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merge_and_viz.py
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merge_and_viz.py
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'''
Usage: python merge_and_viz.py -m [mode] -g [graph] -o [prefix] file1.tsv file2.tsv ... filen.tsv
Purpose: merge tables generated by make_tables.py and make several graphs to visualize GO Slim data
Arguments:
-m, --mode Runmode (choices: both, merge)
-g, --graph Type of graph(s) to make (choices: all, none, bar, heatmap, bubble, pie)
-o, --out Prefix for output files
Files with tables to be merged are given positional arguments after keyword arguments
Jessie Pelosi 2023
'''
import pandas as pd
import argparse
import matplotlib.pyplot as plt
from matplotlib import colors
import numpy as np
import re
import sys
from functools import reduce
import seaborn as sns
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--mode', dest = "run", choices = ['both', 'merge'])
parser.add_argument('-g', '--graph', dest = "graph", choices = ['all', 'bar','heatmap', 'bubble', 'pie', 'none'])
parser.add_argument('-o', '--out', dest='out')
parser.add_argument('file', nargs='*')
args = parser.parse_args()
RUNMODE=args.run
GRAPHS=args.graph
OUTFILE=args.out
if RUNMODE=="both":
dataframe_list = []
for fname in args.file:
temp_df = pd.read_table(fname)
temp_df.columns = ['GOSlimTerm',fname]
dataframe_list.append(temp_df)
names = []
for fname in args.file:
fname = fname.strip()
res = re.sub(r'([.]*)\.blast.*', r'\1', fname)
res2 = res.strip()
if not res2: continue
names.append(res2)
Output_df = reduce(lambda x, y: pd.merge(x, y, on = 'GOSlimTerm', how = 'outer'),dataframe_list)
Output_df = Output_df.fillna(0)
Output_df.columns = Output_df.columns.str.replace("([.]*)\.blast.*", "\1", regex = True)
Output_df.to_csv(OUTFILE+"_aggregatedTable.tsv", sep='\t')
if GRAPHS=="all":
### Generate bubble plot ####
df_bub=pd.melt(Output_df, id_vars = ['GOSlimTerm'])
## Use these lines to edit the size and parameteres of the bubble graph figure
plt.figure(figsize = (6,12))
plot=sns.scatterplot(data=df_bub, x="variable", y="GOSlimTerm", size="value",
legend=False, sizes=(5, 750), hue = 'variable', palette = 'Set3', alpha = 0.5)
plot.set_xticks(np.arange(len(names)))
plot.set_xticklabels(names, size = 8)
plt.xticks(rotation=45, ha='right')
plt.xlabel('')
plt.ylabel('')
plt.tight_layout()
plt.savefig(OUTFILE+'_bubbleGraph.png', format='png', dpi=300)
### Generate heatmaps ###
table_merge_num = Output_df.iloc[0:,1:]
GOSlim_list = list(Output_df["GOSlimTerm"])
fig, ax = plt.subplots(figsize=(15,15))
im = ax.imshow(table_merge_num, cmap="rainbow")
ax.set_title("", size=20)
ax.set_xticks(np.arange(len(names)))
ax.set_xticklabels(names)
plt.setp(ax.get_xticklabels(),rotation=45, ha="right", rotation_mode="anchor")
ax.set_yticks(np.arange(len(GOSlim_list)))
ax.set_yticklabels(GOSlim_list)
fig.tight_layout()
cbar = ax.figure.colorbar(im, ax=ax, shrink=0.5)
ax.set_facecolor("white")
plt.savefig(OUTFILE+"_heatmap.png", format='png',dpi=300)
fig, ax = plt.subplots(figsize=(15,15))
im = ax.imshow(table_merge_num, cmap="rainbow", norm=colors.SymLogNorm(vmin=0, vmax=table_merge_num.to_numpy().max(), linthresh=0.001, base=10))
ax.set_xticks(np.arange(len(names)))
ax.set_xticklabels(names)
plt.setp(ax.get_xticklabels(),rotation=45, ha="right", rotation_mode="anchor")
ax.set_yticks(np.arange(len(GOSlim_list)))
ax.set_yticklabels(GOSlim_list)
fig.tight_layout()
cbar = ax.figure.colorbar(im, ax=ax, shrink=0.5)
ax.set_facecolor("white")
plt.savefig(OUTFILE+"_heatmap_lognorm.png", format='png',dpi=300)
### Generate barplots ###
plotdata = Output_df.set_index(list(Output_df)[0])
plotdata.plot(kind="barh", figsize=(20,15), fontsize=12, width = 1, colormap = 'Set3')
plt.xlabel("Proportion of Gene Set")
plt.ylabel("")
plt.legend(frameon=False, labels = names)
plt.gca().invert_yaxis()
plt.tight_layout()
plt.savefig(OUTFILE+'_barGraph.png', format='png', dpi=300)
