Prepare environment¶

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib_venn import venn3, venn2
import seaborn as sns
import scanpy as sc
import glob

import sys

sys.path.append("./../../../../utilities_folder/")

from utilities import intTable

Set R environment with rpy2:

In [2]:
import rpy2.rinterface_lib.callbacks
import anndata2ri
import logging

from rpy2.robjects import pandas2ri
import rpy2.robjects as ro
from scipy import stats

rpy2.rinterface_lib.callbacks.logger.setLevel(logging.ERROR)

pandas2ri.activate()
anndata2ri.activate()

%load_ext rpy2.ipython

Set up parameters for Python plots:

In [3]:
%matplotlib inline
sc.set_figure_params(dpi = 600, fontsize = 20)

plt.rcParams['pdf.fonttype'] = 'truetype'

cmap_up = sns.light_palette("red", as_cmap=True)
cmap_down = sns.light_palette("blue", as_cmap=True)
cmap_all = sns.light_palette("seagreen", as_cmap=True)

Set folder paths

In [4]:
tables_folder = './tables/'

Compare DEGs¶

In [5]:
egVSipsc = pd.read_excel(tables_folder + 'Bulk_hEGCLCs_vs_hiPSC_filtered.xlsx', index_col = 0)
egVSpgc = pd.read_excel(tables_folder + 'Bulk_hPGCLCs_vs_EGCLC_filtered.xlsx', index_col = 0)
ipscVSpgc = pd.read_excel(tables_folder + 'Bulk_hPGCLCs_vs_hiPSC_filtered.xlsx', index_col = 0)
In [6]:
up_eg_vs_ipsc = egVSipsc[egVSipsc.logFC > 0].index.tolist()
len(up_eg_vs_ipsc)
Out[6]:
82
In [7]:
down_eg_vs_ipsc = egVSipsc[egVSipsc.logFC < 0].index.tolist()
len(down_eg_vs_ipsc)
Out[7]:
34
In [8]:
egVSpgc
Out[8]:
logFC logCPM LR PValue FDR Gene -log10(FDR)
IGF1 15.896291 6.846814 2094.605002 0.000000 1.671936e-316 IGF1 315.776780
SOX17 13.895054 8.632823 2928.672152 0.000000 1.671936e-316 SOX17 315.776780
CHI3L2 13.443653 6.417250 2160.317252 0.000000 1.671936e-316 CHI3L2 315.776780
IRX6 12.703410 6.493450 2260.318922 0.000000 1.671936e-316 IRX6 315.776780
NANOS3 12.560026 8.623343 1951.820313 0.000000 1.671936e-316 NANOS3 315.776780
... ... ... ... ... ... ... ...
ATP4A -2.398388 -0.822223 8.925405 0.002812 4.787297e-03 ATP4A 2.319910
KCNE2 -2.142631 -0.700751 8.582153 0.003395 5.709500e-03 KCNE2 2.243402
PPP1R36 -2.143626 0.183762 8.424237 0.003703 6.191570e-03 PPP1R36 2.208199
BDKRB2 -2.144906 -0.085745 8.318019 0.003925 6.543553e-03 BDKRB2 2.184186
EXOC3L2 -2.121139 1.862711 7.898945 0.004946 8.133181e-03 EXOC3L2 2.089740

2663 rows × 7 columns

In [9]:
up_pgc_vs_eg = egVSpgc[egVSpgc.logFC > 0].index.tolist()
down_pgc_vs_eg = egVSpgc[egVSpgc.logFC < 0].index.tolist()

len(up_pgc_vs_eg)
Out[9]:
1024
In [10]:
len(down_pgc_vs_eg)
Out[10]:
1639
In [11]:
ipscVSpgc
Out[11]:
logFC logCPM LR PValue FDR Gene -log10(FDR)
SOX17 17.549030 8.894461 2025.498153 0.000000 5.866289e-316 SOX17 315.231637
IGF1 15.801469 7.113052 1664.385070 0.000000 5.866289e-316 IGF1 315.231637
IRX6 15.460311 6.758986 1915.660894 0.000000 5.866289e-316 IRX6 315.231637
WNT2 15.321818 6.614517 2056.095895 0.000000 5.866289e-316 WNT2 315.231637
CHI3L2 12.600977 6.683063 1781.461462 0.000000 5.866289e-316 CHI3L2 315.231637
... ... ... ... ... ... ... ...
LRRC73 -2.415578 -0.129566 11.514055 0.000691 1.260929e-03 LRRC73 2.899309
CAPS2 -2.001239 -0.344132 11.222459 0.000808 1.464992e-03 CAPS2 2.834165
ERBB4 -2.021953 -0.359235 10.757452 0.001039 1.856177e-03 ERBB4 2.731381
OR14K1 -2.033268 -0.115466 9.782277 0.001762 3.062090e-03 OR14K1 2.513982
ZNF540 2.169959 0.052795 7.592525 0.005861 9.460098e-03 ZNF540 2.024104

2585 rows × 7 columns

In [12]:
up_pgc_vs_ipsc = ipscVSpgc[ipscVSpgc.logFC > 0].index.tolist()
down_pgc_vs_ipsc = ipscVSpgc[ipscVSpgc.logFC < 0].index.tolist()

len(up_pgc_vs_ipsc)
Out[12]:
953
In [13]:
len(down_pgc_vs_ipsc)
Out[13]:
1632

Venn diagrams¶

All¶

In [14]:
_ = venn3([set(egVSipsc.index), set(egVSpgc.index), set(ipscVSpgc.index)], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))

All positive¶

Let's put all together:

  • up in PGC vs EGCLC
  • up in PGC vs iPSC
  • up in EG vs iPSC
In [15]:
_ = venn3([set(up_pgc_vs_eg), set(up_pgc_vs_ipsc), set(up_eg_vs_ipsc)], set_labels=('UP hPGCLCs_vs_hEGCLC', 'UP hPGCLCs_vs_hiPSC', 'UP hEGCLCs_vs_hiPSC') )

All negative¶

Let's put all together:

  • down in PGC vs EGCLC
  • down in PGC vs iPSC
  • down in EG vs iPSC
In [16]:
_ = venn3([set(down_pgc_vs_eg), set(down_pgc_vs_ipsc), set(down_eg_vs_ipsc)], set_labels=('DOWN hPGCLCs_vs_hEGCLC', 'DOWN hPGCLCs_vs_hiPSC', 'DOWN hEGCLCs_vs_hiPSC') )

