Multi-slice spatial transcriptome domain analysis with SpaDo

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Abstract

With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo’s effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo’s potential to reveal novel biological insights in multi-slice spatial transcriptomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-024-03213-x.

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Most cited references 47

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Basic local alignment search tool.

Stephen F Altschul, Warren Gish, Webb Miller … (1990)

A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Fast, sensitive, and accurate integration of single cell data with Harmony

Ilya Korsunsky, Nghia Millard, Jean Fan … (2019)

The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.

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Author and article information

Contributors

Bin Duan: bioinfo_db@tongji.edu.cn

Qi Liu:

ORCID: http://orcid.org/0000-0003-2578-1221

qiliu@tongji.edu.cn

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 19 March 2024

Publication date PMC-release: 19 March 2024

Publication date Collection: 2024

Volume: 25

Electronic Location Identifier: 73

Affiliations

[1 ]GRID grid.24516.34, ISNI 0000000123704535, State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, , Tongji University, ; Shanghai, 200092 China

[2 ]GRID grid.24516.34, ISNI 0000000123704535, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, , Tongji University, ; Shanghai, 200092 China

[3 ]Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804 China

[4 ]Research Institute of Intelligent Computing, Zhejiang Lab, ( https://ror.org/02m2h7991) Hangzhou, 311121 China

Author information

Qi Liu http://orcid.org/0000-0003-2578-1221

Article

Publisher ID: 3213

DOI: 10.1186/s13059-024-03213-x

PMC ID: 10949687

PubMed ID: 38504325

SO-VID: ce33f867-42db-4e56-967a-6b5f660f26ac

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 14 August 2023

Date accepted : 8 March 2024

Funding

Funded by: National Key Research and Development Program of China

Award ID: Grant No. 2021YFF1201200

Award ID: No. 2021YFF1200900

Award ID: 2021YFF1201200

Award ID: 2021YFF1200900

Award Recipient : Qi Liu

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: Grant No. 31970638

Award ID: 61572361

Award ID: 31970638

Award ID: 61572361

Award ID: 62302336

Award Recipient : Bin Duan Qi Liu

Funded by: FundRef http://dx.doi.org/10.13039/501100013105, Shanghai Rising-Star Program;

Award ID: 23YF1450200

Award Recipient : Bin Duan

Funded by: FundRef http://dx.doi.org/10.13039/501100002858, China Postdoctoral Science Foundation;

Award ID: 2022M722418

Award ID: 2023T160485

Award Recipient : Bin Duan

Funded by: Shanghai Post-doctoral Excellence Program

Award ID: 2022529

Award Recipient : Bin Duan

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: spatial transcriptomics,multiple slice analysis,spatial domain detection

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ScienceOpen disciplines: Genetics

Keywords: spatial transcriptomics, multiple slice analysis, spatial domain detection

Multi-slice spatial transcriptome domain analysis with SpaDo

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Supplementary Information

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Genome Integrity

Most cited references 47

Basic local alignment search tool.

Comprehensive Integration of Single-Cell Data

Fast, sensitive, and accurate integration of single cell data with Harmony

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