Appendix E — Citation

Guidelines

If OSTA has been useful to your research, please consider citing:

HL Crowell*°, Y Dong*, I Billato, P Cai, M Emons, S Gunz, B Guo, M Li, A Mahmoud, A Manukyan, H Pagès, P Panwar, S Rao, CJ Sargeant, L Shepherd Kern, M Ramos, J Sun, M Totty, VJ Carey, Y Chen, L Collado-Torres, S Ghazanfar, KD Hansen, K Martinowich, KR Maynard, E Patrick, D Righelli, D Risso, S Tiberi, L Waldron, R Gottardo†°, MD Robinson†°, SC Hicks†°, LM Weber†°. Orchestrating spatial transcriptomics analysis with Bioconductor. bioRxiv (2025). DOI: 10.1101/2025.11.20.688607

(* co-first. † co-senior. ° correspondence.)

Notably, OSTA pieces together work and information from numerous researchers. As such, we suggest readers cite the pieces of work they have either used or consulted to conduct their research – be it (R, Python, or other) software, benchmarks that informed their methodological decisions, review or perspective pieces, or other. To facilitate this, we provide a list in the section below.

Collection

We here make an attempt to collect all biotechnology, dataset, software, review, benchmark, or other, references and resources mentioned throughout the book.

If you spot any obvious omissions or mistakes, please let us know!
Bioconductor Gentleman et al. (2004); Huber et al. (2015); Carey (2025)
OSCA Amezquita et al. (2020)
benchmark Hartman and Satija (2024)
CosMx He et al. (2022)
Curio Seeker Curio Bioscience
MERFISH Chen et al. (2015)
MERSCOPE Vizgen
Slide-seqV2 Stickels et al. (2021)
ST Ståhl et al. (2016)
Stereo-seq STOmics
Visium 10x Genomics
Visium HD 10x Genomics
Xenium 10x Genomics
3D ST Vickovic et al. (2022)
Open-ST Schott et al. (2024); Schott et al. (2025)
PASTE Zeira et al. (2022)
PASTE2 X. Liu et al. (2023)
STalign Clifton et al. (2023)
STARmap Wang et al. (2018)
Deep-STARmap Sui et al. (2025)
3D MERFISH Fang et al. (2024)
C3PO Cotterell et al. (2024)
CLARITY Tomer et al. (2014)
3DISCO Ertürk et al. (2012)
DISCO-MS Bhatia et al. (2022)
sc4D Rao et al. (2025)
MOSAICA Vu et al. (2022)
scMAGS Baran and Doğan (2023)
CITE-seq Y. Liu et al. (2023)
spatial omics Moffitt et al. (2022); Bressan et al. (2023)
spatial analyses Palla, Fischer, et al. (2022)
spatial proteomics Semba and Ishimoto (2024); Lundberg and Borner (2019)
spatial epigenomics D. Zhang et al. (2023)
spatial transcriptomics Rao et al. (2021); Moses and Pachter (2022); Cheng et al. (2023); Tian et al. (2023)
single-cell spatial proteomics Paul et al. (2021); Mund et al. (2022)
single-cell & spatial transcriptomics Gulati et al. (2025)
probe selection & panel design Kuemmerle et al. (2024); Y. Zhang et al. (2024)
mass spectrometry imaging H. Zhang et al. (2024); Palomino and Muddiman (2025)
sf Pebesma (2018)
sp Pebesma and Bivand (2005)
Rarr Smith and Gruson (2023)
pizzarr n/a
alabaster.base Lun (2023)
MoleculeExperiment Peters Couto et al. (2023)
SingleCellExperiment Amezquita et al. (2020)
SpatialExperiment Righelli et al. (2022)
SpatialFeatureExperiment Moses et al. (2023)
Seurat Hao et al. (2023)
Scanpy Wolf et al. (2018)
Squidpy Palla, Spitzer, et al. (2022)
Giotto J. G. Chen et al. (2025); Dries et al. (2021)
VoltRon Manukyan et al. (2023)
spatialGE Ospina et al. (2022)
SpatialData Marconato et al. (2025)
Bruker’s CosMx AtoMx
Vizgen’s MERSCOPE Visualizer
10 Genomic’s Xenium Xenium Explorer
10 Genomic’s Visium Loupe Browser
Seurat-Loupe conversion loupeR
iSEE Rue-Albrecht et al. (2018)
napari Sofroniew et al. (2025)
BiocPkgTools Su et al. (2025)
BiocViews Carey et al. (2025)
10x Genomics’ Visium processing Space Ranger
10x Genomics’ Xenium processing Xenium Ranger
Bruker’s CosMx processing AtoMx
Bruker’s CosMx tutorials scratch space
SpatialExperimentIO
SpatialFeatureExperiment Moses et al. (2023)
VisiumIO
XeniumIO
human breast cancer (Visium, Xenium) Janesick et al. (2023)
colorectal carcinoma (Visium/HD, Xenium) de Oliveira et al. (2025)
mouse brain (1k-plex CosMx) Bruker
human brain (6k-plex CosMx) Bruker
human brain (Visium) Maynard et al. (2021)
type I diabetes (IMC) Damond et al. (2019)
axolotl brain (Stereo-seq) Wei et al. (2022)
spatialLIBD Pardo et al. (2022)
STexampleData Righelli et al. (2022)
anndata` Virshup et al. (2024)
anndataR Deconinck et al. (2026)
basilisk Lun (2022)
reticulate Ushey et al. (2017)
zellkonverter Zappia et al. (2020)
technical publishing systems R Markdown, Quarto
Rsubread Liao et al. (2019)
stPipe Xu et al. (2025)
10x Genomics’ Visium SpaceRanger
STOmics’ Stereo-seq SAW; Gong et al. (2024)
theory Bhuva et al. (2024)
scrapper
scater McCarthy et al. (2017)
SpotSweeper Totty et al. (2025)
SpaceTrooper Banzi et al. (2025)
CARDspa Ma and Zhou (2022)
cell2location Kleshchevnikov et al. (2022)
DSTG Song and Su (2021)
NMFreg_tutorial
novosparc Nitzan et al. (2019)
Giotto’s SpatialDWLS J. G. Chen et al. (2025)
scvi-tools’s DestVI Lopez et al. (2022)
SD2 H. Li et al. (2022)
spacexr’s RCTD Cable, Murray, Zou, et al. (2022)
SpaOTsc Cang and Nie (2020)
SpatialDecon Danaher et al. (2022)
SpiceMIx Chidester et al. (2023)
SPOTlight Elosua-Bayes et al. (2021)
std-poisson Berglund et al. (2018)
STdeconvolve Miller et al. (2022)
stereoscope Andersson et al. (2020)
STRIDE Sun et al. (2022)
Tangram Biancalani et al. (2021)
benchmarks Sang-aram et al. (2023); H. Li et al. (2023); Gaspard-Boulinc et al. (2025)
bin2cell)) Polański et al. (2024)
stardist Weigert and Schmidt (2022)
SMURF Guo et al. (2025)
SpaceRanger v4 10x Genomics
Sanofi’s ENACT pipeline Kamel et al. (2025)
Baysor Petukhov et al. (2022)
cellpose Stringer et al. (2021)
proseg Jones et al. (2025)
ssam Park et al. (2021)
SPLIT Bilous et al. (2025)
cellAdmix Mitchel et al. (2026)
segger_dev Heidari et al. (2025)
FastReseg Wu et al. (2024)
hoodscanR Liu et al. (2025)
imcRtools Windhager et al. (2023)
scider Li et al. (2025)
limma Baldoni et al. (2025)
edgeR Y. Chen et al. (2025)
DeepST Xu et al. (2022)
GraphST Long et al. (2023)
NicheCompass Birk et al. (2025)
Novae Blampey et al. (2025)
SpaGCN Hu et al. (2021)
STAGATE Dong and Zhang (2022)
review Behanova et al. (2022), Summers et al. (2022)
CCPlotR Ennis et al. (2023)
CellChatDB Jin et al. (2021)
CellphoneDB Efremova et al. (2020)
COMMOT Cang et al. (2023)
Giotto J. G. Chen et al. (2025)
mistyR Tanevski et al. (2022)
SpaOTsc Cang and Nie (2020)
SpatialDM Zhuoxuan Li et al. (2023)
theory Oyler-Yaniv et al. (2017)
review Armingol et al. (2020)
benchmark Liu et al. (2022)
theory Cassella and Ephrussi (2022)
Visium HD Novoselsky et al. (2026)
Bento Mah et al. (2024)
MoleculeExperiment Peters Couto et al. (2023)
FISHFactor Walter et al. (2023)
ClusterMap He et al. (2021)
CellSP Aggarwal and Sinha (2025)
MAGIC Dijk et al. (2018)
Tangram Biancalani et al. (2021)
InSTAnT (Kumar2024-Instant?)
