Regulator and moonlighter T_gcon set value

[s-janewanthana]

Hi, according to your GitHub and tutorial on https://churchman.med.harvard.edu/genewalk tutorial

I saw the code that you have provided for scatterplot.
As you have mention that T_gcon: minimal gene connectivity (to other
genes), set to 75th quartile of distribution.

May I ask why do you choose 75th quartile of distribution as you threshold?
Thanks for your future information.
Jane

Here by is your code

#!/usr/bin/env python

coding: utf-8

### import other packages (numpy, pandas, matplotlib and seaborn)

Install these with pip install before importing for plotting purposes below.

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42 #embed fonts into pdf figure
import seaborn as sns

### Load GeneWalk scatterplots file

project_folder = '/home/genewalk/qki/'

filename = 'genewalk_scatterplots.csv'
scatter_data = pd.read_csv(os.path.join(project_folder,'figures',filename))

### Regulator and Moonlighting plot functions

- If necessary customize plot sizes, plot filenames etc in functions below

- The plots are automatically saved as png and pdf in project_folder/figures with default filenames starting with custom_regulators and custom_moonlighters

- Amended from source file:

https://github.com/churchmanlab/genewalk/blob/master/genewalk/plot.py

def scatterplot_regulators(named_genes,sd=scatter_data,name_space='hgnc_symbol'):
"""Scatter plot with fraction of (globally) relevant GO annotations
as a function of gene connectivity (to other genes) for all input
genes.

Arguments
scat_data : genewalk_scatterplots.csv loaded as pandas dataframe
named_genes: list with genes of type name_space. These will be labeled
             in the plot.
name_space : which column name from genewalk_scatterplots.csv used as
             identifier for genes. Default value: 'hgnc_symbol'. Other 
             possible values: {'mgi_id','hgnc_id'}

Default visualization thresholds:
T_frac: minimal fraction of relevant GO annotations, set to 0.5
T_gcon: minimal gene connectivity (to other
genes), set to 75th quartile of distribution
"""

xlab = 'Connections with other genes (per gene)'
ylab = 'Fraction of relevant GO terms (per gene)'
xvar = 'gene_con'
yvar = 'frac_rel_go'

plot_filename = 'custom_regulators_x_' + xvar + '_y_' + yvar #adjust as needed
plot_title = 'GeneWalk regulator genes' #Adjust as needed
sizex = 4 #unit: inch, adjust as needed
sizey = 4 #unit: inch, adjust as needed
font_sz = 10 # adjust as needed

xmin = 0.45
xmax = max(sd[xvar])*1.2
T_gcon = sd[xvar].quantile(q=0.75)
T_frac = 0.5

# seaborn static plot
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(sizex, sizey))  # inches
g = sns.scatterplot(x=xvar, y=yvar, hue=yvar,
                    linewidth=0, alpha=0.5,

sizes=(40, 400),

                    data=sd,
                    ax=ax, legend=False)
plt.axvline(x=T_gcon, color='grey', linestyle='--')
plt.axhline(y=T_frac, color='grey', linestyle='--')
plt.xlabel(xlab, size=font_sz)
plt.ylabel(ylab, size=font_sz)
plt.xlim([xmin, xmax])
plt.xticks(size=font_sz)
plt.yticks(size=font_sz)

dreg = sd[sd[name_space].isin(named_genes)]
for r in dreg.index:
    gname = dreg[name_space][r]
    x_txt = dreg[xvar][r]
    y_txt = dreg[yvar][r]
    g.text(x_txt, y_txt, gname, size=font_sz*0.8, 
           horizontalalignment='center',
           color='black', weight='semibold', fontstyle='italic')
g.set(xscale="log")
plt.title(plot_title, size=font_sz)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.pdf'),
            bbox_inches="tight", transparent=True)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.png'),
            bbox_inches="tight", transparent=True)

def scatterplot_moonlighters(named_genes,sd=scatter_data,name_space='hgnc_symbol'):
"""Scatter plot with fraction of (globally) relevant GO annotations
as a function of number of GO annotations for all input genes.

