pykegg.utils module¶

pykegg.utils.append_colors(node_df, candidate, new_column_name='color', candidate_column='graphics_name', delim=',', true_color='#ff0000', false_color='#ffffff')¶

Append discrete colors to the node_df based on intersection with candidate ID list.

Parameters:¶

node_df: DataFrame: node data obtained by get_nodes().
candidate: list: list of candidate IDs.
new_column_name: str: the name of the new column.
candidate_column: str: the column in node_df specifying candidate IDs.
delim: str: the delimiter of the node IDs. Typically “,” for graphics_name, and “ “ for name.
true_color: str: the color of the candidate nodes.
false_color: str: the color of the non-candidate nodes.

pykegg.utils.append_colors_continuous_values(node_df, lfc_dict, node_name_column='graphics_name', new_color_column='color', delim=',', colors=None, two_slope=True, center_value='median', orig_value=None, fix_min=None, fix_max=None, fix_center=None)¶

Append continuous colors to the node_df based on the values in dict.

Parameters:¶

node_df: DataFrame: node data obtained by get_nodes().
lfc_dict: dict: dict of values.
node_name_column: str: the column in node_df specifying node IDs.
new_color_column: str: the name of the new column.
delim: str: the delimiter of the node IDs. Typically “,” for graphics_name, and “ “ for name.
colors: list: the colors to be used. Default is [“#0000ff”, “#ffffff”, “#ff0000”].
two_slope: bool: whether to use two-slope color scheme. Default is True.
center_value: str or float: the center value of the color scheme. Default is “median”.
orig_value: str: If specified, append the values used to compute color in the DataFrame.
fix_min: float: fixed minimum value to calculate color
fix_max: float: fixed maximum value to calculate color
fix_center: float: fixed center value to calculate color

pykegg.utils.append_legend(image, min_value=-2, max_value=2, center_value=0, two_slope=True, colors=None, width=1, height=0.6, bottom=0.8, pos='topright', label='Label')¶

Add specified legend to image array

Parameters:¶

image: numpy array: Image numpy array
min_value: float: Minimum value of the color bar
max_value: float: Maximum value of the color bar
center_value: float: Center value of the color bar
two_slope: bool: If True, use two slope color bar
colors: list: List of colors, if None, use blue-white-red color bar
width: float: Width of the color bar
height: float: Height of the color bar
bottom: float: Bottom position of the color bar
pos: str: Position specification, “topright”, “bottomright”, “bottomleft”, “topleft”
label: str: Label of the color bar

pykegg.utils.check_cache(response)¶

Check if response is from cache.

Parameters:¶

response: requests.Response: Response object.

pykegg.utils.convert_id(x, c_dic, first_only=True)¶

convert ID based on dict

Parameters:¶

x: str: node name
c_dic: dict: dictionary obtained typically obtained by id_to_name_dict keys correspond to KEGG ID and values correspond to name
first_only:: return only first string separated by space

pykegg.utils.deseq2_raw_map(results_df, path=None, pid=None, node_name_column='graphics_name', delim=',', color_column='log2FoldChange', highlight_sig=False, highlight_color='#ff0000', highlight_padj_thresh=0.05, colors=None, two_slope=True, center_value=0, show_legend=True, legend_label=None, legend_position='topright', legend_width=1, legend_height=0.6, legend_bottom=0.8)¶

Plot PyDESeq2 results on KEGG pathway map

Parameters:¶

results_df: pandas.DataFrame: PyDESeq2 results dataframe
path: str: Path to the KEGG pathway map
pid: str: KEGG pathway ID
node_name_column: str: Column name of the node name
delim: str: Delimiter of the node name
color_column: str: Column name of the color
highlight_sig: bool: If True, highlight significant genes
highlight_color: str: Color of the highlight
highlight_padj_thresh: float: P-value threshold for the highlight
colors: list: List of colors, if None, use blue-white-red color bar
two_slope: bool: If True, use two slope color bar
center_value: float: Center value of the color bar
show_legend: bool: If True, show legend
legend_label: str: Label of the legend
legend_position: str: Position of the legend
legend_width: float: Width of the legend
legend_height: float: Height of the legend
legend_bottom: float: Bottom position of the legend

pykegg.utils.hex2rgb(hex_str)¶

Convert hex string to rgb tuple.

Parameters:¶

hex_str: str: hex string, e.g. “#ffffff”.

pykegg.utils.id_to_name_dict(list_id='hsa', column=3, semicolon=True, comma=True)¶

Get KEGG ID to name dictionary.

