Visualization of Bulk RNA-seq¶

In this part, we will introduce the tutorial of special plot of omicverse.

In [1]:

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import omicverse as ov
import scanpy as sc
import matplotlib.pyplot as plt
ov.plot_set()
import omicverse as ov
import scanpy as sc
import matplotlib.pyplot as plt
ov.plot_set()

   ____            _     _    __                  
  / __ \____ ___  (_)___| |  / /__  _____________ 
 / / / / __ `__ \/ / ___/ | / / _ \/ ___/ ___/ _ \ 
/ /_/ / / / / / / / /__ | |/ /  __/ /  (__  )  __/ 
\____/_/ /_/ /_/_/\___/ |___/\___/_/  /____/\___/                                              

Version: 1.5.8, Tutorials: https://omicverse.readthedocs.io/

Venn plot¶

In transcriptome analyses, we often have to study differential genes that are common to different groups. Here, we provide ov.pl.venn to draw venn plots to visualise differential genes.

Function: ov.pl.venn:

sets: Subgroups requiring venn plots, Dictionary format, keys no more than 4
palette: You can also re-specify the colour bar that needs to be drawn, just set palette=['#FFFFFF','#000000'], we have prepared ov.pl.red_color,ov.pl.blue_color,ov.pl.green_color,ov.pl.orange_color, by default.
fontsize: the fontsize and linewidth to visualize, fontsize will be multiplied by 2

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fig,ax=plt.subplots(figsize = (4,4))
#dict of sets
sets = {
    'Set1:name': {1,2,3},
    'Set2': {1,2,3,4},
    'Set3': {3,4},
    'Set4': {5,6}
}
#plot venn   
ov.pl.venn(sets=sets,palette=ov.pl.sc_color,
           fontsize=5.5,ax=ax,
           )


#If we need to annotate genes, we can use plt.annotate for this purpose, 
#we need to modify the text content, xy and xytext parameters.
plt.annotate('gene1,gene2', xy=(50,30), xytext=(0,-100),
             ha='center', textcoords='offset points', 
             bbox=dict(boxstyle='round,pad=0.5', fc='gray', alpha=0.1),
             arrowprops=dict(arrowstyle='->', color='gray'),size=12)

#Set the title
plt.title('Venn4',fontsize=13)

#save figure
fig.savefig("figures/bulk_venn4.png",dpi=300,bbox_inches = 'tight')
fig,ax=plt.subplots(figsize = (4,4))
#dict of sets
sets = {
    'Set1:name': {1,2,3},
    'Set2': {1,2,3,4},
    'Set3': {3,4},
    'Set4': {5,6}
}
#plot venn   
ov.pl.venn(sets=sets,palette=ov.pl.sc_color,
           fontsize=5.5,ax=ax,
           )


#If we need to annotate genes, we can use plt.annotate for this purpose, 
#we need to modify the text content, xy and xytext parameters.
plt.annotate('gene1,gene2', xy=(50,30), xytext=(0,-100),
             ha='center', textcoords='offset points', 
             bbox=dict(boxstyle='round,pad=0.5', fc='gray', alpha=0.1),
             arrowprops=dict(arrowstyle='->', color='gray'),size=12)

#Set the title
plt.title('Venn4',fontsize=13)

#save figure
fig.savefig("figures/bulk_venn4.png",dpi=300,bbox_inches = 'tight')

Out[20]:

Text(0, -100, 'gene1,gene2')

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fig,ax=plt.subplots(figsize = (4,4))
#dict of sets
sets = {
    'Set1:name': {1,2,3},
    'Set2': {1,2,3,4},
    'Set3': {3,4},
}
    
ov.pl.venn(sets=sets,ax=ax,fontsize=5.5,
           palette=ov.pl.red_color)

plt.title('Venn3',fontsize=13)
fig,ax=plt.subplots(figsize = (4,4))
#dict of sets
sets = {
    'Set1:name': {1,2,3},
    'Set2': {1,2,3,4},
    'Set3': {3,4},
}
    
ov.pl.venn(sets=sets,ax=ax,fontsize=5.5,
           palette=ov.pl.red_color)

plt.title('Venn3',fontsize=13)

Out[9]:

Text(0.5, 1.0, 'Venn3')

Volcano plot¶

For differentially expressed genes, we tend to visualise them only with volcano plots. Here, we present a method for mapping volcanoes using Python ov.pl.volcano.

Function: ov.pl.venn:

main argument

result: the DEGs result
pval_name: the names of the columns whose vertical coordinates need to be plotted, stored in result.columns. In Bulk RNA-seq experiments, we usually set this to qvalue.
fc_name: The names of the columns for which you need to plot the horizontal coordinates, stored in result.columns. In Bulk RNA-seq experiments, we typically set this to log2FC.
fc_max: We need to set the threshold for the difference foldchange
fc_min: We need to set the threshold for the difference foldchange
pval_threshold: We need to set the threshold for the qvalue
pval_max: We also need to set boundary values so that the data is not too large to affect the visualisation
FC_max: We also need to set boundary values so that the data is not too large to affect the visualisation

plot argument

figsize: The size of the generated figure, by default (4,4).
title: The title of the plot, by default ''.
titlefont: A dictionary of font properties for the plot title, by default {'weight':'normal','size':14,}.
up_color: The color of the up-regulated genes in the plot, by default '#e25d5d'.
down_color: The color of the down-regulated genes in the plot, by default '#7388c1'.
normal_color: The color of the non-significant genes in the plot, by default '#d7d7d7'.
legend_bbox: A tuple containing the coordinates of the legend's bounding box, by default (0.8, -0.2).
legend_ncol: The number of columns in the legend, by default 2.
legend_fontsize: The font size of the legend, by default 12.
plot_genes: A list of genes to be plotted on the volcano plot, by default None.
plot_genes_num: The number of genes to be plotted on the volcano plot, by default 10.
plot_genes_fontsize: The font size of the genes to be plotted on the volcano plot, by default 10.
ticks_fontsize: The font size of the ticks, by default 12.

