Major plot function used by SZQ lab from China Agricultural University

Project description

cvmcore

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Introduction

The core function of data analysis for plot or data process used by SZQ lab from China Agricultural University

Example usage

from Bio import Phylo
import matplotlib as mpl
import matplotlib.pyplot as plt
from io import StringIO
import matplotlib.collections as mpcollections
from copy import copy

import pandas as pd
import numpy as np
import seaborn as sn

from cvmcore.cvmcore import cvmplot

from scipy.cluster.hierarchy import linkage, dendrogram, complete, to_tree
from scipy.spatial.distance import squareform

mlst = [[np.nan, 19., 12.,  9.,  5.,  9.,  2.],
        [np.nan, 19., 12.,  9.,  5.,  9.,  2.],
        [10., 17., 12.,  9., np.nan,  9.,  2.],
        [10., 19., 12., np.nan,  5.,  9.,  2.],
        [np.nan, 19., 13.,  9.,  5.,  9.,  2.]]
genes = np.char.replace(np.array(np.arange(1, 8), dtype='str'), '', 'gene_', count=1)
samples = np.char.replace(np.array(np.arange(1, 6), dtype='str'), '', 'sample_', count=1)
df_mlst = pd.DataFrame(mlst, index=samples, columns=genes)
diff_matrix = cvmplot.get_diff_df(df_mlst)

diff_matrix

	sample_1	sample_2	sample_3	sample_4	sample_5
sample_1	0	0	1	0	1
sample_2	0	0	1	0	1
sample_3	1	1	0	1	2
sample_4	0	0	1	0	1
sample_5	1	1	2	1	0

link_matrix =linkage(squareform(diff_matrix), method='complete')

link_matrix

array([[0., 1., 0., 2.],
       [3., 5., 0., 3.],
       [2., 6., 1., 4.],
       [4., 7., 2., 5.]])

1. Plot a rectangular dendrogram

fig, ax= plt.subplots(1,1)
lableorder, ax = cvmplot.rectree(link_matrix, scale_max=7, labels=samples, ax=ax)
fig.tight_layout()
fig.savefig('screenshots/dendrogram.png')

2. Plot rectangular dendrogram with heatmap

#create dataframe
mat = np.random.randint(70, 100, (5, 10))
loci = np.char.replace(np.array(np.arange(1, 11), dtype='str'), '', 'loci_', count=1)
sample = np.char.replace(np.array(np.arange(1, 6), dtype='str'), '', 'sample', count=1)
df_heatmap = pd.DataFrame(mat, index=sample, columns=loci)

#create linkage matrix
diff_matrix = [[0, 0, 1, 0, 1],
               [0, 0, 1, 0, 1],
               [1, 1, 0, 1, 2],
               [0, 0, 1, 0, 1],
               [1, 1, 2, 1, 0]]

linkage_matrix = linkage(squareform(diff_matrix),'complete')

fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})

fig.tight_layout(w_pad=-2)

row_order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)
cvmplot.heatmap(df_heatmap, order=row_order, ax=ax2, cbar=True, yticklabel=False)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

# fig.tight_layout()
fig.savefig('screenshots/dendrogram_with_heatmap.png', bbox_inches='tight')

[ 5 15 25 35 45]
['sample5', 'sample3', 'sample4', 'sample1', 'sample2']

fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(12,3), gridspec_kw={'width_ratios': [1, 2, 2]})

fig.tight_layout(w_pad=-2)

row_order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)

# remove the yticklabels in ax2
ax2 = cvmplot.heatmap(df_heatmap, order=row_order, ax=ax2, cbar=True, yticklabel=False)
# add ax3 heatmap
ax3 = cvmplot.heatmap(df_heatmap, order=row_order, ax=ax3, cmap='Blues', cbar=True, yticklabel=True)

#set ticklabels property of x or y from ax1, ax2, ax3
ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.xaxis.tick_top()

ax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)
ax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)
ax3.xaxis.tick_top()


# fig.tight_layout()
fig.savefig('screenshots/multiple_heatmap.png', bbox_inches='tight')

2.1 set minimum value of heatmap

fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})
fig.tight_layout(w_pad=-2)

order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

fig.savefig('screenshots/dendrogram_heatmap_minimumvalue.pdf', bbox_inches='tight')

