ovrawm/t_visualize_bulk.txt

#!/usr/bin/env python
# coding: utf-8

# # Visualization of Bulk RNA-seq
# 
# In this part, we will introduce the tutorial of special plot of `omicverse`.

# In[1]:


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


# ## 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

# In[20]:


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')


# In[9]:


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)


# ## 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.

# In[3]:


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


# In[4]:


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,)


# ## 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

# In[3]:


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


# In[7]:


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',)