bio-single-cell-trajectory-inference

Version Compatibility

Reference examples tested with: Cell Ranger 8.0+, scanpy 1.10+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed

package and adapt the example to match the actual API rather than retrying.

Trajectory Inference

Monocle3 (R)

Goal: Infer developmental trajectories and pseudotime ordering using Monocle3's principal graph approach.

Approach: Learn a principal graph through the data manifold, order cells along the graph from a root state, and extract pseudotime values.

"Find the developmental trajectory in my data" → Construct a tree-like graph through the cell embedding, assign pseudotime from a root population, and identify branch points.

library(monocle3)

# Create cell_data_set from Seurat
cds <- as.cell_data_set(seurat_obj)

# Preprocess (if not already done)
cds <- preprocess_cds(cds, num_dim = 50)
cds <- reduce_dimension(cds, reduction_method = 'UMAP')

# Cluster cells
cds <- cluster_cells(cds)

# Learn trajectory graph
cds <- learn_graph(cds)

# Order cells (select root interactively or programmatically)
cds <- order_cells(cds, root_cells = root_cell_ids)

# Plot trajectory with pseudotime
plot_cells(cds, color_cells_by = 'pseudotime', label_branch_points = TRUE, label_leaves = TRUE)

# Get pseudotime values
pseudotime <- pseudotime(cds)

Set Root Programmatically

Goal: Automatically select the trajectory root node based on a known progenitor cluster.

Approach: Identify the principal graph node closest to cells in the specified progenitor cluster and use it as the root for pseudotime ordering.

# Find root by marker gene expression
get_earliest_principal_node <- function(cds, cluster_name) {
    cell_ids <- which(colData(cds)$seurat_clusters == cluster_name)
    closest_vertex <- cds@principal_graph_aux[['UMAP']]$pr_graph_cell_proj_closest_vertex
    closest_vertex <- as.matrix(closest_vertex[cell_ids, ])
    root_pr_nodes <- igraph::V(principal_graph(cds)[['UMAP']])$name[as.numeric(names(which.max(table(closest_vertex))))]
    root_pr_nodes
}

cds <- order_cells(cds, root_pr_nodes = get_earliest_principal_node(cds, 'stem_cluster'))

Slingshot (R)

Goal: Infer smooth lineage trajectories and pseudotime using Slingshot's minimum spanning tree and principal curves.

Approach: Build a minimum spanning tree through cluster centroids to define lineage structure, then fit smooth principal curves for per-lineage pseudotime.

library(slingshot)
library(SingleCellExperiment)

# From Seurat object
sce <- as.SingleCellExperiment(seurat_obj)
reducedDims(sce)$UMAP <- Embeddings(seurat_obj, 'umap')

# Run slingshot
sce <- slingshot(sce, clusterLabels = 'seurat_clusters', reducedDim = 'UMAP')

# Get pseudotime for each lineage
pseudotime_mat <- slingPseudotime(sce)

# Get lineage curves
curves <- slingCurves(sce)

# Plot trajectories
plot(reducedDims(sce)$UMAP, col = sce$seurat_clusters, pch = 16)
lines(SlingshotDataSet(sce), lwd = 2)

Slingshot with Start/End Clusters

# Specify starting cluster
sce <- slingshot(sce, clusterLabels = 'seurat_clusters', reducedDim = 'UMAP', start.clus = 'HSC')

# Specify start and end
sce <- slingshot(sce, clusterLabels = 'seurat_clusters', reducedDim = 'UMAP',
                 start.clus = 'HSC', end.clus = c('Erythroid', 'Myeloid'))

scVelo RNA Velocity (Python)

Goal: Estimate RNA velocity to predict future cell states from spliced/unspliced transcript ratios.

Approach: Model the dynamics of splicing using stochastic or dynamical models, compute velocity vectors, and project directional flow onto UMAP.

import scvelo as scv
import scanpy as sc

# Load data with spliced/unspliced counts
adata = scv.read('data.h5ad')

# Or merge loom files from velocyto
ldata = scv.read('velocyto_output.loom')
adata = scv.utils.merge(adata, ldata)

# Preprocess
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)

# Compute velocity (stochastic model)
scv.tl.velocity(adata, mode='stochastic')
scv.tl.velocity_graph(adata)

# Visualize velocity streams
scv.pl.velocity_embedding_stream(adata, basis='umap', color='clusters')

scVelo Dynamical Model

# More accurate but slower
scv.tl.recover_dynamics(adata, n_jobs=8)
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata)

# Latent time (pseudotime)
scv.tl.latent_time(adata)
scv.pl.scatter(adata, color='latent_time', cmap='gnuplot')

# Velocity confidence
scv.tl.velocity_confidence(adata)
scv.pl.scatter(adata, color=['velocity_confidence', 'velocity_length'])

Gene Dynamics Along Trajectory

# Monocle3: Find genes varying over pseudotime
graph_test_res <- graph_test(cds, neighbor_graph = 'principal_graph', cores = 4)
sig_genes <- graph_test_res %>% filter(q_value < 0.05) %>% arrange(desc(morans_I))

# Plot gene expression over pseudotime
plot_genes_in_pseudotime(cds[rownames(cds) %in% top_genes, ], color_cells_by = 'cluster')

# scVelo: Top likelihood genes
scv.tl.rank_velocity_genes(adata, groupby='clusters', min_corr=0.3)
top_genes = adata.uns['rank_velocity_genes']['names']

# Plot phase portraits
scv.pl.velocity(adata, var_names=['gene1', 'gene2'], basis='umap')

Branch Point Analysis

# Monocle3: Genes differentially expressed at branch points
branch_genes <- graph_test(cds, neighbor_graph = 'principal_graph', cores = 4)

# Slingshot + tradeSeq for branch analysis
library(tradeSeq)
sce <- fitGAM(sce, nknots = 6)
branch_res <- earlyDETest(sce, knots = c(3, 4))

Velocyto Preprocessing

# Generate loom file with spliced/unspliced counts
velocyto run10x -m repeat_mask.gtf /path/to/cellranger_output annotation.gtf

# For SmartSeq2
velocyto run_smartseq2 -o output -m repeat_mask.gtf -e sample bam_files/*.bam annotation.gtf

PAGA Trajectory (Scanpy)

import scanpy as sc

# Compute PAGA
sc.tl.paga(adata, groups='leiden')
sc.pl.paga(adata, color='leiden', threshold=0.03)

# PAGA-initialized UMAP
sc.tl.draw_graph(adata, init_pos='paga')
sc.pl.draw_graph(adata, color='leiden')

# Diffusion pseudotime
adata.uns['iroot'] = np.flatnonzero(adata.obs['leiden'] == 'root_cluster')[0]
sc.tl.dpt(adata)
sc.pl.draw_graph(adata, color='dpt_pseudotime')

Related Skills

• single-cell/clustering - Prerequisite clustering
• single-cell/cell-communication - Downstream signaling analysis
• differential-expression/deseq2-basics - DE along trajectory