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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
Published on: July 6, 2022
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TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine
1Frank G. Zarb School of Business, Hofstra University, 11549, Hempstead, NY, USA.
Genes & Genomics
|May 5, 2022
Summary
This study introduces TissueSpace, a novel tool that transforms gene expression data into a standardized vector format. This enables direct comparison across different experiments and platforms, improving transcriptome data analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Comparing transcriptome data across different platforms and experiments is challenging due to variations in gene expression values.
- Existing methods often fail to leverage the inherent gene expression ranking information.
Purpose of the Study:
- To develop a platform-independent method for representing transcriptome data.
- To enable direct comparison of gene expression profiles from diverse sources.
Main Methods:
- Transcriptome expression profiles were converted into rank vectors.
- Latent semantic analysis (LSA) was applied to generate 100-dimensional vector representations for samples.
- A user-friendly tool, TissueSpace, was developed to facilitate these analyses.
Main Results:
- The reconstructed vectors achieved 96.7% precision in identifying tissue labels from independent datasets.
- TissueSpace offers functionalities for gene ID conversion, vector projection, and functional enrichment analysis.
- Case studies demonstrated the tool's utility in analyzing human common diseases.
Conclusions:
- TissueSpace provides a robust method for cross-platform transcriptome data analysis.
- The tool can generate testable hypotheses for translational medicine research.
- TissueSpace is accessible online for public use.
