import numpy as np
import pandas as pd
import matplotlib.pyplot as pl
import scanpy.api as sc
sc.settings.verbosity = 2 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.settings.set_figure_params(dpi=150) # low dpi (dots per inch) yields small inline figures
sc.logging.print_versions()
scanpy==1.0.3 anndata==0.5.8 numpy==1.13.1 scipy==1.0.0 pandas==0.22.0 scikit-learn==0.19.1 statsmodels==0.8.0 python-igraph==0.7.1 louvain==0.6.1
Some plotting parameters.
paga_plot_params = dict(
legend_fontsize=5,
solid_edges='confidence_tree',
dashed_edges='confidence',
root='neoblast 1',
layout='rt_circular',
node_size_scale=0.5,
node_size_power=0.9,
max_edge_width=0.7,
fontsize=3.5)
Get the data.
adata = sc.read('./write/planaria.h5ad')
adata_full = sc.read('./data/dge.txt', cache=True).T
... reading from cache file ./cache/data-dge.h5ad
Reduce to the 7377 wildtype cells.
adata = adata[adata_full.obs_names.str.startswith(('plan1', 'plan2', 'dmso', 'life'))]
adata.n_obs
7377
Compute the tSNE coordinates.
sc.tl.tsne(adata)
computing tSNE using data matrix X directly using the 'MulticoreTSNE' package by Ulyanov (2017) finished (0:01:18.53)
Compute PAGA.
sc.pp.neighbors(adata, n_neighbors=30)
sc.tl.paga(adata, groups='clusters')
computing neighbors initialized `.distances` `.connectivities` `.eigen_values` `.eigen_basis` `.distances_dpt` using data matrix X directly finished (0:00:06.40) running partition-based graph abstraction (PAGA) initialized `.distances` `.connectivities` `.eigen_values` `.eigen_basis` `.distances_dpt` finished (0:00:00.71)
sc.pl.paga_compare(adata, title='wildtype cells only, tSNE and abstracted graph', **paga_plot_params, save='_wildtype')
saving figure to file ./figures/paga_compare_wildtype.png