Arguments
sd : genewalk_scatterplots.csv loaded as pandas dataframe: scatter_data
named_genes: list with genes of type name_space. These will be labeled
             in the plot.
name_space : which column name from genewalk_scatterplots.csv used as
             identifier for genes. Default value: 'hgnc_symbol'. Other 
             possible values: {'mgi_id','hgnc_id'}


Visualization thresholds:
T_frac: maximal fraction of relevant GO annotations, set to 0.5
T_gocon: minimal number of GO annotations per gene,
set to max of 30 and 75th quartile of distribution.
"""
xlab = 'Number of GO annotations (per gene)'
ylab = 'Fraction of relevant GO terms (per gene)'
xvar = 'go_con'
yvar = 'frac_rel_go'

plot_filename = 'custom_moonlighters_x_' + xvar + '_y_' + yvar #adjust as needed
plot_title = 'GeneWalk moonlighting genes' #Adjust as needed
sizex = 4 #unit: inch, adjust as needed
sizey = 4 #unit: inch, adjust as needed
font_sz = 10 # adjust as needed

xmin = 0.45
xmax = max(sd[xvar])*1.2
T_gocon = max(30, sd[xvar].quantile(q=0.75))
T_frac = 0.5

#seaborn static plot
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(sizex, sizey))  # inches
g = sns.scatterplot(x=xvar, y=yvar, hue=yvar,
                    linewidth=0, alpha=0.5,

sizes=(40, 400),

                    data=sd,
                    ax=ax, legend=False)
plt.axvline(x=T_gocon, color='grey', linestyle='--')
plt.axhline(y=T_frac, color='grey', linestyle='--')
plt.xlabel(xlab, size=font_sz)
plt.ylabel(ylab, size=font_sz)
plt.xlim([xmin, xmax])
plt.xticks(size=font_sz)
plt.yticks(size=font_sz)

dmoon = sd[sd[name_space].isin(named_genes)]
for m in dmoon.index:
    gname = dmoon[name_space][m]
    x_txt = dmoon[xvar][m]
    y_txt = dmoon[yvar][m]
    g.text(x_txt, y_txt, gname, size=font_sz*0.8, 
           horizontalalignment='center',
           color='black', weight='semibold', fontstyle='italic')
g.set(xscale="log")
plt.title(plot_title, size=font_sz)

plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.pdf'),
            bbox_inches="tight", transparent=True)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.png'),
            bbox_inches="tight", transparent=True)

### Plot Regulator scatter plots with custom chosen genes

specify genes in labeled_regulators.

labeled_regulators = ['JUN','TCF3','STAT3']

scatterplot_regulators(labeled_regulators,
scatter_data,
'hgnc_symbol')

### Plot Moonlighting scatter plots with custom chosen genes

specify genes in labeled_moonlighters

labeled_moonlighters = ['WNT5A','TLR4']

scatterplot_moonlighters(labeled_moonlighters,
scatter_data,
'hgnc_symbol')

`#!/usr/bin/env python

coding: utf-8

### import other packages (numpy, pandas, matplotlib and seaborn)

Install these with pip install before importing for plotting purposes below.

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42 #embed fonts into pdf figure
import seaborn as sns

### Load GeneWalk scatterplots file

project_folder = '/home/genewalk/qki/'

filename = 'genewalk_scatterplots.csv'
scatter_data = pd.read_csv(os.path.join(project_folder,'figures',filename))

### Regulator and Moonlighting plot functions

- If necessary customize plot sizes, plot filenames etc in functions below

- The plots are automatically saved as png and pdf in project_folder/figures with default filenames starting with custom_regulators and custom_moonlighters

- Amended from source file:

https://github.com/churchmanlab/genewalk/blob/master/genewalk/plot.py

def scatterplot_regulators(named_genes,sd=scatter_data,name_space='hgnc_symbol'):
"""Scatter plot with fraction of (globally) relevant GO annotations
as a function of gene connectivity (to other genes) for all input
genes.

