def test_computed_y_column():
p = (ggplot(df, aes('x'))
+ stat_ecdf(size=2)
# Should be able to used computed y column & create a
# new mapped column also called y
+ stat_ecdf(aes(y=after_stat('y-0.2')), size=2, color='blue')
)
assert p == 'computed_y_column'
def test_ecdf():
p = ggplot(df, aes('x')) + stat_ecdf(size=2)
assert p == 'ecdf'
import pandas as pd
from plotnine import ggplot, aes, after_stat, stat_ecdf
df = pd.DataFrame({'x': range(10)})
p = ggplot(df, aes('x')) + stat_ecdf(size=2)
def test_ecdf():
p = ggplot(df, aes('x')) + stat_ecdf(size=2)
assert p == 'ecdf'
def test_computed_y_column():
p = (ggplot(df, aes('x'))
+ stat_ecdf(size=2)
# Should be able to used computed y column & create a
# new mapped column also called y
+ stat_ecdf(aes(y=after_stat('y-0.2')), size=2, color='blue')
)
assert p == 'computed_y_column'
def plot_ecdf(df_plot,
variable_column,
color_column='none',
output_file='plot_distribution',
facet_column='none',
x_log10=False):
"""Plot plot_distribution to png.
Parameters
----------
df_plot : pandas.DataFrame
DataFrame with <variable_column> as a column.
variable_column : string
String of variable_column column to plot.
color_column : string
String of color column to plot.
output_file : string
Basename of output file.
facet_column : string
Column to facet the plot by.
Returns
-------
NULL
"""
n_colors = 0
if color_column != 'none':
gplt = plt9.ggplot(df_plot,
plt9.aes(x=variable_column, color=color_column))
n_colors = df_plot[color_column].nunique()
else:
gplt = plt9.ggplot(df_plot, plt9.aes(x=variable_column))
gplt = gplt + plt9.theme_bw()
gplt = gplt + plt9.stat_ecdf(alpha=0.8)
if x_log10:
gplt = gplt + plt9.scale_x_continuous(
trans='log10',
# labels=comma_labels,
minor_breaks=0)
else:
gplt = gplt + plt9.scale_x_continuous(
# trans='log10',
# labels=comma_labels,
minor_breaks=0)
gplt = gplt + plt9.scale_y_continuous(
# trans='log10',
# labels=comma_labels,
minor_breaks=0)
gplt = gplt + plt9.labs(y='Cumulative density', title='')
if n_colors != 0 and n_colors > 20:
gplt = gplt + plt9.theme(legend_position='none')
elif n_colors != 0 and n_colors < 9:
gplt = gplt + plt9.scale_colour_brewer(palette='Dark2', type='qual')
if facet_column != 'none':
gplt = gplt + plt9.facet_wrap('~ {}'.format(facet_column), ncol=5)
n_facets = df_plot[facet_column].nunique()
gplt.save('{}.png'.format(output_file),
dpi=300,
width=6 * (n_facets / 4),
height=4 * (n_facets / 4),
limitsize=False)
else:
gplt.save('{}.png'.format(output_file), dpi=300, width=4, height=4)
return 0
def main():
"""Run CLI."""
parser = argparse.ArgumentParser(description="""
Calcualte and compare LISI across a series of reduced dims and
categorical variables.
""")
parser.add_argument(
'-v',
'--version',
action='version',
version='%(prog)s {version}'.format(version=__version__))
# parser.add_argument(
# '-h5', '--h5_anndata',
# action='store',
# dest='h5',
# required=True,
# help='H5 AnnData file.'
# )
parser.add_argument(
'-rf',
'--reduced_dims_tsv',
action='store',
dest='reduced_dims',
required=True,
help='List of tab-delimited files of reduced dimensions (e.g., PCs)\
for each cell. First column is cell_barcode. List should be\
split by "::" (e.g. file1.tsv.gz::file2.tsv.gz).')
parser.add_argument(
'-lbl',
'--reduced_dims_tsv_labels',
action='store',
dest='reduced_dims_labels',
required=True,
help='String of labels for each reduced_dims_tsv file. List should be\
split by "::".')
parser.add_argument(
'-mf',
'--metadata_tsv',
action='store',
dest='metadata_tsv',
required=True,
help='Tab-delimited file of metadata for each cell. First column\
is cell_barcode.')
parser.add_argument(
'-mv',
'--metadata_columns',
action='store',
dest='metadata_columns',
default='experiment_id',
help='Comma separated string of categorical variables to calculate\
LISI with.\
(default: %(default)s)')
parser.add_argument('-p',
'--perplexity',
action='store',
dest='perplexity',
default=30.0,
type=float,
help='Perplexity.\
(default: %(default)s)')
parser.add_argument(
'-of',
'--output_file',
action='store',
dest='of',
default='',
help='Basename of output files, assuming output in current working \
directory.\
(default: <metadata_tsv>-lisi)')
options = parser.parse_args()
# Fixed settings.
# verbose = True
# Get the out file base.
out_file_base = options.of
if out_file_base == '':
out_file_base = '{}-lisi'.format(
os.path.basename(
options.metadata_tsv.rstrip('tsv.gz').rstrip('.')))