[ 5 15 25 35 45]
['sample5', 'sample3', 'sample4', 'sample1', 'sample2']

png

2.2 using cmap to change color

fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3), gridspec_kw={'width_ratios': [1, 2]})
fig.tight_layout(w_pad=-2)

order, ax1 = cvmplot.rectree(linkage_matrix,labels=sample, no_labels=True, scale_max=3, ax=ax1)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cmap='tab20', cbar=True)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()
fig.savefig('screenshots/dendrogram_heatmap_cmap.pdf', bbox_inches='tight')

[ 5 15 25 35 45]
['sample5', 'sample3', 'sample4', 'sample1', 'sample2']

3. Plot a circular dendrogram

# generate two clusters: a with 100 points, b with 50:
np.random.seed(4711)  # for repeatability of this tutorial
a = np.random.multivariate_normal([10, 0], [[3, 1], [1, 4]], size=[100,])
b = np.random.multivariate_normal([0, 20], [[3, 1], [1, 4]], size=[50,])
X = np.concatenate((a, b),)

Z = linkage(X, 'ward')

Z2 = dendrogram(Z, no_plot=True)

# set open angle
fig, ax= plt.subplots(1,1,figsize=(10,10))

cvmplot.circulartree(Z2,addlabels=True, fontsize=10, ax=ax)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram.png', bbox_inches='tight')

3.1 color label

colors = [{'#0070c7':'2021'}, {'#3a9245':'2022'}, {'#f8d438':'2023'}]
result = np.random.choice(colors, size=150)
label_colors_map = dict(zip(Z2['ivl'], result))
point_colors_map = dict(zip(Z2['ivl'], result))

fig, ax= plt.subplots(1,1,figsize=(10,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_color_label.png')

3.2 set open angle

fig, ax= plt.subplots(1,1,figsize=(10,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, open_angle=30)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_openangle.png')

3.3 set start angle

fig, ax= plt.subplots(1,1,figsize=(10,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, open_angle=90,
                     start_angle=30
                    )
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_startangle.png')

3.4 add point

fig, ax= plt.subplots(1,1,figsize=(12,10))
cvmplot.circulartree(Z2, addlabels=True, branch_color=False, label_colors= label_colors_map, fontsize=15, addpoints=True,
                     point_colors = point_colors_map, point_legend_title='Species', pointsize=25)
fig.tight_layout()
fig.savefig('screenshots/circular_dendrogram_tippoints.png')

4. Plot phylogenetic tree

tree = "(((A:0.2, B:0.3):0.3,(C:0.5, D:0.3):0.2):0.3, E:0.7):1.0;"
tree = Phylo.read(StringIO(tree), 'newick')

fig, ax= plt.subplots(1,1, figsize=(10, 10))
ax, lable_order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax, show_label=True, align_label=True, labelsize=15)
fig.tight_layout()
fig.savefig('screenshots/phylogenetic tree.png')

4.1 Plot tree with heatmap

#create dataframe
mat = np.random.randint(70, 100, (5, 10))
col = np.char.replace(np.array(np.arange(1, 11), dtype='str'), '', 'column_', count=1)
strains = ['A', 'B', 'C', 'D', 'E']
df_heatmap = pd.DataFrame(mat, index=strains, columns=col)

df_heatmap

	column_1	column_2	column_3	column_4	column_5	column_6	column_7	column_8	column_9	column_10
A	89	73	91	75	95	90	93	74	99	97
B	73	90	75	89	85	72	82	85	96	82
C	84	82	86	74	72	75	91	83	97	98
D	72	77	72	98	79	73	87	91	98	94
E	88	75	88	73	77	72	74	73	99	86

fig,(ax1, ax2)= plt.subplots(1,2, figsize=(8, 3), gridspec_kw={'width_ratios':[1, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

fig.savefig('screenshots/phylotree_with_heatmap.pdf')

4.2 remove labels at the tip of the tree

fig,(ax1, ax2)= plt.subplots(1,2, figsize=(8, 3), gridspec_kw={'width_ratios':[1, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=False)
cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90)

ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.set_yticklabels(ax2.get_yticklabels(), fontsize=15)
ax2.xaxis.tick_top()

fig.savefig('screenshots/phylotree_with_heatmap-remove_tiplable.pdf', bbox_inches='tight')