Pairwise comparisons¶

Up in PGC vs EGCLC and up in PGC vs iPSC¶

In [17]:
venn2([set(up_pgc_vs_eg), set(up_pgc_vs_ipsc)], set_labels=('UP hPGCLCs_vs_hEGCLC', 'UP hPGCLCs_vs_hiPSC') )
Out[17]:
<matplotlib_venn._common.VennDiagram at 0x7fa16cefc220>

Up in PGC vs EGCLC and up in EG vs iPSC¶

In [18]:
venn2([set(up_pgc_vs_eg), set(up_eg_vs_ipsc)], set_labels=('UP hPGCLCs_vs_hEGCLC', 'UP hEGCLCs_vs_hiPSC') )
Out[18]:
<matplotlib_venn._common.VennDiagram at 0x7fa16cf1c940>

Up in PGC vs iPSC and up in EG vs iPSC¶

In [19]:
venn2([set(up_pgc_vs_ipsc), set(up_eg_vs_ipsc)], set_labels=('UP hPGCLCs_vs_hiPSC', 'UP hEGCLCs_vs_hiPSC') )
Out[19]:
<matplotlib_venn._common.VennDiagram at 0x7fa16ce75700>

Up in EG vs iPSC and up in EG vs PGC¶

In [20]:
venn2([set(up_eg_vs_ipsc), set(down_pgc_vs_eg)], set_labels=('UP hEGCLC_vs_hiPSC', 'UP hEGCLC_vs_hPGCLC') )
Out[20]:
<matplotlib_venn._common.VennDiagram at 0x7fa16ce47070>

Up in EG vs iPSC and up in iPSC vs PGC¶

In [21]:
venn2([set(up_eg_vs_ipsc), set(down_pgc_vs_ipsc)], set_labels=('UP hEGCLC_vs_hiPSC', 'UP hPGCLC_vs_hiPSC') )
Out[21]:
<matplotlib_venn._common.VennDiagram at 0x7fa16d559ac0>

Up in iPSC vs EG and up in PGC vs EG¶

In [22]:
venn2([set(down_eg_vs_ipsc), set(up_pgc_vs_eg)], set_labels=('UP hiPSC_vs_hEGCLC', 'UP hPGCLC_vs_hEGCLC') )
Out[22]:
<matplotlib_venn._common.VennDiagram at 0x7fa16d027940>

Up in iPSC vs EG and up in EG vs PGC¶

In [23]:
venn2([set(down_eg_vs_ipsc), set(down_pgc_vs_eg)], set_labels=('UP hiPSC_vs_hEGCLC', 'UP hEGCLC_vs_hPGCLC') )
Out[23]:
<matplotlib_venn._common.VennDiagram at 0x7fa151d75220>

Up in iPSC vs EG and up in PGC vs iPSC¶

In [24]:
venn2([set(down_eg_vs_ipsc), set(up_pgc_vs_ipsc)], set_labels=('UP hiPSC_vs_hEGCLC', 'UP hPGCLC_vs_hiPSC') )
Out[24]:
<matplotlib_venn._common.VennDiagram at 0x7fa1675cef70>

Up in iPSC vs EG and up in iPSC vs PGC¶

In [25]:
venn2([set(down_eg_vs_ipsc), set(down_pgc_vs_ipsc)], set_labels=('UP hiPSC_vs_hEGCLC', 'UP hiPSC_vs_hPGCLC') )
Out[25]:
<matplotlib_venn._common.VennDiagram at 0x7fa16cd6dfd0>

Up in PGC vs EG and up in iPSC vs PGC¶

In [26]:
venn2([set(up_pgc_vs_eg), set(down_pgc_vs_ipsc)], set_labels=('UP hPGCLC_vs_hEGCLC', 'UP hiPSC_vs_hPGCLC') )
Out[26]:
<matplotlib_venn._common.VennDiagram at 0x7fa16cd9b2e0>

Up in EG vs PGC and up in PGC vs iPSC¶

In [27]:
venn2([set(down_pgc_vs_eg), set(up_pgc_vs_ipsc)], set_labels=('UP hEGCLC_vs_hPGCLC', 'UP hPGCLC_vs_hiPSC') )
Out[27]:
<matplotlib_venn._common.VennDiagram at 0x7fa16cd63070>

Up in EG vs PGC and up in iPSC vs PGC¶

In [28]:
venn2([set(down_pgc_vs_eg), set(down_pgc_vs_ipsc)], set_labels=('UP hEGCLC_vs_hPGCLC', 'UP hiPSC_vs_hPGCLC') )
Out[28]:
<matplotlib_venn._common.VennDiagram at 0x7fa16ccb0610>

Statistical overlaps¶

In [29]:
%%R -i up_eg_vs_ipsc -i down_eg_vs_ipsc -i up_pgc_vs_eg -i down_pgc_vs_eg -i up_pgc_vs_ipsc -i down_pgc_vs_ipsc

loc <- './../../../../R_loc' # pointing to the renv environment

.libPaths(loc)

library(GeneOverlap) 

up <- list(up_eg_vs_ipsc, up_pgc_vs_eg, up_pgc_vs_ipsc)
names(up) <- c('up_eg_vs_ipsc', 'up_pgc_vs_eg', 'up_pgc_vs_ipsc')

down <- list(down_eg_vs_ipsc, down_pgc_vs_eg, down_pgc_vs_ipsc)
names(down) <- c('down_eg_vs_ipsc', 'down_pgc_vs_eg', 'down_pgc_vs_ipsc')

gom.obj_up_down <- newGOM(up, down, genome.size = 14582)
gom.obj_up_up <- newGOM(up, genome.size = 14582)
gom.obj_down_down <- newGOM(down, genome.size = 14582)
In [30]:
%%R
drawHeatmap(gom.obj_up_down, what = 'Jaccard', grid.col="Greens", note.col="white")
In [31]:
%%R
drawHeatmap(gom.obj_up_up, what = 'Jaccard', grid.col="Reds", note.col="white")
In [32]:
%%R
drawHeatmap(gom.obj_down_down, what = 'Jaccard', grid.col="Blues",  note.col="white")