Arguments
scat_data : genewalk_scatterplots.csv loaded as pandas dataframe
named_genes: list with genes of type name_space. These will be labeled
             in the plot.
name_space : which column name from genewalk_scatterplots.csv used as
             identifier for genes. Default value: 'hgnc_symbol'. Other 
             possible values: {'mgi_id','hgnc_id'}

Default visualization thresholds:
T_frac: minimal fraction of relevant GO annotations, set to 0.5
T_gcon: minimal gene connectivity (to other
genes), set to 75th quartile of distribution
"""

xlab = 'Connections with other genes (per gene)'
ylab = 'Fraction of relevant GO terms (per gene)'
xvar = 'gene_con'
yvar = 'frac_rel_go'

plot_filename = 'custom_regulators_x_' + xvar + '_y_' + yvar #adjust as needed
plot_title = 'GeneWalk regulator genes' #Adjust as needed
sizex = 4 #unit: inch, adjust as needed
sizey = 4 #unit: inch, adjust as needed
font_sz = 10 # adjust as needed

xmin = 0.45
xmax = max(sd[xvar])*1.2
T_gcon = sd[xvar].quantile(q=0.75)
T_frac = 0.5

# seaborn static plot
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(sizex, sizey))  # inches
g = sns.scatterplot(x=xvar, y=yvar, hue=yvar,
                    linewidth=0, alpha=0.5,

sizes=(40, 400),

                    data=sd,
                    ax=ax, legend=False)
plt.axvline(x=T_gcon, color='grey', linestyle='--')
plt.axhline(y=T_frac, color='grey', linestyle='--')
plt.xlabel(xlab, size=font_sz)
plt.ylabel(ylab, size=font_sz)
plt.xlim([xmin, xmax])
plt.xticks(size=font_sz)
plt.yticks(size=font_sz)

dreg = sd[sd[name_space].isin(named_genes)]
for r in dreg.index:
    gname = dreg[name_space][r]
    x_txt = dreg[xvar][r]
    y_txt = dreg[yvar][r]
    g.text(x_txt, y_txt, gname, size=font_sz*0.8, 
           horizontalalignment='center',
           color='black', weight='semibold', fontstyle='italic')
g.set(xscale="log")
plt.title(plot_title, size=font_sz)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.pdf'),
            bbox_inches="tight", transparent=True)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.png'),
            bbox_inches="tight", transparent=True)

def scatterplot_moonlighters(named_genes,sd=scatter_data,name_space='hgnc_symbol'):
"""Scatter plot with fraction of (globally) relevant GO annotations
as a function of number of GO annotations for all input genes.

Arguments
sd : genewalk_scatterplots.csv loaded as pandas dataframe: scatter_data
named_genes: list with genes of type name_space. These will be labeled
             in the plot.
name_space : which column name from genewalk_scatterplots.csv used as
             identifier for genes. Default value: 'hgnc_symbol'. Other 
             possible values: {'mgi_id','hgnc_id'}


Visualization thresholds:
T_frac: maximal fraction of relevant GO annotations, set to 0.5
T_gocon: minimal number of GO annotations per gene,
set to max of 30 and 75th quartile of distribution.
"""
xlab = 'Number of GO annotations (per gene)'
ylab = 'Fraction of relevant GO terms (per gene)'
xvar = 'go_con'
yvar = 'frac_rel_go'

plot_filename = 'custom_moonlighters_x_' + xvar + '_y_' + yvar #adjust as needed
plot_title = 'GeneWalk moonlighting genes' #Adjust as needed
sizex = 4 #unit: inch, adjust as needed
sizey = 4 #unit: inch, adjust as needed
font_sz = 10 # adjust as needed

xmin = 0.45
xmax = max(sd[xvar])*1.2
T_gocon = max(30, sd[xvar].quantile(q=0.75))
T_frac = 0.5

#seaborn static plot
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(sizex, sizey))  # inches
g = sns.scatterplot(x=xvar, y=yvar, hue=yvar,
                    linewidth=0, alpha=0.5,

sizes=(40, 400),

                    data=sd,
                    ax=ax, legend=False)
plt.axvline(x=T_gocon, color='grey', linestyle='--')
plt.axhline(y=T_frac, color='grey', linestyle='--')
plt.xlabel(xlab, size=font_sz)
plt.ylabel(ylab, size=font_sz)
plt.xlim([xmin, xmax])
plt.xticks(size=font_sz)
plt.yticks(size=font_sz)

dmoon = sd[sd[name_space].isin(named_genes)]
for m in dmoon.index:
    gname = dmoon[name_space][m]
    x_txt = dmoon[xvar][m]
    y_txt = dmoon[yvar][m]
    g.text(x_txt, y_txt, gname, size=font_sz*0.8, 
           horizontalalignment='center',
           color='black', weight='semibold', fontstyle='italic')
g.set(xscale="log")
plt.title(plot_title, size=font_sz)

plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.pdf'),
            bbox_inches="tight", transparent=True)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.png'),
            bbox_inches="tight", transparent=True)

