scBio

scBio implements the Cell Population Mapping (CPM) deconvolution algorithm as an R package to infer cell-type and cell-state composition of bulk transcriptome datasets using reference single-cell RNA sequencing (scRNA-seq) profiles.

Key Features:

• Deconvolution Algorithm: Cell Population Mapping (CPM) maps individual cell types and states to deconvolve complex tissue samples and infer composition from bulk transcriptomes.
• Integration with Bulk Data: Uses reference scRNA-seq profiles to interpret bulk transcriptome datasets and attribute signals to specific cell populations and states.
• Continuous Cell-State Reconstruction: Reconstructs the continuous spectrum of cell states to represent gradual changes along activation or differentiation axes.
• Mathematical Modeling of Clinical Associations: Applies mathematical modeling to relate cell-state dynamics and abundance to clinical outcomes.

Scientific Applications:

• Study of Complex Tissues: Enables exploration of cellular heterogeneity in complex tissues such as the lung by linking cell abundance and activation states to biological phenomena.
• Disease Research: Facilitates analysis of cell-state dynamics in disease contexts, exemplified by studies in influenza-virus-infected mice correlating cell-state changes with disease progression and symptomatology.

Methodology:

The CPM deconvolution algorithm uses reference scRNA-seq profiles to infer cell-type and cell-state composition from bulk transcriptomes, reconstructs a continuous spectrum of cell states, and employs mathematical modeling to elucidate relationships between cell-state dynamics and clinical outcomes.