SPRAWL Bierman et al. (2024)
SpaGNN Fang et al. (2023)
scrapper
scater McCarthy et al. (2017)
SpaNorm Salim et al. (2025)
theory Bhuva et al. (2024); Atta et al. (2024)
bulk vs. scRNA-seq Vallejos et al. (2017)
scRNA-seq benchmark Ahlmann-Eltze and Huber (2023)
quick tips Nguyen and Holmes (2019)
perspective Chari and Pachter (2023)
benchmarks Xiang et al. (2021); Pandit et al. (2022)
BANKSY Singhal et al. (2024)
BayesSpace Zhao et al. (2021)
CellCharter Varrone et al. (2024)
Leiden Traag et al. (2019)
PRECAST W. Liu et al. (2023)
PROST Liang et al. (2024)
SpaceFlow Ren et al. (2022)
STAGATE Dong and Zhang (2022)
CellAssign Zhang et al. (2019)
scType Ianevski et al. (2022)
SingleR Aran et al. (2019)
azimuth Hao et al. (2021)
scvi-tools’s scANVI (Xu et al. 2021)
celltypist Domínguez Conde et al. (2022)
scFoundation Hao et al. (2024)
scGPT Cui et al. (2024)
Geneformer Theodoris et al. (2023)
HCA Regev et al. (2017)
Tabula Sapiens Tabula Sapiens Consortium* et al. (2022)
Tabula Muris Tabula Muris Consortium et al. (2018)
Tabula Muris Senis Tabula Muris Consortium (2020)
SAHA Park et al. (2025)
CELLxGENE Megill et al. (2021)
HPA Thul and Lindskog (2018)
scDiagnostics Christidis et al. (2026)
review Wang et al. (2024)
benchmarks R. Chen et al. (2025); Sun et al. (2025)
Cheng et al. (2022); Hu et al. (2024)
T. Liu et al. (2024); Xiong et al. (2025)
C-SIDE Cable, Murray, Shanmugam, et al. (2022)
CTSV Yu and Luo (2022)
DESpace Cai et al. (2024)
nnSVG Weber et al. (2023)
scran Lun et al. (2016)
SPARK Sun et al. (2020); Zhu et al. (2021)
spatialDE Svensson et al. (2018)
spVC Yu and Li (2024)
reviews Adhikari et al. (2024); Yan et al. (2024)
benchmarks Zhijian Li et al. (2023); Chen et al. (2024)
msigdb n/a
msigdbr n/a
AUCell Aibar et al. (2017)
Molecular Signatures Database (MSigDB) Subramanian et al. (2005)
pasta Emons et al. (2025)
spatstat Baddeley and Turner (2005)
1st law of geography Tobler (1970)
spatial analysis Dale and Fortin (2014)
spatial data science Pebesma and Bivand (2023)
point pattern analysis Baddeley et al. (2015)
Lee’s L Lee (2001)
Ripley’s K Ripley (1976)
LISA metric Anselin (1995); Anselin (2019)
joint count statistics Getis (2009)
napari Sofroniew et al. (2025)
QuPath Bankhead et al. (2017)
Prov-GigaPath Xu et al. (2024)
The Cancer Genome Atlas (TCGA) Tomczak et al. (2015)
The Cancer Imaging Archive (TCIA) n/a
imageTCGA n/a
TCIAAPI n/a
C-SIDE Cable, Murray, Shanmugam, et al. (2022)
DESpace Cai et al. (2024)
lme4 Bates et al. (2015)
lmerTest Kuznetsova et al. (2017)
sosta Gunz et al. (2025)
spatialFDA Emons et al. (2026)
spicyR Canete et al. (2022)
silhouette width Rautenstrauch and Ohler (2025)
harmony Korsunsky et al. (2019)
CellMixS Lütge et al. (2021)
rliger Welch et al. (2019)
scvi-tool’s Tangram Biancalani et al. (2021)
SLAT Xia et al. (2023)
CeLEry Q. Zhang et al. (2023)
novosparc Nitzan et al. (2019)
SpaOTsc Cang and Nie (2020)
imputation benchmark B. Li et al. (2022)
integration review Argelaguet et al. (2021)
integration benchmarks Tran et al. (2020); Chazarra-Gil et al. (2021); Luecken et al. (2022)
registration benchmark Hu et al. (2024); Dong et al. (2025); Yan et al. (2026)
registration challenge Weitz et al. (2024)
infercnv n/a
infercnvpy n/a
CNV benchmarks Jensen et al. (2025); Schmid et al. (2025)
monocle Trapnell et al. (2014); Qiu et al. (2017)
slingshot Street et al. (2018)
SpaceFlow Ren et al. (2022)
stlearn Pham et al. (2023)
spaTrack Shen et al. (2025)
trajectory benchmarks Cannoodt et al. (2016); Saelens et al. (2019)
scGPT Cui et al. (2024)
scFoundation Hao et al. (2024)
Geneformer Theodoris et al. (2023)
Prov-GigaPath Xu et al. (2024)
FM review Szałata et al. (2024); Ahlmann-Eltze et al. (2026)
annotation benchmark Kedzierska et al. (2025)
perturbation prediction benchmark Ahlmann-Eltze et al. (2025)
spatial multi-omics review Vandereyken et al. (2023); X. Liu et al. (2024); Isik et al. (2026)
MOFA2 Velten et al. (2022)
MultiAssayExperiment n/a
SpatialGlue Long et al. (2024)
scvi-tool’s MultiVI Ashuach et al. (2023)

References

Adhikari, Sikta Das, Jiaxin Yang, Jianrong Wang, and Yuehua Cui. 2024. “Recent Advances in Spatially Variable Gene Detection in Spatial Transcriptomics.” Computational and Structural Biotechnology Journal 23: 883–91. https://doi.org/10.1016/j.csbj.2024.01.016.
Aggarwal, Bhavay, and Saurabh Sinha. 2025. “CellSP: Module Discovery and Visualization for Subcellular Spatial Transcriptomics Data.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.01.12.632553.
Ahlmann-Eltze, Constantin, Florian Barkmann, Jan Lause, Valentina Boeva, and Dmitry Kobak. 2026. Representation learning of single-cell RNA-seq data.” RNA (New York, N.Y.), ahead of print. https://doi.org/10.1261/rna.080889.125.
Ahlmann-Eltze, Constantin, and Wolfgang Huber. 2023. Comparison of transformations for single-cell RNA-seq data.” Nature Methods 20 (5): 665–72. https://doi.org/10.1038/s41592-023-01814-1.
Ahlmann-Eltze, Constantin, Wolfgang Huber, and Simon Anders. 2025. Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.” Nature Methods 22 (8): 1657–61. https://doi.org/10.1038/s41592-025-02772-6.
Aibar, Sara, Carmen Bravo González-Blas, Thomas Moerman, et al. 2017. “SCENIC: Single-Cell Regulatory Network Inference and Clustering.” Nature Methods 14: 1083–86. https://doi.org/10.1038/nmeth.4463.
Amezquita, Robert A., Aaron T. L. Lun, Etienne Becht, et al. 2020. “Orchestrating Single-Cell Analysis with Bioconductor.” Nature Methods 17: 137–45. https://doi.org/10.1038/s41592-019-0654-x.
Andersson, Alma, Joseph Bergenstråhle, Michaela Asp, et al. 2020. “Single-Cell and Spatial Transcriptomics Enables Probabilistic Inference of Cell Type Topography.” Communications Biology 3 (1): 565. https://doi.org/10.1038/s42003-020-01247-y.
Anselin, Luc. 1995. “Local Indicators of Spatial Association—LISA.” Geographical Analysis 27: 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x.
Anselin, Luc. 2019. “A Local Indicator of Multivariate Spatial Association: Extending Geary’s c.” Geographical Analysis 51: 133–50. https://doi.org/10.1111/gean.12164.
Aran, Dvir, Agnieszka P Looney, Leqian Liu, et al. 2019. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.” Nature Immunology 20 (2): 163–72. https://doi.org/10.1038/s41590-018-0276-y.
Argelaguet, Ricard, Anna S E Cuomo, Oliver Stegle, and John C Marioni. 2021. Computational principles and challenges in single-cell data integration.” Nature Biotechnology 39: 1202–15. https://doi.org/10.1038/s41587-021-00895-7.
Armingol, Erick, Adam Officer, Olivier Harismendy, and Nathan E Lewis. 2020. “Deciphering Cell–Cell Interactions and Communication from Gene Expression.” Nature Reviews Genetics 22 (2): 71–88. https://doi.org/10.1038/s41576-020-00292-x.
Ashuach, Tal, Mariano I Gabitto, Rohan V Koodli, Giuseppe-Antonio Saldi, Michael I Jordan, and Nir Yosef. 2023. MultiVI: deep generative model for the integration of multimodal data.” Nature Methods 20 (8): 1222–31. https://doi.org/10.1038/s41592-023-01909-9.
Atta, Lyla, Kalen Clifton, Manjari Anant, Gohta Aihara, and Jean Fan. 2024. “Gene Count Normalization in Single-Cell Imaging-Based Spatially Resolved Transcriptomics.” Genome Biology 25 (153). https://doi.org/10.1186/s13059-024-03303-w.
Baddeley, Adrian, Ege Rubak, and Rolf Turner. 2015. Spatial Point Patterns: Methodology and Applications with r. 1st Edition. Chapman; Hall/CRC. https://doi.org/10.1201/b19708.
Baddeley, Adrian, and Rolf Turner. 2005. “Spatstat: An r Package for Analyzing Spatial Point Patterns.” Journal of Statistical Software 12: 1–42. https://doi.org/10.18637/jss.v012.i06.
Baldoni, Pedro L., Lizhong Chen, Mengbo Li, Yunshun Chen, and Gordon K. Smyth. 2025. “Dividing Out Quantification Uncertainty Enables Assessment of Differential Transcript Usage with Limma and edgeR.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.04.07.647659.
Bankhead, Peter, Maurice B Loughrey, José A Fernández, et al. 2017. “QuPath: Open Source Software for Digital Pathology Image Analysis.” Scientific Reports 7 (1): 1–7. https://doi.org/10.1038/s41598-017-17204-5.
Banzi, Benedetta, Dario Righelli, Matteo Marchionni, et al. 2025. “SpaceTrooper: A Quality Control Framework for Imaging-Based Spatial Omics Data.” bioRxiv, ahead of print. https://doi.org/10.64898/2025.12.24.696336.
Baran, Yusuf, and Berat Doğan. 2023. “scMAGS: Marker Gene Selection from scRNA-Seq Data for Spatial Transcriptomics Studies.” Computers in Biology and Medicine 155 (106634). https://doi.org/10.1016/j.compbiomed.2023.106634.
Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. 2015. “Fitting Linear Mixed-Effects Models Using Lme4.” Journal of Statistical Software 67: 1–48. https://doi.org/10.18637/jss.v067.i01.
