Seqtometry

Seqtometry analyzes single-cell transcriptomic and epigenomic data by computing signature (gene set) enrichment scores across biologically relevant dimensions, bypassing unsupervised clustering to enable direct qualitative and quantitative cell-type identification and biological process characterization.

Key Features:

• Signature enrichment scoring: Computes enrichment scores using multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems.
• Flow cytometry-like workflow: Uses scores as standalone features for progressive partitioning of cells without cluster-based analysis.
• Cluster-based workflow compatibility: Generates scores as features of clusters for conventional single-cell analysis approaches.
• MAGIC imputation: Exports an imputation function ported from Python's MAGIC-impute (van Dijk et al. 2018) for single-cell data recovery.

Scientific Applications:

• Cell annotation and characterization: Annotates or characterizes cells in scRNA-seq or scATAC-seq datasets using signature-based scores.
• Immune cell analysis: Enables comprehensive analysis of various immune cell types across different biological contexts.
• Disease state investigation: Characterizes diverse biological processes under experimental or disease conditions including multiple myeloma and Alzheimer's disease.
• Cross-organ system analysis: Examines gene expression and chromatin accessibility across various organ systems and cell types.

Methodology:

Seqtometry computes signature (gene set) enrichment scores for single-cell data analysis, with an exported imputation function ported from Python's MAGIC-impute (van Dijk et al. 2018).