### Plot Regulator scatter plots with custom chosen genes

specify genes in labeled_regulators.

labeled_regulators = ['JUN','TCF3','STAT3']

scatterplot_regulators(labeled_regulators,
scatter_data,
'hgnc_symbol')

### Plot Moonlighting scatter plots with custom chosen genes

specify genes in labeled_moonlighters

labeled_moonlighters = ['WNT5A','TLR4']

scatterplot_moonlighters(labeled_moonlighters,
scatter_data,
'hgnc_symbol')

#!/usr/bin/env python

coding: utf-8

### import other packages (numpy, pandas, matplotlib and seaborn)

Install these with pip install before importing for plotting purposes below.

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42 #embed fonts into pdf figure
import seaborn as sns

### Load GeneWalk scatterplots file

project_folder = '/home/genewalk/qki/'

filename = 'genewalk_scatterplots.csv'
scatter_data = pd.read_csv(os.path.join(project_folder,'figures',filename))

### Regulator and Moonlighting plot functions

- If necessary customize plot sizes, plot filenames etc in functions below

- The plots are automatically saved as png and pdf in project_folder/figures with default filenames starting with custom_regulators and custom_moonlighters

- Amended from source file:

https://github.com/churchmanlab/genewalk/blob/master/genewalk/plot.py

def scatterplot_regulators(named_genes,sd=scatter_data,name_space='hgnc_symbol'):
"""Scatter plot with fraction of (globally) relevant GO annotations
as a function of gene connectivity (to other genes) for all input
genes.

Arguments
scat_data : genewalk_scatterplots.csv loaded as pandas dataframe
named_genes: list with genes of type name_space. These will be labeled
             in the plot.
name_space : which column name from genewalk_scatterplots.csv used as
             identifier for genes. Default value: 'hgnc_symbol'. Other 
             possible values: {'mgi_id','hgnc_id'}

Default visualization thresholds:
T_frac: minimal fraction of relevant GO annotations, set to 0.5
T_gcon: minimal gene connectivity (to other
genes), set to 75th quartile of distribution
"""

xlab = 'Connections with other genes (per gene)'
ylab = 'Fraction of relevant GO terms (per gene)'
xvar = 'gene_con'
yvar = 'frac_rel_go'

plot_filename = 'custom_regulators_x_' + xvar + '_y_' + yvar #adjust as needed
plot_title = 'GeneWalk regulator genes' #Adjust as needed
sizex = 4 #unit: inch, adjust as needed
sizey = 4 #unit: inch, adjust as needed
font_sz = 10 # adjust as needed

xmin = 0.45
xmax = max(sd[xvar])*1.2
T_gcon = sd[xvar].quantile(q=0.75)
T_frac = 0.5

# seaborn static plot
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(sizex, sizey))  # inches
g = sns.scatterplot(x=xvar, y=yvar, hue=yvar,
                    linewidth=0, alpha=0.5,

sizes=(40, 400),

                    data=sd,
                    ax=ax, legend=False)
plt.axvline(x=T_gcon, color='grey', linestyle='--')
plt.axhline(y=T_frac, color='grey', linestyle='--')
plt.xlabel(xlab, size=font_sz)
plt.ylabel(ylab, size=font_sz)
plt.xlim([xmin, xmax])
plt.xticks(size=font_sz)
plt.yticks(size=font_sz)

dreg = sd[sd[name_space].isin(named_genes)]
for r in dreg.index:
    gname = dreg[name_space][r]
    x_txt = dreg[xvar][r]
    y_txt = dreg[yvar][r]
    g.text(x_txt, y_txt, gname, size=font_sz*0.8, 
           horizontalalignment='center',
           color='black', weight='semibold', fontstyle='italic')
g.set(xscale="log")
plt.title(plot_title, size=font_sz)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.pdf'),
            bbox_inches="tight", transparent=True)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.png'),
            bbox_inches="tight", transparent=True)

def scatterplot_moonlighters(named_genes,sd=scatter_data,name_space='hgnc_symbol'):
"""Scatter plot with fraction of (globally) relevant GO annotations
as a function of number of GO annotations for all input genes.

