SpaFlow

SpaFlow performs quality control, unsupervised clustering, and classification of multiplex immunofluorescence (MxIF) datasets through a Nextflow pipeline that integrates Seurat, SCIMAP, and CELESTA to enable reproducible and scalable identification of cell populations based on phenotypic markers.

Key Features:

• Quality Control: Robust quality control steps include signal summation and cell density filtering to mitigate artifacts that may impact clustering accuracy.
• Multi-package Integration: Integrates three clustering and classification packages-Seurat, SCIMAP, and CELESTA-each providing unique methodologies for identifying cell types based on phenotypic markers.
• Meta-clustering: A novel meta-clustering approach condenses clusters across multiple regions of interest into common meta-clusters, streamlining cell-type identification in large datasets.
• Post-clustering Analysis: Performs post-clustering analysis on segmented and quantified MxIF data.
• Scalable Implementation: Implemented using Nextflow to facilitate reproducible and scalable analyses across various computing platforms.

Scientific Applications:

• Tumor Microenvironment Analysis: Identification of biologically meaningful cell populations in ovarian tumor cores, including tumor-infiltrating lymphocytes.
• Reproducibility Validation: Consistent identification of distinctive cell populations across matched regions of interest in serial tonsil sections.
• Cell Phenotyping Standardization: Addresses challenges in standardizing cell phenotyping arising from flexibility in MxIF panel design.

Methodology:

SpaFlow is implemented using Nextflow and performs quality control, clustering, and post-clustering analysis on segmented and quantified MxIF data. The pipeline integrates Seurat, SCIMAP, and CELESTA for clustering and classification. Quality control steps include signal summation and cell density filtering. A meta-clustering approach condenses clusters across multiple regions of interest into common meta-clusters.