# Get the columns to use
lisi_columns = options.metadata_columns.split(',')
# lisi_columns = ['experiment_id', 'batch']
lisi_columns_dtype = dict(
zip(lisi_columns, ['category'] * len(lisi_columns)))
# Load the metadata file
file_meta = options.metadata_tsv
df_meta = pd.read_csv(file_meta,
sep='\t',
index_col='cell_barcode',
dtype=lisi_columns_dtype)
# Load the reduced dims.
files = options.reduced_dims.split('::')
labels = options.reduced_dims_labels.split('::')
assert len(files) == len(labels), 'ERROR: check files and labels input'
# Make a dict of theoretical maximum LISI value for each label.
lisi_limit = {}
for col in lisi_columns:
n_cat = len(df_meta[col].cat.categories)
lisi_limit[col] = n_cat
list_lisi = []
for i in range(len(files)):
df_reduced_dims = pd.read_csv(files[i],
sep='\t',
index_col='cell_barcode')
# Run lisi and save results to dataframe
_df_lisi = pd.DataFrame(hm.compute_lisi(
df_reduced_dims.loc[df_meta.index, :], df_meta[lisi_columns],
lisi_columns),
columns=lisi_columns)
_df_lisi['file'] = files[i]
_df_lisi['label'] = labels[i]
_df_lisi['cell_barcode'] = df_meta.index
list_lisi.append(_df_lisi)
# Make one long dataframe.
df_lisi = pd.concat(list_lisi)
# Make cell_barcode the first column.
cols = list(df_lisi.columns)
cols = [cols[-1]] + cols[:-1]
# Save the results
df_lisi[cols].to_csv('{}.tsv.gz'.format(out_file_base),
sep='\t',
index=False,
quoting=csv.QUOTE_NONNUMERIC,
na_rep='',
compression='gzip')
# Compare the lisi distributions
n_labels = len(labels)
for lisi_column in lisi_columns:
# Make density plot.
gplt = plt9.ggplot(df_lisi,
plt9.aes(
fill='label',
x='label',
y=lisi_column,
))
gplt = gplt + plt9.theme_bw(base_size=12)
gplt = gplt + plt9.geom_violin(alpha=0.9)
gplt = gplt + plt9.geom_boxplot(
group='label',
position=plt9.position_dodge(width=.9),
width=.1,
fill='white',
outlier_alpha=0 # Do not know how to totally remove outliers.
)
# Add a line at the theoretical maximum
gplt = gplt + plt9.geom_hline(
plt9.aes(yintercept=lisi_limit[lisi_column]))
# gplt = gplt + plt9.facet_grid('{} ~ .'.format(label))
gplt = gplt + plt9.labs(x='Reduced dimensions', y='LISI', title='')
gplt = gplt + plt9.theme(
axis_text_x=plt9.element_text(angle=-45, hjust=0))
gplt = gplt + plt9.theme(legend_position='none')
if n_labels != 0 and n_labels < 9:
gplt = gplt + plt9.scale_fill_brewer(palette='Dark2', type='qual')
gplt.save(
'{}-{}-violin.png'.format(out_file_base, lisi_column),
dpi=300,
width=4 * (n_labels / 4),
height=10,
# height=4*(n_samples/4),
limitsize=False)
# Make ecdf.
gplt = plt9.ggplot(df_lisi, plt9.aes(
x=lisi_column,
color='label',
))
gplt = gplt + plt9.theme_bw(base_size=12)
gplt = gplt + plt9.stat_ecdf(alpha=0.8)
gplt = gplt + plt9.labs(
x='LISI',
y='Cumulative density',
# color='Reduction',
title='')
if n_labels != 0 and n_labels < 9:
gplt = gplt + plt9.scale_color_brewer(palette='Dark2', type='qual')
gplt.save('{}-{}-ecdf.pdf'.format(out_file_base, lisi_column),
dpi=300,
width=10,
height=4,
limitsize=False)
def test_ecdf():
p = ggplot(df, aes('x')) + stat_ecdf(size=2)
assert p == 'ecdf'
# ## Univariate, Continuous Distribution
# ### Histogram
(
p9.ggplot(df[~df["age"].isna()], p9.aes(x="age"))
+ p9.geom_histogram(binwidth=5)
+ p9.ggtitle("Histogram")
)
# ## ECDF
(
p9.ggplot(df[~df["age"].isna()], p9.aes(x="age"))
+ p9.stat_ecdf()
+ p9.ggtitle("ECDF")
)
# ## Continuous Distribution, grouped by Categorical
# ### Box Plots
(
p9.ggplot(df[~df["age"].isna()], p9.aes(x="sex", y="age"))
+ p9.geom_boxplot()
+ p9.coord_flip()
+ p9.ggtitle("Box plot")
)