Jaccard index - precise numbers¶

In [33]:
l1 = list(set(egVSipsc.index))
l1.sort()

l2 = list(set(egVSpgc.index))
l2.sort()

l3 = list(set(ipscVSpgc.index))
l3.sort()


upsetplot_df = pd.DataFrame([l1,l2,l3]).T
upsetplot_df.columns = ['hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC']
In [34]:
stats_pos = {}

for i, n in zip([l1,l2,l3], upsetplot_df.columns):
    for j, m in zip([l1,l2,l3], upsetplot_df.columns):
        key = n + '-' + m
        set1 = set(i)
        set2 = set(j)
        intersection = len(set1.intersection(set2))
        union = len(set1.union(set2))
        stats_pos[key] = intersection/union
In [35]:
stats_pos
Out[35]:
{'hEGCLCs_vs_hiPSC-hEGCLCs_vs_hiPSC': 1.0,
 'hEGCLCs_vs_hiPSC-hPGCLCs_vs_hEGCLC': 0.025082995204721504,
 'hEGCLCs_vs_hiPSC-hPGCLCs_vs_hiPSC': 0.014650638617580767,
 'hPGCLCs_vs_hEGCLC-hEGCLCs_vs_hiPSC': 0.025082995204721504,
 'hPGCLCs_vs_hEGCLC-hPGCLCs_vs_hEGCLC': 1.0,
 'hPGCLCs_vs_hEGCLC-hPGCLCs_vs_hiPSC': 0.7297297297297297,
 'hPGCLCs_vs_hiPSC-hEGCLCs_vs_hiPSC': 0.014650638617580767,
 'hPGCLCs_vs_hiPSC-hPGCLCs_vs_hEGCLC': 0.7297297297297297,
 'hPGCLCs_vs_hiPSC-hPGCLCs_vs_hiPSC': 1.0}
In [36]:
df_to_plot = pd.DataFrame(stats_pos.values(), index = stats_pos.keys())
i1 = pd.Series(df_to_plot.index).apply(lambda x: x.split('-')[0])
i2 = pd.Series(df_to_plot.index).apply(lambda x: x.split('-')[1])
df_to_plot = df_to_plot.reset_index()
df_to_plot['i1'] = i1
df_to_plot['i2'] = i2
df_to_plot = df_to_plot.pivot(index='i1', columns='i2', values=0)
In [37]:
plt.figure(figsize = (5, 5))
mask = np.triu(np.ones_like(df_to_plot, dtype=bool))
sns.heatmap(df_to_plot, mask=mask,
            square=True, linewidths=.5, cbar_kws={"shrink": .5}, cmap = 'PuBu')
Out[37]:
<AxesSubplot:xlabel='i2', ylabel='i1'>

Comparisons of GO term¶

GO BP¶

In [38]:
egVSipsc_up = pd.read_excel(tables_folder + 'GO_upregulated_hEGCLCs_vs_hiPSC_BP.xlsx', index_col = 0)
pgcVSeg_up = pd.read_excel(tables_folder + 'GO_upregulated_hPGCLCs_vs_EGCLC_BP.xlsx', index_col = 0)
pgcVSipsc_up = pd.read_excel(tables_folder + 'GO_upregulated_hPGCLCs_vs_hiPSC_BP.xlsx', index_col = 0)
In [39]:
egVSipsc_down = pd.read_excel(tables_folder + 'GO_downregulated_hEGCLCs_vs_hiPSC_BP.xlsx', index_col = 0)
pgcVSeg_down = pd.read_excel(tables_folder + 'GO_downregulated_hPGCLCs_vs_EGCLC_BP.xlsx', index_col = 0)
pgcVSipsc_down = pd.read_excel(tables_folder + 'GO_downregulated_hPGCLCs_vs_hiPSC_BP.xlsx', index_col = 0)
In [40]:
egVSipsc_all = pd.read_excel(tables_folder + 'GO_allSignificant_hEGCLCs_vs_hiPSC_BP.xlsx', index_col = 0)
pgcVSeg_all = pd.read_excel(tables_folder + 'GO_allSignificant_hPGCLCs_vs_EGCLC_BP.xlsx', index_col = 0)
pgcVSipsc_all = pd.read_excel(tables_folder + 'GO_allSignificant_hPGCLCs_vs_hiPSC_BP.xlsx', index_col = 0)
In [41]:
len(egVSipsc_up)
Out[41]:
15
In [42]:
len(pgcVSeg_up)
Out[42]:
159
In [43]:
len(pgcVSipsc_up)
Out[43]:
128

Venn diagrams¶

Terms enriched in upregulated genes¶

In [44]:
_ = venn3([set(egVSipsc_up['GO.ID']), set(pgcVSeg_up['GO.ID']), set(pgcVSipsc_up['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))
In [45]:
pgcVSeg_up
Out[45]:
GO.ID Term Annotated Significant Expected Statistics ER -log10(pvalue) Significant/Annotated
1 GO:0045165 cell fate commitment 170 46 12.49 8.400000e-08 3.68 7.075721 0.270588
2 GO:0001709 cell fate determination 22 10 1.62 1.200000e-06 6.17 5.920819 0.454545
3 GO:0003161 cardiac conduction system development 27 11 1.98 1.400000e-06 5.56 5.853872 0.407407
4 GO:0060070 canonical Wnt signaling pathway 258 39 18.95 3.700000e-06 2.06 5.431798 0.151163
5 GO:0007422 peripheral nervous system development 68 16 4.99 3.900000e-06 3.21 5.408935 0.235294
... ... ... ... ... ... ... ... ... ...
155 GO:0019369 arachidonic acid metabolic process 33 7 2.42 8.900000e-03 2.89 2.050610 0.212121
156 GO:0001947 heart looping 50 9 3.67 9.750000e-03 2.45 2.010995 0.180000
157 GO:0055007 cardiac muscle cell differentiation 82 13 6.02 9.800000e-03 2.16 2.008774 0.158537
158 GO:0031954 positive regulation of protein autophosp... 26 6 1.91 9.950000e-03 3.14 2.002177 0.230769
159 GO:0051385 response to mineralocorticoid 26 6 1.91 9.950000e-03 3.14 2.002177 0.230769

159 rows × 9 columns

In [46]:
intersection = set(pgcVSeg_up['Term']).intersection(set(pgcVSipsc_up['Term']))
df = pd.DataFrame(index = intersection)
pgcVSeg_up.index = pgcVSeg_up.Term
pgcVSipsc_up.index = pgcVSipsc_up.Term
df['Scores_pgcVSeg_up'] = pgcVSeg_up[pgcVSeg_up.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_up'] = pgcVSipsc_up[pgcVSipsc_up.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_pgcVSeg_up'] * df['Scores_pgcVSipsc_up']
In [47]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[47]:
In [48]:
set(egVSipsc_up['Term']).intersection(set(pgcVSipsc_up['Term']))
Out[48]:
set()
In [49]:
set(egVSipsc_up['Term'])
Out[49]:
{'DNA methylation',
 'aging',
 'aromatic compound catabolic process',
 'cellular process involved in reproductio...',
 'gene silencing by RNA',
 'germ cell development',
 'learning or memory',
 'locomotory behavior',
 'methylation',
 'negative regulation of phosphorylation',
 'negative regulation of protein-containin...',
 'organic cyclic compound catabolic proces...',
 'piRNA metabolic process',
 'response to oxidative stress',
 'spermatogenesis'}

