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zWFvuN5`GCsUOtGvr}WBN#@(7mTsa_EM$WQsgNr?}{ev#4WN`M#p-QVx>84+uCF8;m=#*=yq+0@HToi}Dp;MkP^JFM%4tkXIh`QcS za^(qnWh142k{dr#FaIQ?J&npId8FEN--Bo(>v%U}`*Wmx|5gvbd*Z zMVr^UthK12bNWfL08E7It-)nBJhYBa8?g@uWP!z9;j&oS%gsbD_7j7sQP&xgl+IoJ zk6AL&=l~IyvNs-1w?1k2p$X>ePye#*i@>?q;c;i^?#!_p*J(-FkWI7`w~ zco;Fl)ag>4E=P^p#B=}Ad6GI}DqQLY4}OnS<8-i4KRHjvNTx#r1g|2RIY1IpIaaWr zO_q%+M-`dUGw+jBv}+h3cz@A7_z+1=I!97Q)YapxwYXJwJ{Q^7Ik+RfC-7u|OcXC6 zZyIgU$?@=HV}u3+Db>j@Am&z`jIRoML_99(gB0{Pmx5?;xySwr;eA6fd^?aja-pr&c#y|UE^qUT3l+WI2c$XcACvE zm}j9fHmEGe&$I@0zS__bRL?P2pjwNA#ZoGSource code for PyWGCNA.utils
 import pickle
 import os
 import biomart
+import pandas as pd
 import requests
 import matplotlib.pyplot as plt
 import networkx as nx
@@ -230,10 +231,11 @@ 
Source code for PyWGCNA.utils
     # Store the data in a dict
     for line in data.splitlines():
         line = line.split('\t')
+        tmp = pd.DataFrame(line, index=attributes).T
         dict = {}
         for i in range(len(attributes)):
             dict[attributes[i]] = line[i]
-        geneInfo = geneInfo.append(dict, ignore_index=True)
+        geneInfo = pd.concat([geneInfo, tmp], ignore_index=True)
 
     geneInfo.index = geneInfo[attributes[0]]
     geneInfo.drop(attributes[0], axis=1, inplace=True)
diff --git a/docs/html/_modules/PyWGCNA/wgcna.html b/docs/html/_modules/PyWGCNA/wgcna.html
index b07197c..1ee4ac1 100644
--- a/docs/html/_modules/PyWGCNA/wgcna.html
+++ b/docs/html/_modules/PyWGCNA/wgcna.html
@@ -1848,7 +1848,7 @@ 
Source code for PyWGCNA.wgcna
                                     useObjects = ColorsX in np.unique(labelsOnBranch)
                                     DistSClustClust = distM.iloc[InCluster, useObjects]
                                     MeanDist = DistSClustClust.mean(axis=0)
-                                    useColorsFac = pd.Categorical(ColorsX[useObjects])
+                                    useColorsFac = ColorsX[useObjects]#pd.Categorical(ColorsX[useObjects])
                                     MeanDist = pd.DataFrame({'MeanDist': MeanDist, 'useColorsFac': useColorsFac})
                                     MeanMeanDist = MeanDist.groupby(
                                         'useColorsFac').mean()  # tapply(MeanDist, useColorsFac, mean)
@@ -1867,7 +1867,7 @@ 
Source code for PyWGCNA.wgcna
                                 InCluster = np.where(SmallLabels == sclust)[0].tolist()
                                 DistSClustClust = distM.iloc[InCluster, useObjects]
                                 MeanDist = DistSClustClust.mean(axis=0)
-                                useColorsFac = pd.Categorical(ColorsX[useObjects])
+                                useColorsFac = ColorsX[useObjects]#pd.Categorical(ColorsX[useObjects])
                                 MeanDist = pd.DataFrame({'MeanDist': MeanDist, 'useColorsFac': useColorsFac})
                                 MeanMeanDist = MeanDist.groupby(
                                     'useColorsFac').mean()  # tapply(MeanDist, useColorsFac, mean)
@@ -1889,7 +1889,7 @@ 
Source code for PyWGCNA.wgcna
                             basicOnBranch = branch_basicClusters[onBr - 1]
                             labelsOnBranch = branchLabels[basicOnBranch]
                             useObjects = ColorsX in np.unique(labelsOnBranch)
-                            useColorsFac = pd.Categorical(ColorsX[useObjects])
+                            useColorsFac = ColorsX[useObjects]#pd.Categorical(ColorsX[useObjects])
                             UnassdToClustDist = distM.iloc[useObjects, obj].groupby(
                                 'useColorsFac').mean()  # tapply(distM[useObjects, obj], useColorsFac, mean)
                             nearest = UnassdToClustDist.idxmin().astype(int) - 1
@@ -1901,7 +1901,7 @@ 
Source code for PyWGCNA.wgcna
                                 nPAMed = nPAMed + 1
                     else:
                         useObjects = np.where(ColorsX != 0)[0].tolist()
-                        useColorsFac = pd.Categorical(ColorsX[useObjects])
+                        useColorsFac = ColorsX[useObjects]#pd.Categorical(ColorsX[useObjects])
                         tmp = pd.DataFrame(distM.iloc[useObjects, Unlabeled])
                         tmp['group'] = useColorsFac
                         UnassdToClustDist = tmp.groupby(
@@ -2107,9 +2107,9 @@ 
Source code for PyWGCNA.wgcna
                 u = u[:, 0:min(n, p, nPC)]
                 v = v[0:min(n, p, nPC), :]
                 tmp = datModule.copy()
-                tmp = tmp.append(pd.DataFrame(v[0:min(n, p, nVarExplained), :],
-                                              columns=tmp.columns.tolist()),
-                                 ignore_index=True)
+                tmp = pd.concat([tmp, pd.DataFrame(v[0:min(n, p, nVarExplained), :],
+                                                   columns=tmp.columns.tolist())],
+                                ignore_index=True)
                 veMat = pd.DataFrame(np.corrcoef(tmp.values)).iloc[:-1, -1].T
                 varExpl.iloc[0:min(n, p, nVarExplained), i] = (veMat ** 2).mean(axis=0)
                 pc = v[0].tolist()
@@ -2127,7 +2127,7 @@ 
Source code for PyWGCNA.wgcna
                         scaledExpr = pd.DataFrame(scale(datModule.T).T, index=datModule.index,
                                                   columns=datModule.columns)
                         covEx = np.cov(scaledExpr)
-                        covEx[not np.isfinite(covEx)] = 0
+                        covEx[~ np.isfinite(covEx)] = 0
                         modAdj = np.abs(covEx) ** softPower
                         kIM = (modAdj.mean(axis=0)) ** 3
                         if np.max(kIM) > 1:
@@ -2870,6 +2870,7 @@ 
Source code for PyWGCNA.wgcna
 
