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PyComplexHeatmap进阶教程：用python画热图的【行】/【列】注释信息

Python教程 信息 进阶 注释 画热图
2023-06-13 09:15:18 时间

今天，给大家详细地介绍一下PyComplexheatmap(https://github.com/DingWB/PyComplexHeatmap) 中annotation的使用方法，也就是如何用python在热图中添加【行】/【列】注释信息。比如样本的疾病状态（肿瘤或者正常样本、年龄、性别、分型等）。

如何用python画热图上, 下, 左、右不同方向的【行】/【列】注释信息

# 导入示例数据
with open(os.path.join(os.path.dirname(PyComplexHeatmap.__file__),"../data/mammal_array.pkl"), 'rb') as f:
    data = pickle.load(f)
df, df_rows, df_cols, col_colors_dict = data

df

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

df_rows

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

df_cols

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

注释信息放在热图「上方」

#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)
#plt.savefig("clustermap.pdf", bbox_inches='tight')
plt.show()

Starting calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..
Incresing ncol
Incresing ncol
More than 3 cols is not supported
Legend too long, generating a new column..

如果有很多图例，PyComplexHeatmap会自动将所有的图例按顺序排列，如果一列放不下，就「自动增加一列」，用两列来画图例（figure legends）。如上图所示，就有2列图例。此外，在注释文字（比如Bovidae）与热图之间曲线的形状和颜色都会随着文字的旋转角度和颜色一起变化，会自动调整角度，使之与注释文字的角度相匹配。

注释信息放在热图「下方」

#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=(7, 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 calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..

如果想要把列注释信息放在热图下方，那就需要「改变HeatmapAnnotation的顺序」, anno_label 应该放在「最后」：anno_label(df_cols.Family) 应该是倒数第二个。anno_label中的rotation参数应该从45度变为-45度，因为旋转方向发生了改变。 此外，可以通过设置row_names_side='right'来将【行】名字row labels挪到热图右边。值得注意的是，「热图与图例之间的间隙是自动调节」的，比如，当row_names_side='right'时，热图右边有了文字，图例就自动往右边挪了，不用我们额外设置。 【行】注释（annotation bar）的高度，可以通过height (mm)参数来设置，比如，在上图的anno_simple中，我们将Family这个bar图的高度设置成了5mm，而Tissue的高度仍然是默认（4mm）。

注释信息放在热图「左方」

#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=False,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, 7))
cm = ClusterMapPlotter(data=df.T,left_annotation=row_ha,
                       show_rownames=False, show_colnames=True,col_names_side='top',
                       row_split=df_cols.Family, cmap='exp1', label='AUC',
                       rasterized=True, legend=True,
                       xticklabels_kws={'labelrotation':45,'labelcolor':'blue'})
plt.show()

Starting calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..

我们首先把df进行转制（df.T），col_ha改成row_ha（现在变成了【行】注释）。我们可以通过改变参数col_names_side='top'来把【行】注释的标签（xlabel）放到热图上方（或者bottom，放到热图下方），另外，改变xticklabels_kws参数可以改变【行】注释标签的旋转角度和颜色（比如上图中的Tissue和Family，旋转45度，颜色为红色）：label_kws={'rotation':45,'rotation_mode':'anchor','color':'red'}。

注释信息放在热图「右方」

#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,'rotation_mode':'anchor','color':'red'},
                           axis=0)
plt.figure(figsize=(4, 7))
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,
                       xticklabels_kws={'labelrotation':-45,'labelcolor':'blue'})
#plt.savefig("annotation.pdf", bbox_inches='tight')
plt.show()

Starting calculating row orders..
Starting calculating col orders..
Starting plotting HeatmapAnnotations
Collecting annotation legends..

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