scphylo.tl.gpps#

scphylo.tl.gpps(df_input, alpha, beta, k_dollo=0, max_del=-1, neighbor_size=30, n_iters=100, time_limit=86400, n_threads=1)[source]#

Solving using gpps.

an ILP-based approach for inferring cancer progression with mutation losses from single cell data [gpps].

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.

  • k_dollo (int, optional) – k for Dollo model, by default 0

  • max_del (int, optional) – Maximum number of deletion allowed, by default -1

  • neighbor_size (int, optional) – Hill climbing neighborhood size, by default 30

  • n_iters (int, optional) – Hill climbing maximum iterations, by default 100

  • time_limit (int, optional) – Time limit (in seconds), by default 86400

  • n_threads (int, optional) – Number of threads, by default 1

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 gpps

Reconstruct tree by gpps