 
[docs]    def module_trait_relationships_heatmap(self,
                                            metaData,
+                                           figsize=None,
                                            show=True,
                                            file_name='module-traitRelationships'):
         """
@@ -2877,6 +2878,8 @@ 
Source code for PyWGCNA.wgcna
 
         :param metaData: traits you would like to see the relationship with topics (must be column name of datExpr.obs)
         :type metaData: list
+        :param figsize: indicate the size of plot
+        :type figsize: tuple of float
         :param show: indicate if you want to show the plot or not (default: True)
         :type show: bool
         :param file_name: name and path of the plot use for save (default: topic-traitRelationships)
@@ -2884,8 +2887,10 @@ 
Source code for PyWGCNA.wgcna
         """
         datTraits = self.getDatTraits(metaData)
 
-        fig, ax = plt.subplots(figsize=(max(20, int(self.moduleTraitPvalue.shape[0] * 1.5)),
-                                        self.moduleTraitPvalue.shape[1] * 1.5), facecolor='white')
+        if figsize is None:
+            figsize = (max(20, int(self.moduleTraitPvalue.shape[0] * 1.5)),
+                       self.moduleTraitPvalue.shape[1] * 1.5)
+        fig, ax = plt.subplots(figsize=figsize, facecolor='white')
         # names
         xlabels = []
         for label in self.MEs.columns:
@@ -3217,7 +3222,7 @@ 
Source code for PyWGCNA.wgcna
                 else:
                     return axs

 
-
[docs]    def functional_enrichment_analysis(self, type, moduleName, sets=None, p_value=1, file_name=None):
+
[docs]    def functional_enrichment_analysis(self, type, moduleName, sets=None, p_value=1, file_name=None, **kwargs):
         """
         Doing functional enrichment analysis including GO, KEGG and REACTOME
 