Behanova, Andrea, Anna Klemm, and Carolina Wählby. 2022. Spatial Statistics for Understanding Tissue Organization.” Frontiers in Physiology 13: 832417. https://doi.org/10.3389/fphys.2022.832417.
Berglund, Emelie, Jonas Maaskola, Niklas Schultz, et al. 2018. “Spatial Maps of Prostate Cancer Transcriptomes Reveal an Unexplored Landscape of Heterogeneity.” Nature Communications 9 (1): 2419. https://doi.org/10.1038/s41467-018-04724-5.
Bhatia, Harsharan Singh, Andreas-David Brunner, Furkan Öztürk, et al. 2022. Spatial proteomics in three-dimensional intact specimens.” Cell 185 (26): 5040–5058.e19. https://doi.org/10.1016/j.cell.2022.11.021.
Bhuva, Dharmesh D., Chin Wee Tan, Agus Salim, et al. 2024. “Library Size Confounds Biology in Spatial Transcriptomics Data.” Genome Biology 25 (99). https://doi.org/10.1186/s13059-024-03241-7.
Biancalani, Tommaso, Gabriele Scalia, Lorenzo Buffoni, et al. 2021. “Deep Learning and Alignment of Spatially Resolved Single-Cell Transcriptomes with Tangram.” Nature Methods 18: 1352–62. https://doi.org/10.1038/s41592-021-01264-7.
Bierman, Rob, Jui M Dave, Daniel M Greif, and Julia Salzman. 2024. “Statistical Analysis Supports Pervasive RNA Subcellular Localization and Alternative 3’ UTR Regulation.” eLife 12. https://doi.org/10.7554/elife.87517.
Bilous, Mariia, Daria Buszta, Jonathan Bac, et al. 2025. From transcripts to cells: Dissecting sensitivity, signal contamination, and specificity in Xenium spatial transcriptomics.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.04.23.649965.
Birk, Sebastian, Irene Bonafonte-Pardàs, Adib Miraki Feriz, et al. 2025. Quantitative characterization of cell niches in spatially resolved omics data.” Nature Genetics 57 (4): 897–909. https://doi.org/10.1038/s41588-025-02120-6.
Blampey, Quentin, Hakim Benkirane, Nadège Bercovici, et al. 2025. Novae: a graph-based foundation model for spatial transcriptomics data.” Nature Methods 22 (12): 2539–50. https://doi.org/10.1038/s41592-025-02899-6.
Bressan, Dario, Giorgia Battistoni, and Gregory J. Hannon. 2023. “The Dawn of Spatial Omics.” Science 381. https://doi.org/10.1126/science.abq4964.
Cable, Dylan M., Evan Murray, Vignesh Shanmugam, et al. 2022. “Cell Type-Specific Inference of Differential Expression in Spatial Transcriptomics.” Nature Methods 19: 1076–87. https://doi.org/10.1038/s41592-022-01575-3.
Cable, Dylan M., Evan Murray, Luli S. Zou, et al. 2022. “Robust Decomposition of Cell Type Mixtures in Spatial Transcriptomics.” Nature Biotechnology 40: 517–26. https://doi.org/10.1038/s41587-021-00830-w.
Cai, Peiying, Mark D Robinson, and Simone Tiberi. 2024. “DESpace: Spatially Variable Gene Detection via Differential Expression Testing of Spatial Clusters.” Bioinformatics 40 (btae027, 2). https://doi.org/10.1093/bioinformatics/btae027.
Canete, Nicolas P, Sourish S Iyengar, John T Ormerod, Heeva Baharlou, Andrew N Harman, and Ellis Patrick. 2022. “spicyR: Spatial Analysis of in Situ Cytometry Data in r.” Bioinformatics 38: 3099–105. https://doi.org/10.1093/bioinformatics/btac268.
Cang, Zixuan, and Qing Nie. 2020. “Inferring Spatial and Signaling Relationships Between Cells from Single Cell Transcriptomic Data.” Nature Communications 11 (2084). https://doi.org/10.1038/s41467-020-15968-5.
Cang, Zixuan, Yanxiang Zhao, Axel A. Almet, et al. 2023. “Screening Cell–Cell Communication in Spatial Transcriptomics via Collective Optimal Transport.” Nature Methods 20: 218–28. https://doi.org/10.1038/s41592-022-01728-4.
Cannoodt, Robrecht, Wouter Saelens, and Yvan Saeys. 2016. Computational methods for trajectory inference from single-cell transcriptomics.” European Journal of Immunology 46 (11): 2496–506. https://doi.org/10.1002/eji.201646347.
Carey, V. J., B. J. Harshfield, S. Falcon, S. Arora, and L. Shepherd. 2025. “biocViews: Categorized Views of r Package Repositories.” R Package, ahead of print. https://doi.org/10.18129/B9.bioc.biocViews.
Carey, Vincent J. 2025. “Bioconductor: Planning a Third Decade of Comprehensive Support for Genomic Data Science.” Patterns 6 (101319, 7). https://doi.org/10.1016/j.patter.2025.101319.
Cassella, Lucia, and Anne Ephrussi. 2022. “Subcellular Spatial Transcriptomics Identifies Three Mechanistically Different Classes of Localizing RNAs.” Nature Communications 13 (1). https://doi.org/10.1038/s41467-022-34004-2.
Chari, Tara, and Lior Pachter. 2023. “The Specious Art of Single-Cell Genomics.” PLoS Computational Biology 19 (e1011288, 8). https://doi.org/10.1371/journal.pcbi.1011288.
Chazarra-Gil, Ruben, Stijn van Dongen, Vladimir Yu Kiselev, and Martin Hemberg. 2021. Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench.” Nucleic Acids Research 49 (7): e42. https://doi.org/10.1093/nar/gkab004.
Chen, Carissa, Hani Jieun Kim, and Pengyi Yang. 2024. “Evaluating Spatially Variable Gene Detection Methods for Spatial Transcriptomics Data.” Genome Biology 25 (18). https://doi.org/10.1186/s13059-023-03145-y.
Chen, Jiaji G, Joselyn C Chávez-Fuentes, Matthew O’Brien, et al. 2025. “Giotto Suite: A Multiscale and Technology-Agnostic Spatial Multiomics Analysis Ecosystem.” Nature Methods, 1–13. https://doi.org/10.1038/s41592-025-02817-w.
Chen, Kok Hao, Alistair N. Boettiger, Jeffrey R. Moffitt, Siyuan Wang, and Xiaowei Zhuang. 2015. “Spatially Resolved, Highly Multiplexed RNA Profiling in Single Cells.” Science 348. https://doi.org/10.1126/science.aaa6090.
Chen, Renjie, Yue Yao, Jingyang Qian, Xin Peng, Xin Shao, and Xiaohui Fan. 2025. A comprehensive benchmarking for spatially resolved transcriptomics clustering methods across variable technologies, organs, and replicates.” iMeta 4 (6): e70084. https://doi.org/10.1002/imt2.70084.
Chen, Yunshun, Lizhong Chen, Aaron T L Lun, Pedro L Baldoni, and Gordon K Smyth. 2025. “edgeR V4: Powerful Differential Analysis of Sequencing Data with Expanded Functionality and Improved Support for Small Counts and Larger Datasets.” Nucleic Acids Research 53 (gkaf018, 2). https://doi.org/10.1093/nar/gkaf018.
Cheng, Andrew, Guanyu Hu, and Wei Vivian Li. 2022. Benchmarking cell-type clustering methods for spatially resolved transcriptomics data.” Briefings in Bioinformatics 24 (1): bbac475. https://doi.org/10.1093/bib/bbac475.
Cheng, Mengnan, Yujia Jiang, Jiangshan Xu, et al. 2023. “Spatially Resolved Transcriptomics: A Comprehensive Review of Their Technological Advances, Applications, and Challenges.” Journal of Genetics and Genomics 50: 625–40. https://doi.org/10.1016/j.jgg.2023.03.011.
Chidester, Benjamin, Tianming Zhou, Shahul Alam, and Jian Ma. 2023. SpiceMix Enables Integrative Single-Cell Spatial Modeling of Cell Identity.” Nat. Genet. 55 (1): 78–88. https://doi.org/10.1038/s41588-022-01256-z.
Christidis, Anthony, Andrew R Ghazi, Smriti Chawla, Nitesh Turaga, Robert Gentleman, and Ludwig Geistlinger. 2026. scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.” bioRxiv, 2026.01.29.701618. https://doi.org/10.64898/2026.01.29.701618.
Clifton, Kalen, Manjari Anant, Gohta Aihara, et al. 2023. “STalign: Alignment of Spatial Transcriptomics Data Using Diffeomorphic Metric Mapping.” Nature Communications 14 (8123). https://doi.org/10.1038/s41467-023-43915-7.
Cotterell, James, Jim Swoger, Alexandre Robert-Moreno, Heura Cardona, Marco Musy, and James Sharpe. 2024. Cell 3D Positioning by Optical encoding (C3PO) and its application to spatial transcriptomics.” bioRxiv, 2024.03.12.584578. https://doi.org/10.1101/2024.03.12.584578.
Cui, Haotian, Chloe Wang, Hassaan Maan, et al. 2024. scGPT: toward building a foundation model for single-cell multi-omics using generative AI.” Nature Methods 21 (8): 1470–80. https://doi.org/10.1038/s41592-024-02201-0.
Dale, Mark R. T., and Marie-Josée Fortin. 2014. Spatial Analysis: A Guide for Ecologists. 2nd Edition. Cambridge University Press. https://doi.org/10.1017/CBO9780511978913.
Damond, Nicolas, Stefanie Engler, Vito R. T. Zanotelli, et al. 2019. “A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry.” Cell Metabolism 29: 755–68. https://doi.org/10.1016/j.cmet.2018.11.014.