### Generate pie charts (outputs one file for each input) ###
for fname in args.file:
temp_df = pd.read_table(fname)
temp_df.columns = ['GOSlimTerm',fname]
temp_df.sort_values(fname, ascending = False, inplace=True)
df2=temp_df[:10].copy()
new_row=pd.DataFrame(data = {'GOSlimTerm' : ['others'],
fname : [temp_df[fname][10:].sum()]
})
df2=pd.concat([df2, new_row])
fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (9,4))
reg_name = re.sub(r'([.]*)\.blast.*', r'\1', fname)
df2.plot(kind = 'pie', y = fname, title = reg_name, labels = df2['GOSlimTerm'], ax = axes, colormap = 'Set3', normalize=True)
plt.ylabel('')
plt.legend('',frameon=False)
plt.tight_layout()
plt.savefig(OUTFILE+'_'+fname+'_pieChart.png', format='png', dpi=300)
elif GRAPHS=="heatmap":
table_merge_num = Output_df.iloc[0:,1:]
GOSlim_list = list(Output_df["GOSlimTerm"])
fig, ax = plt.subplots(figsize=(15,15))
im = ax.imshow(table_merge_num, cmap="rainbow")
ax.set_title("", size=20)
ax.set_xticks(np.arange(len(names)))
ax.set_xticklabels(names)
plt.setp(ax.get_xticklabels(),rotation=45, ha="right", rotation_mode="anchor")
ax.set_yticks(np.arange(len(GOSlim_list)))
ax.set_yticklabels(GOSlim_list)
fig.tight_layout()
cbar = ax.figure.colorbar(im, ax=ax, shrink=0.5)
ax.set_facecolor("white")
plt.savefig(OUTFILE+"_heatmap.png", format='png',dpi=300)
fig, ax = plt.subplots(figsize=(15,15))
im = ax.imshow(table_merge_num, cmap="rainbow", norm=colors.SymLogNorm(vmin=0, vmax=table_merge_num.to_numpy().max(), linthresh=0.001, base=10))
ax.set_title("", size=20)
ax.set_xticks(np.arange(len(names)))
ax.set_xticklabels(names)
plt.setp(ax.get_xticklabels(),rotation=45, ha="right", rotation_mode="anchor")
ax.set_yticks(np.arange(len(GOSlim_list)))
ax.set_yticklabels(GOSlim_list)
fig.tight_layout()
cbar = ax.figure.colorbar(im, ax=ax, shrink=0.5)
ax.set_facecolor("white")
plt.savefig(OUTFILE+"_heatmap_lognorm.png", format='png',dpi=300)
elif GRAPHS=="bar":
plotdata = Output_df.set_index(list(Output_df)[0])
plotdata.plot(kind="barh", figsize=(20,15), fontsize=12, width = 1, colormap = 'Set3')
plt.xlabel("Proportion of Gene Set")
plt.ylabel("")
plt.legend(frameon=False, labels = names)
plt.gca().invert_yaxis()
plt.tight_layout()
plt.savefig(OUTFILE+'_barGraph.png', format='png', dpi=300)
elif GRAPHS=="pie":
for fname in args.file:
temp_df = pd.read_table(fname)
temp_df.columns = ['GOSlimTerm',fname]
temp_df.sort_values(fname, ascending = False, inplace=True)
df2=temp_df[:10].copy()
new_row=pd.DataFrame(data = {'GOSlimTerm' : ['others'],
fname : [temp_df[fname][10:].sum()]
})
df2=pd.concat([df2, new_row])
fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (9,4))
reg_name = re.sub(r'([.]*)\.blast.*', r'\1', fname)
df2.plot(kind = 'pie', y = fname, title = reg_name, labels = df2['GOSlimTerm'], ax = axes, colormap = 'Set3', normalize=True)
plt.ylabel('')
plt.legend('',frameon=False)
plt.tight_layout()
plt.savefig(OUTFILE+'_'+fname+'_pieChart.png', format='png', dpi=300)
elif GRAPHS==("bubble"):
df_bub=pd.melt(Output_df, id_vars = ['GOSlimTerm'])
## Use these lines to edit the size and parameteres of the bubble graph figure
sns.set(font_scale=0.5, style = "white", rc={"figure.figsize":(5, 12)})
plot=sns.scatterplot(data=df_bub, x="variable", y="GOSlimTerm", size="value",
legend=False, sizes=(5, 750), hue = 'variable', palette = 'Set3', alpha = 0.5)
plot.set_xticks(np.arange(len(names)))
plot.set_xticklabels(names)
plt.xticks(rotation=45, ha='right')
plt.xlabel('')
plt.ylabel('')
plt.tight_layout()
plt.savefig(OUTFILE+'_bubbleGraph.png', format='png', dpi=300)
elif RUNMODE=="merge":
dataframe_list = []
for fname in args.file:
temp_df = pd.read_table(fname)
temp_df.columns = ['GOSlimTerm',fname]
dataframe_list.append(temp_df)
names = []
for fname in args.file:
res = re.findall("(.*).blast", fname)
if not res: continue
names.append(res[0])
Output_df = reduce(lambda x, y: pd.merge(x, y, on = 'GOSlimTerm', how = 'outer'),dataframe_list)
Output_df = Output_df.fillna(0)
Output_df.to_csv(OUTFILE+"_aggregatedTable.tsv", sep='\t')