exFINDER

exFINDER identifies external signals activating specific target genes in single-cell transcriptomics (scRNA-seq) data and constructs external signal–target networks (exSigNet) to quantify how external signaling influences cellular responses.

Key Features:

• Identification of external signals: Detects signals originating outside the measured cell population by evaluating activation of target genes in scRNA-seq data.
• Construction of exSigNet: Infers an external signal–target signaling network (exSigNet) that maps external signals to their corresponding target genes.
• Quantitative analysis of exSigNets: Performs quantitative assessments on inferred exSigNets to evaluate the strength and significance of external interactions.
• Robustness across species: Applies to scRNA-seq datasets from multiple species to identify signaling activities related to cellular transitions.
• Clustering and evaluation of signal-target paths: Clusters signal–target paths to identify candidate signal-sending cells and evaluates associated biological events.

Scientific Applications:

• Revealing external influences on cell decisions: Identifies external signals that influence cellular decision-making processes in single-cell transcriptomics datasets.
• Mapping signal transduction to targets: Links external signaling inputs to specific target gene programs via the exSigNet framework.
• Comparative analysis across species: Enables detection of conserved or species-specific external signaling activities across scRNA-seq datasets.
• Identification of candidate signal-sending cell types: Uses clustering of signal–target paths to nominate novel cells potentially responsible for emitting external signals.

Methodology:

Utilizes prior knowledge of signaling pathways to detect external signals that activate target genes in scRNA-seq data, infers an external signal–target network called exSigNet, performs quantitative analyses on exSigNets, and clusters signal–target paths to evaluate associated biological events.