Danaher, Patrick, Youngmi Kim, Brenn Nelson, et al. 2022. “Advances in Mixed Cell Deconvolution Enable Quantification of Cell Types in Spatial Transcriptomic Data.” Nature Communications 13 (1): 385. https://doi.org/10.1038/s41467-022-28020-5.
de Oliveira, Michelli Faria, Juan Pablo Romero, Meii Chung, et al. 2025. “High-Definition Spatial Transcriptomic Profiling of Immune Cell Populations in Colorectal Cancer.” Nature Genetics 57: 1512–23. https://doi.org/10.1038/s41588-025-02193-3.
Deconinck, Louise, Luke Zappia, Robrecht Cannoodt, et al. 2026. anndataR improves interoperability between R and Python in single-cell transcriptomics.” Bioinformatics 42 (6): btag288. https://doi.org/10.1093/bioinformatics/btag288.
Dijk, David van, Roshan Sharma, Juozas Nainys, et al. 2018. “Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.” Cell 174 (3): 716–729.e27. https://doi.org/10.1016/j.cell.2018.05.061.
Domínguez Conde, C, C Xu, L B Jarvis, et al. 2022. Cross-tissue immune cell analysis reveals tissue-specific features in humans.” Science (New York, N.Y.) 376 (6594): eabl5197. https://doi.org/10.1126/science.abl5197.
Dong, Kangning, and Shihua Zhang. 2022. “Deciphering Spatial Domains from Spatially Resolved Transcriptomics with an Adaptive Graph Attention Auto-Encoder.” Nature Communications 13 (1739). https://doi.org/10.1038/s41467-022-29439-6.
Dong, Kejing, Yicheng Gao, Qi Zou, et al. 2025. “Benchmarking Multi-Slice Integration and Downstream Applications in Spatial Transcriptomics Data Analysis.” Genome Biology 26 (1): 318. https://doi.org/10.1186/s13059-025-03796-z.
Dries, Ruben, Qian Zhu, Rui Dong, et al. 2021. “Giotto: A Toolbox for Integrative Analysis and Visualization of Spatial Expression Data.” Genome Biology 22 (78). https://doi.org/10.1186/s13059-021-02286-2.
Efremova, Mirjana, Miquel Vento-Tormo, Sarah A Teichmann, and Roser Vento-Tormo. 2020. CellPhoneDB: Inferring Cell-Cell Communication from Combined Expression of Multi-Subunit Ligand-Receptor Complexes.” Nature Protocols 15 (4): 1484–506. https://doi.org/10.1038/s41596-020-0292-x.
Elosua-Bayes, Marc, Paula Nieto, Elisabetta Mereu, Ivo Gut, and Holger Heyn. 2021. SPOTlight: Seeded NMF Regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes.” Nucleic Acids Research 49 (9): e50. https://doi.org/10.1093/nar/gkab043.
Emons, Martin, Samuel Gunz, Helena L. Crowell, Izaskun Mallona, Reinhard Furrer, and Mark D. Robinson. 2025. “Harnessing the Potential of Spatial Statistics for Spatial Omics Data with Pasta.” Nucleic Acids Research 53 (17): gkaf870. https://doi.org/10.1038/s41467-022-28020-5.
Emons, Martin, Fabian Scheipl, Samuel Gunz, Elizabeth Purdom, and Mark D Robinson. 2026. Differential co-localisation analysis of multi-sample and multi-condition experiments with spatialFDA.” bioRxiv, ahead of print. https://doi.org/10.64898/2026.04.13.718197.
Ennis, Sarah, Pilib Ó Broin, and Eva Szegezdi. 2023. CCPlotR: An R Package for the Visualization of Cell-Cell Interactions.” Bioinformatics Advances 3 (1): vbad130. https://doi.org/10.1093/bioadv/vbad130.
Ertürk, Ali, Klaus Becker, Nina Jährling, et al. 2012. Three-dimensional imaging of solvent-cleared organs using 3DISCO.” Nature Protocols 7 (11): 1983–95. https://doi.org/10.1038/nprot.2012.119.
Fang, Rongxin, Aaron Halpern, Mohammed Mostafizur Rahman, et al. 2024. Three-dimensional single-cell transcriptome imaging of thick tissues.” eLife 12 (RP90029). https://doi.org/10.7554/eLife.90029.
Fang, Zhou, Adam J. Ford, Thomas Hu, Nicholas Zhang, Athanasios Mantalaris, and Ahmet F. Coskun. 2023. “Subcellular Spatially Resolved Gene Neighborhood Networks in Single Cells.” Cell Reports Methods 3 (5): 100476. https://doi.org/10.1016/j.crmeth.2023.100476.
Gaspard-Boulinc, Lucie C., Luca Gortana, Thomas Walter, Emmanuel Barillot, and Florence M. G. Cavalli. 2025. “Cell-Type Deconvolution Methods for Spatial Transcriptomics.” Nature Reviews Genetics, ahead of print. https://doi.org/https://doi.org/10.1038/s41576-025-00845-y.
Gentleman, Robert C., Vincent J. Carey, Ben Bolstad Douglas M Bates, et al. 2004. “Bioconductor: Open Software Development for Computational Biology and Bioinformatics.” Genome Biology 5 (R80). https://doi.org/10.1186/gb-2004-5-10-r80.
Getis, Arthur. 2009. “Spatial Weights Matrices.” Geographical Analysis 41: 404–10. https://doi.org/10.1111/j.1538-4632.2009.00768.x.
Gong, Chun, Shengkang Li, Leying Wang, et al. 2024. “SAW: An Efficient and Accurate Data Analysis Workflow for Stereo-Seq Spatial Transcriptomics.” Gigabyte, 1–12. https://doi.org/10.46471/gigabyte.111.
Gulati, Gunsagar S., Jeremy Philip D’Silva, Yunhe Liu, Linghua Wang, and Aaron M. Newman. 2025. “Profiling Cell Identity and Tissue Architecture with Single-Cell and Spatial Transcriptomics.” Nature Reviews Molecular Cell Biology 26: 11–31. https://doi.org/10.1038/s41580-024-00768-2.
Gunz, Samuel, Helena L. Crowell, and Mark D. Robinson. 2025. “Analysis of Anatomical Multi-Cellular Structures from Spatial Omics Data Using Sosta.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.10.13.682065.
Guo, Juanru, Simona Sarafinovska, Ryan A Hagenson, et al. 2025. SMURF: soft-segmentation for single-cell reconstruction and topological analysis of spatial transcriptomic data.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.05.28.656357.
Hao, Minsheng, Jing Gong, Xin Zeng, et al. 2024. Large-scale foundation model on single-cell transcriptomics.” Nature Methods 21 (8): 1481–91. https://doi.org/10.1038/s41592-024-02305-7.
Hao, Yuhan, Stephanie Hao, Erica Andersen-Nissen, et al. 2021. Integrated analysis of multimodal single-cell data.” Cell 184 (13): 3573–3587.e29. https://doi.org/10.1016/j.cell.2021.04.048.
Hao, Yuhan, Tim Stuart, Madeline H Kowalski, et al. 2023. “Dictionary Learning for Integrative, Multimodal and Scalable Single-Cell Analysis.” Nature Biotechnology, ahead of print. https://doi.org/10.1038/s41587-023-01767-y.
Hartman, Austin, and Rahul Satija. 2024. “Comparative Analysis of Multiplexed in Situ Gene Expression Profiling Technologies.” eLife, ahead of print. https://doi.org/10.7554/eLife.96949.1.
He, Shanshan, Ruchir Bhatt, Carl Brown, et al. 2022. “High-Plex Imaging of RNA and Proteins at Subcellular Resolution in Fixed Tissue by Spatial Molecular Imaging.” Nature Biotechnology 40: 1794–806. https://doi.org/10.1038/s41587-022-01483-z.
He, Yichun, Xin Tang, Jiahao Huang, et al. 2021. “ClusterMap for Multi-Scale Clustering Analysis of Spatial Gene Expression.” Nature Communications 12 (1). https://doi.org/10.1038/s41467-021-26044-x.
Heidari, Elyas, Andrew Moorman, Dániel Unyi, et al. 2025. “Segger: Fast and Accurate Cell Segmentation of Imaging-Based Spatial Transcriptomics Data.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.03.14.643160.
Hu, Jian, Xiangjie Li, Kyle Coleman, et al. 2021. “SpaGCN: Integrating Gene Expression, Spatial Location and Histology to Identify Spatial Domains and Spatially Variable Genes by Graph Convolutional Network.” Nature Methods 18: 1342–51. https://doi.org/10.1038/s41592-021-01255-8.
Hu, Yunfei, Manfei Xie, Yikang Li, et al. 2024. “Benchmarking Clustering, Alignment, and Integration Methods for Spatial Transcriptomics.” Genome Biology 25 (212). https://doi.org/10.1186/s13059-024-03361-0.
Huber, Wolfgang, Vincent J. Carey, Robert Gentleman, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nature Methods 12: 115–21. https://doi.org/10.1038/nmeth.3252.
Ianevski, Aleksandr, Anil K Giri, and Tero Aittokallio. 2022. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data.” Nature Communications 13 (1): 1246. https://doi.org/10.1038/s41467-022-28803-w.
Isik, Esra Busra, Yusuf Hakan Usta, Haozhe Liu, et al. 2026. Multimodal spatial omics: From data acquisition to computational integration.” arXiv, ahead of print. https://doi.org/10.48550/arXiv.2601.12381.
Janesick, Amanda, Robert Shelansky, Andrew D. Gottscho, et al. 2023. “High Resolution Mapping of the Tumor Microenvironment Using Integrated Single-Cell, Spatial and in Situ Analysis.” Nature Communications 14 (8353). https://doi.org/10.1038/s41467-023-43458-x.
Jensen, Augusta Elisabeth Vang, Helena Crowell, Anna Pascual Reguant, et al. 2025. In situ inference of copy number variations in image-based spatial transcriptomics.” bioRxiv, 2025.07.02.662761. https://doi.org/10.1101/2025.07.02.662761.