Terms enriched in downregulated genes¶

In [50]:
_ = venn3([set(egVSipsc_down['GO.ID']), set(pgcVSeg_down['GO.ID']), set(pgcVSipsc_down['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))
In [51]:
set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term'])).intersection(set(egVSipsc_down['Term']))
Out[51]:
set()
In [52]:
set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term']))
Out[52]:
{'B cell receptor signaling pathway',
 'G protein-coupled receptor signaling pat...',
 'activation of transmembrane receptor pro...',
 'adenylate cyclase-activating G protein-c...',
 'adult locomotory behavior',
 'antimicrobial humoral immune response me...',
 'behavioral fear response',
 'calcium ion transmembrane import into cy...',
 'calcium ion-regulated exocytosis of neur...',
 'cellular response to calcium ion',
 'cellular sodium ion homeostasis',
 'chemokine-mediated signaling pathway',
 'chloride transmembrane transport',
 'chondrocyte proliferation',
 'complement activation, classical pathway',
 'detection of temperature stimulus',
 'dopamine metabolic process',
 'engulfment of apoptotic cell',
 'excitatory postsynaptic potential',
 'exploration behavior',
 'eye photoreceptor cell development',
 'gamma-aminobutyric acid signaling pathwa...',
 'ionotropic glutamate receptor signaling ...',
 'locomotory behavior',
 'lymph vessel morphogenesis',
 'membrane depolarization',
 'memory',
 'monocyte chemotaxis',
 'multicellular organismal response to str...',
 'negative regulation of angiogenesis',
 'negative regulation of blood pressure',
 'negative regulation of fibroblast growth...',
 'negative regulation of leukocyte apoptot...',
 'negative regulation of vascular permeabi...',
 'neuron maturation',
 'neuronal action potential',
 'neuropeptide signaling pathway',
 'neurotransmitter metabolic process',
 'neurotransmitter transport',
 'neurotransmitter-gated ion channel clust...',
 'nitric oxide mediated signal transductio...',
 'phagocytosis, engulfment',
 'phagocytosis, recognition',
 'positive regulation of ERK1 and ERK2 cas...',
 'positive regulation of G protein-coupled...',
 'positive regulation of calcium ion trans...',
 'positive regulation of cytosolic calcium...',
 'positive regulation of dendrite extensio...',
 'positive regulation of developmental gro...',
 'positive regulation of excitatory postsy...',
 'positive regulation of phosphatidylinosi...',
 'positive regulation of positive chemotax...',
 'positive regulation of protein kinase B ...',
 'positive regulation of stem cell prolife...',
 'positive regulation of synapse assembly',
 'positive regulation of synaptic transmis...',
 'potassium ion import across plasma membr...',
 'potassium ion transmembrane transport',
 'prostaglandin secretion',
 'purinergic nucleotide receptor signaling...',
 'receptor localization to synapse',
 'regulation of AMPA receptor activity',
 'regulation of action potential',
 'regulation of cytosolic calcium ion conc...',
 'regulation of dopamine secretion',
 'regulation of humoral immune response',
 'regulation of ion transmembrane transpor...',
 'regulation of membrane potential',
 'regulation of neuronal synaptic plastici...',
 'regulation of neurotransmitter secretion',
 'regulation of neutrophil chemotaxis',
 'regulation of postsynaptic membrane pote...',
 'regulation of postsynaptic neurotransmit...',
 'regulation of presynapse assembly',
 'regulation of ryanodine-sensitive calciu...',
 'regulation of short-term neuronal synapt...',
 'regulation of smooth muscle contraction',
 'regulation of synaptic vesicle exocytosi...',
 'regulation of ventricular cardiac muscle...',
 'relaxation of muscle',
 'sensory perception of smell',
 'skeletal muscle contraction',
 'sodium ion transmembrane transport',
 'synaptic membrane adhesion',
 'vascular endothelial growth factor signa...',
 'visual learning'}
In [53]:
pgcVSeg_down= pgcVSeg_down[~pgcVSeg_down.index.duplicated()]
pgcVSipsc_down= pgcVSipsc_down[~pgcVSipsc_down.index.duplicated()]
In [54]:
intersection = set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term']))
df = pd.DataFrame(index = intersection)
#pgcVSeg_down.index = pgcVSeg_down.Term
#pgcVSipsc_down.index = pgcVSipsc_down.Term
df['Scores_pgcVSeg_up'] = pgcVSeg_down[pgcVSeg_down.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_up'] = pgcVSipsc_down[pgcVSipsc_down.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_pgcVSeg_up'] * df['Scores_pgcVSipsc_up']
In [55]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[55]:
In [56]:
set(egVSipsc_down['Term']).intersection(set(pgcVSipsc_down['Term']))
Out[56]:
set()
In [57]:
intersection = set(egVSipsc_down['Term']).intersection(set(pgcVSipsc_down['Term']))
df = pd.DataFrame(index = intersection)
egVSipsc_down.index = egVSipsc_down.Term
pgcVSipsc_down.index = pgcVSipsc_down.Term
df['Scores_egVSipsc_down'] = egVSipsc_down[egVSipsc_down.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_down'] = pgcVSipsc_down[pgcVSipsc_down.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_egVSipsc_down'] * df['Scores_pgcVSipsc_down']
In [58]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[58]:

Terms enriched in all genes¶

In [59]:
_ = venn3([set(egVSipsc_all['GO.ID']), set(pgcVSeg_all['GO.ID']), set(pgcVSipsc_all['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))
In [60]:
set(pgcVSeg_all['Term']).intersection(set(pgcVSipsc_all['Term'])).intersection(set(egVSipsc_all['Term']))
Out[60]:
{'postsynaptic membrane'}
In [61]:
set(pgcVSeg_all['Term']).intersection(set(pgcVSipsc_all['Term']))
Out[61]:
{'AMPA glutamate receptor complex',
 'GABA-ergic synapse',
 'Golgi lumen',
 'anchored component of membrane',
 'anchored component of plasma membrane',
 'blood microparticle',
 'chloride channel complex',
 'collagen trimer',
 'dendrite membrane',
 'dense core granule',
 'excitatory synapse',
 'external side of plasma membrane',
 'extracellular matrix',
 'glial cell projection',
 'hippocampal mossy fiber to CA3 synapse',
 'integral component of postsynaptic densi...',
 'integral component of postsynaptic speci...',
 'integral component of presynaptic membra...',
 'intrinsic component of synaptic vesicle ...',
 'postsynaptic density membrane',
 'postsynaptic membrane',
 'receptor complex',
 'sodium channel complex',
 'terminal bouton',
 'voltage-gated potassium channel complex'}
In [62]:
set(egVSipsc_all['Term']).intersection(set(pgcVSipsc_all['Term']))
Out[62]:
{'postsynaptic membrane'}

