[5]:
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
import numpy as np
import matplotlib as mpl
mpl.rcParams['pdf.fonttype']=42
mpl.rcParams['ps.fonttype']=42
import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
%matplotlib inline
import scanpy as sc
import anndata as ad
from scipy.cluster import hierarchy
from scipy.spatial import distance
from collections import defaultdict
[7]:
grnboost = pd.read_table("/Users/yuanzan/Documents/github/seqyuan/tomato_graft_omics/data/grn/grn_notbook/gene_energy_sugar_grnboost2_addtype.csv", sep="\t")
adata = ad.read_h5ad("/Users/yuanzan/Documents/github/seqyuan/tomato_graft_omics/data/grn/grn_notbook/CC_DD_Exp_energy_sugar.h5ad")
[8]:
grnboost_sub = grnboost.query('importance>=35')
grnboost_sub['targetN'] = grnboost_sub.groupby(["TF"]).sum()['cc'][grnboost_sub['TF']].to_list()
grnboost_sub = grnboost_sub.query('targetN>=6')
grnboost_sub['targetN'] = grnboost_sub.groupby(["TF"]).sum()['cc'][grnboost_sub['TF']].to_list()
grnboost_sub = grnboost_sub.sort_values(by=['targetN'])
#grnboost_sub['dupTarget'] = grnboost_sub.duplicated('target')
<ipython-input-8-96841fc8ce7a>:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
grnboost_sub['targetN'] = grnboost_sub.groupby(["TF"]).sum()['cc'][grnboost_sub['TF']].to_list()
<ipython-input-8-96841fc8ce7a>:3: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
grnboost_sub['targetN'] = grnboost_sub.groupby(["TF"]).sum()['cc'][grnboost_sub['TF']].to_list()
<ipython-input-8-96841fc8ce7a>:5: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
grnboost_sub['targetN'] = grnboost_sub.groupby(["TF"]).sum()['cc'][grnboost_sub['TF']].to_list()
[9]:
grnboost_sub.groupby(["TF"]).sum()['cc']
<ipython-input-9-2367075c054e>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
grnboost_sub.groupby(["TF"]).sum()['cc']
[9]:
TF
2-Phospho-D-glycerate 10
ADP 103
AMP 13
ATP 151
Acetyl-CoA 151
Adenine 9
Ara 14
Beta-Leucine 21
Citric-acid 122
D-Erythrose-4-phosphate 16
D-Glucose-1-phosphate 19
D-Glucose-6-phosphate 10
D-Ribulose-5-phosphate 8
Dihydroxyacetone-phosphate 14
Fru 25
Gal 50
Glycerol-3-phosphate 11
L-Alanine 50
L-Arginine 19
L-Asparagine 44
L-Citrulline 9
L-Glutamate 32
L-Lactate 8
L-Threonine 12
L-Tyrosine 127
Lysine 105
Phosphoenolpyruvic-acid 62
Phosphorylethanolamine 450
Pyruvic-acid 11
Rha 51
Sedoheptulose-7-phosphate 9
Suc 790
Succinic-acid 60
UDP-GlcNAc 12
UMP 11
Uracil 160
cyclic-AMP 52
Name: cc, dtype: int64
[18]:
grnboost_sub.groupby(["TF", 'target_type']).sum().loc['Suc',:]
<ipython-input-18-f7e8530505ac>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
grnboost_sub.groupby(["TF", 'target_type']).sum().loc['Suc',:]
[18]:
| importance | cc | targetN | |
|---|---|---|---|
| target_type | |||
| TF | 2452.356619 | 55 | 43450 |
| TR | 471.433371 | 11 | 8690 |
| gene | 31330.979607 | 724 | 571960 |
[21]:
grnboost_sub.to_csv('../../data/grn/suc_grnboost_importance35_targetN6.csv', sep="\t", index=False)
[24]:
grnboost_sub.query('TF=="Suc" and (target_type=="TF" or target_type=="TR")').to_csv('../../data/grn/suc_importance35_targetN6.csv', sep="\t", index=False)
[25]:
grnboost_sub.query('TF=="Suc" and (target_type=="TF" or target_type=="TR")')
