRT analytical methods for a wide variety of SRT-specific analyses."
SSAM,https://github.com/HiDiHlabs/ssam,https://doi.org/10.1038/s41467-021-23807-4,python,iST,1,cell type identifiation;spatial domain,https://ssam.readthedocs.io/,Nature Communications,2021/6/10,42,agpl-3.0,SSAM (Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation)
SSAM-lite,https://github.com/HiDiHlabs/ssam-lite,https://doi.org/10.3389/fgene.2022.785877,webtools,iST,1,cell type identifiation;spatial domain,https://ssam.readthedocs.io/en/release/,Frontiers in Genetics,2022/2/28,2,mit,"SSAM-lite is a lightweight, browser-based implementation of the SSAM framework. "
ST Spot Detector,https://github.com/SpatialTranscriptomicsResearch/st_spot_detector,https://doi.org/10.1093/bioinformatics/bty030,webtools,sST,1,image&spot alignment,https://github.com/SpatialTranscriptomicsResearch/st_spot_detector/wiki/ST-Spot-Detector-Usage-Guide,Bioinformatics,2018/1/16,29,other,A web tool to do automatic spots detections and positional adjustments for Spatial Transcriptomics Datasets. 
ST viewer,https://github.com/jfnavarro/st_viewer,https://doi.org/10.1093/bioinformatics/bty714,GUI,sST&iST,1,quality control;normalization;dimensionality reduction;visualization;interactive;differential expression;toolboxes,https://github.com/jfnavarro/st_viewer,Bioinformatics,2018/8/22,30,other,A GUI tool for easy and smooth visualisation and analysis of Spatial Transcriptomics datasets
st_analysis,https://github.com/jfnavarro/st_analysis,https://doi.org/10.1186/s13058-019-1242-9,command line,sST,1,quality control;normalization;dimensionality reduction;slices alignment;integration;differential expression;clustering;toolboxes,https://github.com/jfnavarro/st_analysis,Breast Cancer Research,2020/1/13,50,other,A toolset for analysis and visualisation of Spatial Transcriptomics datasets.
st_gridnet,https://github.com/flatironinstitute/st_gridnet,https://doi.org/10.1093/bioinformatics/btab447,command line,sST,1,integration;spatial domain,https://github.com/flatironinstitute/st_gridnet,Bioinformatics,2021/6/14,5,other,"A Python implementation of the model described in our publication ""A convolutional neural network for common-coordinate registration of high-resolution histology images""  developed principally for applications to registration of spatial transcriptomics image data."
st_pipeline,https://github.com/jfnavarro/st_pipeline,https://doi.org/10.1093/bioinformatics/btx211 ,command line,sST,1,raw data processing pipeline,https://github.com/jfnavarro/st_pipeline,Bioinformatics,2017/4/6,74,other,ST Pipeline contains the tools and scripts needed to process and analyze the raw files generated with the Spatial Transcriptomics method in FASTQ format.
st2cell,https://github.com/Svvord/St2cell,https://doi.org/10.1101/2022.10.13.512059,NA,sST,2,super resolution;imputation and denoise,https://github.com/Svvord/St2cell,bioRxiv,2022/10/18,1,NA,Reconstruction of in situ single-cell spatial transcriptomics by integrating high-resolution histological image
STACI,https://github.com/uhlerlab/STACI,https://doi.org/10.1038/s41467-022-35233-1,python,sST,1,nuclei segmentation;imputation and denoise,https://github.com/uhlerlab/STACI,Nature Communications,2022/12/3,14,other,A computational framework to integrate Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data (STACI) to identify molecular and functional alterations in tissues.
STAGATE,https://github.com/zhanglabtools/STAGATE,https://doi.org/10.1038/s41467-022-29439-6,python,sST,1,integration;spatial domain;imputation and denoise,https://stagate.readthedocs.io/en/latest/index.html,Nature Communications,2022/4/1,111,mit,Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
STAligner,https://github.com/zhoux85/STAligner,https://doi.org/10.1101/2022.12.26.521888,python,sST&iST,2,slices alignment;3D reconstruction;visualization;integration,https://staligner.readthedocs.io/en/latest/index.html,bioRxiv,2022/12/27,5,mit,STAligner is designed for alignment and integration of spatially resolved transcriptomics data.
