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fix issue #67
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@nargesr
nargesr committed Oct 19, 2023
1 parent 5ffb266 commit 822885c
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4 changes: 2 additions & 2 deletions PyWGCNA/geneExp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,8 @@ class GeneExp:
:type level: str
:param anndata: if the expression data is in anndata format you should pass it through this parameter. X should be expression matrix. var is a sample information and obs is a gene information.
:param anndata: anndata
:param geneExpr: expression matrix which genes are in the rows and samples are columns
:type geneExpr: pandas dataframe
:param geneExp: expression matrix which genes are in the rows and samples are columns
:type geneExp: pandas dataframe
:param geneExpPath: path of expression matrix
:type geneExpPath: str
:param sep: separation symbol to use for reading data in geneExpPath properly
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172 changes: 106 additions & 66 deletions PyWGCNA/wgcna.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,12 +74,24 @@ class WGCNA(GeneExp):
:type name: str
:param save: indicate if you want to save result of important steps in a figure directory (default: False)
:type save: bool
:param species: species of the data you use i.e mouse, human
:type species: str
:param level: which type of data you use including gene, transcript (default: gene)
:type level: str
:param outputPath: path you want to save all you figures and object (default: '', where you rau your script)
:type outputPath: str
:param geneExpr: expression matrix
:type geneExpr: geneExp class
:param datExpr: data expression data that contains preprocessed data
:type datExpr: anndata
:param anndata: if the expression data is in anndata format you should pass it through this parameter. X should be expression matrix. var is a sample information and obs is a gene information.
:param anndata: anndata
:param geneExp: expression matrix which genes are in the rows and samples are columns
:type geneExp: pandas dataframe
:param geneExpPath: path of expression matrix
:type geneExpPath: str
:param sep: separation symbol to use for reading data in geneExpPath properly
:type sep: str
:param geneInfo: dataframe that contains genes information it should have a same index as gene expression column names (gene/transcript ID)
:type geneInfo: pandas dataframe
:param sampleInfo: dataframe that contains samples information it should have a same index as gene expression index (sample ID)
:type sampleInfo: pandas dataframe
:param TPMcutoff: cut off for removing genes that expressed under this number along samples
:type TPMcutoff: int
:param cut: number to remove outlier sample (default: 'inf') By default we don't remove any sample by hierarchical clustering
Expand All @@ -90,30 +102,36 @@ class WGCNA(GeneExp):
:type RsquaredCut: float
:param MeanCut: mean connectivity to choose power for having scale free network (default: 100)
:type MeanCut: int
:param power: power to have scale free network (default: 6)
:type power: int
:param sft: soft threshold table which has information for each powers
:type sft: pandas dataframe
:param networkType: Type of network we can create including "unsigned", "signed" and "signed hybrid" (default: "signed hybrid")
:type networkType: str
:param adjacency: adjacency matrix calculating base of the type of network
:type adjacency: ndarray
:param geneTree: average hierarchical clustering of dissTOM matrix
:type geneTree: ndarray
:param TOMType: Type of topological overlap matrix(TOM) including "unsigned", "signed" (default: "signed")
:type TOMType: str
:param TOM: topological overlap measure using average linkage hierarchical clustering which inputs a measure of interconnectedness
:param TOM: ndarray
:param minModuleSize: We like large modules, so we set the minimum module size relatively high (default: 50)
:type minModuleSize: int
:param dynamicMods: name of modules by clustering similar genes together
:type dynamicMods: list
:param naColor: color we used to identify genes we don't find any cluster for them (default: "grey")
:type naColor: str
:param MEs: eigengenes
:type MEs: ndarray
:param MEDissThres: diss similarity threshold (default: 0.2)
:type MEDissThres: float
:param figureType: extension of figure (default: "pdf")
:type figureType: str
:param MEs: eigengenes
:type MEs: ndarray
:param geneExpr: gene expression object that contains raw gene expression along with gene and sample information.
:type geneExpr: geneExp class
:param datExpr: data expression data that contains preprocessed data
:type datExpr: anndata
:param dynamicMods: name of modules by clustering similar genes together
:type dynamicMods: list
:param TOM: topological overlap measure using average linkage hierarchical clustering which inputs a measure of interconnectedness
:param TOM: ndarray
:param adjacency: adjacency matrix calculating base of the type of network
:type adjacency: ndarray
:param geneTree: average hierarchical clustering of dissTOM matrix
:type geneTree: ndarray
:param power: power to have scale free network (default: 6)
:type power: int
:param sft: soft threshold table which has information for each powers
:type sft: pandas dataframe
:param datME:
:type datME: pandas dataframe
:param signedKME:(signed) eigengene-based connectivity (module membership)
Expand All @@ -122,7 +140,6 @@ class WGCNA(GeneExp):
:type moduleTraitCor: pandas dataframe
:param moduleTraitPvalue: p-value of correlation between each module and metadata
:type moduleTraitPvalue: pandas dataframe
:param figureType: extension of figure (default: "pdf")
"""

def __init__(self, name='WGCNA',
Expand All @@ -132,11 +149,11 @@ def __init__(self, name='WGCNA',
minModuleSize=50, naColor="grey", cut=float('inf'),
MEDissThres=0.2,
species=None, level='gene', anndata=None, geneExp=None,
geneExpPath=None, sep=',',
geneExpPath=None, sep=',', geneInfo=None, sampleInfo=None,
save=False, outputPath=None, figureType='pdf'):

super().__init__(species=species, level=level, anndata=anndata, geneExp=geneExp,
geneExpPath=geneExpPath, sep=sep)
geneExpPath=geneExpPath, sep=sep, geneInfo=geneInfo, sampleInfo=sampleInfo)

if powers is None:
powers = list(range(1, 11)) + list(range(11, 21, 2))
Expand Down Expand Up @@ -384,7 +401,7 @@ def analyseWGCNA(self, order=None, geneList=None, show=True):
self.moduleTraitCor.loc[i, j] = tmp[0]
self.moduleTraitPvalue.loc[i, j] = tmp[1]

if self.save:
if self.save and self.datExpr.obs.shape[1] != 0:
self.module_trait_relationships_heatmap(metaData=self.datExpr.obs.columns.tolist(),
show=show,
file_name='module-traitRelationships')
Expand Down Expand Up @@ -416,9 +433,12 @@ def analyseWGCNA(self, order=None, geneList=None, show=True):

if self.save:
print(f"{OKCYAN}plotting module barplot eigengene...{ENDC}")
for module in modules:
self.barplotModuleEigenGene(module, metadata, colorBar=metadata[-1], show=show)
print("\tDone..\n")
if self.datExpr.obs.shape[1] == 0:
print(f"{WARNING}There is not any sample information in given object! Skip running barplotModuleEigenGene() function.{ENDC}")
else:
for module in modules:
self.barplotModuleEigenGene(module, metadata, colorBar=metadata[-1], show=show)
print("\tDone..\n")

if self.save:
print(f"{OKCYAN}doing Enrichr GO analysis for each module...{ENDC}")
Expand Down Expand Up @@ -2889,50 +2909,67 @@ def plotModuleEigenGene(self, moduleName, metadata, show=True):
heatmap = heatmap.iloc[labels_order, :]
ME = pd.DataFrame(self.datME["ME" + moduleName].values, columns=['eigengeneExp'])
ME['sample_name'] = self.datME.index
ind = [i + 0.5 for i in range(ME.shape[0])]

fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(26, len(metadata) * 5),
sharex='col', gridspec_kw={
'height_ratios': [len(metadata) * 0.4, len(metadata) * 0.5, len(metadata) * 1.5],
'width_ratios': [20, 3]})
gs = axs[0, 1].get_gridspec()
# remove the underlying axes
for ax in axs[:, 1]:
ax.remove()
ax_legend = fig.add_subplot(gs[:, 1])
ax_legend.axis('off')
axs_legend = gridspec.GridSpecFromSubplotSpec(len(metadata), 1, subplot_spec=ax_legend,
height_ratios=height_ratios)
if len(metadata) != 0:
fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(ME.shape[0], len(metadata) * 5),
sharex='col', gridspec_kw={
'height_ratios': [len(metadata) * 0.4, len(metadata) * 0.5, len(metadata) * 1.5],
'width_ratios': [20, 3]})