[25]:
| TF | target | importance | cc | TF_type | target_type | targetN | |
|---|---|---|---|---|---|---|---|
| 2501 | Suc | Solyc10g076460.2 | 35.962453 | 1 | sugar | TF | 790 |
| 412 | Suc | Solyc05g052610.3 | 49.789240 | 1 | sugar | TF | 790 |
| 2480 | Suc | Solyc04g078770.4 | 36.027338 | 1 | sugar | TF | 790 |
| 564 | Suc | Solyc04g080740.1 | 47.305903 | 1 | sugar | TF | 790 |
| 474 | Suc | Solyc04g009440.3 | 48.620021 | 1 | sugar | TF | 790 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 884 | Suc | Solyc06g005680.4 | 44.026730 | 1 | sugar | TF | 790 |
| 983 | Suc | Solyc02g092460.3 | 43.194171 | 1 | sugar | TR | 790 |
| 982 | Suc | Solyc09g090130.3 | 43.203793 | 1 | sugar | TF | 790 |
| 975 | Suc | Solyc01g109970.3 | 43.284457 | 1 | sugar | TR | 790 |
| 953 | Suc | Solyc10g078720.2 | 43.410027 | 1 | sugar | TF | 790 |
66 rows × 7 columns
[ ]:
[ ]:
[10]:
nodes = list(set(pd.concat([grnboost_sub["TF"], grnboost_sub["target"]], axis=0)))
nodes = adata.var.loc[nodes,:]
nodes.head(2)
[10]:
| type | |
|---|---|
| Solyc06g082070.3 | gene |
| Solyc06g076350.3 | TF |
[11]:
def subG(df):
G = nx.Graph()
G.clear()
nodes = list(set(pd.concat([df["TF"], df["target"]], axis=0)))
subG = nx.Graph()
for i in nodes:
G.add_node(i)
for i,row in df.iterrows():
G.add_edge(row['TF'], row['target'], length=row['importance'], weight=row['importance'])
return G
[ ]:
[152]:
def create_hc(G):
"""Creates hierarchical cluster of graph G from distance matrix"""
path_length = nx.all_pairs_shortest_path_length(G)
distances = np.zeros((len(G), len(G)))
for u, p in path_length:
for v, d in p.items():
distances[u][v] = d
# Create hierarchical cluster
Y = distance.squareform(distances)
Z = hierarchy.complete(Y) # Creates HC using farthest point linkage
# This partition selection is arbitrary, for illustrive purposes
membership = list(hierarchy.fcluster(Z, t=1.15))
# Create collection of lists for blockmodel
partition = defaultdict(list)
for n, p in zip(list(range(len(G))), membership):
partition[p].append(n)
return list(partition.values())
[170]:
import matplotlib.colors as mcolors
keys = list(mcolors.CSS4_COLORS)
[174]:
#keys
[272]:
G = nx.Graph()
G.clear()
nodes = list(set(pd.concat([grnboost_sub["TF"], grnboost_sub["target"]], axis=0)))
nodes_df = adata.var.loc[nodes,:]
nodes_df['range'] = range(nodes_df.shape[0])
for i in nodes_df.index:
G.add_node(i)
for i, row in grnboost_sub.iterrows():
G.add_edge(row['TF'], row['target'], length=row['importance'], weight=row['importance'])
for i in grnboost_sub["TF"].unique():
subdf = grnboost_sub[grnboost_sub["TF"]==i]
sG = subG(subdf)
U = nx.disjoint_union(U, sG)
#break
#### draw graph ####
plt.figure(1, figsize=(8, 8))
# layout graphs with positions using graphviz neato
H = G.subgraph(next(nx.connected_components(G)))
# Makes life easier to have consecutively labeled integer nodes
H = nx.convert_node_labels_to_integers(H)
# Create parititions with hierarchical clustering
partitions = create_hc(H)
# Build blockmodel graph
BM = nx.quotient_graph(H, partitions, relabel=True)
# Draw original graph
pos = nx.spring_layout(H, iterations=20, seed=83) # Seed for reproducibility
