Does Cassiopeia have the Function to directly get fitness score?

[tenlives]

Hi, Thanks for developling the very cool tool. Does the cassiopeia have the function to get the fitness score from the tree?

[colganwi]

Cassiopeia does not implement single-cell fitness estimation. We recommend using infer_fitness from the jungle package

[mattjones315]

HI @tenlives,

Sorry for the confusion - we do actually have this implemented in Cassiopeia, under the tools module. You can invoke it as so:

fitness_estimator = cas.tools.fitness_estimator.LBIJungle()
fitness_estimator.estimate_fitness(tree)

You can then normalize the fitnesses to the maximum as so:

fitnesses = np.array([tree.get_attribute(cell, 'fitness') for cell in _tree.leaves])
fitnesses /= np.max(fitnesses)

Please note that the algorithm utilizes the branch lengths stored in the tree. The approach we've used in our work is to set edge lengths without any mutations to a length of 0, and any edge with mutations to a length of 1. You can also check out the branch length estimators we've implemented.

Hope this helps, Matt

[tenlives]

HI @tenlives,

Sorry for the confusion - we do actually have this implemented in Cassiopeia, under the tools module. You can invoke it as so:

fitness_estimator = cas.tools.fitness_estimator.LBIJungle()
fitness_estimator.estimate_fitness(tree)

You can then normalize the fitnesses to the maximum as so:

fitnesses = np.array([tree.get_attribute(cell, 'fitness') for cell in _tree.leaves])
fitnesses /= np.max(fitnesses)

Please note that the algorithm utilizes the branch lengths stored in the tree. The approach we've used in our work is to set edge lengths without any mutations to a length of 0, and any edge with mutations to a length of 1. You can also check out the branch length estimators we've implemented.

Hope this helps, Matt

Thanks for your kind reply, I have the tree with branch lengths calculated from other tools, according to your approach ,"fitness_estimator = cas.tools.fitness_estimator.LBIJungle(), fitness_estimator.estimate_fitness(tree)", these commands set edge lengths to 0 or 1, I don`t need to set it again,right? And I have many tree files, could the tree file list as a input for "fitness_estimator.estimate_fitness" ?

[mattjones315]

Hi @tenlives,

No - this function will not assume edge lengths are 0 or 1, you must set these explicitly or expect the procedure to use the branch lengths provided in the tree.

And no, you cannot pass a tree list to this function, it takes exactly one tree as input. If you want to do a list, here's some pseudocode to get you started:

fitness_estimator = cas.tools.fitness_estimator.LBIJungle()
for _tree in trees:
    fitness_estimator.estimate_fitness(_tree)
    fitnesses = np.array([tree.get_attribute(cell, 'fitness') for cell in _tree.leaves])
    fitnesses /= np.max(fitnesses)
    _tree.cell_meta['fitness'] = fitnesses

Best, Matt

[tenlives]

Hi @mattjones315 , My data is also the barcode mutation data, if I want to set edge lengths 0 or 1 according to you approach, could you tell me how to use Cassiopeia to set it?

[mattjones315]

Hi @tenlives,

This is a code snippet that I've used, it could be helpful:

tree.reconstruct_ancestral_characters()
tree.set_character_states(tree.root, [0] * tree.n_character)

for edge in tree.depth_first_traverse_edges():
        branch_length = len(tree.get_mutations_along_edge(edge[0], edge[1]))
        branch_length = min(1, branch_length) # you can remove this if you want to keep branch lengths to be the number of mutations
        tree.set_branch_length(edge[0], edge[1], branch_length)

[tenlives]

Hi @tenlives,

This is a code snippet that I've used, it could be helpful:

tree.reconstruct_ancestral_characters()
tree.set_character_states(tree.root, [0] * tree.n_character)

for edge in tree.depth_first_traverse_edges():
        branch_length = len(tree.get_mutations_along_edge(edge[0], edge[1]))
        branch_length = min(1, branch_length) # you can remove this if you want to keep branch lengths to be the number of mutations
        tree.set_branch_length(edge[0], edge[1], branch_length)

Thanks, I have tried it, I found that I should run "tree.set_character_states_at_leaves(character_matrix=character_matrix)" before you code snippet, and it works!

[tenlives]

Does it make sense that I set the node branch length to 1 directly?

[mattjones315]

I think this will introduce artifacts to your analysis- namely, giving weight to edges that don’t carry any mutations.

On Fri, Jul 12, 2024 at 10:45 PM tenlives @.***> wrote:

Does it make sense that I set the node edge length to 1 directly?

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