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Updated: Aug 6, 2025
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
18.6K
A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data.
Oscar Ospina1, Alex Soupir1, Brooke L Fridley2
1Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
Summary
Spatially resolved transcriptomics (ST) reveals tissue architecture and cell interactions. This review covers ST data analysis algorithms and highlights challenges for barcode-based RNA quantitation techniques.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Spatially resolved transcriptomics (ST) enables detailed tissue architecture and cell-type interaction studies.
- ST holds potential for identifying novel drug targets and understanding complex disease etiology.
Purpose of the Study:
- To review existing algorithms for analyzing ST data.
- To highlight unmet analytical challenges in ST data processing and discovery.
Main Methods:
- Focus on barcode-based RNA quantitation techniques within ST.
- Review of algorithms for quality control, preprocessing, visualization, clustering, and cell phenotyping.
Main Results:
- Identified various algorithms applicable to ST data analysis.
- Highlighted specific analytical challenges requiring further algorithmic development.
Conclusions:
- The analysis of ST data requires specialized algorithms due to unique spatial information.
- Further development is needed to fully leverage ST for biological discovery, particularly for barcode-based methods.