nx.draw(H, pos, with_labels=False, node_size=10, edge_color="gainsboro", alpha=0.4)
[ ]:
[325]:
pos = nx.nx_agraph.graphviz_layout(U, prog="neato")
plt.figure(1, figsize=(10, 8))
def plot_target_node(GG, poss, nodeDF, nodetype="gene", nodecolor="#2E7EB7", node_size=20, alpha=0.4):
nodeDF = nodeDF[nodeDF["type"]==nodetype]
nodelist = nodeDF['range'].to_list()
nodes_s = nx.draw_networkx_nodes(G, pos, nodelist=nodelist, node_size=node_size, node_color=nodecolor, alpha=alpha)
nodes1 = plot_target_node(H, pos, nodes_df[nodes_df.index.isin(grn_DF['TF'])==False], nodetype="gene", nodecolor="#2E7EB7", node_size=10)
nodes2 = plot_target_node(H, pos, nodes_df[nodes_df.index.isin(grn_DF['TF'])==False], nodetype="sugar", nodecolor="#581642", node_size=10)
nodes3 = plot_target_node(H, pos, nodes_df, nodetype="energy", nodecolor="#DBB53E", node_size=10)
nodes4 = plot_target_node(H, pos, nodes_df, nodetype="TF", nodecolor="#ED3833", node_size=10)
nodes5 = plot_target_node(H, pos, nodes_df, nodetype="TR", nodecolor="#ED3833", node_size=10)
def plot_TF_node(GG, poss, grn_DF, nodeDF, nodetype="sugar", nodecolor="red", node_size=20, alpha=0.4):
nodeDF = nodeDF[nodeDF["type"]==nodetype]
nodelist = nodeDF['range'].to_list()
target_count = pd.DataFrame(grn_DF.groupby(["TF"]).sum()['cc'])
aa = nodeDF.index.isin(target_count.index)==False
notIntarget_count = nodeDF.index[aa].to_list()
notIntarget_count = pd.DataFrame({'cc':[1]*len(notIntarget_count)}, index=notIntarget_count)
target_count = target_count.append(notIntarget_count)
#target_count.loc[notIntarget_count, 'cc'] = [1]
#print(target_count)
node_size = node_size * target_count.loc[nodeDF.index, 'cc']/6
nodes_s = nx.draw_networkx_nodes(G, pos, nodelist=nodelist, node_size=node_size,
node_color=nodecolor,
#label=nodeDF.index.to_list(),
alpha=alpha)
nodes6 = plot_TF_node(H, pos, grnboost_sub, nodes_df, nodetype="sugar", nodecolor="#581642", node_size=20)
nodes7 = plot_TF_node(H, pos, grnboost_sub, nodes_df, nodetype="energy", nodecolor="#DBB53E", node_size=20)
top7TFs = grnboost_sub[['TF', 'targetN']].sort_values(['targetN'], ascending=False).drop_duplicates().head(10)['TF'].to_list()
top7TFs = nodes_df.loc[top7TFs,:]
bbox={'facecolor': 'w', #填充色
'edgecolor': 'None',#外框色
'alpha': 0.5, #框透明度
'pad': 2,#本文与框周围距离
}
nx.draw_networkx_labels(H, pos, labels={row['range']:i for i,row in top7TFs.iterrows()},
font_size=10,
font_family="sans-serif",
font_color='k',
bbox=bbox
)
edge_colors = grnboost_sub['importance'].to_list()
cmap = plt.cm.plasma
edges = nx.draw_networkx_edges(H, pos, edge_color="gainsboro", alpha=0.4)
ax = plt.gca()
ax.scatter(None,None, label='sugar', color='#581642')
ax.scatter(None,None, label='energy', color='#DBB53E')
ax.scatter(None,None, label='gene', color='#2E7EB7')
ax.scatter(None,None, label='TF/TR', color='#ED3833')
ax.legend(loc=5)
ax.margins(0.15)
plt.axis("off")
plt.tight_layout()
#ax.set_axis_off()
#plt.show()
plt.savefig("tomato_geneExp_energy_sugar_Comodule_network.pdf")
/tmp/ipykernel_1036807/2239920485.py:19: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
target_count = pd.DataFrame(grn_DF.groupby(["TF"]).sum()['cc'])