STAMarker,http://github.com/zhanglabtools/STAMarker,https://doi.org/10.1101/2022.11.07.515535,python,sST&iST,2,spatial domain;spatial variable genes,https://github.com/zhanglabtools/STAMarker/blob/main/tutorial/Mouse-cerebellum.ipynb,bioRxiv,2022/11/8,5,other,STAMarker: Determining spatial domain-specific variable genes with saliency maps in deep learning
standR,https://bioconductor.org/packages/release/bioc/html/standR.html,https://doi.org/10.1093/nar/gkad1026,R,sST,1,toolboxes,https://github.com/DavisLaboratory/standR,Nucleic Acids Research,2024/1/11,2,mit,"standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations."
STANN,https://github.com/sameelab/STANN,https://doi.org/10.1016/j.cels.2021.09.004,command line,iST,1,cell type identifiation,https://github.com/sameelab/STANN/blob/main/notebooks/demo.ipynb,Cell Systems,2021/10/9,14,mit,"Given ST and scRNA-seq data of a tissue, STANN models cell-types in the scRNA-seq dataset from the genes that are profiled by both ST and scRNA-seq. "
stAPAminer,https://github.com/BMILAB/stAPAminer,https://doi.org/10.1016/j.gpb.2023.01.003,R,iST,1,alternative polyadenylation,https://github.com/BMILAB/stAPAminer,"Genomics, Proteomics &amp; Bioinformatics",2023/1/18,2,other,Mining Spatial Patterns of Alternative Polyadenylation for Spatially Resolved Transcriptomic Studies.
STARCH,https://github.com/raphael-group/STARCH,https://doi.org/10.1088/1478-3975/abbe99,command line,iST,1,cnv detection;clone inference,https://github.com/raphael-group/STARCH,Physical Biology,2020/10/6,30,mit,Spatial Transcriptomics Algorithm Reconstructing Copy-number Heterogeneity
Stardist,https://github.com/stardist/stardist,https://doi.org/10.48550/arXiv.1806.03535,GUI,iST,2,cell segmentation,https://github.com/stardist/stardist,arXiv,2018/11/8,508,bsd-3-clause,StarDist - Object Detection with Star-convex Shapes
Stardust,https://github.com/InfOmics/stardust/,https://doi.org/10.1093/gigascience/giac075,R,sST,1,spatial domain,https://github.com/InfOmics/stardust/,GigaScience,2022/8/10,2,mit,"The main aim of the tool is, given as input an expression matrix, the positions of spots and, possibly, a space weight configuration, to derive a vector of cluster identities for each spot in the input data. Stardust depends on Seurat (for clustering and data visualization) and on rCASC for the computation of the stability scores and generation of the distributions comparison."
Starfish,https://github.com/spacetx/starfish,https://doi.org/10.21105/joss.02440,python,iST,1,visualization,https://spacetx-starfish.readthedocs.io/en/latest/,Journal of Open Source Software,2021/5/4,18,mit,starfish: unified pipelines for image-based transcriptomics
starmaoVR,https://github.com/holab-hku/starmapVis,https://doi.org/10.1101/2020.09.01.277079,webtools,iST,2,visualization,https://holab-hku.github.io/starmapVis/ ,bioRxiv,2020/9/5,1,mit,StarmapVis: animated visual display of single cell and spatial omic data
STARmap,https://github.com/weallen/STARmap,https://doi.org/10.1126/science.aat5691,python,iST,1,raw data processing pipeline,https://github.com/weallen/STARmap,Science,2018/6/21,837,mit,Software for processing and analyzing STARmap experiments.