GO MF¶

In [63]:
egVSipsc_up = pd.read_excel(tables_folder + 'GO_upregulated_hEGCLCs_vs_hiPSC_MF.xlsx', index_col = 0)
pgcVSeg_up = pd.read_excel(tables_folder + 'GO_upregulated_hPGCLCs_vs_EGCLC_MF.xlsx', index_col = 0)
pgcVSipsc_up = pd.read_excel(tables_folder + 'GO_upregulated_hPGCLCs_vs_hiPSC_MF.xlsx', index_col = 0)
In [64]:
egVSipsc_down = pd.read_excel(tables_folder + 'GO_downregulated_hEGCLCs_vs_hiPSC_MF.xlsx', index_col = 0)
pgcVSeg_down = pd.read_excel(tables_folder + 'GO_downregulated_hPGCLCs_vs_EGCLC_MF.xlsx', index_col = 0)
pgcVSipsc_down = pd.read_excel(tables_folder + 'GO_downregulated_hPGCLCs_vs_hiPSC_MF.xlsx', index_col = 0)
In [65]:
egVSipsc_all = pd.read_excel(tables_folder + 'GO_allSignificant_hEGCLCs_vs_hiPSC_MF.xlsx', index_col = 0)
pgcVSeg_all = pd.read_excel(tables_folder + 'GO_allSignificant_hPGCLCs_vs_EGCLC_MF.xlsx', index_col = 0)
pgcVSipsc_all = pd.read_excel(tables_folder + 'GO_allSignificant_hPGCLCs_vs_hiPSC_MF.xlsx', index_col = 0)
In [66]:
len(egVSipsc_up)
Out[66]:
9
In [67]:
len(pgcVSeg_up)
Out[67]:
22
In [68]:
len(pgcVSipsc_up)
Out[68]:
19

Venn diagrams¶

Terms enriched in upregulated genes¶

In [69]:
_ = venn3([set(egVSipsc_up['GO.ID']), set(pgcVSeg_up['GO.ID']), set(pgcVSipsc_up['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))
In [70]:
pgcVSeg_up
Out[70]:
GO.ID Term Annotated Significant Expected Statistics ER -log10(pvalue) Significant/Annotated
1 GO:0001228 DNA-binding transcription activator acti... 329 57 24.10 7.200000e-10 2.37 9.142668 0.173252
2 GO:0005109 frizzled binding 25 12 1.83 4.700000e-08 6.56 7.327902 0.480000
3 GO:0005125 cytokine activity 102 20 7.47 4.700000e-05 2.68 4.327902 0.196078
4 GO:0005201 extracellular matrix structural constitu... 123 24 9.01 5.400000e-05 2.66 4.267606 0.195122
5 GO:0070851 growth factor receptor binding 97 18 7.10 1.500000e-04 2.54 3.823909 0.185567
6 GO:0004896 cytokine receptor activity 50 12 3.66 2.000000e-04 3.28 3.698970 0.240000
7 GO:0019955 cytokine binding 89 20 6.52 4.100000e-04 3.07 3.387216 0.224719
8 GO:0008201 heparin binding 111 19 8.13 4.200000e-04 2.34 3.376751 0.171171
9 GO:0008528 G protein-coupled peptide receptor activ... 66 13 4.83 7.100000e-04 2.69 3.148742 0.196970
10 GO:0017046 peptide hormone binding 35 9 2.56 7.200000e-04 3.52 3.142668 0.257143
11 GO:0002020 protease binding 109 18 7.98 9.000000e-04 2.26 3.045757 0.165138
12 GO:0004623 phospholipase A2 activity 23 7 1.68 9.600000e-04 4.17 3.017729 0.304348
13 GO:0140416 transcription regulator inhibitor activi... 18 6 1.32 1.310000e-03 4.55 2.882729 0.333333
14 GO:0008373 sialyltransferase activity 18 6 1.32 1.310000e-03 4.55 2.882729 0.333333
15 GO:0019838 growth factor binding 105 20 7.69 1.770000e-03 2.60 2.752027 0.190476
16 GO:0004714 transmembrane receptor protein tyrosine ... 51 11 3.74 2.600000e-03 2.94 2.585027 0.215686
17 GO:0005044 scavenger receptor activity 29 7 2.12 4.140000e-03 3.30 2.383000 0.241379
18 GO:0004930 G protein-coupled receptor activity 196 31 14.36 5.960000e-03 2.16 2.224754 0.158163
19 GO:0015144 carbohydrate transmembrane transporter a... 24 6 1.76 6.590000e-03 3.41 2.181115 0.250000
20 GO:0008374 O-acyltransferase activity 45 7 3.30 7.990000e-03 2.12 2.097453 0.155556
21 GO:0004622 lysophospholipase activity 18 5 1.32 7.990000e-03 3.79 2.097453 0.277778
22 GO:0005126 cytokine receptor binding 147 22 10.77 8.590000e-03 2.04 2.066007 0.149660
In [71]:
pgcVSeg_up[pgcVSeg_up.Term.isin(intersection)]
Out[71]:
GO.ID Term Annotated Significant Expected Statistics ER -log10(pvalue) Significant/Annotated
In [72]:
len(pgcVSeg_up[pgcVSeg_up.Term.isin(intersection)])
Out[72]:
0
In [73]:
len(intersection)
Out[73]:
0
In [74]:
pgcVSipsc_up[pgcVSipsc_up.Term.isin(intersection)]
Out[74]:
GO.ID Term Annotated Significant Expected Statistics ER -log10(pvalue) Significant/Annotated
In [75]:
intersection = set(pgcVSeg_up['Term']).intersection(set(pgcVSipsc_up['Term']))
df = pd.DataFrame(index = intersection)
pgcVSeg_up.index = pgcVSeg_up.Term
pgcVSipsc_up.index = pgcVSipsc_up.Term
df['Scores_pgcVSeg_up'] = pgcVSeg_up[pgcVSeg_up.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_up'] = pgcVSipsc_up[pgcVSipsc_up.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_pgcVSeg_up'] * df['Scores_pgcVSipsc_up']
In [76]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[76]:
In [77]:
set(egVSipsc_up['Term']).intersection(set(pgcVSipsc_up['Term']))
Out[77]:
set()
In [78]:
set(egVSipsc_up['Term'])
Out[78]:
{'DNA-binding transcription repressor acti...',
 'active ion transmembrane transporter act...',
 'active transmembrane transporter activit...',
 'cation transmembrane transporter activit...',
 'inorganic cation transmembrane transport...',
 'mRNA binding',
 'phosphoric ester hydrolase activity',
 'structural molecule activity'}

