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)
Using the full data as computed in the main notebook.
adata = sc.read('./write/planaria.h5ad')
sc.pl.paga_compare(adata, title='100% of data, tSNE and abstracted graph', **paga_plot_params)
Subsampling to 80% of the data, 17289 cells.
adata_subsampled = sc.read('./write/planaria.h5ad')
sc.pp.subsample(adata_subsampled, fraction=0.8)
adata_subsampled.n_obs
17289
sc.pp.neighbors(adata_subsampled, n_neighbors=30)
sc.tl.paga(adata_subsampled, groups='clusters')
computing neighbors initialized `.distances` `.connectivities` `.eigen_values` `.eigen_basis` `.distances_dpt` using data matrix X directly finished (0:00:14.96) running partition-based graph abstraction (PAGA) initialized `.distances` `.connectivities` `.eigen_values` `.eigen_basis` `.distances_dpt` finished (0:00:01.34)
We recover the same graph as with 100% of the data.
sc.pl.paga_compare(adata_subsampled, title='80% of data, tSNE and abstracted graph', **paga_plot_params, save='_80percent')
saving figure to file ./figures/paga_compare_80percent.png
Subsampling to 10% of the data, 2161 cells.
adata_subsampled = sc.read('./write/planaria.h5ad')
sc.pp.subsample(adata_subsampled, fraction=0.1)
adata_subsampled.n_obs
2161
sc.pp.neighbors(adata_subsampled, n_neighbors=30)
sc.tl.paga(adata_subsampled, groups='clusters')
computing neighbors initialized `.distances` `.connectivities` `.eigen_values` `.eigen_basis` `.distances_dpt` using data matrix X directly finished (0:00:02.30) running partition-based graph abstraction (PAGA) initialized `.distances` `.connectivities` `.eigen_values` `.eigen_basis` `.distances_dpt` finished (0:00:00.57)
sc.pl.paga_compare(adata_subsampled, title='10% of data, tSNE and abstracted graph', threshold_dashed=0.02, **paga_plot_params, save='_10percent')
saving figure to file ./figures/paga_compare_10percent.png