STdeconvolve,https://github.com/JEFworks-Lab/STdeconvolve,https://doi.org/10.1038/s41467-022-30033-z,R,sST,1,spatial decomposition,https://github.com/JEFworks-Lab/STdeconvolve,Nature Communications,2022/4/29,61,other,Reference-free cell-type deconvolution of multi-cellular spatially resolved transcriptomics data
std-nb,https://github.com/SpatialTranscriptomicsResearch/std-nb,https://doi.org/10.1101/362624,command line,sST&iST,2,spatial expression modules;spatial domain,https://github.com/SpatialTranscriptomicsResearch/std-nb,bioRxiv,2018/7/6,10,gpl-3.0, Negative binomial regression for Spatial Transcriptomics data as described in Maaskola et al. 2018 
STEEL,https://steel-st.sourceforge.io/,https://doi.org/10.21203/rs.3.rs-1240258/v1,command line,sST&iST,2,spatial domain;integration,https://steel-st.sourceforge.io/,Research Square,2022/1/12,2,gpl-3.0,STEEL is an unsupervised algorithm based on manifold learning to analyze spatial transcriptome data.
STELLAR,https://github.com/snap-stanford/stellar,https://doi.org/10.1038/s41592-022-01651-8,command line,iST,1,cell type identifiation,https://github.com/snap-stanford/stellar,Nature Methods,2022/10/24,30,mit,"STELLAR, a geometric deep learning tool for cell-type discovery and identification in spatially resolved single-cell datasets."
STellaris,https://spatial.rhesusbase.com/,https://doi.org/10.1093/nar/gkad419,webtools,sST,1,spatial reconstruction of sc,https://spatial.rhesusbase.com/,Nucleic Acids Research,2023/7/5,5,other,"STellaris is an integrated web server for accurate and flexible spatial mapping of user-uploaded single-cell RNA-seq (scRNA-seq) data. It is founded on a manually curated compilation of spatial transcriptomics datasets across diverse organs, developmental stages and pathological states. The tissue-scale spatial cellular maps would subsequently facilitate the interpretation of intercellular communications in the spatial context."
STEM,https://github.com/WhirlFirst/STEM,https://doi.org/10.1101/2022.09.23.509186,python,sST,2,spatial reconstruction of sc,https://github.com/WhirlFirst/STEM,bioRxiv,2022/9/26,1,gpl-3.0,STEM is a tool for building single-cell level spatial transcriptomic landscapes using SC data with ST data.
stereo,https://github.com/melobio/Stereo,https://doi.org/10.21203/rs.3.rs-1067780/v1,python,sST,2,spatial domain,https://stereopy.readthedocs.io/en/latest/Tutorials/Examples.html,Research Square,2022/1/20,1,NA,NA
StereoCell,https://github.com/STOmics/StereoCell,https://doi.org/10.1101/2023.02.28.530414,python,iST,2,cell segmentation,https://github.com/STOmics/StereoCell,BioRxiv,2023/3/5,5,mit,StereoCell: a highly accurate single-cell gene expression generation software for high resolution spatial transcriptomics.
stereoscope,https://github.com/almaan/stereoscope,https://doi.org/10.1038/s42003-020-01247-y,command line,sST,1,spatial decomposition,https://github.com/almaan/stereoscope,Communications Biology,2020/10/9,247,mit,Spatial mapping of cell types by integration of transcriptomics data
StereoSiTE,https://github.com/STOmics/StereoSiTE,https://doi.org/10.1101/2022.12.31.522366,python,sST,2,cell-cell/gene-gene interaction;spatial domain,https://github.com/STOmics/StereoSiTE,bioRxiv,2022/12/31,0,mit,StereoSiTE is a package for the analysis and visualization of spatial transcriptome data. 
Stfusion,https://bitbucket.org/sonnhammergroup/stfusion/src/master/#markdown-header-usage,https://doi.org/10.1186/s12920-020-00738-5,R,sST,1,fusion transcript,https://bitbucket.org/sonnhammergroup/stfusion/src/master/#markdown-header-usage,BMC Medical Genomics,2020/8/4,17,other,A fusion transcript lacks a poly(A) tail for the 5´ gene and has an elevated number of poly(A) tails for the 3´ gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score.