Parameters:¶

list_id: str: organism ID.
column: int: Column to use for name.
semicolon: bool: Whether to split by semicolon.
comma: bool: Whether to split by comma.

pykegg.utils.overlay(rects, kegg_map)¶

overlay two images with transparency

Parameters:¶

rects: np.array: Image numpy array
kegg_map: np.array: Image numpy array

pykegg.utils.overlay_continuous_values_with_legend(node_df, value_dict, path=None, pid=None, node_name_column='graphics_name', delim=',', colors=None, legend_label='value', legend_position='topright', legend_width=1, legend_height=0.6, legend_bottom=0.8, transparent_colors=None, two_slope=True, center_value='median')¶

Obtain the raw image of pathway and color the nodes, return the overlaid image with legend.

Parameters:¶

node_df: DataFrame: node data obtained by get_nodes().

pykegg.utils.overlay_opencv_image(node_df, path=None, pid=None, fill_color='color', transparent_colors=None, highlight_nodes=None, highlight_color='#ff0000', highlight_expand=2)¶

Obtain the raw image of pathway and color the nodes.

Parameters:¶

node_df: DataFrame: node data obtained by get_nodes().
path: str: path to the image if already downloaded.
pid: str: KEGG pathway identifier.
fill_color: str: the column in node_df specifying color in HEX. If list is given, split the width according to the color number. Skip the node if None.
transparent_color: list of str: specify which color to be transparent. If None, default [“#FFFFFF”, “#BFFFBF”, “#BFBFFF”] is used.
highlight_nodes: str: the column in node_df specifying which nodes to be highlighted.
hihglight_color: str: the color of the highlighted nodes.
highlight_expand: int: the number of pixels to expand the highlighted nodes.

pykegg.utils.parallel_edges(df, move_param=5)¶

Experimental function moving x and y positions if multiple edges are to be plotted in plotnine based on whether the y position is the same between two points

Parameters:

df (pd.DataFrame) – data frame returned by return_segments
move_params (float or int) – parameter to control edge nudge

pykegg.utils.parallel_edges2(df, move_param=5)¶

Experimental function moving x and y positions if multiple edges are to be plotted in plotnine based on degrees between points

Parameters:

df (pd.DataFrame) – data frame returned by return_segments
move_params (float or int) – parameter to control edge nudge

pykegg.utils.pathway_name_to_id_dict(list_id='hsa')¶

Get pathway name to ID dictionary.

Parameters:¶

list_id: str: organism ID.

pykegg.utils.return_color_bar(width=1, height=0.6, bottom=0.8, min_value=-2, max_value=2, two_slope=True, center_value=0, colors=None, label='Label')¶

Return color bar as a numpy array.

Parameters:¶

width: int: width of the color bar.
height: int: height of the color bar.
bottom: int: bottom of the color bar.
min_value: int: minimum value of the color bar.
max_value: int: maximum value of the color bar.
two_slope: bool: if True, use two slope norm.
center_value: int: center value of the color bar.
colors: list: list of colors.
label: str: label of the color bar.

pykegg.utils.return_segments(graph, node_df=None, edge_df=None)¶

Return edge dataframe to having xend and yend

Parameters:¶

graph: KGML_graph: KGML_graph class object
node_df: DataFrame: node data obtained by get_nodes().
edge_df: DataFrame: edge data obtained by get_edges().

pykegg.utils.shorten_end(row, pct=0.8, absolute=None)¶

shorten segments by moving yend and xend

Parameters:¶

row: pd.Series: Series of edge data frame
pct: float: scaling factor
absolute: float: absolute distance to shorten

pykegg.utils.visualize(pathway_name, genes, db=None, org=None, column_name='graphics_name', false_color='#707070', true_color='#FA8072', output=None)¶

Output pathway image based on pathway name and gene symbol list

Parameters:¶

pathway_name: str: pathway name (not ID)
genes: str: list of genes
db: str: database name
org: str: if not specified db, the parameter will be used to convert pathway name to ID
column_name: str: column name to match for in node data
true_color: str: HEX specifying color for matched nodes
false_color: str: HEX specifying color for not matched nodes
output: str: output image file, default to None, meaning return the Image

pykegg.utils.visualize_gseapy(gsea_res, colors, pathway_name=None, pathway_id=None, org='hsa', column_name='graphics_name', false_color='#707070')¶

Visualize GSEApy results.

Parameters:¶

gsea_res: GSEApy object or list of GSEApy objects: GSEApy results.
colors: str or list of str: Colors to use for each gsea results.
pathway_name: str: Pathway name.
pathway_id: str: Pathway ID.
org: str: KEGG organism ID.
column_name: str: Column name to use for visualization.
false_color: str: Color to use for false nodes.

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