Basic Dendrogram¶
A dendrogram is a diagram representing a tree. The figure factory called create_dendrogram
performs hierarchical
clustering on data and represents the resulting tree. Values on the tree depth axis correspond to distances between clusters.
Dendrogram plots are commonly used in computational biology to show the clustering of genes or samples, sometimes in the margin of heatmaps.
In [1]:
import plotly.figure_factory as ff import numpy as np np.random.seed[1] X = np.random.rand[15, 12] # 15 samples, with 12 dimensions each fig = ff.create_dendrogram[X] fig.update_layout[width=800, height=500] fig.show[]
Set Color Threshold¶
In [2]:
import plotly.figure_factory as ff import numpy as np X = np.random.rand[15, 10] # 15 samples, with 10 dimensions each fig = ff.create_dendrogram[X, color_threshold=1.5] fig.update_layout[width=800, height=500] fig.show[]
Set Orientation and Add Labels¶
In [3]:
import plotly.figure_factory as ff import numpy as np X = np.random.rand[10, 12] names = ['Jack', 'Oxana', 'John', 'Chelsea', 'Mark', 'Alice', 'Charlie', 'Rob', 'Lisa', 'Lily'] fig = ff.create_dendrogram[X, orientation='left', labels=names] fig.update_layout[width=800, height=800] fig.show[]
In [4]:
import plotly.graph_objects as go import plotly.figure_factory as ff import numpy as np from scipy.spatial.distance import pdist, squareform # get data data = np.genfromtxt["//files.figshare.com/2133304/ExpRawData_E_TABM_84_A_AFFY_44.tab", names=True,usecols=tuple[range[1,30]],dtype=float, delimiter="\t"] data_array = data.view[[np.float, len[data.dtype.names]]] data_array = data_array.transpose[] labels = data.dtype.names # Initialize figure by creating upper dendrogram fig = ff.create_dendrogram[data_array, orientation='bottom', labels=labels] for i in range[len[fig['data']]]: fig['data'][i]['yaxis'] = 'y2' # Create Side Dendrogram dendro_side = ff.create_dendrogram[data_array, orientation='right'] for i in range[len[dendro_side['data']]]: dendro_side['data'][i]['xaxis'] = 'x2' # Add Side Dendrogram Data to Figure for data in dendro_side['data']: fig.add_trace[data] # Create Heatmap dendro_leaves = dendro_side['layout']['yaxis']['ticktext'] dendro_leaves = list[map[int, dendro_leaves]] data_dist = pdist[data_array] heat_data = squareform[data_dist] heat_data = heat_data[dendro_leaves,:] heat_data = heat_data[:,dendro_leaves] heatmap = [ go.Heatmap[ x = dendro_leaves, y = dendro_leaves, z = heat_data, colorscale = 'Blues' ] ] heatmap[0]['x'] = fig['layout']['xaxis']['tickvals'] heatmap[0]['y'] = dendro_side['layout']['yaxis']['tickvals'] # Add Heatmap Data to Figure for data in heatmap: fig.add_trace[data] # Edit Layout fig.update_layout[{'width':800, 'height':800, 'showlegend':False, 'hovermode': 'closest', }] # Edit xaxis fig.update_layout[xaxis={'domain': [.15, 1], 'mirror': False, 'showgrid': False, 'showline': False, 'zeroline': False, 'ticks':""}] # Edit xaxis2 fig.update_layout[xaxis2={'domain': [0, .15], 'mirror': False, 'showgrid': False, 'showline': False, 'zeroline': False, 'showticklabels': False, 'ticks':""}] # Edit yaxis fig.update_layout[yaxis={'domain': [0, .85], 'mirror': False, 'showgrid': False, 'showline': False, 'zeroline': False, 'showticklabels': False, 'ticks': "" }] # Edit yaxis2 fig.update_layout[yaxis2={'domain':[.825, .975], 'mirror': False, 'showgrid': False, 'showline': False, 'zeroline': False, 'showticklabels': False, 'ticks':""}] # Plot! fig.show[]
What About Dash?¶
Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn about how to install Dash at //dash.plot.ly/installation.
Everywhere in this page that you see fig.show[]
, you can display the same figure in a Dash application by passing it to the figure
argument of
the Graph
component from the built-in dash_core_components
package like this:
import plotly.graph_objects as go # or plotly.express as px fig = go.Figure[] # or any Plotly Express function e.g. px.bar[...] # fig.add_trace[ ... ] # fig.update_layout[ ... ] import dash import dash_core_components as dcc import dash_html_components as html app = dash.Dash[] app.layout = html.Div[[ dcc.Graph[figure=fig] ]] app.run_server[debug=True, use_reloader=False] # Turn off reloader if inside Jupyter