monocle3

Monocle 3: Single-Cell Trajectory Analysis (R)

Overview

Monocle 3 is an algorithm for reconstructing single-cell trajectories and analyzing cell fate decisions. It learns the sequence of gene expression changes cells undergo during dynamic biological processes (differentiation, response to stimuli, disease progression) and places each cell at its proper position along this trajectory.

Key Concepts:

• Pseudotime: A measure of how much progress a cell has made through a biological process (distance from trajectory start)
• Trajectory graph: A principal graph that represents the overall path cells follow
• Branches: Points where cells can diverge into different fates
• Partitions: Separate trajectory components for cells with distinct starting states

When to Use This Skill

Use this skill when:

• Analyzing cell differentiation trajectories
• Studying dynamic biological processes (development, disease progression, treatment response)
• Identifying gene expression changes over pseudotime
• Analyzing branch points and cell fate decisions
• Working with time-series single-cell data
• You need to order cells along a continuum rather than discrete clusters

Installation

# Install Monocle3 from Bioconductor
if (!require("BiocManager")) install.packages("BiocManager")
BiocManager::install("monocle3")

# Load library
library(monocle3)
library(ggplot2)
library(dplyr)

Basic Workflow

1. Create CellDataSet Object

Monocle3 uses the CellDataSet (CDS) object to store expression data:

# Method 1: From expression matrix + metadata
# expression_matrix: genes x cells matrix
# cell_metadata: data.frame with cell information
# gene_metadata: data.frame with gene information (must include gene_id column)

cds <- new_cell_data_set(expression_matrix,
                         cell_metadata = cell_metadata,
                         gene_metadata = gene_metadata)

# Method 2: From Seurat object
cds <- SeuratWrappers::as.cell_data_set(seurat_object)

# Method 3: From 10X data
expression_matrix <- Read10X("path/to/filtered_feature_bc_matrix/")
cds <- new_cell_data_set(expression_matrix)