bio-single-cell-lineage-tracing

Version Compatibility

Reference examples tested with: Cassiopeia 2.0+, matplotlib 3.8+, numpy 1.26+, 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

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Lineage Tracing Analysis

"Reconstruct cell lineage trees from CRISPR barcodes" → Build phylogenetic trees of cell relationships from lineage barcode mutations to study clonal dynamics and cell fate decisions.

• Python: cassiopeia.tl.ILPSolver(cas_tree) or GreedySolver for tree reconstruction

Cassiopeia Tree Reconstruction

Goal: Reconstruct a cell lineage tree from CRISPR barcode character matrices to reveal clonal relationships among single cells.

Approach: Load a character matrix (cells x barcode sites with mutation states), create a CassiopeiaTree object, then solve with a greedy or ILP maximum parsimony solver.

import cassiopeia as cas
import numpy as np

# Load character matrix (cells x barcode sites)
# Values: mutation states at each editing site
# -1 = missing, 0 = unedited, 1+ = mutation states
tree = cas.data.CassiopeiaTree(
    character_matrix=char_matrix,
    cell_meta=cell_metadata
)

# Check data quality
print(f'Cells: {tree.n_cell}')
print(f'Characters: {tree.n_character}')
print(f'Missing fraction: {(char_matrix == -1).mean():.2%}')

# Reconstruct tree with greedy solver
solver = cas.solver.VanillaGreedySolver()
solver.solve(tree)

# Alternative: maximum parsimony
solver = cas.solver.ILPSolver()
solver.solve(tree, convergence_time_limit=600)

Hybrid Solvers

# Hybrid approach: greedy for large trees, ILP refinement
solver = cas.solver.HybridSolver(
    top_solver=cas.solver.VanillaGreedySolver(),
    bottom_solver=cas.solver.ILPSolver(),
    cell_cutoff=200
)
solver.solve(tree)

# Neighbor-joining for comparison
nj_solver = cas.solver.NeighborJoiningSolver(
    dissimilarity_function=cas.solver.dissimilarity_functions.weighted_hamming_distance
)
nj_solver.solve(tree)

From CRISPR Barcodes

# Parse barcode sequences from alignment
barcodes = cas.pp.call_alleles(
    alignment_file='aligned_barcodes.bam',
    reference='barcode_reference.fa',
    min_base_quality=20,
    min_read_quality=10
)

# Filter low-quality calls
barcodes = cas.pp.filter_cells(barcodes, min_umi_per_cell=10)
barcodes = cas.pp.filter_alleles(barcodes, min_cells_per_allele=3)

# Build character matrix
char_matrix = cas.pp.convert_alleles_to_character_matrix(
    barcodes,
    missing_state_indicator=-1
)

Character Matrix QC

# Assess barcode diversity
n_states = (char_matrix > 0).sum(axis=0)
print(f'Mean states per site: {n_states.mean():.1f}')

# Filter uninformative characters
informative = (char_matrix > 0).sum(axis=0) > 1
char_matrix = char_matrix[:, informative]

# Missing data analysis
missing_per_cell = (char_matrix == -1).mean(axis=1)
missing_per_site = (char_matrix == -1).mean(axis=0)

# Remove cells with too much missing data
keep_cells = missing_per_cell < 0.5
char_matrix = char_matrix[keep_cells]

CoSpar for Clonal Dynamics

Goal: Infer cell fate transition maps and clonal dynamics from time-series lineage tracing data.

Approach: Load lineage-traced AnnData with clone annotations, compute a transition map using intraclone smoothing, then estimate fate probabilities from source to sink populations.

import cospar as cs

adata = cs.read_h5ad('lineage_traced.h5ad')

# Clone information in obs
# 'clone_id' or 'barcode' column required

# Infer transition map
cs.tl.infer_Tmap(
    adata,
    smooth_array=[15, 10, 5],
    intraclone_threshold=0.2,
    neighbor_method='embedding'
)

# Visualize clonal structure
cs.pl.clonal_embedding(adata, color='clone_id')

# Fate probabilities from source to sink
cs.tl.fate_map(
    adata,
    source='HSC',
    sink='Monocyte',
    method='norm-sum'
)