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result=ov.read('data/dds_result.csv',index_col=0)
result.head()
result=ov.read('data/dds_result.csv',index_col=0)
result.head()

Out[3]:

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	qvalue	-log(pvalue)	-log(qvalue)	BaseMean	log2(BaseMean)	log2FC	abs(log2FC)	sig
4931428F04Rik	44.747673	-0.592738	0.680476	-0.871063	0.383720	0.650810	0.650810	0.415986	0.186545	44.747673	5.515626	-0.592738	0.592738	normal
Fam135a	1679.272500	0.065358	0.269188	0.242797	0.808163	0.922122	0.922122	0.092501	0.035211	1679.272500	10.714479	0.065358	0.065358	normal
Cxcl5	18.194110	0.391243	0.954836	0.409749	0.681990	0.859639	0.859639	0.166222	0.065684	18.194110	4.262592	0.391243	0.391243	normal
Gm2830	85.257000	0.960027	0.363169	2.643472	0.008206	0.047828	0.047828	2.085866	1.320314	85.257000	6.430570	0.960027	0.960027	normal
Stat2	821.361600	-0.409115	0.448145	-0.912907	0.361292	0.630416	0.630416	0.442142	0.200373	821.361600	9.683629	-0.409115	0.409115	normal

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ov.pl.volcano(result,pval_name='qvalue',fc_name='log2FoldChange',
                     pval_threshold=0.05,fc_max=1.5,fc_min=-1.5,
                      pval_max=10,FC_max=10,
                    figsize=(4,4),title='DEGs in Bulk',titlefont={'weight':'normal','size':14,},
                     up_color='#e25d5d',down_color='#7388c1',normal_color='#d7d7d7',
                     up_fontcolor='#e25d5d',down_fontcolor='#7388c1',normal_fontcolor='#d7d7d7',
                     legend_bbox=(0.8, -0.2),legend_ncol=2,legend_fontsize=12,
                     plot_genes=None,plot_genes_num=10,plot_genes_fontsize=11,
                     ticks_fontsize=12,)
ov.pl.volcano(result,pval_name='qvalue',fc_name='log2FoldChange',
                     pval_threshold=0.05,fc_max=1.5,fc_min=-1.5,
                      pval_max=10,FC_max=10,
                    figsize=(4,4),title='DEGs in Bulk',titlefont={'weight':'normal','size':14,},
                     up_color='#e25d5d',down_color='#7388c1',normal_color='#d7d7d7',
                     up_fontcolor='#e25d5d',down_fontcolor='#7388c1',normal_fontcolor='#d7d7d7',
                     legend_bbox=(0.8, -0.2),legend_ncol=2,legend_fontsize=12,
                     plot_genes=None,plot_genes_num=10,plot_genes_fontsize=11,
                     ticks_fontsize=12,)

Out[4]:

<AxesSubplot: title={'center': 'DEGs in Bulk'}, xlabel='$log_{2}FC$', ylabel='$-log_{10}(qvalue)$'>

Box plot¶

For differentially expressed genes in different groups, we sometimes need to compare the differences between different groups, and this is when we need to use box-and-line plots to do the comparison

Function: ov.pl.boxplot:

data: the data to visualize the boxplt example could be found in seaborn.load_dataset("tips")
x_value, y_value, hue: Inputs for plotting long-form data. See examples for interpretation.
figsize: The size of the generated figure, by default (4,4).
fontsize: The font size of the tick and labels, by default 12.
title: The title of the plot, by default ''.

Function: ov.pl.add_palue:

ax: the axes of bardotplot
line_x1: The left side of the p-value line to be plotted
line_x2: The right side of the p-value line to be plotted|
line_y: The height of the p-value line to be plotted
text_y: How much above the p-value line is plotted text
text: the text of p-value, you can set *** to instead p<0.001
fontsize: the fontsize of text
fontcolor: the color of text
horizontalalignment: the location of text

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import seaborn as sns
data = sns.load_dataset("tips")
data.head()
import seaborn as sns
data = sns.load_dataset("tips")
data.head()

Out[3]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

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fig,ax=ov.pl.boxplot(data,hue='sex',x_value='day',y_value='total_bill',
              palette=ov.pl.red_color,
              figsize=(4,2),fontsize=12,title='Tips',)

ov.pl.add_palue(ax,line_x1=-0.5,line_x2=0.5,line_y=40,
          text_y=0.2,
          text='$p={}$'.format(round(0.001,3)),
          fontsize=11,fontcolor='#000000',
          horizontalalignment='center',)
fig,ax=ov.pl.boxplot(data,hue='sex',x_value='day',y_value='total_bill',
              palette=ov.pl.red_color,
              figsize=(4,2),fontsize=12,title='Tips',)

ov.pl.add_palue(ax,line_x1=-0.5,line_x2=0.5,line_y=40,
          text_y=0.2,
          text='$p={}$'.format(round(0.001,3)),
          fontsize=11,fontcolor='#000000',
          horizontalalignment='center',)