Jin, Suoqin, Christian F Guerrero-Juarez, Lihua Zhang, et al. 2021. “Inference and Analysis of Cell-Cell Communication Using CellChat.” Nature Communications 12 (1): 1088. https://doi.org/10.1038/s41467-021-21246-9.
Jones, Daniel C., Anna E. Elz, Azadeh Hadadianpour, Heeju Ryu, David R. Glass, and Evan W. Newell. 2025. “Cell Simulation as Cell Segmentation.” Nature Methods 22: 1331–42. https://doi.org/10.1038/s41592-025-02697-0.
Kamel, Mena, Yiwen Song, Ana Solbas, et al. 2025. ENACT: End-to-end analysis of Visium High Definition (HD) data.” Bioinformatics 41 (3): btaf094. https://doi.org/10.1093/bioinformatics/btaf094.
Kedzierska, Kasia Z, Lorin Crawford, Ava P Amini, and Alex X Lu. 2025. Zero-shot evaluation reveals limitations of single-cell foundation models.” Genome Biology 26 (1): 101. https://doi.org/10.1186/s13059-025-03574-x.
Kleshchevnikov, Vitalii, Artem Shmatko, Emma Dann, et al. 2022. Cell2location Maps Fine-Grained Cell Types in Spatial Transcriptomics.” Nature Biotechnology 40: 661–71. https://doi.org/10.1038/s41587-021-01139-4.
Korsunsky, Ilya, Nghia Millard, Jean Fan, et al. 2019. Fast, sensitive and accurate integration of single-cell data with Harmony.” Nature Methods 16 (12): 1289–96. https://doi.org/10.1038/s41592-019-0619-0.
Kuemmerle, Louis B., Malte D. Luecken, Alexandra B. Firsova, et al. 2024. “Probe Set Selection for Targeted Spatial Transcriptomics.” Nature Methods 21: 2260–70. https://doi.org/10.1038/s41592-024-02496-z.
Kuznetsova, Alexandra, Per B. Brockhoff, and Rune H. B. Christensen. 2017. “lmerTest Package: Tests in Linear Mixed Effects Models.” Journal of Statistical Software 82: 1–26. https://doi.org/10.18637/jss.v082.i13.
Lee, Sang-Il. 2001. “Developing a Bivariate Spatial Association Measure: An Integration of Pearson’s r and Moran’s i.” Journal of Geographical Systems 3: 369–85. https://doi.org/10.1007/s101090100064.
Li, Bin, Wen Zhang, Chuang Guo, et al. 2022. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution.” Nature Methods 19: 662–70. https://doi.org/10.1038/s41592-022-01480-9.
Li, Haoyang, Hanmin Li, Juexiao Zhou, and Xin Gao. 2022. SD2: Spatially Resolved Transcriptomics Deconvolution Through Integration of Dropout and Spatial Information.” Bioinformatics 38 (21): 4878–84. https://doi.org/10.1093/bioinformatics/btac605.
Li, Haoyang, Juexiao Zhou, Zhongxiao Li, et al. 2023. “A Comprehensive Benchmarking with Practical Guidelines for Cellular Deconvolution of Spatial Transcriptomics.” Nature Communications 14 (1548). https://doi.org/10.1038/s41467-023-37168-7.
Li, Mengbo, Ning Liu, Quoc Hoang Nguyen, and Yunshun Chen. 2025. “Preserving Tissue Structure Through Density-Based Spatial Analysis with Scider.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.09.11.675745.
Li, Zhijian, Zain M. Patel, Dongyuan Song, Guanao Yan, Jingyi Jessica Li, and Luca Pinello. 2023. “Benchmarking Computational Methods to Identify Spatially Variable Genes and Peaks.” bioRxiv, ahead of print. https://doi.org/10.1101/2023.12.02.569717.
Li, Zhuoxuan, Tianjie Wang, Pengtao Liu, and Yuanhua Huang. 2023. SpatialDM: Rapid Identification of Spatially Co-Expressed Ligand-Receptor Reveals Cell-Cell Communication Patterns.” bioRxiv, 2022.08.19.504616. https://doi.org/10.1101/2022.08.19.504616.
Liang, Yuchen, Guowei Shi, Runlin Cai, et al. 2024. “PROST: Quantitative Identification of Spatially Variable Genes and Domain Detection in Spatial Transcriptomics.” Nature Communications 15 (600). https://doi.org/10.1038/s41467-024-44835-w.
Liao, Yang, Gordon K Smyth, and Wei Shi. 2019. “The r Package Rsubread Is Easier, Faster, Cheaper and Better for Alignment and Quantification of RNA Sequencing Reads.” Nucleic Acids Research 47: e47. https://doi.org/10.1093/nar/gkz114.
Liu, Ning, Jarryd Martin, Dharmesh D Bhuva, et al. 2025. “hoodscanR: Profiling Single-Cell Neighborhoods in Spatial Transcriptomics Data.” bioRxiv, ahead of print. https://doi.org/10.1101/2024.03.26.586902.
Liu, Teng, Zhao-Yu Fang, Zongbo Zhang, Yongxiang Yu, Min Li, and Ming-Zhu Yin. 2024. “A Comprehensive Overview of Graph Neural Network-Based Approaches to Clustering for Spatial Transcriptomics.” Computational and Structural Biotechnology Journal 23: 106–28. https://doi.org/10.1016/j.csbj.2023.11.055.
Liu, Wei, Xu Liao, Ziye Luo, et al. 2023. “Probabilistic Embedding, Clustering, and Alignment for Integrating Spatial Transcriptomics Data with PRECAST.” Nature Communications 14 (296). https://doi.org/10.1038/s41467-023-35947-w.
Liu, Xiaojie, Ting Peng, Miaochun Xu, et al. 2024. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications.” Journal of Hematology & Oncology 17 (1): 72. https://doi.org/10.1186/s13045-024-01596-9.
Liu, Xinhao, Ron Zeira, and Benjamin J Raphael. 2023. Partial alignment of multislice spatially resolved transcriptomics data.” Genome Research 33 (7): 1124–32. https://doi.org/10.1101/gr.277670.123.
Liu, Yang, Marcello DiStasio, Graham Su, et al. 2023. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq.” Nature Biotechnology 41 (10): 1405–9. https://doi.org/10.1038/s41587-023-01676-0.
Liu, Zhaoyang, Dongqing Sun, and Chenfei Wang. 2022. “Evaluation of Cell-Cell Interaction Methods by Integrating Single-Cell RNA Sequencing Data with Spatial Information.” Genome Biology 23 (1): 218. https://doi.org/10.1186/s13059-022-02783-y.
Long, Yahui, Kok Siong Ang, Mengwei Li, et al. 2023. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.” Nature Communications 14 (1): 1–19. https://doi.org/10.1038/s41467-023-36796-3.
Long, Yahui, Kok Siong Ang, Raman Sethi, et al. 2024. Deciphering spatial domains from spatial multi-omics with SpatialGlue.” Nature Methods 21 (9): 1658–67. https://doi.org/10.1038/s41592-024-02316-4.
Lopez, Romain, Baoguo Li, Hadas Keren-Shaul, et al. 2022. DestVI Identifies Continuums of Cell Types in Spatial Transcriptomics Data.” Nature Biotechnology 40: 1360–69. https://doi.org/10.1038/s41587-022-01272-8.
Luecken, Malte D, M Büttner, K Chaichoompu, et al. 2022. Benchmarking atlas-level data integration in single-cell genomics.” Nature Methods 19 (1): 41–50. https://doi.org/10.1038/s41592-021-01336-8.
Lun, Aaron. 2022. “Basilisk: A Bioconductor Package for Managing Python Environments.” Journal of Open Source Software 7. https://doi.org/10.21105/joss.04742.
Lun, Aaron. 2023. “Alabaster.base: Save Bioconductor Objects to File.” R Package, ahead of print. https://doi.org/10.18129/B9.bioc.alabaster.base.
Lun, Aaron T. L., Davis J. McCarthy, and John C. Marioni. 2016. “A Step-by-Step Workflow for Low-Level Analysis of Single-Cell RNA-Seq Data with Bioconductor.” F1000Research 5 (2122). https://doi.org/10.12688/f1000research.9501.2.
Lundberg, Emma, and Georg H. H. Borner. 2019. “Spatial Proteomics: A Powerful Discovery Tool for Cell Biology.” Nature Reviews Molecular Cell Biology 20: 285–302. https://doi.org/10.1038/s41580-018-0094-y.
Lütge, Almut, Joanna Zyprych-Walczak, Urszula Brykczynska Kunzmann, et al. 2021. CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data.” Life Science Alliance 4 (6): e202001004. https://doi.org/10.26508/lsa.202001004.
Ma, Ying, and Xiang Zhou. 2022. “Spatially Informed Cell-Type Deconvolution for Spatial Transcriptomics.” Nature Biotechnology 40 (9): 1349–59. https://doi.org/10.1038/s41587-022-01273-7.
Mah, Clarence K., Noorsher Ahmed, Nicole A. Lopez, et al. 2024. “Bento: A Toolkit for Subcellular Analysis of Spatial Transcriptomics Data.” Genome Biology 25 (1). https://doi.org/10.1186/s13059-024-03217-7.
Manukyan, Artür, Ella Bahry, Emanuel Wyler, et al. 2023. “VoltRon: A Spatial Omics Analysis Platform for Multi-Resolution and Multi-Omics Integration Using Image Registration.” bioRxiv, ahead of print. https://doi.org/10.1101/2023.12.15.571667.
Marconato, Luca, Giovanni Palla, Kevin A. Yamauchi, et al. 2025. “SpatialData: An Open and Universal Data Framework for Spatial Omics.” Nature Methods 22: 58–62. https://doi.org/10.1038/s41592-024-02212-x.
Maynard, Kristen R., Leonardo Collado-Torres, Lukas M. Weber, et al. 2021. “Transcriptome-Scale Spatial Gene Expression in the Human Dorsolateral Prefrontal Cortex.” Nature Neuroscience 24: 425–36. https://doi.org/10.1038/s41593-020-00787-0.