# Plot fate map
cs.pl.fate_map(adata, source='HSC')

CoSpar Trajectory Analysis

# Fate coupling between cell types
cs.tl.fate_coupling(adata, source='HSC')
cs.pl.fate_coupling(adata, source='HSC')

# Transition probabilities over time
cs.tl.transition_map(adata, time_key='day')
cs.pl.transition_map(adata)

# Clone size dynamics
cs.tl.clone_size(adata, time_key='day')
cs.pl.clone_size(adata)

Mitochondrial Lineage (MitoTracing)

# Use mtDNA mutations as natural barcodes
# No engineering required, works on any scRNA-seq

import mito_utils as mu

# Call mtDNA variants from scRNA-seq BAM
variants = mu.call_variants(
    adata,
    bam_path='possorted_genome_bam.bam',
    min_cell_quality=0.9,
    min_coverage=10
)

# Filter variants by quality
variants = mu.filter_variants(
    variants,
    min_cells=10,
    max_af=0.9,
    min_af=0.01
)

# Build distance matrix
distances = mu.compute_distances(variants, method='jaccard')

# Infer tree
tree = mu.build_tree(distances, method='nj')

LARRY Barcode Processing

# For LARRY lentiviral barcoding
import larry

# Parse LARRY barcodes from FASTQ
barcodes = larry.parse_barcodes(
    r1='barcodes_R1.fastq.gz',
    r2='barcodes_R2.fastq.gz',
    whitelist='cell_barcodes.txt'
)

# Match to expression data
adata.obs['clone_id'] = barcodes.loc[adata.obs_names, 'clone_id']

# Clone analysis
clone_sizes = adata.obs['clone_id'].value_counts()
print(f'Number of clones: {len(clone_sizes)}')
print(f'Median clone size: {clone_sizes.median():.0f}')

Tree Visualization

# Plot tree with cell type colors
cas.pl.local.plot_matplotlib(
    tree,
    meta_data=['cell_type'],
    clade_colors=cell_type_colors,
    orient='down',
    figsize=(15, 10)
)

# Interactive tree with itol
cas.pl.local.export_to_itol(tree, 'tree_for_itol.txt')

# ETE3 visualization
cas.pl.local.plot_ete3(
    tree,
    meta_data='cell_type',
    show_internal=False
)

Tree Quality Metrics

# Robinson-Foulds distance between trees
from cassiopeia.critique import compare

rf_distance = compare.robinson_foulds(tree1, tree2)

# Triplet accuracy
triplet_acc = compare.triplets_correct(tree, ground_truth_tree)

# Bootstrap support
bootstrapped_trees = cas.solver.bootstrap(
    tree,
    solver=solver,
    n_replicates=100
)
support = cas.critique.bootstrap_support(tree, bootstrapped_trees)

Integrate with scRNA-seq

import scanpy as sc

# Match tree leaves to expression data
common_cells = set(tree.leaves).intersection(adata.obs_names)
adata_matched = adata[list(common_cells)]
tree_matched = tree.copy()
tree_matched.subset_leaves(list(common_cells))

# Add tree distances to adata
for i, cell in enumerate(adata_matched.obs_names):
    for j, cell2 in enumerate(adata_matched.obs_names):
        if i < j:
            dist = tree_matched.get_distance(cell, cell2)
            # Store in obsp sparse matrix

# Correlate clonal relatedness with transcriptomic similarity

Clonal Expansion Analysis

# Find expanded clones
clone_sizes = adata.obs['clone_id'].value_counts()
expanded = clone_sizes[clone_sizes > 10].index

# Differential expression: expanded vs non-expanded
sc.tl.rank_genes_groups(
    adata,
    groupby='is_expanded',
    method='wilcoxon'
)

# Clone-specific signatures
for clone in expanded[:5]:
    clone_cells = adata[adata.obs['clone_id'] == clone]
    sc.tl.score_genes(clone_cells, gene_list=signature_genes)

Tree Statistics

Metric	Description	Typical Range
Tree depth	Max root-to-leaf distance	10-50
Balance (Colless)	Tree asymmetry	0-1
Sackin index	Sum of root-leaf depths	Varies
Gamma statistic	Tempo of diversification	-3 to 3

Related Skills

• single-cell/trajectory-inference - Pseudotime inference
• single-cell/preprocessing - Preprocessing
• phylogenetics/modern-tree-inference - Tree inference concepts
• single-cell/clustering - Cell type assignment