From 1feba7367dfb76b61cda4577c9978737ccc72a57 Mon Sep 17 00:00:00 2001 From: Narges Rezaie Date: Wed, 24 Jan 2024 11:59:56 -0800 Subject: [PATCH] update docs --- PyWGCNA/wgcna.py | 3 +- docs/doctrees/api.doctree | Bin 576857 -> 578906 bytes docs/doctrees/environment.pickle | Bin 272794 -> 273385 bytes docs/doctrees/suggested_reading.doctree | Bin 5180 -> 4726 bytes docs/html/_modules/PyWGCNA/geneExp.html | 2 +- docs/html/_modules/PyWGCNA/wgcna.html | 84 +++++++++++-------- docs/html/_sources/suggested_reading.rst.txt | 2 +- docs/html/api.html | 10 +-- docs/suggested_reading.rst | 2 +- setup.py | 4 +- 10 files changed, 58 insertions(+), 49 deletions(-) diff --git a/PyWGCNA/wgcna.py b/PyWGCNA/wgcna.py index 4933c83..3d80fb0 100644 --- a/PyWGCNA/wgcna.py +++ b/PyWGCNA/wgcna.py @@ -65,7 +65,6 @@ BOLD = "\033[1m" UNDERLINE = "\033[4m" - class WGCNA(GeneExp): """ A class used to do weighted gene co-expression network analysis. @@ -2360,7 +2359,7 @@ def orderMEs(MEs, greyLast=True, greyName="MEgrey", orderBy=0, order=None, useSe :param MEs: Module eigengenes in a multi-set format. :type MEs: dict :param greyLast: Normally the color grey is reserved for unassigned genes; hence the grey module is not a proper module and it is conventional to put it last. If this is not desired, set the parameter to FALSE. (default = True) - :type greyLast:bool + :type greyLast: bool :param greyName: Name of the grey module eigengene. (default = "MEgrey") :type greyName: str :param orderBy: Specifies the set by which the eigengenes are to be ordered (in all other sets as well). Defaults to the first set in useSets (or the first set, if useSets is not given). 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a/docs/html/_modules/PyWGCNA/geneExp.html +++ b/docs/html/_modules/PyWGCNA/geneExp.html @@ -123,7 +123,7 @@

Source code for PyWGCNA.geneExp

     :type species: str
     :param level: which type of data you use including gene, transcript (default: gene)
     :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: if the expression data is in anndata format you should pass it through this parameter. X should be expression matrix. var is a gene information and obs is a sample information.
     :param anndata: anndata
     :param geneExp: expression matrix which genes are in the rows and samples are columns
     :type geneExp: pandas dataframe
diff --git a/docs/html/_modules/PyWGCNA/wgcna.html b/docs/html/_modules/PyWGCNA/wgcna.html
index c5ed54d..b2dfb70 100644
--- a/docs/html/_modules/PyWGCNA/wgcna.html
+++ b/docs/html/_modules/PyWGCNA/wgcna.html
@@ -187,8 +187,8 @@ 
Source code for PyWGCNA.wgcna
     :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 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 anndata: if the expression data is in anndata format you should pass it through this parameter. X should be expression matrix. var is a gene information and obs is a sample information.
+    :type 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
@@ -331,11 +331,13 @@ 
Source code for PyWGCNA.wgcna
             if np.size(goodGenes) - np.count_nonzero(goodGenes) > 0:
                 print(
                     f"{OKGREEN} {np.size(goodGenes) - np.count_nonzero(goodGenes)} gene(s) detected as an outlier!{ENDC}")
-                print(f"{OKGREEN}Removing genes: {', '.join(self.datExpr.var.index[[not goodGene for goodGene in goodGenes]].tolist())}{ENDC}")
+                print(
+                    f"{OKGREEN}Removing genes: {', '.join(self.datExpr.var.index[[not goodGene for goodGene in goodGenes]].tolist())}{ENDC}")
             if np.size(goodSamples) - np.count_nonzero(goodSamples) > 0:
                 print(
                     f"{OKGREEN} {np.size(goodSamples) - np.count_nonzero(goodSamples)} sample(s) detected as an outlier!{ENDC}")
-                print(f"{OKGREEN}Removing samples: {', '.join(self.datExpr.obs.index[[not goodSample for goodSample in goodSamples]].tolist())}{ENDC}")
+                print(
+                    f"{OKGREEN}Removing samples: {', '.join(self.datExpr.obs.index[[not goodSample for goodSample in goodSamples]].tolist())}{ENDC}")
             # Remove the offending genes and samples from the data:
             self.datExpr = self.datExpr[goodSamples, goodGenes]
 
@@ -497,27 +499,12 @@ 
Source code for PyWGCNA.wgcna
         """
         print(f"{BOLD}{OKBLUE}Analysing WGCNA...{ENDC}")
 
-        datTraits = self.getDatTraits(self.datExpr.obs.columns.tolist())
-
         print(f"{OKCYAN}Calculating module trait relationship ...{ENDC}")
 
-        self.moduleTraitCor = pd.DataFrame(index=self.MEs.columns,
-                                           columns=datTraits.columns,
-                                           dtype="float")
-        self.moduleTraitPvalue = pd.DataFrame(index=self.MEs.columns,
-                                              columns=datTraits.columns,
-                                              dtype="float")
-
-        for i in self.MEs.columns:
-            for j in datTraits.columns:
-                tmp = stats.pearsonr(self.MEs[i], datTraits[j], alternative=alternative)
-                self.moduleTraitCor.loc[i, j] = tmp[0]
-                self.moduleTraitPvalue.loc[i, j] = tmp[1]
-
-        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')
+        self.module_trait_relationships_heatmap(metaData=self.datExpr.obs.columns.tolist(),
+                                                alternative=alternative,
+                                                show=show,
+                                                file_name='module-traitRelationships')
         print("\tDone..\n")
 
         print(f"{OKCYAN}Adding (signed) eigengene-based connectivity (module membership) ...{ENDC}")
@@ -547,7 +534,8 @@ 
Source code for PyWGCNA.wgcna
         if self.save:
             print(f"{OKCYAN}plotting module barplot eigengene...{ENDC}")
             if self.datExpr.obs.shape[1] == 0:
-                print(f"{WARNING}There is not any sample information in given object! Skip running barplotModuleEigenGene() function.{ENDC}")
+                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)
@@ -629,7 +617,7 @@ 
Source code for PyWGCNA.wgcna
         """
         Checks data for missing entries, entries with weights below a threshold, and zero-variance genes. If necessary, the filtering is iterated.
 
-        :param datExpr:expression data. A data frame in which columns are genes and rows ar samples.
+        :param datExpr:expression data. A data frame in which columns are samples and rows are gene.
         :type datExpr: pandas dataframe
         :param weights: optional observation weights in the same format (and dimensions) as datExpr.
         :type weights: pandas dataframe
@@ -1852,7 +1840,7 @@ 
Source code for PyWGCNA.wgcna
                                     useObjects = ColorsX in np.unique(labelsOnBranch)
                                     DistSClustClust = distM.iloc[InCluster, useObjects]
                                     MeanDist = DistSClustClust.mean(axis=0)
-                                    useColorsFac = ColorsX[useObjects]#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)
@@ -1871,7 +1859,7 @@ 
Source code for PyWGCNA.wgcna
                                 InCluster = np.where(SmallLabels == sclust)[0].tolist()
                                 DistSClustClust = distM.iloc[InCluster, useObjects]
                                 MeanDist = DistSClustClust.mean(axis=0)
-                                useColorsFac = ColorsX[useObjects]#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)
@@ -1893,7 +1881,7 @@ 
Source code for PyWGCNA.wgcna
                             basicOnBranch = branch_basicClusters[onBr - 1]
                             labelsOnBranch = branchLabels[basicOnBranch]
                             useObjects = ColorsX in np.unique(labelsOnBranch)
-                            useColorsFac = ColorsX[useObjects]#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
@@ -1905,7 +1893,7 @@ 
Source code for PyWGCNA.wgcna
                                 nPAMed = nPAMed + 1
                     else:
                         useObjects = np.where(ColorsX != 0)[0].tolist()
-                        useColorsFac = ColorsX[useObjects]#pd.Categorical(ColorsX[useObjects])
+                        useColorsFac = ColorsX[useObjects]  # pd.Categorical(ColorsX[useObjects])
                         tmp = pd.DataFrame(distM.iloc[useObjects, Unlabeled])
                         tmp['group'] = useColorsFac
                         UnassdToClustDist = tmp.groupby(
@@ -2054,7 +2042,8 @@ 
Source code for PyWGCNA.wgcna
         if expr.shape[1] != len(colors):
             sys.exit("moduleEigengenes: Error: ncol(expr) and length(colors) must be equal (one color per gene).")
         if len(pd.Categorical(colors).categories) == 1:
-            sys.exit(f"{WARNING}All your genes ended up in one modules! Reconsider your input parameters in order to get more modules.{ENDC}")
+            sys.exit(
+                f"{WARNING}All your genes ended up in one modules! Reconsider your input parameters in order to get more modules.{ENDC}")
         # TODO: "Argument 'colors' contains unused levels (empty modules). Use colors[, drop=TRUE] to get rid of them."
         if softPower < 0:
             sys.exit("softPower must be non-negative")
@@ -2874,6 +2863,7 @@ 
Source code for PyWGCNA.wgcna
 
