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pmid:37846051

Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization.

Ludvig Larsson · Lovisa Franzén · Patrik L Ståhl · Joakim Lundeberg

Bioinformatics (Oxford, England) · 2023 doi:10.1093/bioinformatics/btad626

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization.
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Ludvig Larsson
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Lovisa Franzén
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Patrik L Ståhl
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Joakim Lundeberg
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Bioinformatics (Oxford, England)
year
2023
abstract
SUMMARY: Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation. AVAILABILITY AND IMPLEMENTATION: The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.
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Semla includes an approach for cell type deconvolution based on Non-Negative Least Squares (NNLS), which runs in a matter of seconds on Visium data with up to 100 000 spots (Fig. 1C, Supplementary Section S3.2).
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Semla includes an approach for cell type deconvolution based on Non-Negative Least Squares (NNLS), which runs in a matter of seconds on Visium data with up to 100 000 spots (Fig. 1C, Supplementary Section S3.2).
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