def test_violin():
sc.pl.set_rcParams_defaults()
sc.set_figure_params(dpi=80, color_map='viridis')
pbmc = sc.datasets.pbmc68k_reduced()
sc.pl.violin(pbmc, ['n_genes', 'percent_mito', 'n_counts'],
stripplot=True,
multi_panel=True,
jitter=True,
show=False)
save_and_compare_images('master_violin_multi_panel', tolerance=40)
def make_violin_images_multi():
####
# tests based on pbmc68k_reduced dataset
sc.pl.set_rcParams_defaults()
sc.set_figure_params(dpi=80, color_map='viridis')
pbmc = sc.datasets.pbmc68k_reduced()
sc.pl.violin(pbmc, ['n_genes', 'percent_mito', 'n_counts'],
stripplot=False,
multi_panel=True,
jitter=False)
pl.savefig("master_violin_multi_panel.png", dpi=80)
pl.close()
def test_violin():
sc.pl.set_rcParams_defaults()
sc.set_figure_params(dpi=80, color_map='viridis')
pbmc = sc.datasets.pbmc68k_reduced()
outfile = NamedTemporaryFile(suffix='.png', prefix='scanpy_test_violin_', delete=False)
sc.pl.violin(pbmc, ['n_genes', 'percent_mito', 'n_counts'],
stripplot=True, multi_panel=True, jitter=True, show=False)
pl.savefig(outfile.name, dpi=80)
pl.close()
res = compare_images(ROOT + '/master_violin_multi_panel.png', outfile.name, 40)
assert res is None, res
os.remove(outfile.name)
# -*- coding: utf-8 -*-
import matplotlib as mpl
mpl.use('agg')
import matplotlib.pyplot as pl
import scanpy.api as sc
sc.set_figure_params(dpi=80, color_map='viridis')
def make_heatmaps():
adata = sc.datasets.krumsiek11()
# make heatmap
sc.pl.heatmap(adata, adata.var_names, 'cell_type', use_raw=False)
pl.savefig('master_heatmap.png', dpi=80)
pl.close()
# make heatmap with continues data
adata.obs['Gata2'] = adata.X[:, 0]
sc.pl.heatmap(adata,
adata.var_names,
'Gata2',
use_raw=False,
num_categories=4,
figsize=(4.5, 5))
pl.savefig('master_heatmap2.png', dpi=80)
pl.close()
df = df.replace(regex=r' ', value='_').replace(regex=r'/', value='_')
df.to_csv('%s/DEGs/cell_groups.csv' %(outdir), sep="\t", index=False)
grpList = ','.join(list(set(df['sampleCluster'].values)))
os.system('Rscript %s/diff_expr.R %s/DEGs/expr.csv %s/DEGs %s/DEGs/cell_groups.csv %s kw 0.01 5000 TRUE 4 v2 1 conover FALSE median FALSE' %(indir, outdir, outdir, outdir, outdir))
## Trajectory analysis
cell_annot = df.loc[rpkms.columns]
import numpy as np
import pandas as pd
import matplotlib.pyplot as pl
from matplotlib import rcParams
import scanpy.api as sc
rcParams['pdf.fonttype'] = 42
sc.set_figure_params(color_map='viridis')
expr_data = rpkms.copy()
sample_cluster=cell_annot['sampleCluster']
X = expr_data.values
gene_names = list(expr_data.index)
sample_names = list(expr_data.columns)
adata = sc.AnnData(X.transpose())
adata.var_names = gene_names
adata.row_names = sample_names
sc.settings.figdir = outdir
genes = list(set(list(set(sigVarGenes))
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as pl
import scanpy.api as sc
#print("hello world")
#pl.plot(arange(5))
sc.set_figure_params(
dpi=100) # low dpi (dots per inch) yields small inline figures
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_versions()
results_file = '../results/paga_test/planaria_extended.h5ad'
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)