Mudata scFates test

muon_to_scFates.py

import mudatasets
import numpy as np
import pandas as pd
import scanpy as sc
import anndata as ad
import muon as mu

mdata = mudatasets.load("brain3k_multiome", full=True)
mdata.var_names_make_unique()

rna = mdata['rna']

rna.var['mt'] = rna.var_names.str.startswith('MT-')  # annotate the group of mitochondrial genes as 'mt'
sc.pp.calculate_qc_metrics(rna, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)

mu.pp.filter_obs(rna, 'n_genes_by_counts', lambda x: (x >= 200) & (x < 8000))
mu.pp.filter_obs(rna, 'total_counts', lambda x: x < 40000)
mu.pp.filter_obs(rna, 'pct_counts_mt', lambda x: x < 2)

rna.layers["counts"] = rna.X.copy()
sc.pp.normalize_total(rna, target_sum=1e4)
sc.pp.log1p(rna)
rna.layers["lognorm"] = rna.X.copy()

sc.pp.highly_variable_genes(rna, min_mean=0.02, max_mean=4, min_disp=0.5)
sc.pp.scale(rna, max_value=10)
sc.tl.pca(rna)
sc.pp.neighbors(rna, n_neighbors=10, n_pcs=20)
sc.tl.leiden(rna, resolution=.5)
sc.tl.umap(rna, spread=1., min_dist=.5, random_state=11)
sc.pl.umap(rna, color="leiden", legend_loc="on data")

rna=rna[rna.obs.leiden.isin(["2","11","12","13"])]

sc.pp.neighbors(rna, n_neighbors=10, n_pcs=20)
sc.tl.umap(rna)

import scFates as scf
scf.tl.tree(rna,Nodes=100,use_rep="X_umap",method="ppt",ppt_sigma=1,ppt_lambda=100,seed=42)
scf.tl.cleanup(rna,minbranchlength=10)

scf.pl.graph(rna)

scf.tl.root(rna,22)
scf.tl.pseudotime(rna,n_jobs=5,n_map=50,seed=42)
scf.pl.trajectory(rna)
scf.pl.milestones(rna,annotate=True)
rna.X=rna.layers["lognorm"]
scf.tl.test_fork(rna,"22",milestones=["40","65","19"],rescale=True,features=rna.var_names[:100],n_jobs=5)
print(rna.uns['22->40<>65<>19']['fork'])