Terms enriched in downregulated genes¶

In [79]:
_ = venn3([set(egVSipsc_down['GO.ID']), set(pgcVSeg_down['GO.ID']), set(pgcVSipsc_down['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))
In [80]:
set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term'])).intersection(set(egVSipsc_down['Term']))
Out[80]:
set()
In [81]:
set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term']))
Out[81]:
{'CoA-ligase activity',
 'G protein-coupled peptide receptor activ...',
 'G protein-coupled receptor activity',
 'G-protein beta-subunit binding',
 'Wnt-protein binding',
 'alkali metal ion binding',
 'amyloid-beta binding',
 'antigen binding',
 'calcium channel regulator activity',
 'calcium-dependent phospholipid binding',
 'chloride channel activity',
 'cytokine activity',
 'cytokine receptor activity',
 'excitatory extracellular ligand-gated io...',
 'extracellular matrix binding',
 'extracellular matrix structural constitu...',
 'fibroblast growth factor receptor bindin...',
 'glutamate receptor activity',
 'growth factor activity',
 'hormone activity',
 'inward rectifier potassium channel activ...',
 'ligand-gated cation channel activity',
 'neuropeptide receptor activity',
 'neuropeptide receptor binding',
 'organic acid:sodium symporter activity',
 'peptide hormone binding',
 'phosphatidylserine binding',
 'protein tyrosine kinase activator activi...',
 'proteoglycan binding',
 'serine-type endopeptidase activity',
 'serine-type endopeptidase inhibitor acti...',
 'sulfotransferase activity',
 'transmembrane receptor protein tyrosine ...',
 'transmembrane signaling receptor activit...',
 'transmitter-gated ion channel activity i...',
 'voltage-gated ion channel activity'}
In [82]:
pgcVSeg_down= pgcVSeg_down[~pgcVSeg_down.index.duplicated()]
pgcVSipsc_down= pgcVSipsc_down[~pgcVSipsc_down.index.duplicated()]
In [83]:
intersection = set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term']))
df = pd.DataFrame(index = intersection)

df['Scores_pgcVSeg_up'] = pgcVSeg_down[pgcVSeg_down.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_up'] = pgcVSipsc_down[pgcVSipsc_down.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_pgcVSeg_up'] * df['Scores_pgcVSipsc_up']
In [84]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[84]:
In [85]:
set(egVSipsc_down['Term']).intersection(set(pgcVSipsc_down['Term']))
Out[85]:
set()
In [86]:
intersection = set(egVSipsc_down['Term']).intersection(set(pgcVSipsc_down['Term']))
df = pd.DataFrame(index = intersection)
egVSipsc_down.index = egVSipsc_down.Term
pgcVSipsc_down.index = pgcVSipsc_down.Term
df['Scores_egVSipsc_down'] = egVSipsc_down[egVSipsc_down.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_down'] = pgcVSipsc_down[pgcVSipsc_down.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_egVSipsc_down'] * df['Scores_pgcVSipsc_down']
In [87]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[87]:

Terms enriched in all genes¶

In [88]:
_ = venn3([set(egVSipsc_all['GO.ID']), set(pgcVSeg_all['GO.ID']), set(pgcVSipsc_all['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))
In [89]:
set(pgcVSeg_all['Term']).intersection(set(pgcVSipsc_all['Term'])).intersection(set(egVSipsc_all['Term']))
Out[89]:
{'G protein-coupled receptor activity'}
In [90]:
set(pgcVSeg_all['Term']).intersection(set(pgcVSipsc_all['Term']))
Out[90]:
{'G protein-coupled peptide receptor activ...',
 'G protein-coupled receptor activity',
 'G-protein beta-subunit binding',
 'Wnt-protein binding',
 'antigen binding',
 'calcium channel regulator activity',
 'chloride channel activity',
 'cholesterol binding',
 'cytokine activity',
 'cytokine binding',
 'cytokine receptor activity',
 'delayed rectifier potassium channel acti...',
 'excitatory extracellular ligand-gated io...',
 'extracellular matrix binding',
 'extracellular matrix structural constitu...',
 'fibroblast growth factor receptor bindin...',
 'fibronectin binding',
 'frizzled binding',
 'glutamate receptor activity',
 'growth factor activity',
 'growth factor receptor binding',
 'heparin binding',
 'hormone activity',
 'inward rectifier potassium channel activ...',
 'ligand-gated cation channel activity',
 'neuropeptide receptor activity',
 'neuropeptide receptor binding',
 'organic acid:sodium symporter activity',
 'outward rectifier potassium channel acti...',
 'peptide hormone binding',
 'protein tyrosine kinase activator activi...',
 'proteoglycan binding',
 'scavenger receptor activity',
 'serine-type endopeptidase inhibitor acti...',
 'sialyltransferase activity',
 'sulfotransferase activity',
 'transmembrane receptor protein tyrosine ...',
 'transmembrane signaling receptor activit...',
 'transmitter-gated ion channel activity i...'}
In [91]:
set(egVSipsc_all['Term']).intersection(set(pgcVSipsc_all['Term']))
Out[91]:
{'G protein-coupled receptor activity'}

GO CC¶

In [92]:
egVSipsc_up = pd.read_excel(tables_folder + 'GO_upregulated_hEGCLCs_vs_hiPSC_CC.xlsx', index_col = 0)
pgcVSeg_up = pd.read_excel(tables_folder + 'GO_upregulated_hPGCLCs_vs_EGCLC_CC.xlsx', index_col = 0)
pgcVSipsc_up = pd.read_excel(tables_folder + 'GO_upregulated_hPGCLCs_vs_hiPSC_CC.xlsx', index_col = 0)
In [93]:
egVSipsc_down = pd.read_excel(tables_folder + 'GO_downregulated_hEGCLCs_vs_hiPSC_CC.xlsx', index_col = 0)
pgcVSeg_down = pd.read_excel(tables_folder + 'GO_downregulated_hPGCLCs_vs_EGCLC_CC.xlsx', index_col = 0)
pgcVSipsc_down = pd.read_excel(tables_folder + 'GO_downregulated_hPGCLCs_vs_hiPSC_CC.xlsx', index_col = 0)
In [94]:
#egVSipsc_all = pd.read_excel(tables_folder + 'GO_allSignificant_hEGCLCs_vs_hiPSC_CC.xlsx', index_col = 0)
#pgcVSeg_all = pd.read_excel(tables_folder + 'GO_allSignificant_hPGCLCs_vs_EGCLC_CC.xlsx', index_col = 0)
#pgcVSipsc_all = pd.read_excel(tables_folder + 'GO_allSignificant_hPGCLCs_vs_hiPSC_CC.xlsx', index_col = 0)
In [95]:
len(egVSipsc_up)
Out[95]:
6
In [96]:
len(pgcVSeg_up)
Out[96]:
15
In [97]:
len(pgcVSipsc_up)
Out[97]:
15