McCarthy, Davis J, Kieran R Campbell, Aaron T L Lun, and Quin F Wills. 2017. “Scater: Pre-Processing, Quality Control, Normalization and Visualization of Single-Cell RNA-Seq Data in r.” Bioinformatics 33: 1179–86. https://doi.org/10.1093/bioinformatics/btw777.
Megill, Colin, Bruce Martin, Charlotte Weaver, et al. 2021. Cellxgene: A performant, scalable exploration platform for high dimensional sparse matrices.” bioRxiv, 2021.04.05.438318. https://doi.org/10.1101/2021.04.05.438318.
Miller, Brendan F, Feiyang Huang, Lyla Atta, Arpan Sahoo, and Jean Fan. 2022. “Reference-Free Cell Type Deconvolution of Multi-Cellular Pixel-Resolution Spatially Resolved Transcriptomics Data.” Nature Communications 13 (1): 2339. https://doi.org/10.1038/s41467-022-30033-z.
Mitchel, Jonathan, Teng Gao, Viktor Petukhov, Eli Cole, and Peter V Kharchenko. 2026. Impact and correction of segmentation errors in spatial transcriptomics.” Nature Genetics 58 (2): 434–44. https://doi.org/10.1038/s41588-025-02497-4.
Moffitt, Jeffrey R., Emma Lundberg, and Holger Heyn. 2022. “The Emerging Landscape of Spatial Profiling Technologies.” Nature Reviews Genetics 23: 741–59. https://doi.org/10.1038/s41576-022-00515-3.
Moses, Lambda, Pétur Helgi Einarsson, Kayla Jackson, et al. 2023. “Voyager: Exploratory Single-Cell Genomics Data Analysis with Geospatial Statistics.” bioRxiv, ahead of print. https://doi.org/10.1101/2023.07.20.549945.
Moses, Lambda, and Lior Pachter. 2022. “Museum of Spatial Transcriptomics.” Nature Methods 19: 534–46. https://doi.org/10.1038/s41592-022-01409-2.
Mund, Andreas, Andreas-David Brunner, and Matthias Mann. 2022. “Unbiased Spatial Proteomics with Single-Cell Resolution in Tissues.” Molecular Cell 82: 2335–49. https://doi.org/10.1016/j.molcel.2022.05.022.
Nguyen, Lan Huong, and Susan Holmes. 2019. Ten quick tips for effective dimensionality reduction.” PLoS Computational Biology 15 (6): 1006907. https://doi.org/10.1371/journal.pcbi.1006907.
Nitzan, Mor, Nikos Karaiskos, Nir Friedman, and Nikolaus Rajewsky. 2019. “Gene Expression Cartography.” Nature 576: 132–37. https://doi.org/10.1038/s41586-019-1773-3.
Novoselsky, Roy, Ofra Golani, Tal Barkai, et al. 2026. Subcellular mRNA localization patterns across tissues resolved with spatial transcriptomics.” Nature Communications, 1–13. https://doi.org/10.1038/s41467-026-72156-7.
Ospina, Oscar E, Christopher M Wilson, Alex C Soupir, et al. 2022. spatialGE: Quantification and Visualization of the Tumor Microenvironment Heterogeneity Using Spatial Transcriptomics.” Bioinformatics 38 (9): 2645–47. https://doi.org/10.1093/bioinformatics/btac145.
Oyler-Yaniv, Alon, Jennifer Oyler-Yaniv, Benjamin M Whitlock, et al. 2017. “A Tunable Diffusion-Consumption Mechanism of Cytokine Propagation Enables Plasticity in Cell-to-Cell Communication in the Immune System.” Immunity 46 (4): 609–20. https://doi.org/10.1016/j.immuni.2017.03.011.
Palla, Giovanni, David S. Fischer, Aviv Regev, and Fabian J. Theis. 2022. “Spatial Components of Molecular Tissue Biology.” Nature Biotechnology 40: 308–18. https://doi.org/10.1038/s41587-021-01182-1.
Palla, Giovanni, Hannah Spitzer, Michal Klein, et al. 2022. “Squidpy: A Scalable Framework for Spatial Omics Analysis.” Nature Methods 19: 171–78. https://doi.org/10.1038/s41592-021-01358-2.
Palomino, Tana V., and David C. Muddiman. 2025. “Mass Spectrometry Imaging of n-Linked Glycans: Fundamentals and Recent Advances.” Mass Spectrometry Reviews, 1–25. https://doi.org/10.1002/mas.21895.
Pandit, Vrushali, Asish Kumar Swain, and Pankaj Yadav. 2022. Comparison of Dimensionality Reduction and Clustering Methods for Single-Cell Transcriptomics Data.” bioRxiv, 2022.10.15.512334. https://doi.org/10.1101/2022.10.15.512334.
Pardo, Brenda, Abby Spangler, Lukas M. Weber, et al. 2022. “spatialLIBD: An r/Bioconductor Package to Visualize Spatially-Resolved Transcriptomics Data.” BMC Genomics 23 (434). https://doi.org/10.1186/s12864-022-08601-w.
Park, Jeongbin, Wonyl Choi, Sebastian Tiesmeyer, et al. 2021. “Cell Segmentation-Free Inference of Cell Types from in Situ Transcriptomics Data.” Nature Communications 12 (3545). https://doi.org/10.1038/s41467-021-23807-4.
Park, Jiwoon, Roberto De Gregorio, Erika Hissong, et al. 2025. The Spatial Atlas of Human Anatomy (SAHA): A multimodal subcellular-resolution reference across human organs.” bioRxiv, 2025.06.16.658716. https://doi.org/10.1101/2025.06.16.658716.
Paul, Indranil, Carl White, Isabella Turcinovic, and Andrew Emili. 2021. “Imaging the Future: The Emerging Era of Single-Cell Spatial Proteomics.” The FEBS Journal 288: 6990–7001. https://doi.org/10.1111/febs.15685.
Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439–46. https://doi.org/10.32614/rj-2018-009.
Pebesma, Edzer, and Roger Bivand. 2023. Spatial Data Science: With Applications in r. 1st Edition. Chapman; Hall/CRC. https://doi.org/10.1201/9780429459016.
Pebesma, Edzer, and Roger S Bivand. 2005. “Classes and Methods for Spatial Data: The Sp Package.” The Newsletter of the R Project 5 (2).
Peters Couto, Bárbara Zita, Nicholas Robertson, Ellis Patrick, and Shila Ghazanfar. 2023. “MoleculeExperiment Enables Consistent Infrastructure for Molecule-Resolved Spatial Omics Data in Bioconductor.” Bioinformatics 39 (btad550, 9). https://doi.org/10.1093/bioinformatics/btad550.
Petukhov, Viktor, Rosalind J. Xu, Ruslan A. Soldatov, et al. 2022. “Cell Segmentation in Imaging-Based Spatial Transcriptomics.” Nature Biotechnology 40: 345–54. https://doi.org/10.1038/s41587-021-01044-w.
Pham, Duy, Xiao Tan, Brad Balderson, et al. 2023. Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues.” Nature Communications 14 (1): 1–25. https://doi.org/10.1038/s41467-023-43120-6.
Polański, Krzysztof, Raquel Bartolomé-Casado, Ioannis Sarropoulos, et al. 2024. “Bin2cell Reconstructs Cells from High Resolution Visium HD Data.” Bioinformatics 40 (btae546, 9). https://doi.org/10.1093/bioinformatics/btae546.
Qiu, Xiaojie, Qi Mao, Ying Tang, et al. 2017. Reversed graph embedding resolves complex single-cell trajectories.” Nature Methods 14 (10): 979–82. https://doi.org/10.1038/nmeth.4402.
Rao, Anjali, Dalia Barkley, Gustavo S. França, and Itai Yanai. 2021. “Exploring Tissue Architecture Using Spatial Transcriptomics.” Nature 596: 211–20. https://doi.org/https://doi.org/10.1038/s41586-021-03634-9.
Rao, Ishir, Manolis Kellis, and Yosuke Tanigawa. 2025. sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer’s disease pathology.” bioRxiv, 2025.11.19.689166. https://doi.org/10.1101/2025.11.19.689166.
Rautenstrauch, Pia, and Uwe Ohler. 2025. Shortcomings of silhouette in single-cell integration benchmarking.” Nature Biotechnology, 1–5. https://doi.org/10.1038/s41587-025-02743-4.
Regev, Aviv, Sarah A Teichmann, Eric S Lander, et al. 2017. The Human Cell Atlas.” Elife 6: e27041. https://doi.org/10.7554/eLife.27041.
Ren, Honglei, Benjamin L Walker, Zixuan Cang, and Qing Nie. 2022. Identifying multicellular spatiotemporal organization of cells with SpaceFlow.” Nature Communications 13 (1): 4076. https://doi.org/10.1038/s41467-022-31739-w.
Righelli, Dario, Lukas M Weber, Helena L Crowell, et al. 2022. “SpatialExperiment: Infrastructure for Spatially-Resolved Transcriptomics Data in r Using Bioconductor.” Bioinformatics 38: 3128–31. https://doi.org/10.1093/bioinformatics/btac299.
Ripley, B. D. 1976. “The Second-Order Analysis of Stationary Point Processes.” Journal of Applied Probability 13: 255–66. https://doi.org/10.2307/3212829.
Rue-Albrecht, Kevin, Federico Marini, Charlotte Soneson, and Aaron T. L. Lun. 2018. “iSEE: Interactive SummarizedExperiment Explorer.” F1000Research 7 (741). https://doi.org/10.12688/f1000research.14966.1.
Saelens, Wouter, Robrecht Cannoodt, Helena Todorov, and Yvan Saeys. 2019. A comparison of single-cell trajectory inference methods.” Nature Biotechnology 37 (5): 547–54. https://doi.org/10.1038/s41587-019-0071-9.
