def alpha_frequency(output_dir: str,
tables: biom.Table,
metadata_column: str = None,
metadata: qiime2.Metadata = None,
palette: str = 'husl',
style: str = 'white',
context: str = 'paper',
# plot_type: str = 'all',
labels : str = None) -> None:
#first 2 tables
sample_frequencies1 = _frequencies(tables[0], axis='sample')
sample_frequencies2 = _frequencies(tables[1], axis='sample')
sample_frequencies1.sort_values(inplace=True, ascending=False)
sample_frequencies2.sort_values(inplace=True, ascending=False)
sample_frequencies_df1 = sample_frequencies1.to_frame()
sample_frequencies_df2 = sample_frequencies2.to_frame()
sample_frequencies_df1.index.name = "sample-id"
sample_frequencies_df1.reset_index(inplace=True)
sample_frequencies_df2.index.name = "sample-id"
sample_frequencies_df2.reset_index(inplace=True)
#if not metadata & labels:
# raise ValueError("Metadata file was not provided")
#if no labels are given, label the inputs as numbers
if not labels:
merged = pd.merge(sample_frequencies_df1, sample_frequencies_df2, on = "sample-id")
merged = merged.rename(columns = {'0_x':'1', '0_y':'2'})
vars_to_plot = ['1','2']
#if verbose
print('Labeling columns ...')
if len(tables)>2:
for i in range((len(tables))-2) :
sample_frequencies = _frequencies(tables[i+2], axis='sample')
sample_frequencies.sort_values(inplace=True, ascending=False)
sample_frequencies_df = sample_frequencies.to_frame()
sample_frequencies_df.index.name = "sample-id"
sample_frequencies_df.reset_index(inplace=True)
merged = pd.merge(merged, sample_frequencies_df, on = "sample-id")
merged = merged.rename(columns = {0:(i+3)})
vars_to_plot = list(merged.loc[:, merged.columns !='sample-id'])
else:
if len(labels) != len(tables):
raise ValueError("The number of labels is different than the number of tables")
merged = pd.merge(sample_frequencies_df1, sample_frequencies_df2, on = "sample-id")
merged = merged.rename(columns = {'0_x':labels[0], '0_y':labels[1]})
vars_to_plot = list(merged.loc[:, merged.columns !='sample-id'])
# if verbose:
print('Labeling columns ...')
if len(tables)>2:
for i in range((len(tables))-2) :
sample_frequencies = _frequencies(tables[i+2], axis='sample')
sample_frequencies.sort_values(inplace=True, ascending=False)
sample_frequencies_df = sample_frequencies.to_frame()
sample_frequencies_df.index.name = "sample-id"
sample_frequencies_df.reset_index(inplace=True)
merged = pd.merge(merged, sample_frequencies_df, on = "sample-id")
merged = merged.rename(columns = {0:labels[i+2]})
vars_to_plot = list(merged.loc[:, merged.columns !='sample-id'])
melted_merged = pd.melt(merged, id_vars = 'sample-id')
if not metadata:
melted_merged = pd.melt(merged, id_vars = 'sample-id')
melted_merged = melted_merged.rename(columns = {'variable':'Table', 'value':'Sequencing Depth'})
table_preview = melted_merged.to_html()
with open('melted_merged.html', 'w') as file:
file.write(table_preview)
table_preview2 = merged.to_html()
with open('merged.html', 'w') as file:
file.write(table_preview2)
sns.set_style(style)
sns.set_context(context)
pairplot_frequency = sns.pairplot(merged, vars = vars_to_plot, palette = palette)
pairplot_frequency.savefig(os.path.join(output_dir, 'pairplot_frequency.png'))
pairplot_frequency.savefig(os.path.join(output_dir, 'pairplot_frequency.pdf'))
plt.gcf().clear()
print('Plotting pairplot ...')
