scphylo.tl.siclonefit#

scphylo.tl.siclonefit(df_input, alpha, beta, n_restarts=3, n_iters=500, n_burnin=100, return_tree=False, experiment=False)[source]#

Solving using SiCloneFit.

Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data [SiCloneFit].

Parameters

  • df_input (pandas.DataFrame) – Input genotype matrix in which rows are cells and columns are mutations. Values inside this matrix show the presence (1), absence (0) and missing entires (3).

  • alpha (float) – False positive error rate.

  • beta (float) – False negative error rate.

  • n_restarts (int, optional) – Number of restarts, by default 3

  • n_iters (int, optional) – Number of iterations for each Markov Chain after burnin, by default 500

  • n_burnin (int, optional) – Number of iterations for burnin of each Markov Chain, by default 100

  • return_tree (bool, optional) – Return the inferred cell-lineage tree, by default False

  • experiment (bool, optional) – Is in the experiment mode (the log won’t be shown), by default False

Returns

A conflict-free matrix in which rows are cells and columns are mutations. Values inside this matrix show the presence (1) and absence (0).

Return type

pandas.DataFrame

Examples#

Reconstruct tree by SiCloneFit

Reconstruct tree by SiCloneFit