ASAP

ASAP performs integrated analysis of single-cell RNA-sequencing (scRNA-seq) count data post-genome alignment to identify cell populations, differential gene expression, and enriched functional gene sets from whole-transcriptome profiles.

Key Features:

• Integrated Workflow: Parsing, filtering, and normalization of input count data files post-genome alignment to prepare datasets for downstream analyses.
• Visualization: Advanced visualization capabilities for graphical representation of complex scRNA-seq datasets and gene expression patterns.
• Cell Clustering: Identification of cell clusters within scRNA-seq datasets to delineate distinct cell populations.
• Differential Expression Analysis: Detection of genes significantly up- or down-regulated between cell types or conditions, including cluster-specific marker gene identification.
• Functional Gene Set Enrichment: Analysis of enriched biological pathways and processes associated with specific cell clusters.
• Applicability to RNA-seq: Methodologies that overlap with bulk RNA-seq workflows, making the approach conceptually applicable to various RNA-seq datasets.
• Validation and Reproducibility: Reproduction of results from a single-cell study of 91 mouse cells across five distinct cell types demonstrates reliability.

Scientific Applications:

• Cellular heterogeneity analysis: Dissecting cellular diversity and identifying distinct cell populations from scRNA-seq data.
• Disease mechanism investigation: Characterizing cell-type-specific expression changes relevant to disease at single-cell resolution.
• Biomarker discovery: Identifying cluster-specific marker genes for use as potential biomarkers.
• Domain-specific studies: Application to developmental biology, immunology, and cancer research using whole-transcriptome single-cell profiles.

Methodology:

Parsing, filtering, and normalization of count data files post-genome alignment; cell clustering; differential expression analysis including cluster-specific marker identification; functional gene set enrichment; integration of commonly used algorithms within a cohesive framework.