monocle

monocle infers cellular trajectories and analyzes single-cell RNA-seq data to order cells in pseudotime, detect differentially expressed genes, and resolve branching fate decisions.

Key Features:

• Census Algorithm Integration: Transforms relative RNA-seq expression levels into relative transcript counts using the Census algorithm without requiring experimental spike-in controls.
• Trajectory Inference with Reversed Graph Embedding: Infers complex, branched single-cell trajectories using reversed graph embedding to reconstruct fate decisions in an unsupervised manner.
• Unsupervised Progression Ordering: Orders individual cells along biological progressions (pseudotime) without prior specification of genes defining the process.
• Differential Expression and Clustering: Performs differential expression analysis to identify developmentally regulated genes and supports clustering to discover rare cell types and distinct transcriptional states.
• Visualization Capabilities: Visualizes trajectories and gene expression dynamics to aid interpretation of lineage structures and expression changes.

Scientific Applications:

• Developmental Biology: Reconstructs lineage specification and cell fate decisions from single-cell RNA-seq datasets.
• Hematopoiesis and Transcription Factor Studies: Applied to blood development to reveal how mutations in key transcription factors can alter cellular trajectories and produce alternative fates.
• Multi-layer Gene Regulation Analysis: Enables analysis of transcriptional regulation layers, including splicing patterns and allelic imbalances, by leveraging transformed Census counts.

Methodology:

Converts relative RNA-seq expression to relative transcript counts via the Census algorithm; infers trajectories using reversed graph embedding; performs unsupervised pseudotemporal ordering, differential expression analysis, clustering, and trajectory/gene-expression visualization.