/tmp/ipykernel_1036807/2239920485.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
target_count = target_count.append(notIntarget_count)
/tmp/ipykernel_1036807/2239920485.py:19: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
target_count = pd.DataFrame(grn_DF.groupby(["TF"]).sum()['cc'])
/tmp/ipykernel_1036807/2239920485.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
target_count = target_count.append(notIntarget_count)
[355]:
style 3#
[ ]:
[465]:
grnboost_sub_bar = grnboost.query('importance>=35')
grnboost_sub_bar.loc[grnboost_sub_bar[grnboost_sub_bar['target_type']=='TF'].index, "target_type"] = 'TF/TR'
grnboost_sub_bar.loc[grnboost_sub_bar[grnboost_sub_bar['target_type']=='TR'].index, "target_type"] = 'TF/TR'
grnboost_sub_bar = pd.DataFrame(grnboost_sub_bar.groupby(["TF", 'TF_type', 'target_type']).sum()['cc'])
grnboost_sub_bar = grnboost_sub_bar.reset_index()
grnboost_sub_bar = grnboost_sub_bar.sort_values(by=['cc', 'target_type'], ascending=False)
grnboost_sub_bar.columns = ['Sugar_Energy', 'Sugar_Energy_type', 'Gene/metabolite class', 'Cooperative dynamic\nGene/metabolite number']
/tmp/ipykernel_1036807/3719034143.py:4: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
grnboost_sub_bar = pd.DataFrame(grnboost_sub_bar.groupby(["TF", 'TF_type', 'target_type']).sum()['cc'])
states#
[467]:
fig, (ax1, ax2) = plt.subplots(2, 1, sharey=False, dpi=100, figsize=(5,4))
fig.subplots_adjust(hspace=0.1)
palette=['#2E7EB7', '#ED3833', '#581642', '#DBB53E']
sns.barplot(ax=ax1, data=grnboost_sub_bar.query('Sugar_Energy_type=="sugar"'), color=palette, palette=palette,
x="Sugar_Energy", y="Cooperative dynamic\nGene/metabolite number", hue="Gene/metabolite class")
sns.barplot(ax=ax2, data=grnboost_sub_bar.query('Sugar_Energy_type=="sugar"'), color=palette, palette=palette,
x="Sugar_Energy", y="Cooperative dynamic\nGene/metabolite number", hue="Gene/metabolite class")
ax2.set_ylim(0, 80) # 子图1设置y轴范围，只显示部分图
ax1.set_ylim(700, 750) # 子图2设置y轴范围，只显示部分图
ax1.legend(loc=5)
ax2.margins(0.03)
ax1.margins(0.03)
ax1.set_xticks([])
ax1.set_xlabel('')
ax2.set_xlabel('Sugar')
ax1.set_ylabel('')
ax2.set_ylabel('')
ax2.get_legend().remove()
ax1.spines['bottom'].set_visible(False)#关闭子图1中底部脊
ax2.spines['top'].set_visible(False)##关闭子图2中顶部脊
#ax = plt.add_subplot(facecolor=None)
#ax.set_axis_off()1.set_title
#ax1.set_ylabel('Cooperative dynamic\nGene/metabolite number', loc="bottom", halign='center')
#fig.align_labels()
ax1.set_title('Cooperative dynamic\nGene/metabolite number')
#ax.set_xticks([])
#ax.set_yticks([])
fig.savefig('sugar__Cooperative_dynamic_Gene_metabolite_number.pdf')
[ ]:
[472]:
fig, ax = plt.subplots(1, 1, sharey=False, dpi=100, figsize=(5,8))
#fig.subplots_adjust(hspace=0.1)
ax = sns.barplot(ax=ax, data=grnboost_sub_bar.query('Sugar_Energy_type=="energy"'), color=palette, palette=palette,
y="Sugar_Energy", x="Cooperative dynamic\nGene/metabolite number",
hue="Gene/metabolite class")
ax.legend(loc=8)
ax.margins(0.03)
ax.set_ylabel('Energy')
plt.tight_layout()
fig.savefig('energy__Cooperative_dynamic_Gene_metabolite_number.pdf')
[469]:
grnboost_sub_bar.to_csv("sugar_energy_coDynamic_grnboost_module.csv", index=False)
[ ]:
[ ]: