singleCellTK

singleCellTK provides analysis workflows for single-cell RNA-sequencing (scRNA-Seq) data to characterize cell-level transcriptional heterogeneity.

Key Features:

• scRNA-Seq support: Processes single-cell RNA-sequencing (scRNA-Seq) data to analyze transcriptional activity at the individual cell level.
• Differential expression analysis: Performs differential expression analysis to identify genes differentially expressed across cell populations or conditions.
• Downsampling analysis: Performs downsampling analysis to assess how data sampling affects results and conclusions.
• Clustering: Performs clustering to group cells with similar transcriptional profiles and reveal potential cell types or states.
• Study variability handling: Addresses variability arising from study design and data generation processes.

Scientific Applications:

• Developmental biology: Investigates cellular diversity and lineage relationships during development using scRNA-Seq data.
• Cancer research: Characterizes tumor heterogeneity and cell states in cancer studies.
• Immunology: Profiles immune cell populations and functional states in immunological studies.
• Cellular heterogeneity studies: Enables high-resolution characterization of cellular diversity and function in single-cell studies.

Methodology:

Computational methods explicitly include differential expression analysis, downsampling analysis, and clustering applied to scRNA-Seq data to analyze transcriptional activity at the individual cell level.