[1]:

import os,sys
%matplotlib inline
import matplotlib.pylab as plt
import pickle
plt.rcParams['figure.dpi'] = 100
plt.rcParams['savefig.dpi']=300
plt.rcParams['font.family']='sans serif'
plt.rcParams['font.sans-serif']='Arial'
plt.rcParams['pdf.fonttype']=42
# sys.path.append(os.path.expanduser("~/Projects/Github/PyComplexHeatmap/"))
from PyComplexHeatmap import *

A quick example

[4]:

#Generate example dataset (random)
df = pd.DataFrame(['AAAA1'] * 5 + ['BBBBB2'] * 5, columns=['AB'])
df['CD'] = ['C'] * 3 + ['D'] * 3 + ['G'] * 4
df['EF'] = ['E'] * 6 + ['F'] * 2 + ['H'] * 2
df['F'] = np.random.normal(0, 1, 10)
df.index = ['sample' + str(i) for i in range(1, df.shape[0] + 1)]
df_box = pd.DataFrame(np.random.randn(10, 4), columns=['Gene' + str(i) for i in range(1, 5)])
df_box.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
df_bar = pd.DataFrame(np.random.uniform(0, 10, (10, 2)), columns=['TMB1', 'TMB2'])
df_bar.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
df_scatter = pd.DataFrame(np.random.uniform(0, 10, 10), columns=['Scatter'])
df_scatter.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
df_heatmap = pd.DataFrame(np.random.randn(30, 10), columns=['sample' + str(i) for i in range(1, 11)])
df_heatmap.index = ["Fea" + str(i) for i in range(1, df_heatmap.shape[0] + 1)]
df_heatmap.iloc[1, 2] = np.nan

plt.figure(figsize=(5, 8))
col_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True,rotation=15),
                           AB=anno_simple(df.AB,cmap='Set2'),axis=1,
                           CD=anno_simple(df.CD,add_text=True,colors={'C':'tomato','D':'yellowgreen','G':'skyblue'},
                                            legend_kws={'frameon':False}),
                           Exp=anno_boxplot(df_box, cmap='turbo',height=10),
                           Scatter=anno_scatterplot(df_scatter,height=10),
                           TMB_bar=anno_barplot(df_bar,height=10,legend_kws={'color_text':False,'labelcolor':'blue'}))
cm = ClusterMapPlotter(data=df_heatmap, top_annotation=col_ha, col_split=2, row_split=3, col_split_gap=0.5,
                     row_split_gap=1,label='values',row_dendrogram=True,show_rownames=False,show_colnames=False,
                     tree_kws={'row_cmap': 'Dark2'},cmap='Spectral_r',
                       legend_gap=5,legend_hpad=2,legend_vpad=5)
#legend_gap is the gap between two legends, legend_hpad is the horizonal space between legend and heatmap, legend_vpad
# is the verticall space between the first legend and the top of axes (legend_anchor).
# cm.ax_heatmap.set_axis_off()
plt.show()

Starting plotting..
Starting calculating row orders..
Reordering rows..
Starting calculating col orders..
Reordering cols..
Plotting matrix..
Starting plotting HeatmapAnnotations
Collecting legends..
Collecting annotation legends..
Plotting legends..
Estimated legend width: 19.051388888888887 mm
../_images/notebooks_clustermap_2_1.png

Plotting annotations

Only plot the row/column annotation

[3]:

plt.figure(figsize=(6, 4))
col_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True,rotation=15),
                            AB=anno_simple(df.AB,add_text=True,legend=True,cmap='Dark2'), axis=1,
                            CD=anno_simple(df.CD, colors={'C': 'red', 'D': 'gray', 'G': 'yellow'},
                                           add_text=True,legend=True),
                            Exp=anno_boxplot(df_box, cmap='turbo',legend=True),
                            Scatter=anno_scatterplot(df_scatter), TMB_bar=anno_barplot(df_bar,legend=True),
                           plot=True,legend=True,legend_gap=3)
col_ha.show_ticklabels(df.index.tolist())
plt.show()

Starting plotting HeatmapAnnotations
Collecting annotation legends..
../_images/notebooks_clustermap_5_1.png

anno_label:

anno_label is used to add a text label to the annotatin, parameter merge control whether to merge the adjacent labels with the same text, if merge != True, then, texts would be draw for each columns. We can also annotate the selected rows/cols using anno_label. In the following example, we also set annot=True to show the float value for each cell,linewidths=0.05,linecolor='orange' can be used to control the line and line color for the boarder between cells.