STGNNks,https://github.com/plhhnu/STGNNks,https://doi.org/10.1016/j.compbiomed.2023.107440,python,sST,1,spatial decomposition,https://github.com/plhhnu/STGNNks,Computers in Biology and Medicine,2023/9/9,5,other,"STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and sums clustering"
STIM,https://github.com/PreibischLab/STIM,https://doi.org/10.1101/2021.12.07.471629,command line,sST,2,slices alignment;3D reconstruction;visualization;interactive;normalization,https://github.com/PreibischLab/STIM#Tutorial-on-small-example,bioRxiv,2021/12/8,7,other,"Library for managing, storage, viewing, and working with spatial transcriptomics data using Imglib2, N5, BigDataViewer and Fiji."
STitch3D,https://github.com/YangLabHKUST/STitch3D,https://doi.org/10.1101/2023.02.02.526814,python,sST&iST,2,3D reconstruction;integration;slices alignment;spatial domain,https://stitch3d-tutorial.readthedocs.io/en/latest/index.html,bioRxiv,2023/2/4,1,mit,Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices
stlearn,https://github.com/BiomedicalMachineLearning/stLearn,https://doi.org/10.1101/2020.05.31.125658,python,sST&iST,2,quality control;normalization;dimensionality reduction;imputation and denoise;spatial trajectory;cell-cell/gene-gene interaction;interactive;visualization;integration;clustering;toolboxes,https://stlearn.readthedocs.io/en/latest/,bioRxiv,2020/5/31,168,other,A novel machine learning pipeline to analyse spatial transcriptomics data 
STMGCN,https://github.com/sxj204/stmgcn,https://doi.org/10.1093/bib/bbad278,python,sST,1,spatial domain,https://github.com/sxj204/stmgcn,Briefings in Bioinformatics,2023/8/7,2,other,STMGCN is a novel deep computational framework that effectively integrates spatial coordinates and gene expression information for ST data analysis. 
stMLnet,https://github.com/SunXQlab/stMLnet,https://doi.org/10.1101/2022.06.27.497696,R,sST,2,cell-cell/gene-gene interaction,https://www.stmlnet.top/net/,bioRxiv,2022/6/29,4,other,stMLnet: Modeling and inference of spatial intercellular communication and multilayer signaling regulations from spatial transcriptomics data
stmut,https://github.com/limin321/stmut,https://doi.org/10.1101/2022.12.05.519162,R,sST,2,point mutation detection;cnv detection;allelic,https://github.com/limin321/stmut/blob/master/FigTableScripts/FigTables.md#spatial-spot-cnvs-figure-3,bioRxiv,2022/12/7,1,other,Visualizing Somatic Alterations of 10X Spatial Transcriptomics Data 
stMVC,https://github.com/cmzuo11/stMVC,https://doi.org/10.1038/s41467-022-33619-9,command line,sST&iST,1,spatial dimensionality reduction,https://github.com/cmzuo11/stMVC,Nature Communications,2022/10/10,13,other,"stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. "
ST-net,https://github.com/bryanhe/ST-Net,https://doi.org/10.1038/s41551-020-0578-x,command line,iST,1,imputation and denoise,https://github.com/bryanhe/ST-Net,Nature Biomedical Engineering,2020/6/22,190,other,Deep learning on histopathology images.
Stopover,https://github.com/bsungwoo/STopover,https://doi.org/10.1101/2022.11.16.516708,python,sST,2,cell co-location;cell-cell/gene-gene interaction,https://github.com/bsungwoo/STopover,bioRxiv,2022/11/17,0,other,capturing spatial colocalization and interaction in the TME using topological analysis in spatial transcriptomics data
stPlus,https://github.com/xy-chen16/stPlus,https://doi.org/10.1093/bioinformatics/btab298,python,iST,1,imputation and denoise,http://health.tsinghua.edu.cn/software/stPlus/,Bioinformatics,2021/4/25,40,mit,stPlus: reference-based enhancement of spatial transcriptomics
STRIDE,https://github.com/wanglabtongji/STRIDE,https://doi.org/10.1093/nar/gkac150,command line,sST,1,spatial decomposition;visualization;spatial domain;3D reconstruction,https://stridespatial.readthedocs.io/en/latest/,Nucleic Acids Research,2022/2/19,39,gpl-3.0,STRIDE (Spatial TRanscRIptomics DEconvolution by topic modelling) is a cell-type deconvolution tool for spatial transcriptomics by using single-cell transcriptomics data. 
