API

Import scphylo as:

import scphylo as scp

This package is intended for use after mutation calling and building the input data via our suggested mutation calling pipeline.

Read/Write (io)

This module provides functions for reading and writing data.

io.read(filepath)	Read genotype matrix and read-count matrix.
io.write(obj, filepath)	Write genotype matrix or read-count matrix into a file.

Preprocessing (pp)

This module provides functions for filtering and preprocessing data.

pp.remove_mut_by_list(adata, alist)	Remove a list of mutations from the data.
pp.remove_cell_by_list(adata, alist)	Remove a list of cells from the data.
pp.filter_mut_reference_must_present_in_at_least(adata)	Remove mutations based on the wild-type status.
pp.filter_mut_mutant_must_present_in_at_least(adata)	Remove mutations based on the mutant status.
pp.bifiltering(df, cellr, mutr[, time_limit])	Bi-filtering to find maximally inforemed submatrix.
pp.consensus_combine(df)	Combine cells in genotype matrix.

Tools (tl)

This module provides a high-level API to compute conflict-free solutions and calculate the probability of mutations seeding particular cells.

Solving the noisy input genotype matrix (scphylo-Boost)

tl.booster(df_input, alpha, beta[, solver, ...])	Trisicell-Boost solver.
tl.scite(df_input, alpha, beta[, n_iters, ...])	Solving using SCITE.
tl.phiscsb(df_input, alpha, beta[, experiment])	Solving using PhISCS-B (only in single-cell mode, no bulk).
tl.scistree(df_input, alpha, beta[, ...])	Solving using ScisTree.
tl.bnb(df_input, bounding[, time_limit])	Solving using PhISCS-BnB.
tl.onconem(df_input, alpha, beta)	Solving using OncoNEM.
tl.huntress(df_input, alpha, beta[, n_threads])	Solving using HUNTRESS.
tl.siclonefit(df_input, alpha, beta[, ...])	Solving using SiCloneFit.
tl.sphyr(df_input, alpha, beta[, ...])	Solving using SPhyR.
tl.gpps(df_input, alpha, beta[, k_dollo, ...])	Solving using gpps.
tl.phiscsi_bulk(df_input, alpha, beta[, ...])	Solving using PhISCS-I (in single-cell mode with bulk and mutation elimination).
tl.scelestial(df_input)	Solving using Scelestial.

Partition function calculation (scphylo-PartF)

tl.partition_function(df_input, alpha, beta, ...)

Calculate the probability of a mutation seeding particular cells.

Consensus tree building (scphylo-Cons)

tl.consensus(sc1, sc2)

Build the consensus tree between two tumor progression trees.

For comparing two phylogenetic trees

tl.ad(df_grnd, df_sol)	Ancestor-descendent accuracy.
tl.dl(df_grnd, df_sol)	Different-lineage accuracy.
tl.mltd(df_grnd, df_sol)	Multi-labeled tree dissimilarity measure (MLTD).
tl.tpted(df_grnd, df_sol)	Tumor phylogeny tree edit distance measure (TPTED).
tl.caset(df_grnd, df_sol)	Commonly Ancestor Sets score.
tl.disc(df_grnd, df_sol)	Distinctly Inherited Sets score.
tl.mp3(df_grnd, df_sol)	Triplet-based similarity score.
tl.rf(df_grnd, df_sol)	Robinson-Foulds score.
tl.gs(df_grnd, df_sol)	Genotype-similarity accuracy.

Plotting (pl)

This module provides functionality for plotting trees in clonal or dendrogram formats.

pl.clonal_tree(tree[, muts_as_number, ...])	Draw the tree in clonal format.
pl.dendro_tree(tree[, width, height, dpi, ...])	Draw the tree in dendro fromat.

Utils (ul)

This module provides utility functions.

ul.to_tree(df)	Convert a conflict-free matrix to a tree object.
ul.to_cfmatrix(tree)	Convert phylogenetic tree to conflict-free matrix.
ul.to_mtree(tree)	Convert the phylogenetic tree to mutation tree.
ul.hclustering(df[, metric, method, return_dist])	Hierarchical clustering.
ul.is_conflict_free(df_in)	Check conflict-free criteria via Gusfield algorithm.
ul.is_conflict_free_gusfield(df_in)	Check conflict-free criteria via Gusfield algorithm.

Datasets (datasets)

This module provides functions for simulating data and accessing published datasets.

datasets.example([is_expression])	Return an example for sanity checking and playing with scPhylo.
datasets.simulate([n_cells, n_muts, ...])	Simulate single-cell noisy genotype matrix.
datasets.add_noise(df_in, alpha, beta, missing)	Add noise to the input genotype matrix.
datasets.add_readcount(df_in[, ...])	Add readcount to the input genotype matrix.
datasets.melanoma20()	Mouse Melanoma dataset with 20 sublines.
datasets.colorectal1()	Human Colorectal Cancer (Patient 1).
datasets.colorectal2([readcount])	Human Colorectal Cancer (Patient 2).
datasets.colorectal3()	Human Colorectal Cancer.
datasets.acute_myeloid_leukemia1()	Human Acute Myeloid Leukemia dataset (Patient 1).
datasets.acute_myeloid_leukemia2()	Human Acute Myeloid Leukemia dataset (Patient 2).
datasets.acute_lymphocytic_leukemia1()	Human Acute Lymphocytic Leukemia dataset (Patient 1).
datasets.acute_lymphocytic_leukemia2()	Human Acute Lymphocytic Leukemia dataset (Patient 2).
datasets.acute_lymphocytic_leukemia3()	Human Acute Lymphocytic Leukemia dataset (Patient 3).
datasets.acute_lymphocytic_leukemia4()	Human Acute Lymphocytic Leukemia dataset (Patient 4).
datasets.acute_lymphocytic_leukemia5()	Human Acute Lymphocytic Leukemia dataset (Patient 5).
datasets.acute_lymphocytic_leukemia6()	Human Acute Lymphocytic Leukemia dataset (Patient 6).
datasets.high_grade_serous_ovarian_cancer1()	High Grade Serous Ovarian Cancer (Patient 2).
datasets.high_grade_serous_ovarian_cancer2()	High Grade Serous Ovarian Cancer (Patient 3).
datasets.high_grade_serous_ovarian_cancer3()	High Grade Serous Ovarian Cancer (Patient 9).
datasets.high_grade_serous_ovarian_cancer_3celllines()	High Grade Serous Ovarian Cancer (3 cell lines).
datasets.myeloproliferative_neoplasms18()	JAK2-Negative Myeloproliferative Neoplasm.
datasets.myeloproliferative_neoplasms78()	JAK2-Negative Myeloproliferative Neoplasm.
datasets.myeloproliferative_neoplasms712()	JAK2-Negative Myeloproliferative Neoplasm.
datasets.renal_cell_carcinoma()	Clear-cell Renal-cell Carcinoma.
datasets.muscle_invasive_bladder()	Muscle Invasive Bladder Cancer.
datasets.erbc()	Oestrogen-receptor-positive (ER+) Breast Cancer.
datasets.tnbc()	Triple-negative Breast Cancer.