@@ -3231,6 +3236,8 @@ 
Source code for PyWGCNA.wgcna
         :type p_value: float
         :param file_name: name of the file you want to use to save plot (default is moduleName)
         :type file_name: str
+        :param kwargs: Other keyword arguments are passed through to the underlying gseapy.enrichr() finction
+        :type kwargs: key, value pairings
         """
         if type not in ["GO", "KEGG", "REACTOME"]:
             sys.exit("Type is not valid! it should be one of them GO, KEGG, REACTOME")
@@ -3261,7 +3268,8 @@ 
Source code for PyWGCNA.wgcna
                                  gene_sets=sets,
                                  organism=self.species,
                                  outdir=f"{self.outputPath}figures/{type}/{file_name}",
-                                 cutoff=p_value)
+                                 cutoff=p_value,
+                                 **kwargs)
                 dotplot(enr.res2d,
                         title=f"Gene ontology in {moduleName} module",
                         cmap='viridis_r',
@@ -3516,9 +3524,9 @@ 
Source code for PyWGCNA.wgcna
 
         adj = self.TOM.loc[genes, genes]
         adj[adj < minTOM] = 0
-        adj = adj.where(np.triu(np.ones(adj.shape)).astype(bool))
-        adj = adj.where(adj.values != np.diag(adj), 0,
-                        adj.where(adj.values != np.flipud(adj).diagonal(0), 0, inplace=True))
+        adj.where(np.triu(np.ones(adj.shape)).astype(bool), inplace=True)
+        adj.where(adj.values != np.diag(adj), 0, inplace=True)
+        adj.where(adj.values != np.flipud(adj).diagonal(0), 0, inplace=True)
         adj = adj.stack().nlargest(numConnections)
 
         net = Network()
diff --git a/docs/html/api.html b/docs/html/api.html
index 8eaa9a2..0278daf 100644
--- a/docs/html/api.html
+++ b/docs/html/api.html
@@ -574,7 +574,7 @@
 
 

 

-functional_enrichment_analysis(type, moduleName, sets=None, p_value=1, file_name=None)[source]

+functional_enrichment_analysis(type, moduleName, sets=None, p_value=1, file_name=None, **kwargs)[source]
 
Doing functional enrichment analysis including GO, KEGG and REACTOME

 

 
Parameters

@@ -584,6 +584,7 @@
 
  • sets (str, list, tuple) – str, list, tuple of Enrichr Library name(s). or custom defined gene_sets (dict, or gmt file) (you can add any Enrichr Libraries from here: https://maayanlab.cloud/Enrichr/#stats) only need to fill if the type is GO or KEGG
    • 
 
  • p_value (float) – Defines the pValue threshold. (default: 0.05)
    • 
 
  • file_name (str) – name of the file you want to use to save plot (default is moduleName)
    • 
+
  • kwargs (key, value pairings) – Other keyword arguments are passed through to the underlying gseapy.enrichr() finction
    • 
 
 
    
 
    
@@ -875,12 +876,13 @@
 
 
    
 
    
-module_trait_relationships_heatmap(metaData, show=True, file_name='module-traitRelationships')[source]
    
+module_trait_relationships_heatmap(metaData, figsize=None, show=True, file_name='module-traitRelationships')[source]
 
    plot topic-trait relationship heatmap
    
 
    
 
    Parameters
    
 
      
 
    • metaData (list) – traits you would like to see the relationship with topics (must be column name of datExpr.obs)
      • 
+
    • figsize (tuple of float) – indicate the size of plot
      • 
 
    • show (bool) – indicate if you want to show the plot or not (default: True)
      • 
 
    • file_name (str) – name and path of the plot use for save (default: topic-traitRelationships)
      • 
 
    
diff --git a/docs/html/searchindex.js b/docs/html/searchindex.js
index 1177b6a..5266ef2 100644
--- a/docs/html/searchindex.js
+++ b/docs/html/searchindex.js
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\ No newline at end of file
diff --git a/setup.py b/setup.py
index 37367b9..c38c926 100644
--- a/setup.py
+++ b/setup.py
@@ -10,7 +10,7 @@
     author='Narges Rezaie',  # your name
     author_email='nargesrezaie80@gmail.com',  # your email
     url='https://github.com/mortazavilab/PyWGCNA',  # url to your git repo
-    download_url='https://github.com/mortazavilab/PyWGCNA/archive/refs/tags/v1.20.4.tar.gz',  # link to the tar.gz file associated with this release
+    download_url='https://github.com/mortazavilab/PyWGCNA/archive/refs/tags/v2.0.0.zip',  # 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>=2.1.0',  # make sure the packages you put here are those NOT included in the