[4]:

#Annotate the rows with average > 0.3
df_rows = df_heatmap.apply(lambda x:x.name if x.mean() > 0.3 else None,axis=1)
df_rows.name='Selected'

row_ha = HeatmapAnnotation(Avg=anno_simple(df_heatmap.mean(axis=1).apply(lambda x:round(x,2)),
                                           cmap='jet'), #add_text=True,,text_kws={'rotation':0,'fontsize':10,'color':'black'}
                            # Avg=anno_barplot(df_heatmap.mean(axis=1).apply(lambda x:round(x,2)),
                            #                height=10,colors='orangered'),
                           selected=anno_label(df_rows,colors='red'),
                           axis=0,verbose=0,label_kws={'rotation':0,'horizontalalignment':'left'})

col_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True,rotation=15),
                           AB=anno_simple(df.AB,add_text=True,cmap='Set2'),axis=1,
                           Exp=anno_boxplot(df_box, cmap='turbo'),
                           verbose=0) #verbose=0 will turn off the log.

plt.figure(figsize=(6, 8))
cm = ClusterMapPlotter(data=df_heatmap, top_annotation=col_ha,right_annotation=row_ha,
                       col_split=2,row_split=2, col_split_gap=0.5,row_split_gap=1,
                       label='values',row_dendrogram=True,show_rownames=False,show_colnames=False,
                         tree_kws={'row_cmap': 'Set1'},verbose=0,legend_gap=7,
                       annot=True,linewidths=0.05,linecolor='gold',cmap='turbo')
plt.show()
../_images/notebooks_clustermap_7_0.png

anno_simple:

anno_simple is to draw simple annotatin, cmap for anno_simple can be either categorical (Set1, Dark2, tab10 et.al) or continnuous (jet, turbo, parula). Parameter add_text control whether to add text on the annotation, if the color and fontsize in text_kws was not specified, the color and fontsize would be determined automatically, for example, if the background color is deep, then the text color would be white, otherwise the text color would be black. The text color can be changed with parameter text_kws={‘color’:your_color},for example:

[5]:

plt.figure(figsize=(5, 4))
col_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True,rotation=15),
                            AB=anno_simple(df.AB,add_text=True,legend=True,text_kws={'color':'gold'}),
                            CD=anno_simple(df.CD,add_text=True,legend=True,text_kws={'color':'purple'}),
                            Exp=anno_boxplot(df_box, cmap='turbo',legend=True),
                            Scatter=anno_scatterplot(df_scatter), TMB_bar=anno_barplot(df_bar,legend=True),
                           plot=True,legend=True,legend_gap=5,axis=1)
col_ha.show_ticklabels(df.index.tolist())
plt.show()

Starting plotting HeatmapAnnotations
Collecting annotation legends..
../_images/notebooks_clustermap_9_1.png

To add a annotation quickly, you just need a dataframe

if df was given, all columns in dataframe df would be treated as a separately anno_simple annotation.

