TissueSpace

TissueSpace converts transcriptome profiles into rank-based representations and applies Latent Semantic Analysis to produce 100-dimensional vectors that enable cross-platform comparability and downstream analysis of human transcriptome data.

Key Features:

• Rank Vector Transformation: Transforms gene expression profiles into rank vectors that preserve relative gene ordering and mitigate platform-specific biases.
• Latent Semantic Analysis (LSA): Applies LSA to rank vectors to generate compact, continuous 100-dimensional vector representations for each sample.
• 100-dimensional Vector Projection: Projects any human transcriptome profile into the 100-dimensional vector space for comparative analyses across datasets.
• Gene ID Conversion to Ensembl: Converts various gene ID types to Ensembl gene IDs to ensure identifier consistency.
• Functional Enrichment Analysis of Vector Features: Performs functional enrichment analyses for features within the 100-dimensional vectors to support biological interpretation.
• Tissue Label Recovery Precision: Demonstrated 96.7% precision in recovering tissue labels from an independent dataset using reconstructed vector representations.

Scientific Applications:

• Cross-platform comparative transcriptomics: Enables direct comparison of transcriptome data across different platforms and experiments using platform-independent vector representations.
• Tissue classification and annotation: Supports recovery and annotation of tissue labels from transcriptome profiles with high precision (96.7%).
• Molecular medicine and translational research: Facilitates hypothesis generation, biomarker discovery, and exploration of gene expression patterns across tissues and conditions in studies of human common diseases.

Methodology:

Rank-based transformation of raw transcriptome data into rank vectors followed by Latent Semantic Analysis (LSA) to produce 100-dimensional continuous sample vectors; additional explicitly stated steps include conversion of gene IDs to Ensembl, projection of profiles into the vector space, and functional enrichment analysis of vector features.