application and visualization

[rollisuntory]

Thank you for developing the algorithm!
I would like to ask, if it's not too much trouble, could you provide us with the visualization code? I am particularly interested in attempting to replicate the figures involving tumor samples that were presented in the article. Could you provide the visualization code for that purpose?

[RuiqiaoHe]

Thanks for your attention. SEVtras has provided the visualization of UMAP in the output of SEVtras.ESAI_calculator (a pdf file named SEVumap.pdf, embedding sEVs and cells in a UMAP).
If this is not enough, could you please tell me what other types of figures you want to plot? I will try to sort out the corresponding code.

[rollisuntory]

I am still in the exploratory phase of understanding how to utilize the output from the ESAI_recognizer, and since I haven't had much experience with Python before (having conducted most of my analyses in R), I am also trying to figure out how to use the ESAI_calculator. Could you please provide the code used to generate Figure 4-C? Additionally, could you clarify the meaning of the ev_genes.csv generated in the output of the ESAI_recognizer? Thank you very much!

[RuiqiaoHe]

The code corresponding to Figure 4-C is copied as follows:

## for the left UMAP
import scanpy as sc
sc.pl.umap(adata, color = ['clusters'], legend_loc='on data', ax = ax, frameon = False,
           palette = sns.color_palette(['#97bfa4', '#766187']), size = 200, show = False)

## for the right GSEA
import gseapy as gp
###generate gsea_res from gseapy 

import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import colors
import numpy as np
import pandas as pd

### MITOTIC_SPINDLE
sns.set(style="ticks", font_scale=2)
fig, (ax) = plt.subplots(1, 1, figsize = (6, 3))

res = gsea_res[0][1]
term = 'HALLMARK_MITOTIC_SPINDLE'
line_color = '#97bfa4'
sns.lineplot(x = np.arange(len(res.ranking)), y = res.results[term]['RES'], linewidth=4, color = line_color, ax = ax)
ax.set_ylabel("Enrichment score")
ax.set(xticklabels=[])
ax.set(xticks=[])
ax.set_title('Mitotic spindle')

ax1 = fig.add_axes([0.124,0.07,0.776,0.08])# change for different size
import matplotlib.transforms as transforms
trans2 = transforms.blended_transform_factory(ax1.transData, ax1.transAxes)
ax1.vlines(res.results[term]['hit_indices'], 0, 1, linewidth=.5, transform=trans2, color ='black')
ax1.spines['bottom'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.tick_params(axis='y', 
                        bottom=False, top=False,
                        right=False, left=False, 
                        labelbottom=False, labelleft=False)
ax1.set_xlabel("Gene position")

sns.despine()

plt.savefig('../figures/fig4_gsea_0.pdf', dpi = 300, transparent=True, format = 'pdf', bbox_inches='tight')


### PI3K_AKT_MTOR_SIGNALING
sns.set(style="ticks", font_scale=2)
fig, (ax) = plt.subplots(1, 1, figsize = (6, 3))

res = gsea_res[0][1]
term = 'HALLMARK_PI3K_AKT_MTOR_SIGNALING'
line_color = '#766187'
sns.lineplot(x = np.arange(len(res.ranking)), y = res.results[term]['RES'], linewidth=4, color = line_color, ax = ax)
ax.set_ylabel("Enrichment score")
ax.set(xticklabels=[])
ax.set(xticks=[])
ax.set_title('PI3K/Akt/mTOR signaling')

ax1 = fig.add_axes([0.124,0.07,0.776,0.08])# change for different size
import matplotlib.transforms as transforms
trans2 = transforms.blended_transform_factory(ax1.transData, ax1.transAxes)
ax1.vlines(res.results[term]['hit_indices'], 0, 1, linewidth=.5, transform=trans2, color ='black')
ax1.spines['bottom'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.tick_params(axis='y', 
                        bottom=False, top=False,
                        right=False, left=False, 
                        labelbottom=False, labelleft=False)
ax1.set_xlabel("Gene position")
sns.despine()
plt.savefig('../figures/fig3_gsea_1.pdf', dpi = 300, transparent=True, format = 'pdf', bbox_inches='tight')

And the ev_genes.csv file represents sEV-characterized genes enriched during EM iterations in SEVtras. This is a data-driven process, and these genes are used to identify sEV-containing droplets in your single-cell data.