[6]:

plt.figure(figsize=(5, 3))
col_ha = HeatmapAnnotation(df=df,plot=True,legend=True)
plt.show()

Starting plotting HeatmapAnnotations
Collecting annotation legends..
../_images/notebooks_clustermap_11_1.png

Plot the figure and legend separately

Sometimes, one only want to plot the figure without legend, or plot the legend in a separated pdf, you can do that by giving the parameter plot_legend=False, and plot the legend in another pdf with row_ha.plot_legends

[7]:

plt.figure(figsize=(6, 4))
col_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True,rotation=15),
                            AB=anno_simple(df.AB,add_text=True,legend=True), axis=1,
                            CD=anno_simple(df.CD,add_text=True,legend=True),
                            Exp=anno_boxplot(df_box, cmap='turbo',legend=True),
                            Scatter=anno_scatterplot(df_scatter), TMB_bar=anno_barplot(df_bar,legend=True),
                           plot=True,legend=True,plot_legend=False,
                           legend_gap=5,hgap=0)
col_ha.show_ticklabels(df.index.tolist())
plt.show()

plt.figure()
row_ha.plot_legends()
plt.show()

Starting plotting HeatmapAnnotations
../_images/notebooks_clustermap_13_1.png 
No ax was provided, using plt.gca()
../_images/notebooks_clustermap_13_3.png

Top, bottom, left ,right annotations

[8]:

# Load an example dataset
with open("../data/mammal_array.pkl", 'rb') as f:
    data = pickle.load(f)
df, df_rows, df_cols, col_colors_dict = data

[9]:

df

[9]:
GSM4412025 GSM4412026 GSM4412027 GSM4412028 GSM4412029 GSM4412030 GSM4412031 GSM4412032 GSM4412033 GSM4412034 ... GSM4997945 GSM4997946 GSM4997947 GSM4997948 GSM4997949 GSM4997950 GSM4997951 GSM4997952 GSM4997953 GSM4997954
sheep 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 ... 0.435033 0.432900 0.446626 0.449123 0.497180 0.515918 0.483706 0.504681 0.529076 0.446443
beluga whale 0.687488 0.694207 0.706525 0.702734 0.687014 0.704003 0.705887 0.693806 0.719417 0.712677 ... 0.560381 0.571552 0.610392 0.613619 0.675832 0.668502 0.624820 0.658377 0.702334 0.575034
house mouse 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
vaquita 0.693523 0.702525 0.716792 0.725095 0.711261 0.708651 0.717952 0.705486 0.720915 0.724171 ... 0.581862 0.594443 0.628908 0.639457 0.680801 0.707493 0.662338 0.665142 0.751859 0.584952
large flying fox 0.286822 0.269406 0.296796 0.314719 0.305074 0.308419 0.268421 0.297236 0.295019 0.297973 ... 0.281684 0.363926 0.367216 0.359392 0.289821 0.257351 0.234301 0.295840 0.249336 0.344848
greater horseshoe bat 0.530606 0.517989 0.520719 0.525855 0.507583 0.511993 0.528107 0.521563 0.542159 0.509526 ... 0.735151 0.714805 0.708868 0.738207 0.823604 0.828040 0.823382 0.796882 0.878783 0.693625
little brown bat 0.202525 0.215990 0.238977 0.241872 0.267750 0.236729 0.202384 0.240519 0.251148 0.267257 ... 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000

7 rows × 883 columns

[10]:

df_rows

[10]:
PredictedTaxid PredictedSpecies common_names Family
sheep 9940.0 ovis_aries_rambouillet sheep Bovidae
beluga whale 9749.0 delphinapterus_leucas beluga whale Monodontidae
house mouse 10090.0 mus_musculus house mouse Muridae
vaquita 42100.0 phocoena_sinus vaquita Phocoenidae
large flying fox 132908.0 pteropus_vampyrus large flying fox Pteropodidae
greater horseshoe NaN NaN NaN NaN
bat 59479.0 rhinolophus_ferrumequinum greater horseshoe bat Rhinolophidae
little brown bat 59463.0 myotis_lucifugus little brown bat Vespertilionidae 
[11]:

df_cols

[11]:
GSE Basename NCBI_scientific_name taxid Tissue Sex Family Order Species SuccessRate common_names
GSM4412025 GSE147003 GSM4412025 Ovis aries 9940 Blood Female Bovidae Artiodactyla Ovis aries 0.765818 sheep
GSM4412026 GSE147003 GSM4412026 Ovis aries 9940 Blood Female Bovidae Artiodactyla Ovis aries 0.797669 sheep
GSM4412027 GSE147003 GSM4412027 Ovis aries 9940 Blood Female Bovidae Artiodactyla Ovis aries 0.759256 sheep
GSM4412028 GSE147003 GSM4412028 Ovis aries 9940 Blood Male Bovidae Artiodactyla Ovis aries 0.749813 sheep
GSM4412029 GSE147003 GSM4412029 Ovis aries 9940 Blood Female Bovidae Artiodactyla Ovis aries 0.770433 sheep
... ... ... ... ... ... ... ... ... ... ... ...
GSM4997950 GSE164127 GSM4997950 Eptesicus fuscus 29078 Skin Male Vespertilionidae Chiroptera Eptesicus fuscus 0.660425 big brown bat
GSM4997951 GSE164127 GSM4997951 Eptesicus fuscus 29078 Skin Male Vespertilionidae Chiroptera Eptesicus fuscus 0.652822 big brown bat
GSM4997952 GSE164127 GSM4997952 Eptesicus fuscus 29078 Skin Female Vespertilionidae Chiroptera Eptesicus fuscus 0.664746 big brown bat
GSM4997953 GSE164127 GSM4997953 Eptesicus fuscus 29078 Skin Male Vespertilionidae Chiroptera Eptesicus fuscus 0.650848 big brown bat
GSM4997954 GSE164127 GSM4997954 Eptesicus fuscus 29078 Skin Male Vespertilionidae Chiroptera Eptesicus fuscus 0.657170 big brown bat

883 rows × 11 columns

[12]:

col_colors_dict

[12]:

{'Tissue': {'#A40043': 'Blood',
  '#00E5FF': 'Brain',
  '#00BECC': 'Cerebellum',
  '#B2F8FF': 'Striatum',
  '#6CBF00': 'Liver',
  '#FFCCEE': 'Muscle',
  '#1E9351': 'Skin'}}

[13]:

#Put annotations on the top
col_ha = HeatmapAnnotation(label=anno_label(df_cols.Family, merge=True, rotation=45),
                               Family=anno_simple(df_cols.Family, legend=True),
                               Tissue=df_cols.Tissue,label_side='right', axis=1)
plt.figure(figsize=(7, 4))
cm = ClusterMapPlotter(data=df, top_annotation=col_ha,
                       show_rownames=True, show_colnames=False,row_names_side='left',
                       col_split=df_cols.Family, cmap='exp1', label='AUC',
                       rasterized=True, legend=True,legend_anchor='ax_heatmap',legend_width=50)
#legend_pad control the space between heatmap and legend.
#plt.savefig("clustermap.pdf", bbox_inches='tight')
plt.show()

Starting plotting..
Starting calculating row orders..
Reordering rows..
Starting calculating col orders..
Reordering cols..
Plotting matrix..
Starting plotting HeatmapAnnotations
Collecting legends..
Collecting annotation legends..
Plotting legends..
Incresing ncol
Incresing ncol
More than 3 cols is not supported
Legend too long, generating a new column..
../_images/notebooks_clustermap_20_1.png

If you have a very long list of legends, the function will automatically increace another column of legend, for example, in the above plot, there are two columns of legends.

[14]:

#Put annotations on the bottom
col_ha = HeatmapAnnotation(Tissue=anno_simple(df_cols.Tissue,height=5),
                           Family=anno_simple(df_cols.Family, legend=False,height=6),
                           label=anno_label(df_cols.Family, merge=True,rotation=-45),
                           label_side='right',axis=1)
plt.figure(figsize=(6.5, 4))
cm = ClusterMapPlotter(data=df, bottom_annotation=col_ha,
                       show_rownames=True, show_colnames=False,row_names_side='right',
                       col_split=df_cols.Family, cmap='jet', label='AUC',
                       rasterized=True, legend=True)
plt.show()