STtools,https://github.com/seqscope/STtools,https://doi.org/10.1093/bioadv/vbac061,R,sST,1,raw data processing pipeline,https://github.com/seqscope/STtools/blob/main/doc/readme1.md,Bioinformatics Advances,2022/9/1,4,other,"STtools is a software package that is designed to process spatial transciriptomics (ST) data from various platforms including Seq-Scope, SlideSeq, and VISIUM. "
STUtility,https://github.com/ludvigla/STUtility_web_site,https://doi.org/10.1186/s12864-020-06832-3,R,sST,1,quality control;normalization;dimensionality reduction;spatial variable genes;visualization;differential expression;clustering;toolboxes,https://ludvigla.github.io/STUtility_web_site/Neighborhood.html,BMC Genomics,2020/7/14,138,other,STutility is an R-package with the goal of providing an easy-to-use visualization and analysis tool kit for spatial transcriptomics data.
SVCA,https://github.com/damienArnol/svca,https://doi.org/10.1016/j.celrep.2019.08.077,command line,iST,1,cell-cell/gene-gene interaction,https://github.com/damienArnol/svca,Cell Reports,2019/10/1,126,apache-2.0,STutility is an R-package with the goal of providing an easy-to-use visualization and analysis tool kit for spatial transcriptomics data.
TACCO,https://github.com/simonwm/tacco,https://doi.org/10.1101/2022.10.02.508471,python,sST&iST,2,cell type identifiation,https://simonwm.github.io/tacco/index.html,bioRxiv,2022/10/5,3,bsd-3-clause,TACCO: Transfer of Annotations to Cells and their COmbinations
Tangram,https://github.com/broadinstitute/Tangram,https://doi.org/10.1038/s41592-021-01264-7,R,sST&iST,1,imputation and denoise;spatial reconstruction of sc;spatial decomposition,https://github.com/broadinstitute/Tangram/blob/master/tutorial_tangram_with_squidpy.ipynb,Nature Methods,2021/10/28,268,bsd-3-clause,Spatial alignment of single cell transcriptomic data. 
TESLA,https://github.com/jianhuupenn/TESLA,https://doi.org/10.2139/ssrn.4248354,python,sST,2,imputation and denoise;visualization,https://github.com/jianhuupenn/TESLA/blob/main/tutorial/tutorial.md,SSRN Electronic Journal,2022/10/29,0,mit,Deciphering tumor ecosystems at super-resolution from spatial transcriptomics with TESLA
TissUUmaps 3,https://tissuumaps.github.io/,https://doi.org/10.1101/2022.01.28.478131,GUI,sST&iST,1,visualization,https://tissuumaps.github.io/gallery/,bioRxiv,2022/1/28,5,other,TissUUmaps is a free and open source browser-based tool for GPU-accelerated visualization and interactive exploration of tens of millions of datapoints overlaying tissue samples.
TIST,https://github.com/ShanYiran/TIST,https://doi.org/10.1016/j.gpb.2022.11.012,R,sST,1,imputation and denoise;spatial domain,https://github.com/ShanYiran/TIST/wiki/,"Genomics, Proteomics &amp; Bioinformatics",2022/12/19,8,other,TIST: Transcritpome and histopathological Image integrative analysis for Spatial Transcriptomics
TopACT,https://gitlab.com/kfbenjamin/topact,https://doi.org/10.48550/arXiv.2212.06505,python,sST,2,cell type identifiation,https://gitlab.com/kfbenjamin/topact,arXiv,2022/12/13,0,gpl-3.0,TopACT is a method for annotating subcellular spatial transcriptomics data with cell types inferred from a single-cell reference data set.
