Visualization of Bulk RNA-seq

Visualization of Bulk RNA-seq#

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

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

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')

Text(0, -100, 'gene1,gene2')
../_images/87b65d8ee40f23585a5317ebd481dc5b68618fc39567ff1e3c16f0450437f326.png 
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)

Text(0.5, 1.0, 'Venn3')
../_images/54f28ab919466ad8fb3b1b4f53db8c00d879a71615fc19ae13bee706da40eac3.png

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.

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

<AxesSubplot: title={'center': 'DEGs in Bulk'}, xlabel='$log_{2}FC$', ylabel='$-log_{10}(qvalue)$'>
../_images/ad56b342668a86ffcdd0b1554b107a8d082c552348f05b01b0904e8335235696.png

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

import seaborn as sns
data = sns.load_dataset("tips")
data.head()
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 
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',)
../_images/53619fcd13d16f733ba2f7011981fa075b38cf8ee4a24b0462215c05f5784b7f.png
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