Venn diagrams¶

Terms enriched in upregulated genes¶

In [98]:
_ = venn3([set(egVSipsc_up['GO.ID']), set(pgcVSeg_up['GO.ID']), set(pgcVSipsc_up['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))
In [99]:
intersection = set(pgcVSeg_up['Term']).intersection(set(pgcVSipsc_up['Term']))
df = pd.DataFrame(index = intersection)
pgcVSeg_up.index = pgcVSeg_up.Term
pgcVSipsc_up.index = pgcVSipsc_up.Term
df['Scores_pgcVSeg_up'] = pgcVSeg_up[pgcVSeg_up.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_up'] = pgcVSipsc_up[pgcVSipsc_up.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_pgcVSeg_up'] * df['Scores_pgcVSipsc_up']
In [100]:
df.sort_values(by = 'Combined').head(10)
Out[100]:
Scores_pgcVSeg_up Scores_pgcVSipsc_up Combined
external side of plasma membrane 1.600000e-07 5.300000e-07 8.480000e-14
collagen-containing extracellular matrix 4.900000e-05 2.400000e-05 1.176000e-09
intermediate filament 2.600000e-04 1.700000e-05 4.420000e-09
extracellular matrix 1.100000e-05 3.680000e-03 4.048000e-08
basal plasma membrane 1.000000e-04 4.800000e-04 4.800000e-08
blood microparticle 3.660000e-03 3.800000e-04 1.390800e-06
brush border membrane 1.299000e-02 9.300000e-03 1.208070e-04
Golgi lumen 1.688000e-02 7.920000e-03 1.336896e-04
collagen trimer 1.083000e-02 1.994000e-02 2.159502e-04
desmosome 4.764000e-02 6.440000e-03 3.068016e-04
In [101]:
set(egVSipsc_up['Term']).intersection(set(pgcVSipsc_up['Term']))
Out[101]:
set()

Terms enriched in downregulated genes¶

In [102]:
_ = venn3([set(egVSipsc_down['GO.ID']), set(pgcVSeg_down['GO.ID']), set(pgcVSipsc_down['GO.ID'])], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))

/usr/local/lib/python3.8/dist-packages/matplotlib_venn/_venn3.py:53: UserWarning:

Circle A has zero area
In [103]:
set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term'])).intersection(set(egVSipsc_down['Term']))
Out[103]:
set()
In [104]:
egVSipsc_down
Out[104]:
GO.ID Term Annotated Significant Expected Statistics ER -log10(pvalue) Significant/Annotated
In [105]:
set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term']))
Out[105]:
{'AMPA glutamate receptor complex',
 'GABA-ergic synapse',
 'Golgi lumen',
 'T-tubule',
 'acrosomal membrane',
 'anchored component of membrane',
 'anchored component of plasma membrane',
 'axon terminus',
 'cation channel complex',
 'chloride channel complex',
 'collagen trimer',
 'dendrite membrane',
 'dendritic spine',
 'dense core granule',
 'excitatory synapse',
 'external side of plasma membrane',
 'extracellular matrix',
 'glial cell projection',
 'hippocampal mossy fiber to CA3 synapse',
 'inhibitory synapse',
 'integral component of postsynaptic densi...',
 'integral component of postsynaptic speci...',
 'integral component of presynaptic membra...',
 'integral component of synaptic vesicle m...',
 'intrinsic component of synaptic vesicle ...',
 'ionotropic glutamate receptor complex',
 'plasma membrane signaling receptor compl...',
 'postsynaptic density membrane',
 'postsynaptic membrane',
 'receptor complex',
 'sarcoplasmic reticulum membrane',
 'sodium channel complex',
 'voltage-gated potassium channel complex'}
In [106]:
intersection = set(pgcVSeg_down['Term']).intersection(set(pgcVSipsc_down['Term']))
df = pd.DataFrame(index = intersection)
pgcVSeg_down.index = pgcVSeg_down.Term
pgcVSipsc_down.index = pgcVSipsc_down.Term
df['Scores_pgcVSeg_up'] = pgcVSeg_down[pgcVSeg_down.Term.isin(intersection)]['Statistics']
df['Scores_pgcVSipsc_up'] = pgcVSipsc_down[pgcVSipsc_down.Term.isin(intersection)]['Statistics']
df['Combined'] = df['Scores_pgcVSeg_up'] * df['Scores_pgcVSipsc_up']
In [107]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[107]:
In [108]:
set(egVSipsc_down['Term']).intersection(set(pgcVSipsc_down['Term']))
Out[108]:
set()

Reactome¶

In [109]:
egVSipsc_up = pd.read_excel(tables_folder + 'GO_up_hEGCLCs_vs_hiPSC_reactome.xlsx', index_col = 0)
pgcVSeg_up = pd.read_excel(tables_folder + 'GO_up_hPGCLCs_vs_EGCLC_reactome.xlsx', index_col = 0)
pgcVSipsc_up = pd.read_excel(tables_folder + 'GO_up_hPGCLCs_vs_hiPSC_reactome.xlsx', index_col = 0)
In [110]:
egVSipsc_down = pd.read_excel(tables_folder + 'GO_down_hEGCLCs_vs_hiPSC_reactome.xlsx', index_col = 0)
pgcVSeg_down = pd.read_excel(tables_folder + 'GO_down_hPGCLCs_vs_EGCLC_reactome.xlsx', index_col = 0)
pgcVSipsc_down = pd.read_excel(tables_folder + 'GO_down_hPGCLCs_vs_hiPSC_reactome.xlsx', index_col = 0)
In [111]:
egVSipsc_all = pd.read_excel(tables_folder + 'GO_all_hEGCLCs_vs_hiPSC_reactome.xlsx', index_col = 0)
pgcVSeg_all = pd.read_excel(tables_folder + 'GO_all_hPGCLCs_vs_EGCLC_reactome.xlsx', index_col = 0)
pgcVSipsc_all = pd.read_excel(tables_folder + 'GO_all_hPGCLCs_vs_hiPSC_reactome.xlsx', index_col = 0)
In [112]:
#egVSipsc_up