ind = [i + 0.5 for i in range(ME.shape[0])]
for m in metadata:
handles = []
x = ind
y = np.repeat(3000 * metadata.index(m), len(ind))
color = sampleInfo[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)
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)
gs = axs[0, 1].get_gridspec()
# remove the underlying axes
for ax in axs[:, 1]:
ax.remove()
ax_legend = fig.add_subplot(gs[:, 1])
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)
axs[0, 0].grid(False)
axs[0, 0].axis('off')

axs[1, 0].bar(ind, ME.eigengeneExp, align='center', color='black')
axs[1, 0].set_ylabel('eigengeneExp')
axs[1, 0].set_facecolor('white')

cmap = sns.color_palette("dark:red", as_cmap=True)
sns.heatmap(heatmap, cmap=cmap,
cbar=False, # cbar_ax=axs[2,1],
yticklabels=False, xticklabels=False,
ax=axs[2, 0])
axs_legend = gridspec.GridSpecFromSubplotSpec(len(metadata), 1, subplot_spec=ax_legend,
height_ratios=height_ratios)

for m in metadata:
handles = []
x = ind
y = np.repeat(3000 * metadata.index(m), len(ind))
color = sampleInfo[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)
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)
axs[0, 0].grid(False)
axs[0, 0].axis('off')

axs[1, 0].bar(ind, ME.eigengeneExp, align='center', color='black')
axs[1, 0].set_ylabel('eigengeneExp')
axs[1, 0].set_facecolor('white')

cmap = sns.color_palette("dark:red", as_cmap=True)
sns.heatmap(heatmap, cmap=cmap,
cbar=False, # cbar_ax=axs[2,1],
yticklabels=False, xticklabels=False,
ax=axs[2, 0])
else:
fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(ME.shape[0], 20),
sharex='col', gridspec_kw={
'height_ratios': [1, 1.5]}, facecolor='white')

axs[0].set_title(f"Module Eigengene for {moduleName}", size=28, fontweight="bold")
axs[0].bar(ind, ME.eigengeneExp, align='center', color='black')
axs[0].set_ylabel('eigengeneExp')

cmap = sns.color_palette("dark:red", as_cmap=True)
sns.heatmap(heatmap, cmap=cmap,
cbar=False, # cbar_ax=axs[2,1],
yticklabels=False, xticklabels=False,
ax=axs[1])

if self.save:
fig.savefig(f"{self.outputPath}figures/module_heatmap_eigengene_{moduleName}.{self.figureType}")
if not show:
Expand All @@ -2958,6 +2995,9 @@ def barplotModuleEigenGene(self, moduleName, metadata, combine=True, colorBar=No
:type show: bool
"""
sampleInfo = self.datExpr.obs
if sampleInfo.shape[1] == 0:
print(f"{WARNING}There is not any sample information in given object! Skip running barplotModuleEigenGene() function.{ENDC}")
return None

height_ratios = []
for m in metadata:
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4 changes: 2 additions & 2 deletions setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,14 +3,14 @@
setup(
name='PyWGCNA', # the name of your package
packages=['PyWGCNA'], # same as above
version='1.2.4', # version number
version='1.20.2', # version number
license='MIT', # license type
description='PyWGCNA is a Python package designed to do Weighted correlation network analysis (WGCNA)',
# short description
author='Narges Rezaie', # your name
author_email='[email protected]', # your email
url='https://github.com/mortazavilab/PyWGCNA', # url to your git repo
download_url='https://github.com/mortazavilab/PyWGCNA/archive/refs/tags/v1.2.4.tar.gz', # link to the tar.gz file associated with this release
download_url='https://github.com/mortazavilab/PyWGCNA/archive/refs/tags/v1.20.2.tar.gz', # link to the tar.gz file associated with this release
keywords=['PyWGCNA', 'WGCNA', 'bulk', 'gene clustering', 'network analysis'], #
install_requires=[ # these can also include >, <, == to enforce version compatibility
'pandas==1.4.4', # make sure the packages you put here are those NOT included in the
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