4.3 Plot multiple heatmap with phylotree

fig,(ax1, ax2, ax3)= plt.subplots(1,3, figsize=(12, 3), gridspec_kw={'width_ratios':[1, 2, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)
ax2 = cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90, yticklabel=False)
# add ax3 heatmap
ax3 = cvmplot.heatmap(df_heatmap, order=order, ax=ax3, cmap='Blues', cbar=True, yticklabel=True)

#set ticklabels property of x or y from ax1, ax2, ax3
ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.xaxis.tick_top()

ax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)
ax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)
ax3.xaxis.tick_top()


# fig.tight_layout()
fig.savefig('screenshots/phylotree_multiple_heatmap.png', bbox_inches='tight')

5. Gene environment plot

First, you shoud prepare a dataframe from the gff file, The columns should include the feature start, end, strand, label(gene name or whatever you want show next to the arrow) and the arrow color.

TRACK	START	END	STRAND	LABEL	COLOR
A	100	900	-1	label1	#ec9631
A	1100	1300	1	label2	#ec9631
A	1350	1500	1	label3	#ec9631
A	1520	1700	1	label4	#ec9631
A	1900	2200	-1	label5	#ec9631
A	2500	2700	1	label6	#ec9631
A	2700	2800	-1	label7	#ec9631
A	2850	3000	-1	label8	red
A	3100	3500	1	label9	#ec9631
A	3600	3800	-1	label10	#ec9631
A	3900	4200	-1	label11	#ec9631
A	4300	4700	-1	label12	#ec9631
A	4800	4850	1	label13	#ec9631
B	100	900	-1	label14	#ec9631
B	1100	1300	1	label15	#ec9631
B	1350	1500	1	label16	#ec9631
B	1520	1700	1	label17	#ec9631
B	1900	2200	-1	label18	#ec9631
B	2500	2700	1	label19	#ec9631
B	2700	2800	-1	label20	#ec9631
B	2850	3000	-1	label21	#ec9631
B	3100	3500	1	label22	#ec9631
B	3600	3800	-1	label23	#ec9631
B	3900	4200	-1	label24	#ec9631
B	4300	4700	-1	label25	#ec9631
B	4800	4850	1	label26	#ec9631
C	100	900	-1	label27	#ec9631
C	1100	1300	1	label28	#ec9631
C	1350	1500	1	label29	#ec9631
C	1520	1700	1	label30	#ec9631
C	1900	2200	-1	label31	green
C	2500	2700	1	label32	#ec9631
C	2700	2800	-1	label33	#ec9631
C	2850	3000	-1	label34	#ec9631
C	3100	3500	1	label35	#ec9631
C	3600	3800	-1	label36	#ec9631
C	3900	4200	-1	label37	#ec9631
C	4300	4700	-1	label38	#ec9631
C	4800	4850	1	label39	#ec9631
D	100	900	-1	label40	#ec9631
D	1100	1300	1	label41	#ec9631
D	1350	1500	1	label42	#ec9631
D	1520	1700	1	label43	#ec9631
D	1900	2200	-1	label44	#ec9631
D	2500	2700	1	label45	#ec9631
D	2700	2800	-1	label46	#ec9631
D	2850	3000	-1	label47	#ec9631
D	3100	3500	1	label48	#ec9631
D	3600	3800	-1	label49	#ec9631
D	3900	4200	-1	label50	#ec9631
D	4300	4700	-1	label51	#ec9631
D	4800	4850	1	label52	#ec9631
E	100	900	-1	label53	#ec9631
E	1100	1300	1	label54	#ec9631
E	1350	1500	1	label55	#ec9631
E	1520	1700	1	label56	#ec9631
E	1900	2200	-1	label57	#ec9631
E	2500	2700	1	label58	#ec9631
E	2700	2800	-1	label59	#ec9631
E	2850	3000	-1	label60	#ec9631
E	3100	3500	1	label61	#ec9631
E	3600	3800	-1	label62	#ec9631
E	3900	4200	-1	label63	#ec9631
E	4300	4700	-1	label64	#ec9631
E	4800	4850	1	label65	#ec9631

5. Plot genes

# Create arrow dictionary
arrow_dict = {k: g.to_dict(orient='records') for k, g in df.set_index('TRACK').groupby(level=0)}