Salim, Agus, Dharmesh D. Bhuva, Carissa Chen, et al. 2025. “SpaNorm: Spatially-Aware Normalization for Spatial Transcriptomics Data.” Genome Biology 26 (109). https://doi.org/10.1186/s13059-025-03565-y.
Sang-aram, Chananchida, Robin Browaeys, Ruth Seurinck, and Yvan Saeys. 2023. “Spotless, a Reproducible Pipeline for Benchmarking Cell Type Deconvolution in Spatial Transcriptomics.” eLife 12 (RP88431). https://doi.org/10.7554/eLife.88431.
Schmid, Katharina T, Aikaterini Symeonidi, Dmytro Hlushchenko, et al. 2025. Benchmarking scRNA-seq copy number variation callers.” Nature Communications 16 (1): 8777. https://doi.org/10.1038/s41467-025-62359-9.
Schott, Marie, Daniel León-Periñán, Elena Splendiani, et al. 2024. “Open-ST: High-Resolution Spatial Transcriptomics in 3D.” Cell 187: 3953–72. https://doi.org/10.1016/j.cell.2024.05.055.
Schott, Marie, Daniel León-Periñán, Elena Splendiani, et al. 2025. “Protocol for High-Resolution 3D Spatial Transcriptomics Using Open-ST.” STAR Protocols 6 (103521, 1). https://doi.org/10.1016/j.xpro.2024.103521.
Semba, Takashi, and Takatsugu Ishimoto. 2024. “Spatial Analysis by Current Multiplexed Imaging Technologies for the Molecular Characterisation of Cancer Tissues.” British Journal of Cancer 131: 1737–47. https://doi.org/10.1038/s41416-024-02882-6.
Shen, Xunan, Lulu Zuo, Zhongfei Ye, et al. 2025. Inferring cell trajectories of spatial transcriptomics via optimal transport analysis.” Cell Systems 16 (2): 101194. https://doi.org/10.1016/j.cels.2025.101194.
Singhal, Vipul, Nigel Chou, Joseph Lee, et al. 2024. “BANKSY Unifies Cell Typing and Tissue Domain Segmentation for Scalable Spatial Omics Data Analysis.” Nature Genetics 56: 431–41. https://doi.org/10.1038/s41588-024-01664-3.
Smith, Mike, and Hugo Gruson. 2023. “Rarr: Read Zarr Files in r.” R Package, ahead of print. https://doi.org/10.18129/B9.bioc.Rarr.
Sofroniew, Nicholas, Talley Lambert, Grzegorz Bokota, et al. 2025. “Napari: A Multi-Dimensional Image Viewer for Python.” Zenodo, ahead of print. https://doi.org/10.5281/zenodo.16883660.
Song, Qianqian, and Jing Su. 2021. DSTG: Deconvoluting Spatial Transcriptomics Data Through Graph-Based Artificial Intelligence.” Briefings in Bioinformatics 22 (5): bbaa414. https://doi.org/10.1093/bib/bbaa414.
Ståhl, Patrik L., Fredrik Salmén, Sanja Vickovic, et al. 2016. “Visualization and Analysis of Gene Expression in Tissue Sections by Spatial Transcriptomics.” Science 353: 78–82. https://doi.org/10.1126/science.aaf2403.
Stickels, Robert R., Evan Murray, Pawan Kumar, et al. 2021. “Highly Sensitive Spatial Transcriptomics at Near-Cellular Resolution with Slide-seqV2.” Nature Biotechnology 39: 313–19. https://doi.org/10.1038/s41587-020-0739-1.
Street, Kelly, Davide Risso, Russell B Fletcher, et al. 2018. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.” BMC Genomics 19 (1): 477. https://doi.org/10.1186/s12864-018-4772-0.
Stringer, Carsen, Tim Wang, Michalis Michaelos, and Marius Pachitariu. 2021. “Cellpose: A Generalist Algorithm for Cellular Segmentation.” Nature Methods 18: 100–106. https://doi.org/10.1038/s41592-020-01018-x.
Su, Shian, Lori Shepherd, Marcel Ramos, et al. 2025. “BiocPkgTools: Collection of Simple Tools for Learning about Bioconductor Packages.” R Package, ahead of print. https://doi.org/10.18129/B9.bioc.BiocPkgTools.
Subramanian, Aravind, Pablo Tamayo, Vamsi K Mootha, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences of the USA 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.
Sui, Xin, Jennifer A Lo, Shuchen Luo, et al. 2025. Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks.” Nature Methods 22 (12): 2574–84. https://doi.org/10.1038/s41592-025-02867-0.
Summers, Huw D, John W Wills, and Paul Rees. 2022. Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis.” Cell Reports Methods 2 (11): 100348. https://doi.org/10.1016/j.crmeth.2022.100348.
Sun, Dongqing, Zhaoyang Liu, Taiwen Li, Qiu Wu, and Chenfei Wang. 2022. STRIDE: Accurately Decomposing and Integrating Spatial Transcriptomics Using Single-Cell RNA Sequencing.” Nucleic Acids Research 50 (7): e42. https://doi.org/10.1093/nar/gkac150.
Sun, Jieran, Kirti Biharie, Peiying Cai, et al. 2025. Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.06.23.660861.
Sun, Shiquan, Jiaqiang Zhu, and Xiang Zhou. 2020. “Statistical Analysis of Spatial Expression Patterns for Spatially Resolved Transcriptomic Studies.” Nature Methods 17: 193–200. https://doi.org/10.1038/s41592-019-0701-7.
Svensson, Valentine, Sarah A Teichmann, and Oliver Stegle. 2018. “SpatialDE: Identification of Spatially Variable Genes.” Nature Methods 15: 343–46. https://doi.org/10.1038/nmeth.4636.
Szałata, Artur, Karin Hrovatin, Sören Becker, et al. 2024. Transformers in single-cell omics: a review and new perspectives.” Nature Methods 21 (8): 1430–43. https://doi.org/10.1038/s41592-024-02353-z.
Tabula Muris Consortium. 2020. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse.” Nature 583 (7817): 590–95. https://doi.org/10.1038/s41586-020-2496-1.
Tabula Muris Consortium, Overall coordination, Logistical coordination, et al. 2018. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris.” Nature 562 (7727): 367–72. https://doi.org/10.1038/s41586-018-0590-4.
Tabula Sapiens Consortium*, Robert C Jones, Jim Karkanias, et al. 2022. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.” Science (New York, N.Y.) 376 (6594): eabl4896. https://doi.org/10.1126/science.abl4896.
Tanevski, Jovan, Ricardo Omar Ramirez Flores, Attila Gabor, Denis Schapiro, and Julio Saez-Rodriguez. 2022. “Explainable Multiview Framework for Dissecting Spatial Relationships from Highly Multiplexed Data.” Genome Biology 23 (1): 97. https://doi.org/10.1186/s13059-022-02663-5.
Theodoris, Christina V, Ling Xiao, Anant Chopra, et al. 2023. Transfer learning enables predictions in network biology.” Nature 618 (7965): 616–24. https://doi.org/10.1038/s41586-023-06139-9.
Thul, Peter J, and Cecilia Lindskog. 2018. The human protein atlas: A spatial map of the human proteome: The Human Protein Atlas.” Protein Science 27 (1): 233–44. https://doi.org/10.1002/pro.3307.
Tian, Luyi, Fei Chen, and Evan Z. Macosko. 2023. “The Expanding Vistas of Spatial Transcriptomics.” Nature Biotechnology 41: 773–82. https://doi.org/10.1038/s41587-022-01448-2.
Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46: 234–40. https://doi.org/10.2307/143141.
Tomczak, Katarzyna, Patrycja Czerwińska, and Maciej Wiznerowicz. 2015. “The Cancer Genome Atlas (TCGA): An Immeasurable Source of Knowledge.” Contemporary Oncology/Współczesna Onkologia 19: A68–77. https://doi.org/10.5114/wo.2014.47136.
Tomer, Raju, Li Ye, Brian Hsueh, and Karl Deisseroth. 2014. Advanced CLARITY for rapid and high-resolution imaging of intact tissues.” Nature Protocols 9 (7): 1682–97. https://doi.org/10.1038/nprot.2014.123.
Totty, Michael, Stephanie C. Hicks, and Boyi Guo. 2025. “SpotSweeper: Spatially Aware Quality Control for Spatial Transcriptomics.” Nature Methods 22: 1520–30. https://doi.org/10.1038/s41592-025-02713-3.
Traag, V. A., L. Waltman, and N. J. van Eck. 2019. “From Louvain to Leiden: Guaranteeing Well-Connected Communities.” Scientific Reports 9 (5233). https://doi.org/10.1038/s41598-019-41695-z.
Tran, Hoa Thi Nhu, Kok Siong Ang, Marion Chevrier, et al. 2020. A benchmark of batch-effect correction methods for single-cell RNA sequencing data.” Genome Biology 21 (1): 12. https://doi.org/10.1186/s13059-019-1850-9.
Trapnell, Cole, Davide Cacchiarelli, Jonna Grimsby, et al. 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.” Nature Biotechnology 32 (4): 381–86. https://doi.org/10.1038/nbt.2859.
Ushey, Kevin, Joseph J. Allaire, and Yuan Tang. 2017. “Reticulate: Interface to ’Python’.” R Package, ahead of print. https://doi.org/10.32614/CRAN.package.reticulate.
Vallejos, Catalina A, Davide Risso, Antonio Scialdone, Sandrine Dudoit, and John C Marioni. 2017. Normalizing single-cell RNA sequencing data: challenges and opportunities.” Nature Methods 14 (6): 565–71. https://doi.org/10.1038/nmeth.4292.
Vandereyken, Katy, Alejandro Sifrim, Bernard Thienpont, and Thierry Voet. 2023. Methods and applications for single-cell and spatial multi-omics.” Nature Reviews Genetics 24 (8): 494–515. https://doi.org/10.1038/s41576-023-00580-2.
