TissueEnrich

TissueEnrich identifies enrichment of tissue-specific genes within a given gene set to assign tissue identities to samples or clusters by comparison to curated reference tissue-specific gene expression datasets.

Key Features:

• Tissue-specific enrichment detection: Identifies enrichment of tissue-specific genes within input gene sets.
• Curated reference datasets: Leverages curated reference tissue-specific gene expression datasets.
• Compatibility with RNA-Seq workflows: Complements standard functional enrichment analyses commonly used in RNA-Seq workflows.
• Supports differential expression and co-expression inputs: Accepts gene lists derived from differential expression analysis or co-expression networks.
• Single-cell and stem cell applicability: Can be used to assign tissue identities to single-cell populations and differentiated embryonic stem cells.
• Sample and cluster annotation: Maps gene expression signatures to tissue-of-origin or differentiation states for biological samples or clusters.

Scientific Applications:

• Tissue identity assignment: Assigns tissue identities to biological samples or clusters based on tissue-specific gene enrichment.
• Interpretation of differential expression: Determines whether differentially expressed genes show preferential expression in particular tissue types.
• Analysis of co-expression modules: Evaluates tissue specificity of co-expression network modules.
• Single-cell annotation and differentiation state inference: Infers tissue identity or differentiation states for single-cell populations and differentiated embryonic stem cells.
• Complementary enrichment analysis: Supplements standard functional enrichment analyses in RNA-Seq studies to provide tissue-specific context.

Methodology:

Compares input gene sets to curated reference tissue-specific gene expression datasets to identify enrichment of tissue-specific genes.