# Define the display order of your tracks
order = ['D', 'A',  'C', 'B', 'E']

5.1 Plot gene arrows and label on top track

fig, ax = plt.subplots(1,1, figsize=(10,10))
ax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='top')
fig.savefig('screenshots/gene_arrow_top.png', bbox_inches='tight')

5.2 Plot gene arrows and label on bottom track

fig, ax = plt.subplots(1,1, figsize=(10,10))
ax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='bottom')
fig.savefig('screenshots/gene_arrow_bottom.png', bbox_inches='tight')

5.3 Plot gene arrows and label on all tracks

fig, ax = plt.subplots(1,1, figsize=(10,10))
ax = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax, max_track_size=5000, addlabels=True, label_track='all')
fig.savefig('screenshots/gene_arrow_all.png', bbox_inches='tight')

5.4 Plot gene arrows with phylotree and heatmap

Put together!

# Put together
fig,(ax1, ax2, ax3, ax4)= plt.subplots(1,4, figsize=(16, 3), gridspec_kw={'width_ratios':[1, 2, 2, 2]})
fig.tight_layout(w_pad=-2)
ax1, order = cvmplot.phylotree(tree=tree, color='k', lw=1, ax=ax1, show_label=True, align_label=True, labelsize=15)
ax2 = cvmplot.heatmap(df_heatmap, order=order, ax=ax2, cbar=True, vmin=90, yticklabel=False)
# add ax3 heatmap
ax3 = cvmplot.heatmap(df_heatmap, order=order, ax=ax3, cmap='Blues', cbar=True, yticklabel=False)

ax4 = cvmplot.plotgenes(dc=arrow_dict, order=order, ax=ax4, max_track_size=5000, addlabels=True, label_track='top', ylim=(-3, 3))



#set ticklabels property of x or y from ax1, ax2, ax3
ax1.set_xticklabels(ax1.get_xticklabels(), fontsize=15)

ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90, fontsize=15)
ax2.xaxis.tick_top()

ax3.set_xticklabels(ax3.get_xticklabels(), rotation=90, fontsize=15)
ax3.set_yticklabels(ax3.get_yticklabels(), fontsize=15)
ax3.xaxis.tick_top()


# fig.tight_layout()
fig.savefig('screenshots/phylotree_heatmap_withgenes.png', bbox_inches='tight')

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	column_1	column_2	column_3	column_4	column_5	column_6	column_7	column_8	column_9	column_10
A	89	73	91	75	95	90	93	74	99	97
B	73	90	75	89	85	72	82	85	96	82
C	84	82	86	74	72	75	91	83	97	98
D	72	77	72	98	79	73	87	91	98	94
E	88	75	88	73	77	72	74	73	99	86

	column_1	column_2	column_3	column_4	column_5	column_6	column_7	column_8	column_9	column_10
A	89	73	91	75	95	90	93	74	99	97
B	73	90	75	89	85	72	82	85	96	82
C	84	82	86	74	72	75	91	83	97	98
D	72	77	72	98	79	73	87	91	98	94
E	88	75	88	73	77	72	74	73	99	86

cvmcore 0.3.0

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Project description

cvmcore

Introduction

Example usage

1. Plot a rectangular dendrogram

2. Plot rectangular dendrogram with heatmap

2.1 set minimum value of heatmap

2.2 using cmap to change color

3. Plot a circular dendrogram

3.1 color label

3.2 set open angle

3.3 set start angle

3.4 add point

4. Plot phylogenetic tree

4.1 Plot tree with heatmap

4.2 remove labels at the tip of the tree

4.3 Plot multiple heatmap with phylotree

5. Gene environment plot

5. Plot genes

5.1 Plot gene arrows and label on top track

5.2 Plot gene arrows and label on bottom track

5.3 Plot gene arrows and label on all tracks

5.4 Plot gene arrows with phylotree and heatmap

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	column_1	column_2	column_3	column_4	column_5	column_6	column_7	column_8	column_9	column_10
A	89	73	91	75	95	90	93	74	99	97
B	73	90	75	89	85	72	82	85	96	82
C	84	82	86	74	72	75	91	83	97	98
D	72	77	72	98	79	73	87	91	98	94
E	88	75	88	73	77	72	74	73	99	86