#raincloud_frequency = pt.RainCloud( x = 'Table', y = 'Sequencing Depth', data = melted_merged,
# orient = 'h', alpha = 0.65, palette = palette )
#raincloud_frequency.figure.savefig(os.path.join(output_dir, 'raincloud.png'), bbox_inches = 'tight')
#raincloud_frequency.figure.savefig(os.path.join(output_dir, 'raincloud.pdf'), bbox_inches = 'tight')
plt.gcf().clear()
print('Plotting raincloud ...')
boxplot_frequency = sns.boxplot(data=melted_merged,x='Table',y='Sequencing Depth', palette = palette, saturation = 1)
boxplot_frequency.figure.savefig(os.path.join(output_dir, 'boxplot.png'), bbox_inches = 'tight')
boxplot_frequency.figure.savefig(os.path.join(output_dir, 'boxplot.pdf'), bbox_inches = 'tight')
plt.gcf().clear()
print('Plotting boxplot ...')
else:
if not metadata_column:
raise ValueError("Metadata column not provided")
metadata = metadata.to_dataframe()
metadata.index.name = "sample-id"
metadata.reset_index(inplace = True)
merged_metadata = pd.merge(merged,metadata, on = "sample-id")
melted_merged_metadata = pd.merge(melted_merged, metadata, on = "sample-id")
melted_merged_metadata = melted_merged_metadata.rename(columns = {'variable':'Table', 'value':'Sequencing Depth'})
# if verbose:
print('Merging metadata ...')
sns.set_style(style)
sns.set_context(context)
pairplot_frequency = sns.pairplot(merged_metadata, hue = metadata_column, vars = vars_to_plot, palette = palette)
pairplot_frequency.savefig(os.path.join(output_dir, 'pairplot_frequency.png'))
pairplot_frequency.savefig(os.path.join(output_dir, 'pairplot_frequency.pdf'))
plt.gcf().clear()
# if verbose:
print('Plotting pairplot ...')
#raincloud_frequency = pt.RainCloud( x = 'Table', y = 'Sequencing Depth', data = melted_merged_metadata,
# orient = 'h', hue = metadata_column, alpha = 0.65, palette = palette )
#raincloud_frequency.figure.savefig(os.path.join(output_dir, 'raincloud.png'), bbox_inches = 'tight')
#raincloud_frequency.figure.savefig(os.path.join(output_dir, 'raincloud.pdf'), bbox_inches = 'tight')
#plt.gcf().clear()
# if verbose:
print('Plotting raincloud ...')
boxplot_frequency = sns.boxplot(data=melted_merged_metadata,x='Table',y='Sequencing Depth',hue=metadata_column, palette = palette, saturation = 1)
boxplot_frequency.figure.savefig(os.path.join(output_dir, 'boxplot.png'), bbox_inches = 'tight')
boxplot_frequency.figure.savefig(os.path.join(output_dir, 'boxplot.pdf'), bbox_inches = 'tight')
plt.gcf().clear()
# if verbose:
print('Plotting boxplot ...')
# melted_merged.to_numpy()
# for i in range(len(merged.columns)-1):
# col =
index = os.path.join(TEMPLATES, 'frequency_assets', 'index.html')
q2templates.render(index, output_dir)
def denoise_stats(
output_dir: str,
stats: qiime2.
Metadata, #stats type is not a metadata but this is the transformer used by DADA2 plugin to make DADA2Stats into pd.dataframe
plot_type: str = 'line',
labels: str = None,
style: str = 'whitegrid',
context: str = 'talk') -> None:
if not labels:
stats = load_df(stats)
else:
stats = load_df_labels(stats, labels)
stats = pd.concat(stats)
numeric = ['denoised', 'filtered', 'input', 'non-chimeric']
stats[numeric] = stats[numeric].apply(pd.to_numeric)
#makes into a df
stats = stats.groupby('id').sum()
stats = stats.drop(columns=[
'percentage of input passed filter', 'percentage of input non-chimeric'
])
df = pd.melt(stats.reset_index(),
id_vars='id',
var_name='step',
value_name='read_number')
input_read_number = df['read_number'].max()
df['% of Reads Remaining'] = df['read_number'] / input_read_number * 100
step_order = {'input': 0, 'filtered': 1, 'denoised': 2, 'non-chimeric': 3}
df['order'] = df['step'].apply(lambda x: step_order[x])
df = df.reset_index()
df['Run Number'] = 'Run ' + df['id']
hue_order = df.query('step == "non-chimeric"').sort_values(
'% of Reads Remaining', ascending=False)['id']
sns.set_style('whitegrid')
sns.set_context("talk")
line_graph = sns.lineplot(data=df,
y='% of Reads Remaining',
x='order',
hue='Run Number')
plt.ylim(0, 100)
plt.xlim(0, 4)
plt.xticks([x / 2 for x in range(0, 9)],
['Input', '', 'Filtered', '', 'Denoised', "", 'Non-chimeric'])
plt.xlabel('Processing Steps')
plt.legend(loc='center left', bbox_to_anchor=(1.25, 0.5), ncol=1)
# plt.title('allow to give any title or default one')
line_graph.figure.savefig(os.path.join(output_dir, 'line_graph.png'),
bbox_inches='tight')
line_graph.figure.savefig(os.path.join(output_dir, 'line_graph.pdf'),
bbox_inches='tight')
plt.gcf().clear()
#maybe the bargraph
r = range(len(stats))
vars_to_plot = df['id'].values
print(df.shape)
colors = ['darkorange', 'orange', 'sandybrown', 'navajowhite']
Step = ['Input', 'Filtered', 'Denoised', 'Non-chimeric']
plt.bar(r,
df[df['step'] == 'input']['read_number'],
color=colors[0],
edgecolor='white',
width=1)
plt.bar(r,
df[df['step'] == 'filtered']['read_number'],
color=colors[1],
edgecolor='white',
width=1)
plt.bar(r,
df[df['step'] == 'denoised']['read_number'],
color=colors[2],
edgecolor='white',
width=1)
#plt.bar(r, df[df['step']=='merged']['read_number'], color = colors[3], edgecolor = 'white', width = 1)
plt.bar(r,
df[df['step'] == 'non-chimeric']['read_number'],
color=colors[3],
edgecolor='white',
width=1)
plt.xticks(r, vars_to_plot, fontweight='bold')
plt.xlabel('Method')
plt.ylabel('Sequencing Depth')
plt.legend(Step, bbox_to_anchor=(1.05, 1), loc=2)
plt.savefig(os.path.join(output_dir, 'bar_plot.png'), bbox_inches='tight')
plt.savefig(os.path.join(output_dir, 'bar_plot.pdf'), bbox_inches='tight')
plt.gcf().clear()
index = os.path.join(TEMPLATES, 'denoise_assets', 'index.html')
q2templates.render(index, output_dir)
def alpha_diversity(output_dir: str,
alpha_diversity: pd.Series,
metadata_column: str = None,
metadata: qiime2.Metadata = None,
palette: str = 'husl',
style: str = 'white',
context: str = 'paper',
labels : str = None ) -> None:
#first 2 vectors
alpha_div1 = alpha_diversity[0].to_frame()
alpha_div2 = alpha_diversity[1].to_frame()
alpha_div1.index.name = "sample-id"
alpha_div1.reset_index(inplace=True)
alpha_div2.index.name = "sample-id"
alpha_div2.reset_index(inplace=True)
if not labels:
merged = pd.merge(alpha_div1, alpha_div2, on = 'sample-id')
merged = merged.rename(columns = {'shannon_x':'1', 'shannon_y':'2'})
vars_to_plot = ['1', '2']
print ('Labeling columns...')
if len(alpha_diversity) >2:
for i in range((len(alpha_diversity))-2) :
alpha_div = alpha_diversity[i+2].to_frame()
alpha_div.index.name = "sample-id"
alpha_div.reset_index(inplace = True)
merged = pd.merge(merged, alpha_div, on = "sample-id")
merged = merged.rename(columns = {'shannon':(i+3)})
vars_to_plot = list(merged.loc[:, merged.columns !='sample-id'])
else:
if len(labels) != len(alpha_diversity):
raise ValueError("The number of labels is different than the number of tables")
merged = pd.merge(alpha_div1, alpha_div2, on = "sample-id")
merged = merged.rename(columns = {'shannon_x':labels[0], 'shannon_y':labels[1]})
vars_to_plot = list(merged.loc[:, merged.columns !='sample-id'])
print('Labeling columns ...')
if len(alpha_diversity) >2:
for i in range((len(alpha_diversity))-2) :
alpha_div = alpha_diversity[i+2].to_frame()
alpha_div.index.name = "sample-id"
alpha_div.reset_index(inplace = True)
merged = pd.merge(merged, alpha_div, on = "sample-id")
merged = merged.rename(columns = {'shannon':labels[i+2]})
vars_to_plot = list(merged.loc[:, merged.columns !='sample-id'])
melted_merged = pd.melt(merged, id_vars = 'sample-id')
if not metadata:
melted_merged = pd.melt(merged, id_vars = 'sample-id')
melted_merged = melted_merged.rename(columns = {'variable':'Table', 'value':'Alpha Diversity Index'})
print('Plotting pairplot ...')
sns.set_style(style)
sns.set_context(context)
pairplot_diversity = sns.pairplot(merged, vars = vars_to_plot, palette = palette)
pairplot_diversity.savefig(os.path.join(output_dir, 'pairplot_diversity.png'))
pairplot_diversity.savefig(os.path.join(output_dir, 'pairplot_diversity.pdf'))
plt.gcf().clear()
print('Plotting raincloud ...')
#raincloud_diversity = pt.RainCloud(x = 'Table', y = 'Alpha Diversity Index', data = melted_merged,
#orient = 'h', alpha = 0.65, palette = palette)
#raincloud_diversity.figure.savefig(os.path.join(output_dir, 'raincloud_diversity.png'), bbox_inches = 'tight')
#raincloud_diversity.figure.savefig(os.path.join(output_dir, 'raincloud_diversity.pdf'), bbox_inches = 'tight')
#plt.gcf().clear()
print('Plotting boxplot ...')
boxplot_diversity = sns.boxplot(data = melted_merged, x= 'Table', y= 'Alpha Diversity Index', palette = palette,
saturation = 1)
boxplot_diversity.figure.savefig(os.path.join(output_dir, 'boxplot_diversity.png'), bbox_inches = 'tight')
boxplot_diversity.figure.savefig(os.path.join(output_dir, 'boxplot_diversity.pdf'), bbox_inches = 'tight')
plt.gcf().clear()
else:
if not metadata_column:
raise ValueError("Metadata column not provided")
print('Merging metadata ...')
metadata = metadata.to_dataframe()
metadata.index.name = "sample-id"
metadata.reset_index(inplace = True)
merged_metadata = pd.merge(merged, metadata, on = "sample-id")
melted_merged_metadata = pd.merge(melted_merged, metadata, on = "sample-id")
melted_merged_metadata = melted_merged_metadata.rename(columns = {'variable':'Table', 'value':'Alpha Diversity Index'})
print('Plotting pairplot ...')
sns.set_style(style)
sns.set_context(context)
pairplot_diversity = sns.pairplot(merged_metadata, hue = metadata_column, vars = vars_to_plot, palette = palette)
pairplot_diversity.savefig(os.path.join(output_dir, 'pairplot_diversity.png'))
pairplot_diversity.savefig(os.path.join(output_dir, 'pairplot_diversity.pdf'))
plt.gcf().clear()
print('Plotting raincloud ...')
#raincloud_diversity = pt.RainCloud( x = 'Table', y = 'Alpha Diversity Index', data = melted_merged_metadata,
# orient = 'h', hue = metadata_column, alpha = 0.65, palette = palette )
#raincloud_diversity.figure.savefig(os.path.join(output_dir, 'raincloud_diversity.png'), bbox_inches = 'tight')
#raincloud_diversity.figure.savefig(os.path.join(output_dir, 'raincloud_diversity.pdf'), bbox_inches = 'tight')
#plt.gcf().clear()
print('Plotting boxplot ...')
boxplot_diversity = sns.boxplot(data=melted_merged_metadata,x='Table',y='Alpha Diversity Index',hue=metadata_column, palette = palette, saturation = 1)
boxplot_diversity.figure.savefig(os.path.join(output_dir, 'boxplot_diversity.png'), bbox_inches = 'tight')
boxplot_diversity.figure.savefig(os.path.join(output_dir, 'boxplot_diversity.pdf'), bbox_inches = 'tight')
plt.gcf().clear()
index = os.path.join(TEMPLATES, 'diversity_assets', 'index.html')
q2templates.render(index, output_dir)
