Cassiopeia Benchmarking Pipeline#

This notebook serves as an entry point for understanding how to interface with Cassiopeia for the purposes of simulating trees, data, benchmarking algorithms.

You can install Cassiopeia by following the guide here.

All of our documentation is hosted here.

import cProfile
from collections import defaultdict
import copy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
from import tqdm

import cassiopeia as cas
from cassiopeia.solver import missing_data_methods
from IPython.display import Image

Tree Simulation#

We can use a simple birth-death model with fitness to simulate trees.

Specifically, this is a continuous-time birth-death process in which birth and death events are sampled from indepedent waiting distributions. Importnatly, we can incorporate fitness into this framework by modulating the scale of the birth waiting distribution. This is done by sampling a random number of fitness events per generation, each with a fitness effect drawn from a distribution. The documentation for this class can be found here.

bd_sim = cas.sim.BirthDeathFitnessSimulator(
    birth_waiting_distribution = lambda scale: np.random.exponential(scale),
    initial_birth_scale = 0.5,
    death_waiting_distribution = lambda: np.random.exponential(1.5),
    mutation_distribution = lambda: 1 if np.random.uniform() < 0.5 else 0,
    fitness_distribution = lambda: np.random.normal(0, .5),
    fitness_base = 1.3,
    num_extant = 400,
ground_truth_tree = bd_sim.simulate_tree()

Unfortunately, iTOL is no longer free. However, there is local plotting functionality. For tutorials, see Cassiopeia/notebooks/local_plotting.ipynb

[4]:, add_root=True)
(<Figure size 504x504 with 1 Axes>, <AxesSubplot:>)

Overlay Lineage Tracing Data#

We can then use our Cas9-based lineage tracing data simulator to overlay data. Here, this is a very flexible Cas9-data generator in which we can specify the dependence structure between characters (i.e. the cassettes that link togehter cut sites), the mutation rate, silencing rates, and state distributions. This function derives from the DataSimulator class & implements the overlay_data function which operates on a CassiopeiaTree, such as the one you simulated above.

lt_sim = cas.sim.Cas9LineageTracingDataSimulator(
    number_of_cassettes = 40,
    size_of_cassette = 1,
    mutation_rate = 0.1,
    state_generating_distribution = lambda: np.random.exponential(1e-5),
    number_of_states = 50,
    state_priors = None,
    heritable_silencing_rate = 9e-4,
    stochastic_silencing_rate = 0.1,
    heritable_missing_data_state = -1,
    stochastic_missing_data_state = -1,

We can get a feel for the character matrix and the complexity of the data here by extracting the simulated data.

## inspect character matrix

character_matrix = ground_truth_tree.character_matrix

print(f"We simulated {character_matrix.shape[1]} characters across {character_matrix.shape[0]} cells.")

# plot the number of unique states per character
plt.ylabel("Number of unique states ")

missing_data_per_character = character_matrix.apply(lambda x: len(x[x == -1]) / len(x), axis=0)
plt.ylabel("Percentage of missing states")
We simulated 40 characters across 400 cells.

Rebuild trees#

Cassiopeia has implemented several CassiopeiaSolvers for reconstructing trees. Each of these can take in several class-specific parameters and at a minimum implements the solve routine which operates on a CassiopeiaTree.

The full list of solvers can be found here. For a full tutorial on tree reconstruction, refer to the Tree Reconstruction notebook.

Here we use the VanillaGreedySolver, which was described in the Cassiopeia paper published in 2020.

reconstructed_tree = = ground_truth_tree.character_matrix, missing_state_indicator = -1)

greedy_solver = cas.solver.VanillaGreedySolver()

[8]:, add_root=True)
(<Figure size 504x504 with 1 Axes>, <AxesSubplot:>)


Our critique library is for testing the similarity between CassiopeiaTrees. The most pertinent use case is when comparing the reconstruction accuracy. We have two metrics implemented - a triplets correct measure, and the Robinson-Foulds metric.

reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True)

rf, rf_max =, reconstructed_tree)

print(f"Normalized RF: {rf / rf_max}")
Normalized RF: 0.6891891891891891
triplets =, reconstructed_tree, number_of_trials = 500)

Building benchmarking pipelines#

We can link all of these steps together to benchmark the performance of several algorithms.

The briefest of benchmarking pipelines would consist of:

  • Simulating a ground truth phylogeny

  • Overlaying data onto this phylogeny

  • Inferring the phylogeny with potentially many algorithms

  • Assessing performance

Here we show a very simple benchmarking pipeline:


algorithms = {"Vanilla Greedy": cas.solver.VanillaGreedySolver(), 'UPGMA': cas.solver.UPGMASolver(dissimilarity_function=cas.solver.dissimilarity.weighted_hamming_distance), 'NeighborJoining': cas.solver.NeighborJoiningSolver(dissimilarity_function=cas.solver.dissimilarity.weighted_hamming_distance, add_root=True) } iterations = 10 algorithm_to_performance_triplets = defaultdict(list) algorithm_to_performance_rf = defaultdict(list) for _ in tqdm(range(iterations)): ground_truth_tree = bd_sim.simulate_tree() np.random.seed(None) lt_sim.overlay_data(ground_truth_tree) for algorithm_name in tqdm(algorithms.keys()): algorithm = algorithms[algorithm_name] reconstructed_tree = = ground_truth_tree.character_matrix, missing_state_indicator = -1) algorithm.solve(reconstructed_tree) # ground_truth_tree.collapse_mutationless_edges(infer_ancestral_characters = False) reconstructed_tree.collapse_mutationless_edges(infer_ancestral_characters = True) rf, rf_max =, reconstructed_tree) triplets =, reconstructed_tree, number_of_trials=500) algorithm_to_performance_triplets[algorithm_name].append(np.mean(list(triplets[0].values()))) algorithm_to_performance_rf[algorithm_name].append(rf / rf_max)

We can easily visualize results after reformatting the dictionaries of performances

algorithm_to_performance_triplets_df = pd.DataFrame(columns = ['Algorithm', 'Triplets'])
for algorithm in algorithm_to_performance_triplets:

    entries = algorithm_to_performance_triplets[algorithm]
    new_df = pd.DataFrame([algorithm]*len(entries), columns = ['Algorithm'])
    new_df['Triplets'] = entries

    algorithm_to_performance_triplets_df = pd.concat([algorithm_to_performance_triplets_df, new_df])

algorithm_to_performance_rf_df = pd.DataFrame(columns = ['Algorithm', 'RF'])
for algorithm in algorithm_to_performance_rf:

    entries = algorithm_to_performance_rf[algorithm]
    new_df = pd.DataFrame([algorithm]*len(entries), columns = ['Algorithm'])
    new_df['RF'] = entries

    algorithm_to_performance_rf_df = pd.concat([algorithm_to_performance_rf_df, new_df])
sns.boxplot(data=algorithm_to_performance_triplets_df, x = 'Algorithm', y = 'Triplets')

sns.boxplot(data=algorithm_to_performance_rf_df, x = 'Algorithm', y = 'RF')