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DEGWindow.py
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import os
import pandas as pd
import numpy as np
from compute_goea import goea
from matplotlib.backends.qt_compat import QtCore, QtWidgets, is_pyqt5
from matplotlib.backends.backend_qt5agg import (FigureCanvas, NavigationToolbar2QT as NavigationToolbar)
from PyQt5.QtCore import QThread, pyqtSignal, Qt
from matplotlib.figure import Figure
class DEGWindow(QtWidgets.QMainWindow):
def __init__(self, df, id_to_name, cluster1, cluster2, out_dir, goea_dir, adj_p_vals=True):
super().__init__()
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
self.setWindowTitle("DEG result for " + cluster1 + ' and ' + cluster2)
layout = QtWidgets.QVBoxLayout(self._main)
scroll = QtWidgets.QScrollArea()
table = QtWidgets.QTableWidget()
scroll.setWidget(table)
layout.addWidget(table)
self.df = df
self.id_to_name = id_to_name
self.cluster1 = cluster1
self.cluster2 = cluster2
self.out_dir = out_dir
self.goea_dir = goea_dir
if adj_p_vals:
self.p_vals = 'adj-p-value'
else:
self.p_vals = 'p-value'
if not os.path.exists(self.out_dir):
os.mkdir(self.out_dir)
from rpy2.robjects import pandas2ri
from rpy2.robjects import r
import rpy2.robjects as robjects
robjects.r('''
p <- function(ediff) {
library(scde)
p.values <- 2*pnorm(abs(ediff$Z),lower.tail=F) # 2-tailed p-value
p.values
}
''')
robjects.r('''
p.adj <- function(ediff) {
library(scde)
p.values.adj <- 2*pnorm(abs(ediff$cZ),lower.tail=F) # Adjusted to control for FDR
p.values.adj
}
''')
r_p = robjects.globalenv['p']
r_p_adj = robjects.globalenv['p.adj']
pandas2ri.activate()
p_vals = r_p(df)
adj_p_vals = r_p_adj(df)
self.df['p-value'] = pd.Series(pandas2ri.ri2py(p_vals), index = self.df.index)
self.df['adj-p-value'] = pd.Series(pandas2ri.ri2py(adj_p_vals), index = self.df.index)
gene_ids = self.df.index
gene_symbols = np.array([self.id_to_name[gene_id] for gene_id in gene_ids])
self.df['gene_symbols'] = pd.Series(gene_symbols, index = self.df.index)
self.df_up = self.df.loc[df['Z'] > 0]
self.df_down = self.df.loc[df['Z'] < 0]
writer = pd.ExcelWriter(os.path.join(self.out_dir, self.cluster1 + ' deg ' + self.cluster2 + '.xlsx'))
self.df.to_excel(writer,'Up and down')
self.df_up.to_excel(writer,'Up')
self.df_down.to_excel(writer,'Down')
writer.save()
## Get fold change
## Data frame with fold change and p values
volcano_canvas = FigureCanvas(Figure(figsize=(5, 5)))
layout.addWidget(volcano_canvas)
self.addToolBar(QtCore.Qt.BottomToolBarArea,
NavigationToolbar(volcano_canvas, self))
self._volcano_fig = volcano_canvas.figure
self._volcano_ax = volcano_canvas.figure.subplots()
fig, self._volcano_ax = self.volcano_plot(df, fig=self._volcano_fig, ax=self._volcano_ax)
self._volcano_ax.figure.canvas.draw()
go_button = QtWidgets.QPushButton("GO analysis")
go_button.clicked.connect(self._on_click_go)
layout.addWidget(go_button)
table.setColumnCount(len(df.columns))
table.setRowCount(len(df.index))
for i in range(len(df.index)):
table.setVerticalHeaderItem(i,QtWidgets.QTableWidgetItem(self.id_to_name[df.index[i]]))
for j in range(len(df.columns)):
table.setHorizontalHeaderItem(j,QtWidgets.QTableWidgetItem(df.columns[j]))
table.setItem(i,j,QtWidgets.QTableWidgetItem(str(df.iloc[i, j])))
def get_fig(self, fig=None, ax=None, figsize=[4, 4]):
"""fills in any missing axis or figure with the currently active one
:param ax: matplotlib Axis object
:param fig: matplotlib Figure object
"""
if not fig:
fig = plt.figure(figsize=figsize)
if not ax:
ax = plt.gca()
return fig, ax
def volcano_plot(self, df, fig=None, ax=None):
"""Function to highlight specific cells on the tSNE map
"""
fig, ax = self.get_fig(fig=fig, ax=ax)
df['log-p'] = -df[self.p_vals].apply(np.log10)
ax.scatter(df['ce'], df['log-p'], s=5, color='lightgrey')
ax.scatter(df.loc[(df[self.p_vals] < 0.05) & (df['ce'] > 1), 'ce'],
df.loc[(df[self.p_vals] < 0.05) & (df['ce'] > 1), 'log-p'], s=5, color=self.cluster1)
ax.scatter(df.loc[(df[self.p_vals] < 0.05) & (df['ce'] < -1), 'ce'],
df.loc[(df[self.p_vals] < 0.05) & (df['ce'] < -1), 'log-p'], s=5, color=self.cluster2)
fig.savefig(os.path.join(self.out_dir, self.cluster1 + '_' + self.cluster2 + '_volcano.svg'))
return fig, ax
def _on_click_go(self):
cluster = self.df.loc[(self.df[self.p_vals] < 0.05)].index
print('up and down ', len(cluster), ' number of genes')
gene_symbols = [self.id_to_name[gene_id] for gene_id in cluster]
goea(cluster, gene_symbols, self.cluster1, self.cluster2, self.goea_dir, self.out_dir) ## list of genes represented by their ensembl id and gene symbol
cluster = self.df.loc[(self.df[self.p_vals] < 0.05) & (self.df['ce'] > 1)].index
print('up ', len(cluster), ' number of genes')
gene_symbols = [self.id_to_name[gene_id] for gene_id in cluster]
goea(cluster, gene_symbols, self.cluster1, str(self.cluster2) + 'up', self.goea_dir, self.out_dir) ## list of genes represented by their ensembl id and gene symbol
cluster = self.df.loc[(self.df[self.p_vals] < 0.05) & (self.df['ce'] < -1)].index
print('down ', len(cluster), ' number of genes')
gene_symbols = [self.id_to_name[gene_id] for gene_id in cluster]
goea(cluster, gene_symbols, self.cluster1, str(self.cluster2) + 'down', self.goea_dir, self.out_dir)## list of genes represented by their ensembl id and gene symbol