Venn diagrams¶

Terms enriched in upregulated genes¶

In [113]:
pgcVSipsc_up
Out[113]:
estimate pvals log10_pval gene_annotated n_gene_annotated gene_significant n_gene_significant ER genes_score
source
REACTOME_GPCR_LIGAND_BINDING inf 0.000000e+00 inf ['GPR183', 'CCL2', 'CXCL3', 'CXCL10', 'CXCL6',... 463 ['GCGR', 'WNT2B', 'TRH', 'APLNR', 'NMUR1', 'PT... 38 0.082073 [39.49588643299154, 33.297928178377134, 40.062...
REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION 42.519016 3.026805e-43 42.519016 ['BMP2', 'ICAM1', 'TNC', 'SDC4', 'SERPINE1', '... 300 ['HAPLN1', 'COL23A1', 'PLOD1', 'DMD', 'CTSL', ... 32 0.106667 [65.7986538088462, 103.36318933859657, 69.0811...
REACTOME_RHO_GTPASE_CYCLE 34.512749 3.070788e-35 34.512749 ['JAG1', 'TRIP10', 'RHOB', 'CAVIN1', 'AKAP12',... 450 ['ARMCX3', 'PCDH7', 'ACTC1', 'DIAPH2', 'DSP', ... 26 0.057778 [54.70057406879335, 13.91909396274346, 84.6465...
REACTOME_SIGNALING_BY_INTERLEUKINS 33.179810 6.609813e-34 33.179810 ['RIPK2', 'CCL2', 'MAP3K8', 'VEGFA', 'DUSP4', ... 461 ['IL4R', 'CSF3R', 'JUNB', 'NLRC5', 'NFKB2', 'F... 25 0.054230 [23.204773309513616, 81.91921767972629, 6.9696...
REACTOME_NEUTROPHIL_DEGRANULATION 33.179810 6.609813e-34 33.179810 ['PTX3', 'PLAU', 'NFKB1', 'SLC2A3', 'CXCL1', '... 478 ['FUCA1', 'DSP', 'CAMP', 'BST1', 'HGSNAT', 'AG... 25 0.052301 [39.4499262012086, 39.92293469792166, 27.98103...
... ... ... ... ... ... ... ... ... ...
REACTOME_REGULATION_OF_TP53_ACTIVITY 6.614033 2.432021e-07 6.614033 ['SGK1', 'TP53INP1', 'AURKA', 'CDK1', 'TPX2', ... 160 ['CHD3', 'PRDM1', 'POU4F1', 'SGK1', 'SMYD2'] 5 0.031250 [142.44874207108666, 30.476909844062614, 20.59...
REACTOME_DISORDERS_OF_TRANSMEMBRANE_TRANSPORTERS 6.614033 2.432021e-07 6.614033 ['ABCA1', 'SLC2A1', 'CP', 'HK1', 'GCK', 'AVPR1... 177 ['GCKR', 'SLC2A9', 'SLCO2A1', 'ABCA1', 'SLC6A2'] 5 0.028249 [19.426605437676702, 29.234868870075427, 24.49...
REACTOME_CILIUM_ASSEMBLY 6.614033 2.432021e-07 6.614033 ['TUBB2A', 'KIF3B', 'TUBA4A', 'DYNLL2', 'HDAC6... 202 ['KIF3A', 'PRKAR2B', 'CYS1', 'IFT172', 'KIFAP3'] 5 0.024752 [38.279452481075246, 90.7403994556073, 28.8877...
REACTOME_NEDDYLATION 6.614033 2.432021e-07 6.614033 ['SPSB1', 'NFE2L2', 'CDKN1A', 'SOCS3', 'SQSTM1... 245 ['SPSB2', 'EPAS1', 'FBXO10', 'COMMD3', 'SPSB1'] 5 0.020408 [13.38792493848568, 6.904907507229172, 24.8095...
REACTOME_ORGANELLE_BIOGENESIS_AND_MAINTENANCE 6.614033 2.432021e-07 6.614033 ['TUBB2A', 'SOD2', 'PPARGC1A', 'ACSS2', 'KIF3B... 296 ['KIF3A', 'PRKAR2B', 'CYS1', 'IFT172', 'KIFAP3'] 5 0.016892 [38.279452481075246, 90.7403994556073, 28.8877...

189 rows × 9 columns

In [114]:
_ = venn3([set(), set(pgcVSeg_up.index.tolist()), set(pgcVSipsc_up.index.tolist())], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))

/usr/local/lib/python3.8/dist-packages/matplotlib_venn/_venn3.py:53: UserWarning:

Circle A has zero area
In [115]:
intersection = set(pgcVSeg_up.index).intersection(set(pgcVSipsc_up.index))
df = pd.DataFrame(index = intersection)
pgcVSeg_up.index = pgcVSeg_up.index
pgcVSipsc_up.index = pgcVSipsc_up.index
df['Scores_pgcVSeg_up'] = pgcVSeg_up[pgcVSeg_up.index.isin(intersection)]['pvals']
df['Scores_pgcVSipsc_up'] = pgcVSipsc_up[pgcVSipsc_up.index.isin(intersection)]['pvals']
df['Combined'] = df['Scores_pgcVSeg_up'] * df['Scores_pgcVSipsc_up']
In [116]:
intTable(df.sort_values(by = 'Combined'), save = False)
Out[116]:
In [117]:
set(egVSipsc_up.index).intersection(set(pgcVSipsc_up.index))
Out[117]:
set()

Terms enriched in downregulated genes¶

In [118]:
_ = venn3([set(), set(pgcVSeg_down.index), set(pgcVSipsc_down.index)], set_labels=('hEGCLCs_vs_hiPSC', 'hPGCLCs_vs_hEGCLC', 'hPGCLCs_vs_hiPSC'))

/usr/local/lib/python3.8/dist-packages/matplotlib_venn/_venn3.py:53: UserWarning:

Circle A has zero area
In [119]:
set(pgcVSeg_down.index).intersection(set(pgcVSipsc_down.index)).intersection(set(egVSipsc_down.index))
Out[119]:
set()
In [120]:
#set(pgcVSeg_down['source']).intersection(set(pgcVSipsc_down['source']))