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stLearn - A downstream analysis toolkit for Spatial Transcriptomic data

stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.

Getting Started

Citing stLearn

If you have used stLearn in your research, please consider citing us:

Pham, Duy, et al. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues." Nature Communications 14.1 (2023): 7739. https://doi.org/10.1101/2020.05.31.125658

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GitHub - BiomedicalMachineLearning/stLearn: A novel machine learning pipeline to analyse spatial transcriptomics data · GitHub
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stLearn - A downstream analysis toolkit for Spatial Transcriptomic data
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
Getting Started
Citing stLearn
If you have used stLearn in your research, please consider citing us:
Pham, Duy, et al. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues."
Nature Communications 14.1 (2023): 7739.
https://doi.org/10.1101/2020.05.31.125658
About
A novel machine learning pipeline to analyse spatial transcriptomics data 
Resources
Readme
License
Contributing
Contributing
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Stars
257 stars
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5 watching
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31 forks
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GitHub - BiomedicalMachineLearning/stLearn: A novel machine learning pipeline to analyse spatial transcriptomics data · GitHub
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stLearn - A downstream analysis toolkit for Spatial Transcriptomic data
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
Getting Started
Citing stLearn
If you have used stLearn in your research, please consider citing us:
Pham, Duy, et al. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues."
Nature Communications 14.1 (2023): 7739.
https://doi.org/10.1101/2020.05.31.125658
About
A novel machine learning pipeline to analyse spatial transcriptomics data 
Resources
Readme
License
Contributing
Contributing
Activity
Stars
257 stars
Watchers
5 watching
Forks
31 forks
Report repository
Releases
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Used by
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GitHub - BiomedicalMachineLearning/stLearn: A novel machine learning pipeline to analyse spatial transcriptomics data · GitHub
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stLearn - A downstream analysis toolkit for Spatial Transcriptomic data
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
Getting Started
Citing stLearn
If you have used stLearn in your research, please consider citing us:
Pham, Duy, et al. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues."
Nature Communications 14.1 (2023): 7739.
https://doi.org/10.1101/2020.05.31.125658
About
A novel machine learning pipeline to analyse spatial transcriptomics data 
Resources
Readme
License
Contributing
Contributing
Activity
Stars
257 stars
Watchers
5 watching
Forks
31 forks
Report repository
Releases
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Used by
Contributors
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GitHub - BiomedicalMachineLearning/stLearn: A novel machine learning pipeline to analyse spatial transcriptomics data · GitHub
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PackagePyPI VersionPyPI downloads
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stLearn - A downstream analysis toolkit for Spatial Transcriptomic data
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
Getting Started
Citing stLearn
If you have used stLearn in your research, please consider citing us:
Pham, Duy, et al. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues."
Nature Communications 14.1 (2023): 7739.
https://doi.org/10.1101/2020.05.31.125658
About
A novel machine learning pipeline to analyse spatial transcriptomics data 
Resources
Readme
License
Contributing
Contributing
Activity
Stars
257 stars
Watchers
5 watching
Forks
31 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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GitHub - BiomedicalMachineLearning/stLearn: A novel machine learning pipeline to analyse spatial transcriptomics data · GitHub
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PackagePyPI VersionPyPI downloads
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stLearn - A downstream analysis toolkit for Spatial Transcriptomic data
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
Getting Started
Citing stLearn
If you have used stLearn in your research, please consider citing us:
Pham, Duy, et al. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues."
Nature Communications 14.1 (2023): 7739.
https://doi.org/10.1101/2020.05.31.125658
About
A novel machine learning pipeline to analyse spatial transcriptomics data 
Resources
Readme
License
Contributing
Contributing
Activity
Stars
257 stars
Watchers
5 watching
Forks
31 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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GitHub - BiomedicalMachineLearning/stLearn: A novel machine learning pipeline to analyse spatial transcriptomics data · GitHub
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PackagePyPI VersionPyPI downloads
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stLearn - A downstream analysis toolkit for Spatial Transcriptomic data
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
Getting Started
Citing stLearn
If you have used stLearn in your research, please consider citing us:
Pham, Duy, et al. "Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues."
Nature Communications 14.1 (2023): 7739.
https://doi.org/10.1101/2020.05.31.125658
About
A novel machine learning pipeline to analyse spatial transcriptomics data 
Resources
Readme
License
Contributing
Contributing
Activity
Stars
257 stars
Watchers
5 watching
Forks
31 forks
Report repository
Releases
Packages
Used by
Contributors
Languages