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fix #94 #92
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@nargesr
nargesr committed Mar 21, 2024
1 parent c81a0ee commit 943900e
Showing 1 changed file with 77 additions and 24 deletions.
101 changes: 77 additions & 24 deletions PyWGCNA/wgcna.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,13 +259,19 @@ def preprocess(self, show=True):

print("\tDone pre-processing..\n")

def findModules(self, **kwargs):
def findModules(self, kwargs_function={'cutreeHybrid': {'deepSplit': 2, 'pamRespectsDendro': False}}):
"""
Clustering genes through original WGCNA pipeline: 1.pick soft threshold 2.calculating adjacency matrix 3.calculating TOM similarity matrix 4.cluster genes base of dissTOM 5.merge similar cluster dynamically
:param kwargs_function: dictionary where the keys are the name of the function and values are the dictionary contains parameter you want to change within function
:type kwargs_function: dict
"""
print(f"{BOLD}{OKBLUE}Run WGCNA...{ENDC}")

# Call the network topology analysis function
kwargs = dict()
if 'pickSoftThreshold' in list(kwargs_function.keys()):
kwargs = kwargs_function['pickSoftThreshold']
self.power, self.sft = WGCNA.pickSoftThreshold(self.datExpr.to_df(), RsquaredCut=self.RsquaredCut,
MeanCut=self.MeanCut, powerVector=self.powers,
networkType=self.networkType, **kwargs)
Expand Down Expand Up @@ -294,13 +300,19 @@ def findModules(self, **kwargs):
fig.savefig(f"{self.outputPath}figures/summary_power.{self.figureType}")

# Set Power
self.adjacency = WGCNA.adjacency(self.datExpr.to_df(), power=self.power, adjacencyType=self.networkType)
kwargs = dict()
if 'adjacency' in list(kwargs_function.keys()):
kwargs = kwargs_function['adjacency']
self.adjacency = WGCNA.adjacency(self.datExpr.to_df(), power=self.power, adjacencyType=self.networkType, **kwargs)
self.adjacency = pd.DataFrame(self.adjacency,
columns=self.datExpr.to_df().columns,
index=self.datExpr.to_df().columns)

# Turn adjacency into topological overlap
self.TOM = WGCNA.TOMsimilarity(self.adjacency.to_numpy(), TOMType=self.TOMType)
kwargs = dict()
if 'TOMsimilarity' in list(kwargs_function.keys()):
kwargs = kwargs_function['TOMsimilarity']
self.TOM = WGCNA.TOMsimilarity(self.adjacency.to_numpy(), TOMType=self.TOMType, **kwargs)
self.TOM.columns = self.datExpr.to_df().columns
self.TOM.index = self.datExpr.to_df().columns
dissTOM = 1 - self.TOM
Expand All @@ -310,17 +322,23 @@ def findModules(self, **kwargs):
# Call the hierarchical clustering function
self.geneTree = linkage(a, method="average")

# Module identification using dynamic tree cut:
# dynamicMods = WGCNA.cutreeHybrid(dendro=self.geneTree, distM=dissTOM, deepSplit=2, pamRespectsDendro=False,
# minClusterSize=self.minModuleSize, **kwargs)
dynamicMods = WGCNA.cutreeHybrid(dendro=self.geneTree, distM=dissTOM, deepSplit=2, pamRespectsDendro=False,
minClusterSize=self.minModuleSize, **kwargs)
# Module identification using dynamic tree cut
kwargs = dict()
if 'cutreeHybrid' in list(kwargs_function.keys()):
kwargs = kwargs_function['cutreeHybrid']
dynamicMods = WGCNA.cutreeHybrid(dendro=self.geneTree, distM=dissTOM, minClusterSize=self.minModuleSize, **kwargs)

# Convert numeric labels into colors
self.datExpr.var['dynamicColors'] = WGCNA.labels2colors(labels=dynamicMods)
kwargs = dict()
if 'labels2colors' in list(kwargs_function.keys()):
kwargs = kwargs_function['labels2colors']
self.datExpr.var['dynamicColors'] = WGCNA.labels2colors(labels=dynamicMods, **kwargs)

# Calculate eigengenes
MEList = WGCNA.moduleEigengenes(expr=self.datExpr.to_df(), colors=self.datExpr.var['dynamicColors'])
kwargs = dict()
if 'moduleEigengenes' in list(kwargs_function.keys()):
kwargs = kwargs_function['moduleEigengenes']
MEList = WGCNA.moduleEigengenes(expr=self.datExpr.to_df(), colors=self.datExpr.var['dynamicColors'], **kwargs)

self.MEs = MEList['eigengenes']
if 'MEgrey' in self.MEs.columns:
Expand All @@ -345,8 +363,11 @@ def findModules(self, **kwargs):
plt.savefig(f"{self.outputPath}figures/eigenesgenes.{self.figureType}")