Varrone, Marco, Daniele Tavernari, Albert Santamaria-Martínez, Logan A. Walsh, and Giovanni Ciriello. 2024. “CellCharter Reveals Spatial Cell Niches Associated with Tissue Remodeling and Cell Plasticity.” Nature Genetics 56: 74–84. https://doi.org/10.1038/s41588-023-01588-4.
Velten, Britta, Jana M Braunger, Ricard Argelaguet, et al. 2022. Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.” Nature Methods, 1–8. https://doi.org/10.1038/s41592-021-01343-9.
Vickovic, Sanja, Denis Schapiro, Konstantin Carlberg, et al. 2022. “Three-Dimensional Spatial Transcriptomics Uncovers Cell Type Localizations in the Human Rheumatoid Arthritis Synovium.” Communications Biology 5 (129). https://doi.org/10.1038/s42003-022-03050-3.
Virshup, Isaac, Sergei Rybakov, Fabian J Theis, Philipp Angerer, and F Alexander Wolf. 2024. “Anndata: Access and Store Annotated Data Matrices.” Journal of Open Source Software 9 (101): 4371. https://doi.org/10.21105/joss.04371.
Vu, Tam, Alexander Vallmitjana, Joshua Gu, et al. 2022. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis.” Nature Communications 13 (1): 169. https://doi.org/10.1038/s41467-021-27798-0.
Walter, Florin C, Oliver Stegle, and Britta Velten. 2023. “FISHFactor: A Probabilistic Factor Model for Spatial Transcriptomics Data with Subcellular Resolution.” Bioinformatics 39 (5). https://doi.org/10.1093/bioinformatics/btad183.
Wang, Xiao, William E Allen, Matthew A Wright, et al. 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states.” Science 361 (6400): eaat5691. https://doi.org/10.1126/science.aat5691.
Wang, Ziyi, Aoyun Geng, Hao Duan, Feifei Cui, Quan Zou, and Zilong Zhang. 2024. “A Comprehensive Review of Approaches for Spatial Domain Recognition of Spatial Transcriptomes.” Briefings in Functional Genomics 23: 702–12. https://doi.org/10.1093/bfgp/elae040.
Weber, Lukas M., Arkajyoti Saha, Abhirup Datta, Kasper D. Hansen, and Stephanie C. Hicks. 2023. “nnSVG for the Scalable Identification of Spatially Variable Genes Using Nearest-Neighbor Gaussian Processes.” Nature Communications 14 (4059). https://doi.org/10.1038/s41467-023-39748-z.
Wei, Xiaoyu, Sulei Fu, Hanbo Li, et al. 2022. “Single-Cell Stereo-Seq Reveals Induced Progenitor Cells Involved in Axolotl Brain Regeneration.” Science 377. https://doi.org/10.1126/science.abp9444.
Weigert, Martin, and Uwe Schmidt. 2022. “Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist.” The IEEE International Symposium on Biomedical Imaging Challenges (ISBIC). https://doi.org/10.1109/ISBIC56247.2022.9854534.
Weitz, Philippe, Masi Valkonen, Leslie Solorzano, et al. 2024. “The ACROBAT 2022 Challenge: Automatic Registration of Breast Cancer Tissue.” Medical Image Analysis 97: 103257.
Welch, Joshua D., Velina Kozareva, Ashley Ferreira, Charles Vanderburg, Carly Martin, and Evan Z. Macosko. 2019. “Single-Cell Multi-Omic Integration Compares and Contrasts Features of Brain Cell Identity.” Cell 177: 1873–87. https://doi.org/10.1016/j.cell.2019.05.006.
Windhager, Jonas, Vito Riccardo Tomaso Zanotelli, Daniel Schulz, et al. 2023. “An End-to-End Workflow for Multiplexed Image Processing and Analysis.” Nature Protocols 18: 3565–613. https://doi.org/10.1038/s41596-023-00881-0.
Wolf, F. Alexander, Philipp Angerer, and Fabian J. Theis. 2018. “SCANPY: Large-Scale Single-Cell Gene Expression Data Analysis.” Genome Biology 19 (15). https://doi.org/10.1186/s13059-017-1382-0.
Wu, Lidan, Joseph M. Beechem, and Patrick Danaher. 2024. “FastReseg: Using Transcript Locations to Refine Image-Based Cell Segmentation Results in Spatial Transcriptomics.” bioRxiv, ahead of print. https://doi.org/10.1101/2024.12.05.627051.
Xia, Chen-Rui, Zhi-Jie Cao, Xin-Ming Tu, and Ge Gao. 2023. “Spatial-Linked Alignment Tool (SLAT) for Aligning Heterogenous Slices.” Nature Communications 14 (7236). https://doi.org/10.1038/s41467-023-43105-5.
Xiang, Ruizhi, Wencan Wang, Lei Yang, Shiyuan Wang, Chaohan Xu, and Xiaowen Chen. 2021. A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data.” Frontiers in Genetics 12. https://doi.org/10.3389/fgene.2021.646936.
Xiong, Caiwei, Huang Shuai, Muqing Zhou, et al. 2025. A comprehensive comparison on clustering methods for multi-slide spatially resolved transcriptomics data analysis.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.01.19.633631.
Xu, Chang, Xiyun Jin, Songren Wei, et al. 2022. DeepST: identifying spatial domains in spatial transcriptomics by deep learning.” Nucleic Acids Research 50 (22): e131. https://doi.org/10.1093/nar/gkac901.
Xu, Chenling, Romain Lopez, Edouard Mehlman, Jeffrey Regier, Michael I Jordan, and Nir Yosef. 2021. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models.” Molecular Systems Biology 17 (1): e9620. https://doi.org/10.15252/msb.20209620.
Xu, Hanwen, Naoto Usuyama, Jaspreet Bagga, et al. 2024. “A Whole-Slide Foundation Model for Digital Pathology from Real-World Data.” Nature 630 (8015): 181–88. https://doi.org/10.1038/s41586-024-07441-w.
Xu, Yang, Callum J. Sargeant, Yue You, et al. 2025. “stPipe: A Flexible and Streamlined r/Bioconductor Pipeline for Preprocessing Sequencing-Based Spatial Transcriptomics Data.” bioRxiv, ahead of print. https://doi.org/10.1101/2025.04.16.649254.
Yan, Guanao, Shuo Harper Hua, and Jingyi Jessica Li. 2024. “Categorization of 33 Computational Methods to Detect Spatially Variable Genes from Spatially Resolved Transcriptomics Data.” arXiv, ahead of print. https://doi.org/10.48550/arXiv.2405.18779.
Yan, Yunzhi, Tianyi Gu, Chengcheng Sun, et al. 2026. “Benchmarking Alignment Methods for Spatial Transcriptomics Data.” Nature Computational Science 6 (5): 524–41. https://doi.org/10.1038/s43588-026-00977-z.
Yu, Jinge, and Xiangyu Luo. 2022. “Identification of Cell-Type-Specific Spatially Variable Genes Accounting for Excess Zeros.” Bioinformatics 38: 4135–44. https://doi.org/10.1093/bioinformatics/btac457.
Yu, Shan, and Wei Vivian Li. 2024. “spVC for the Detection and Interpretation of Spatial Gene Expression Variation.” Genome Biology 25 (103). https://doi.org/10.1186/s13059-024-03245-3.
Zappia, Luke, Aaron Lun, Jack Kamm, Robrecht Cannoodt, Gabriel Hoffman, and Marek Cmero. 2020. “anndataR Improves Interoperability Between r and Python in Single-Cell Transcriptomics.” R Package, ahead of print. https://doi.org/10.18129/B9.bioc.zellkonverter.
Zeira, Ron, Max Land, Alexander Strzalkowski, and Benjamin J. Raphael. 2022. “Alignment and Integration of Spatial Transcriptomics Data.” Nature Methods 19: 567–75. https://doi.org/10.1038/s41592-022-01459-6.
Zhang, Allen W, Ciara O’Flanagan, Elizabeth A Chavez, et al. 2019. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling.” Nature Methods 16 (10): 1007–15. https://doi.org/10.1038/s41592-019-0529-1.
Zhang, Di, Yanxiang Deng, Petra Kukanja, et al. 2023. Spatial epigenome–transcriptome co-profiling of mammalian tissues.” Nature 616: 113–22. https://doi.org/10.1038/s41586-023-05795-1.
Zhang, Hua, Kelly H. Lu, Malik Ebbini, Penghsuan Huang, Haiyan Lu, and Lingjun Li. 2024. “Mass Spectrometry Imaging for Spatially Resolved Multi-Omics Molecular Mapping.” Npj Imaging 2 (20). https://doi.org/10.1038/s44303-024-00025-3.
Zhang, Qihuang, Shunzhou Jiang, Amelia Schroeder, et al. 2023. “Leveraging Spatial Transcriptomics Data to Recover Cell Locations in Single-Cell RNA-Seq with CeLEry.” Nature Communications 14 (4050). https://doi.org/10.1038/s41467-023-39895-3.
Zhang, Yida, Viktor Petukhov, Evan Biederstedt, Richard Que, Kun Zhang, and Peter V. Kharchenko. 2024. “Gene Panel Selection for Targeted Spatial Transcriptomics.” Genome Biology 25 (35). https://doi.org/10.1186/s13059-024-03174-1.
Zhao, Edward, Matthew R. Stone, Xing Ren, et al. 2021. “Spatial Transcriptomics at Subspot Resolution with BayesSpace.” Nature Biotechnology 39: 1375–84. https://doi.org/10.1038/s41587-021-00935-2.
Zhu, Jiaqiang, Shiquan Sun, and Xiang Zhou. 2021. “SPARK-x: Non-Parametric Modeling Enables Scalable and Robust Detection of Spatial Expression Patterns for Large Spatial Transcriptomic Studies.” Genome Biology 22 (184). https://doi.org/10.1186/s13059-021-02404-0.
Back to top