def balance_taxonomy(output_dir: str, table: pd.DataFrame, tree: TreeNode, taxonomy: pd.DataFrame, balance_name: str, pseudocount: float = 0.5, taxa_level: int = 0, n_features: int = 10, threshold: float = None, metadata: qiime2.MetadataColumn = None) -> None: if threshold is not None and isinstance(metadata, qiime2.CategoricalMetadataColumn): raise ValueError('Categorical metadata column detected. Only specify ' 'a threshold when using a numerical metadata column.') # make sure that the table and tree match up table, tree = match_tips(add_pseudocount(table, pseudocount), tree) # parse out headers for taxonomy taxa_data = list(taxonomy['Taxon'].apply(lambda x: x.split(';')).values) taxa_df = pd.DataFrame(taxa_data, index=taxonomy.index) # fill in NAs def f(x): y = np.array(list(map(lambda k: k is not None, x))) i = max(0, np.where(y)[0][-1]) x[np.logical_not(y)] = [x[i]] * np.sum(np.logical_not(y)) return x taxa_df = taxa_df.apply(f, axis=1) num_clade = tree.find(balance_name).children[NUMERATOR] denom_clade = tree.find(balance_name).children[DENOMINATOR] if num_clade.is_tip(): num_features = pd.DataFrame( {num_clade.name: taxa_df.loc[num_clade.name]} ).T r = 1 else: num_features = taxa_df.loc[num_clade.subset()] r = len(list(num_clade.tips())) if denom_clade.is_tip(): denom_features = pd.DataFrame( {denom_clade.name: taxa_df.loc[denom_clade.name]} ).T s = 1 else: denom_features = taxa_df.loc[denom_clade.subset()] s = len(list(denom_clade.tips())) b = (np.log(table.loc[:, num_features.index]).mean(axis=1) - np.log(table.loc[:, denom_features.index]).mean(axis=1)) b = b * np.sqrt(r * s / (r + s)) balances = pd.DataFrame(b, index=table.index, columns=[balance_name]) # the actual colors for the numerator and denominator num_color = sns.color_palette("Paired")[0] denom_color = sns.color_palette("Paired")[1] fig, (ax_num, ax_denom) = plt.subplots(2) balance_barplots(tree, balance_name, taxa_level, taxa_df, denom_color=denom_color, num_color=num_color, axes=(ax_num, ax_denom)) ax_num.set_title( r'$%s_{numerator} \; taxa \; (%d \; taxa)$' % ( balance_name, len(num_features))) ax_denom.set_title( r'$%s_{denominator} \; taxa \; (%d \; taxa)$' % ( balance_name, len(denom_features))) ax_denom.set_xlabel('Number of unique taxa') plt.tight_layout() fig.savefig(os.path.join(output_dir, 'barplots.svg')) fig.savefig(os.path.join(output_dir, 'barplots.pdf')) dcat = None multiple_cats = False if metadata is not None: fig2, ax = plt.subplots() c = metadata.to_series() data, c = match(balances, c) data[c.name] = c y = data[balance_name] # check if continuous if isinstance(metadata, qiime2.NumericMetadataColumn): ax.scatter(c.values, y) ax.set_xlabel(c.name) if threshold is None: threshold = c.mean() dcat = c.apply( lambda x: '%s < %f' % (c.name, threshold) if x < threshold else '%s > %f' % (c.name, threshold) ) sample_palette = pd.Series(sns.color_palette("Set2", 2), index=dcat.value_counts().index) elif isinstance(metadata, qiime2.CategoricalMetadataColumn): sample_palette = pd.Series( sns.color_palette("Set2", len(c.value_counts())), index=c.value_counts().index) try: pd.to_numeric(metadata.to_series()) except ValueError: pass else: raise ValueError('Categorical metadata column ' f'{metadata.name!r} contains only numerical ' 'values. At least one value must be ' 'non-numerical.') balance_boxplot(balance_name, data, y=c.name, ax=ax, palette=sample_palette) if len(c.value_counts()) > 2: warnings.warn( 'More than 2 categories detected in categorical metadata ' 'column. Proportion plots will not be displayed', stacklevel=2) multiple_cats = True else: dcat = c else: # Some other type of MetadataColumn raise NotImplementedError() ylabel = (r"$%s = \ln \frac{%s_{numerator}}" "{%s_{denominator}}$") % (balance_name, balance_name, balance_name) ax.set_title(ylabel, rotation=0) ax.set_ylabel('log ratio') fig2.savefig(os.path.join(output_dir, 'balance_metadata.svg')) fig2.savefig(os.path.join(output_dir, 'balance_metadata.pdf')) if not multiple_cats: # Proportion plots # first sort by clr values and calculate average fold change ctable = pd.DataFrame(clr(centralize(table)), index=table.index, columns=table.columns) left_group = dcat.value_counts().index[0] right_group = dcat.value_counts().index[1] lidx, ridx = (dcat == left_group), (dcat == right_group) if b.loc[lidx].mean() > b.loc[ridx].mean(): # double check ordering and switch if necessary # careful - the left group is also commonly associated with # the denominator. left_group = dcat.value_counts().index[1] right_group = dcat.value_counts().index[0] lidx, ridx = (dcat == left_group), (dcat == right_group) # we are not performing a statistical test here # we're just trying to figure out a way to sort the data. num_fold_change = ctable.loc[:, num_features.index].apply( lambda x: ttest_ind(x[ridx], x[lidx])[0]) num_fold_change = num_fold_change.sort_values( ascending=False ) denom_fold_change = ctable.loc[:, denom_features.index].apply( lambda x: ttest_ind(x[ridx], x[lidx])[0]) denom_fold_change = denom_fold_change.sort_values( ascending=True ) metadata = pd.DataFrame({dcat.name: dcat}) top_num_features = num_fold_change.index[:n_features] top_denom_features = denom_fold_change.index[:n_features] fig3, (ax_denom, ax_num) = plt.subplots(1, 2) proportion_plot( table, metadata, category=metadata.columns[0], left_group=left_group, right_group=right_group, feature_metadata=taxa_df, label_col=taxa_level, num_features=top_num_features, denom_features=top_denom_features, # Note that the syntax is funky and counter # intuitive. This will need to be properly # fixed here # https://github.com/biocore/gneiss/issues/244 num_color=sample_palette.loc[right_group], denom_color=sample_palette.loc[left_group], axes=(ax_num, ax_denom)) # The below is overriding the default colors in the # numerator / denominator this will also need to be fixed in # https://github.com/biocore/gneiss/issues/244 max_ylim, min_ylim = ax_denom.get_ylim() num_h, denom_h = n_features, n_features space = (max_ylim - min_ylim) / (num_h + denom_h) ymid = (max_ylim - min_ylim) * num_h ymid = ymid / (num_h + denom_h) - 0.5 * space ax_denom.axhspan(min_ylim, ymid, facecolor=num_color, zorder=0) ax_denom.axhspan(ymid, max_ylim, facecolor=denom_color, zorder=0) ax_num.axhspan(min_ylim, ymid, facecolor=num_color, zorder=0) ax_num.axhspan(ymid, max_ylim, facecolor=denom_color, zorder=0) fig3.subplots_adjust( # the left side of the subplots of the figure left=0.3, # the right side of the subplots of the figure right=0.9, # the bottom of the subplots of the figure bottom=0.1, # the top of the subplots of the figure top=0.9, # the amount of width reserved for blank space # between subplots wspace=0, # the amount of height reserved for white space # between subplots hspace=0.2, ) fig3.savefig(os.path.join(output_dir, 'proportion_plot.svg')) fig3.savefig(os.path.join(output_dir, 'proportion_plot.pdf')) index_fp = os.path.join(output_dir, 'index.html') with open(index_fp, 'w') as index_f: index_f.write('<html><body>\n') if metadata is not None: index_f.write('<h1>Balance vs %s </h1>\n' % c.name) index_f.write(('<img src="balance_metadata.svg" ' 'alt="barplots">\n\n' '<a href="balance_metadata.pdf">' 'Download as PDF</a><br>\n')) if not multiple_cats: index_f.write('<h1>Proportion Plot </h1>\n') index_f.write(('<img src="proportion_plot.svg" ' 