# Call an automatic merging function
kwargs = dict()
if 'mergeCloseModules' in list(kwargs_function.keys()):
kwargs = kwargs_function['mergeCloseModules']
merge = WGCNA.mergeCloseModules(self.datExpr.to_df(), self.datExpr.var['dynamicColors'],
cutHeight=self.MEDissThres)
cutHeight=self.MEDissThres, **kwargs)
# The merged module colors; Rename to moduleColors
self.datExpr.var['moduleColors'] = merge['colors']
else:
Expand All @@ -360,10 +381,17 @@ def findModules(self, **kwargs):
self.MEs = merge['newMEs']

# Recalculate MEs with color labels
self.datME = WGCNA.moduleEigengenes(self.datExpr.to_df(), self.datExpr.var['moduleColors'])['eigengenes']
kwargs = dict()
if 'moduleEigengenes' in list(kwargs_function.keys()):
kwargs = kwargs_function['moduleEigengenes']
self.datME = WGCNA.moduleEigengenes(self.datExpr.to_df(), self.datExpr.var['moduleColors'], **kwargs)['eigengenes']
if 'MEgrey' in self.datME.columns:
self.datME.drop(['MEgrey'], axis=1, inplace=True)
self.MEs = WGCNA.orderMEs(self.datME)

kwargs = dict()
if 'orderMEs' in list(kwargs_function.keys()):
kwargs = kwargs_function['orderMEs']
self.MEs = WGCNA.orderMEs(self.datME, **kwargs)

print("\tDone running WGCNA..\n")

Expand Down Expand Up @@ -3025,7 +3053,10 @@ def barplotModuleEigenGene(self, moduleName, metadata, combine=True, colorBar=No

height_ratios = []
for m in metadata:
height_ratios.append(len(list(self.metadataColors[m].keys())))
if isinstance(self.metadataColors[m], dict):
height_ratios.append(len(list(self.metadataColors[m].keys())))
else:
height_ratios.append(1)
height_ratios.reverse()

modules = np.unique(self.datExpr.var['moduleColors']).tolist()
Expand Down Expand Up @@ -3084,16 +3115,38 @@ def barplotModuleEigenGene(self, moduleName, metadata, combine=True, colorBar=No
x = ind
y = np.repeat(3000 * metadata.index(m), len(ind))
color = cat[m].values
for n in list(self.metadataColors[m].keys()):
color = np.where(color == n, self.metadataColors[m][n], color)
patch = mpatches.Patch(color=self.metadataColors[m][n], label=n)
handles.append(patch)
if m != colorBar:
axs[0, 0].scatter(x, y, c=color, s=1600, marker='s')
ax_legend = plt.Subplot(fig, axs_legend[len(metadata) - 1 - metadata.index(m)])
ax_legend.legend(title=m, handles=handles)
ax_legend.axis('off')
fig.add_subplot(ax_legend)
if type(self.metadataColors[m]) == dict:
for n in list(self.metadataColors[m].keys()):
color = np.where(color == n, self.metadataColors[m][n], color)
patch = mpatches.Patch(color=self.metadataColors[m][n], label=n)
handles.append(patch)
if m != colorBar:
axs[0, 0].scatter(x, y, c=color, s=1600, marker='s')
ax_legend = plt.Subplot(fig, axs_legend[len(metadata) - 1 - metadata.index(m)])
ax_legend.legend(title=m, handles=handles)
ax_legend.axis('off')
fig.add_subplot(ax_legend)
else:
cat[m] = cat[m].astype('float')
color = cat[m].values
axs[0, 0].scatter(x, y, c=color,
cmap=self.metadataColors[m].get_cmap(),
s=1600, marker='s')

ax_legend = plt.Subplot(fig, axs_legend[len(metadata) - 1 - metadata.index(m)])
clb = fig.colorbar(mappable=self.metadataColors[m],
ax=ax_legend,
orientation='horizontal',
fraction=0.9)
clb.ax.set_title(m.capitalize())
ax_legend.axis('off')
fig.add_subplot(ax_legend)
# if m != colorBar:
# axs[0, 0].scatter(x, y, c=color, s=1600, marker='s')
# ax_legend = plt.Subplot(fig, axs_legend[len(metadata) - 1 - metadata.index(m)])
# ax_legend.legend(title=m, handles=handles)
# ax_legend.axis('off')
# fig.add_subplot(ax_legend)

axs[0, 0].set_title(f"Module Eigengene for {moduleName}", size=28, fontweight="bold")
axs[0, 0].set_ylim(-2000, np.max(y) + 2000)
Expand Down

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