gExcite

gExcite integrates gene expression, antibody profiling, and hashing deconvolution to perform multimodal single-cell analysis of transcriptomes and surface proteins (CITE-seq) for studying cellular heterogeneity and immune profiling.

Key Features:

• Multimodal analysis: Integrates gene expression and antibody-based surface protein profiling to analyze transcriptomic and proteomic signals from the same cells, including CITE-seq data.
• Gene expression quantification: Performs quantification of single-cell gene expression for downstream transcriptomic analyses.
• Antibody-based protein detection: Detects and processes antibody-derived tags for surface protein profiling at single-cell resolution.
• Hashing deconvolution: Implements hashing deconvolution to assign cells to samples in multiplexed experiments.
• Scalability: Designed to process large datasets and multiple samples efficiently.
• Reproducibility and automation: Encapsulates workflows within the Snakemake workflow manager to ensure reproducible, automated execution.
• Start-to-end preprocessing: Provides end-to-end preprocessing steps to prepare single-cell data for downstream analyses.

Scientific Applications:

• PBMC dissociation protocol comparisons: Enables simultaneous analysis of gene and protein expression to compare effects of different dissociation protocols on peripheral blood mononuclear cells (PBMCs).
• Immune cell profiling: Supports profiling of immune cell populations by combining transcriptomic and surface protein information.
• Oncology research: Facilitates investigation of tumor immune microenvironments by integrating gene and protein expression at single-cell level.
• Cellular heterogeneity analyses: Allows characterization of cellular heterogeneity by jointly analyzing transcriptomic and proteomic modalities.

Methodology:

Start-to-end framework with preprocessing of single-cell data, gene expression quantification, antibody-derived tag (protein) detection, hashing deconvolution for sample assignment, and workflow management via Snakemake.