TransfromerST,https://github.com/Zhaocy-Research/TransformerST,https://doi.org/10.1101/2022.08.11.503261,python,sST,1,imputation and denoise;spatial domain;super resolution,https://github.com/Zhaocy-Research/TransformerST,bioRxiv,2022/8/15,2,other,Transformer Enables Reference Free Analysis of Spatial Transcriptomics
trendsceek,https://github.com/edsgard/trendsceek,https://doi.org/10.1038/nmeth.4634,R,sST&iST,1,spatial variable genes,https://github.com/edsgard/trendsceek/tree/master/vignettes,Nature Methods,2018/3/19,231,other,Identify genes with spatial expression trends in single-cell gene expression data
Tumoroscope,https://github.com/szczurek-lab/Tumoroscope,https://doi.org/10.1101/2022.09.22.508914,python,sST,2,clone inference;visualization;integration,https://github.com/szczurek-lab/Tumoroscope,bioRxiv,2022/9/23,0,gpl-3.0,"tumoroscope is a Python package for inferring the clonal composition of the cancer tissue across the space using spatial transcriptomics, whole exome sequencing and H&E images."
UNet-ImageJ,https://lmb.informatik.uni-freiburg.de/resources/opensource/unet/,https://doi.org/10.1038/s41592-018-0261-2,MATLAB,iST,1,cell segmentation,https://lmb.informatik.uni-freiburg.de/resources/opensource/unet/,Nature Methods,2018/12/4,1199,gpl-3.0,Source of the U-Net Segmentation plugin for Fiji.
UniCell ,https://ucdeconvolve.readthedocs.io/en/latest/,https://doi.org/10.1038/s41467-023-36961-8,python,sST,1,spatial decomposition,https://github.com/dchary/ucdeconvolve/,Nature Communications,2023/3/11,10,gpl-3.0,UniCell Deconvolve: Cell Type Deconvolution For Transcriptomic Data
UTAG,https://github.com/ElementoLab/utag,https://doi.org/10.1038/s41592-022-01657-2,python,iST,1,spatial domain,https://github.com/ElementoLab/utag/blob/main/documentation/UTAG%20Tutorial.ipynb,Nature Methods,2022/10/31,22,other,Unsupervised discovery of tissue architechture with graphs (UTAG)
Vesalius,https://github.com/patrickCNMartin/Vesalius/,https://doi.org/10.15252/msb.202211080,R,sST,1,spatial domain;visualization,https://patrickcnmartin.github.io/Vesalius/index.html,Molecular Systems Biology,2022/9/6,7,gpl-3.0,Vesalius: Dissecting Tissue Anatomy from Spatial Transcriptomic Data
vissE,https://github.com/DavisLaboratory/vissE/,https://doi.org/10.1101/2022.03.06.483195,R,sST&iST,2,spatial expression modules,https://davislaboratory.github.io/vissE/,bioRxiv,2022/3/7,2,gpl-3.0,An R/Bioconductor package to identify higher-order molecular phenotypes
VistoSeg,https://github.com/LieberInstitute/VistoSeg,https://doi.org/10.1101/2021.08.04.452489,MATLAB,sST,2,nuclei segmentation,http://research.libd.org/VistoSeg/index.html,bioRxiv,2021/8/6,9,gpl-3.0,Image processing pipeline for spatial projects
waveST,https://github.com/OliverXUZY/waveST,https://doi.org/10.48550/arXiv.2205.11243,R,sST,1,spatial dimensionality reduction,https://github.com/OliverXUZY/waveST,arXiv,2022/5/19,0,mit,R package about wavelet-guided methods for Spatial Transcriptomics
webSCST,https://github.com/swsoyee/webSCST,https://doi.org/10.1093/bioinformatics/btac350,R,sST&iST,1,spatial reconstruction of sc,http://www.webscst.com/,Bioinformatics,2022/5/23,15,other,A web tool for single-cell RNA-seq data and spatial transcriptome data integration 
xfuse,https://github.com/ludvb/xfuse,https://doi.org/10.1038/s41587-021-01075-3,command line,sST&iST,1,imputation and denoise;super resolution,https://github.com/ludvb/xfuse,Nature Biotechnology,2021/11/29,54,other,Super-resolved spatial transcriptomics by deep data fusion
ZENomix,https://github.com/yasokochi/ZENomix,https://doi.org/10.1101/2022.12.16.520397,python,sST,2,point mutation detection,https://github.com/yasokochi/ZENomix,bioRxiv,2022/12/20,0,mit,ZENomix is a novel computational method for reconstructing mutant spatial transcriptomes in azero-shot manner.