'alt="proportions">\n\n' '<a href="proportion_plot.pdf">' 'Download as PDF</a><br>\n')) index_f.write(('<h1>Balance Taxonomy</h1>\n' '<img src="barplots.svg" alt="barplots">\n\n' '<a href="barplots.pdf">' 'Download as PDF</a><br>\n' '<h3>Numerator taxa</h3>\n' '<a href="numerator.csv">\n' 'Download as CSV</a><br>\n' '<h3>Denominator taxa</h3>\n' '<a href="denominator.csv">\n' 'Download as CSV</a><br>\n')) num_features.to_csv(os.path.join(output_dir, 'numerator.csv'), header=True, index=True) denom_features.to_csv(os.path.join(output_dir, 'denominator.csv'), header=True, index=True) index_f.write('</body></html>\n')
def balance_taxonomy(output_dir: str, balances: pd.DataFrame, tree: TreeNode, taxonomy: pd.DataFrame, balance_name: Str, taxa_level: Int = 0, metadata: MetadataCategory = None) -> None: # parse out headers for taxonomy taxa_data = list(taxonomy['Taxon'].apply(lambda x: x.split(';')).values) taxa_df = pd.DataFrame(taxa_data, index=taxonomy.index) # fill in NAs def f(x): y = np.array(list(map(lambda k: k is not None, x))) i = max(0, np.where(y)[0][-1]) x[np.logical_not(y)] = [x[i]] * np.sum(np.logical_not(y)) return x taxa_df = taxa_df.apply(f, axis=1) num_clade = tree.find(balance_name).children[NUMERATOR] denom_clade = tree.find(balance_name).children[DENOMINATOR] if num_clade.is_tip(): num_features = pd.DataFrame( {num_clade.name: taxa_df.loc[num_clade.name]} ).T else: num_features = taxa_df.loc[num_clade.subset()] if denom_clade.is_tip(): denom_features = pd.DataFrame( {denom_clade.name: taxa_df.loc[denom_clade.name]} ).T else: denom_features = taxa_df.loc[denom_clade.subset()] num_color, denom_color = '#4c72b0', '#4c72b0' fig, (ax_num, ax_denom) = plt.subplots(2) balance_barplots(tree, balance_name, taxa_level, taxa_df, denom_color=denom_color, num_color=num_color, axes=(ax_num, ax_denom)) ax_num.set_title( r'$%s_{numerator} \; taxa \; (%d \; taxa)$' % (balance_name, len(num_features))) ax_denom.set_title( r'$%s_{denominator} \; taxa \; (%d \; taxa)$' % (balance_name, len(denom_features))) ax_denom.set_xlabel('Number of unique taxa') plt.tight_layout() fig.savefig(os.path.join(output_dir, 'barplots.svg')) fig.savefig(os.path.join(output_dir, 'barplots.pdf')) if metadata is not None: fig2, ax = plt.subplots() c = metadata.to_series() data, c = match(balances, c) data[c.name] = c y = data[balance_name] # check if continuous try: c = c.astype(np.float64) ax.scatter(c.values, y) ax.set_xlabel(c.name) except: balance_boxplot(balance_name, data, y=c.name, ax=ax) ylabel = (r"$%s = \ln \frac{%s_{numerator}}" "{%s_{denominator}}$") % (balance_name, balance_name, balance_name) ax.set_title(ylabel, rotation=0) ax.set_ylabel('log ratio') fig2.savefig(os.path.join(output_dir, 'balance_metadata.svg')) fig2.savefig(os.path.join(output_dir, 'balance_metadata.pdf')) index_fp = os.path.join(output_dir, 'index.html') with open(index_fp, 'w') as index_f: index_f.write('<html><body>\n') if metadata is not None: index_f.write('<h1>Balance vs %s </h1>\n' % c.name) index_f.write(('<img src="balance_metadata.svg" ' 'alt="barplots">\n\n' '<a href="balance_metadata.pdf">' 'Download as PDF</a><br>\n')) index_f.write(('<h1>Balance Taxonomy</h1>\n' '<img src="barplots.svg" alt="barplots">\n\n' '<a href="barplots.pdf">' 'Download as PDF</a><br>\n' '<h3>Numerator taxa</h3>\n' '<a href="numerator.csv">\n' 'Download as CSV</a><br>\n' '<h3>Denominator taxa</h3>\n' '<a href="denominator.csv">\n' 'Download as CSV</a><br>\n')) num_features.to_csv(os.path.join(output_dir, 'numerator.csv'), header=True, index=True) denom_features.to_csv(os.path.join(output_dir, 'denominator.csv'), header=True, index=True) index_f.write('</body></html>\n')