Functionality: Implemented
K
, or a fixed color. In the case of node color, a column name in the data and aggregate function can be used too. Use of column name and #samples creates a continuous colormap, whilst the other options result in discrete colors.Functionality: To Add
Install the package with pip:
pip install clustree
The powerhouse function of the library is clustree
. Use
from clustree import clustree
to import the function. A detailed description of the parameters is provided below.
def clustree(
data: Union[Path, str],
prefix: str,
images: Union[Path, str],
output_path: Optional[Union[Path, str]] = None,
draw: bool = True,
node_color: str = "prefix",
node_color_aggr: Optional[Union[Callable, str]] = None,
node_cmap: Union[mpl.colors.Colormap, str] = "inferno",
edge_color: str = "samples",
edge_cmap: Union[mpl.colors.Colormap, str] = "viridis",
orientation: Literal["vertical", "horizontal"] = "vertical",
layout_reingold_tilford: bool = None,
min_cluster_number: Literal[0, 1] = 1,
border_size: float = 0.05,
figsize: tuple[float, float] = None,
arrows: bool = None,
node_size: float = 300,
node_size_edge: Optional[float] = None,
dpi: float = 500,
kk: Optional[int] = None,
) -> DiGraph:
"""
data
: Path of csv or DataFrame object.prefix
: String indicating columns containing clustering information.images
: Path of directory that contains images.output_path
: Absolute path to save clustree drawing at. If file extension is supplied, must be .png. If None, then output not written to file.draw
: Whether to draw the clustree. Defaults to True. If False and output_path supplied, will be overridden.node_color
: For continuous colormap, use 'samples' or the name of a metadata column to color nodes by. For discrete colors, use 'prefix' to color by resolution or specify a fixed color (see Specifying colors in Matplotlib tutorial here: https://matplotlib.org/stable/tutorials/colors/colors.html). If None, default set equal to value of prefix to color by resolution.node_color_aggr
: If node_color is a column name then a function or string giving the name of a function to aggregate that column for samples in each cluster.node_cmap
: If node_color is 'samples' or a column name then a colourmap to use (see Colormap Matplotlib tutorial here: https://matplotlib.org/stable/tutorials/colors/colormaps.html).edge_color
: For continuous colormap, use 'samples'. For discrete colors, use 'prefix' to color by resolution or specify a fixed color (see Specifying colors in Matplotlib tutorial here: https://matplotlib.org/stable/tutorials/colors/colors.html). If None, default set to 'samples'.edge_cmap
: If edge_color is 'samples' then a colourmap to use (see Colormap Matplotlib tutorial here: https://matplotlib.org/stable/tutorials/colors/colormaps.html).orientation
: Orientation of clustree drawing. Defaults to 'vertical'.layout_reingold_tilford
: Whether to use the Reingold-Tilford algorithm for node positioning. Defaults to True if (kk <= 12), False otherwise. Setting True not recommended if (kk > 12) due to memory bottleneck in igraph dependency.min_cluster_number
: Cluster number can take values (0, ..., K-1) or (1, ..., K). If the former option is preferred, parameter should take value 0, and 1 otherwise. Defaults to None, in which case, minimum cluster number is found automatically.border_size
: Border width as proportion of image width. Defaults to 0.05.figsize
: Parsed to matplotlib to determine figure size. Defaults to (kk/2, kk/2), clipped to a minimum of (3,3) and maximum of (10,10).arrows
: Whether to add arrows to graph edges. Removing arrows alleviates appearance issue caused by arrows overlapping nodes. Defaults to True.node_size
: Size of nodes in clustree graph drawing. Parsed directly to networkx.draw_networkx_nodes. Default to 300.node_size_edge
: Controls edge start and end point. Parsed directly to networkx.draw_networkx_edges.dpi
: Controls resolution of output if saved to file.kk
: Choose custom depth of clustree graph.K
. For example, at cluster resolution K=2
data is clustered into 2 distinct clusters.k
. For example, at cluster resolution 2 data is clustered into 2 distinct clusters k=1
and k=2
.K
(cluster resolution) shown in clustree.[1, 1, 2, 2, 2]
would mean the first 2 data points belong to cluster number 1
and the following 3 data points belong to cluster number 2
.