Arguments
sd : genewalk_scatterplots.csv loaded as pandas dataframe: scatter_data
named_genes: list with genes of type name_space. These will be labeled
             in the plot.
name_space : which column name from genewalk_scatterplots.csv used as
             identifier for genes. Default value: 'hgnc_symbol'. Other 
             possible values: {'mgi_id','hgnc_id'}


Visualization thresholds:
T_frac: maximal fraction of relevant GO annotations, set to 0.5
T_gocon: minimal number of GO annotations per gene,
set to max of 30 and 75th quartile of distribution.
"""
xlab = 'Number of GO annotations (per gene)'
ylab = 'Fraction of relevant GO terms (per gene)'
xvar = 'go_con'
yvar = 'frac_rel_go'

plot_filename = 'custom_moonlighters_x_' + xvar + '_y_' + yvar #adjust as needed
plot_title = 'GeneWalk moonlighting genes' #Adjust as needed
sizex = 4 #unit: inch, adjust as needed
sizey = 4 #unit: inch, adjust as needed
font_sz = 10 # adjust as needed

xmin = 0.45
xmax = max(sd[xvar])*1.2
T_gocon = max(30, sd[xvar].quantile(q=0.75))
T_frac = 0.5

#seaborn static plot
sns.set(style="whitegrid")
fig, ax = plt.subplots(figsize=(sizex, sizey))  # inches
g = sns.scatterplot(x=xvar, y=yvar, hue=yvar,
                    linewidth=0, alpha=0.5,

sizes=(40, 400),

                    data=sd,
                    ax=ax, legend=False)
plt.axvline(x=T_gocon, color='grey', linestyle='--')
plt.axhline(y=T_frac, color='grey', linestyle='--')
plt.xlabel(xlab, size=font_sz)
plt.ylabel(ylab, size=font_sz)
plt.xlim([xmin, xmax])
plt.xticks(size=font_sz)
plt.yticks(size=font_sz)

dmoon = sd[sd[name_space].isin(named_genes)]
for m in dmoon.index:
    gname = dmoon[name_space][m]
    x_txt = dmoon[xvar][m]
    y_txt = dmoon[yvar][m]
    g.text(x_txt, y_txt, gname, size=font_sz*0.8, 
           horizontalalignment='center',
           color='black', weight='semibold', fontstyle='italic')
g.set(xscale="log")
plt.title(plot_title, size=font_sz)

plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.pdf'),
            bbox_inches="tight", transparent=True)
plt.savefig(os.path.join(project_folder, 'figures', plot_filename + '.png'),
            bbox_inches="tight", transparent=True)

### Plot Regulator scatter plots with custom chosen genes

specify genes in labeled_regulators.

labeled_regulators = ['JUN','TCF3','STAT3']

scatterplot_regulators(labeled_regulators,
scatter_data,
'hgnc_symbol')

### Plot Moonlighting scatter plots with custom chosen genes

specify genes in labeled_moonlighters

labeled_moonlighters = ['WNT5A','TLR4']

scatterplot_moonlighters(labeled_moonlighters,
scatter_data,
'hgnc_symbol')

[ri23]

Hi @s-janewanthana

Thank you for reaching out. Generally speaking the higher the gene connectivity and fraction of relative GO terms the more relevant the gene in the regulator scatterplot. The threshold choice of 75th percentile has no particular statistical reason, it is just a guidance for plotting purposes.
Please feel free to adjust to other values or omit altogether if that makes more sense for your data.
The same holds for the T_frac default value of 0.5.
It might also be worth considering genes with a high fraction of relevant GO terms that are less connected. I hope this helps.
Kind regards,
Robert