Starting plotting..
Starting calculating row orders..
Reordering rows..
Starting calculating col orders..
Reordering cols..
Plotting matrix..
Starting plotting HeatmapAnnotations
Collecting legends..
Collecting annotation legends..
Plotting legends..
Estimated legend width: 25.930555555555557 mm
../_images/notebooks_clustermap_22_1.png

If you want to put the columns annotations on the bottom, then you need to change the order of HeatmapAnnotation, anno_label should be the last one and anno_label(df_cols.Family) should be the second last one. When the columns annotation is on the top, the rotation of the anno_label is 45, but when it is on the bottom, rotation should be -45 (rotate to the other direction). In addition, you can change the row labels to the right by setting row_names_side='right'. It’s worth noting that the gap betwee the heatmap and the legend could be automatically determined by the code when you set row_names_side to the right. The height of the annotation bar could be changed by the parameter height (mm) in anno_simple or other kinds of annotation functions.

[15]:

#Put annotations on the left
row_ha = HeatmapAnnotation(label=anno_label(df_cols.Family, merge=True,rotation=45),
                           Family=anno_simple(df_cols.Family, legend=True,height=5),
                           Tissue=anno_simple(df_cols.Tissue,height=5),
                           label_side='top',
                           label_kws={'rotation':45,'rotation_mode':'anchor','color':'red'},
                           axis=0)
plt.figure(figsize=(4, 6))
cm = ClusterMapPlotter(data=df.T,left_annotation=row_ha,
                       show_rownames=False, show_colnames=True,col_names_side='top',
                       row_split=df_cols.Family, row_split_gap=0,
                       cmap='exp1', label='AUC',
                       rasterized=True, legend=True,
                       xticklabels_kws={'labelrotation':45,'labelcolor':'blue'},
                       ylabel="Samples",xlabel="Predicted Species",
                       xlabel_kws=dict(labelpad=0),
                       ylabel_kws=dict(labelpad=50),ylabel_bbox_kws=dict(facecolor='green'))
plt.show()

Starting plotting..
Starting calculating row orders..
Reordering rows..
Starting calculating col orders..
Reordering cols..
Plotting matrix..
Starting plotting HeatmapAnnotations
Collecting legends..
Collecting annotation legends..
Plotting legends..
Estimated legend width: 39.68888888888889 mm
../_images/notebooks_clustermap_24_1.png

To put annotation on the left in this example, we tranpose the dataframe by useing df.T and use left_annotation. We can put the columns labels on the top by set col_names_side='top' and use xticklabels_kws to change the rotation and color of the columsn labels. We can also change the rotation and color for the annotation labels (for example, Family and Tissue in this plot) by set label_kws={'rotation':45,'rotation_mode':'anchor','color':'red'}.

[16]:

#Put annotation on the right
row_ha = HeatmapAnnotation(Tissue=df_cols.Tissue,
                           Family=anno_simple(df_cols.Family, legend=False,height=5),
                           label=anno_label(df_cols.Family, merge=True,rotation=45),
                           label_side='bottom',
                           label_kws={'rotation':-45,'color':'red'},
                           axis=0)
plt.figure(figsize=(4, 6))
cm = ClusterMapPlotter(data=df.T,right_annotation=row_ha,
                       show_rownames=False, show_colnames=True,col_names_side='bottom',
                       row_split=df_cols.Family, cmap='jet', label='AUC',
                       rasterized=True, legend=True,row_split_gap=0.1,
                       xticklabels_kws={'labelrotation':-45,'labelcolor':'blue'},
                       ylabel="Samples",xlabel="Predicted Species")
#plt.savefig("annotation.pdf", bbox_inches='tight')
plt.show()

Starting plotting..
Starting calculating row orders..
Reordering rows..
Starting calculating col orders..
Reordering cols..
Plotting matrix..
Starting plotting HeatmapAnnotations
Collecting legends..
Collecting annotation legends..
Plotting legends..
Estimated legend width: 25.930555555555557 mm
../_images/notebooks_clustermap_26_1.png