 
[docs]    def module_trait_relationships_heatmap(self,
                                            metaData,
+                                           alternative='two-sided',
                                            figsize=None,
                                            show=True,
                                            file_name='module-traitRelationships'):
@@ -2882,6 +2872,8 @@ 
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 alternative: Defines the alternative hypothesis for calculating correlation for module-trait relationship. Default is ‘two-sided’. The following options are available: 'two-sided’: the correlation is nonzero, ‘less’: the correlation is negative (less than zero), ‘greater’: the correlation is positive (greater than zero)
+        :type alternative: str
         :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)
@@ -2891,9 +2883,26 @@ 
Source code for PyWGCNA.wgcna
         """
         datTraits = self.getDatTraits(metaData)
 
+        moduleTraitCor = pd.DataFrame(index=self.MEs.columns,
+                                      columns=datTraits.columns,
+                                      dtype="float")
+        moduleTraitPvalue = pd.DataFrame(index=self.MEs.columns,
+                                         columns=datTraits.columns,
+                                         dtype="float")
+
+        for i in self.MEs.columns:
+            for j in datTraits.columns:
+                tmp = stats.pearsonr(self.MEs[i], datTraits[j], alternative=alternative)
+                moduleTraitCor.loc[i, j] = tmp[0]
+                moduleTraitPvalue.loc[i, j] = tmp[1]
+
+        if set(metaData) == set(self.datExpr.obs.columns.tolist()):
+            self.moduleTraitCor = moduleTraitCor
+            self.moduleTraitPvalue = moduleTraitPvalue
+
         if figsize is None:
-            figsize = (max(20, int(self.moduleTraitPvalue.shape[0] * 1.5)),
-                       self.moduleTraitPvalue.shape[1] * 1.5)
+            figsize = (max(20, int(moduleTraitPvalue.shape[0] * 1.5)),
+                       moduleTraitPvalue.shape[1] * 1.5)
         fig, ax = plt.subplots(figsize=figsize, facecolor='white')
         # names
         xlabels = []
@@ -2902,15 +2911,15 @@ 
Source code for PyWGCNA.wgcna
         ylabels = datTraits.columns
 
         # Loop over data dimensions and create text annotations.
-        tmp_cor = self.moduleTraitCor.T.round(decimals=2)
-        tmp_pvalue = self.moduleTraitPvalue.T.round(decimals=3)
+        tmp_cor = moduleTraitCor.T.round(decimals=2)
+        tmp_pvalue = moduleTraitPvalue.T.round(decimals=3)
         labels = (np.asarray(["{0}\n({1})".format(cor, pvalue)
                               for cor, pvalue in zip(tmp_cor.values.flatten(),
                                                      tmp_pvalue.values.flatten())])) \
-            .reshape(self.moduleTraitCor.T.shape)
+            .reshape(moduleTraitCor.T.shape)
 
         sns.set(font_scale=1.5)
-        res = sns.heatmap(self.moduleTraitCor.T, annot=labels, fmt="", cmap='RdBu_r',
+        res = sns.heatmap(moduleTraitCor.T, annot=labels, fmt="", cmap='RdBu_r',
                           vmin=-1, vmax=1, ax=ax, annot_kws={'size': 20, "weight": "bold"},
                           xticklabels=xlabels, yticklabels=ylabels)
         res.set_xticklabels(res.get_xmajorticklabels(), fontsize=20, fontweight="bold", rotation=90)
@@ -3114,7 +3123,8 @@ 
Source code for PyWGCNA.wgcna
         """
         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}")
+            print(
+                f"{WARNING}There is not any sample information in given object! Skip running barplotModuleEigenGene() function.{ENDC}")
             return None
 
         height_ratios = []
diff --git a/docs/html/_sources/suggested_reading.rst.txt b/docs/html/_sources/suggested_reading.rst.txt
index 3d952c3..47a6073 100644
--- a/docs/html/_sources/suggested_reading.rst.txt
+++ b/docs/html/_sources/suggested_reading.rst.txt
@@ -1,5 +1,5 @@
 Suggested Reading
-=========
+=================
 
 If you are unfamiliar with R refrence WGCNA, we suggest reading the original WGCNA publication:
 
diff --git a/docs/html/api.html b/docs/html/api.html
index 05f0a2c..f0233e4 100644
--- a/docs/html/api.html
+++ b/docs/html/api.html
@@ -127,7 +127,7 @@
 
    
 
  • species (str) – species of the data you use i.e mouse, human
    • 
 
  • level (str) – which type of data you use including gene, transcript (default: gene)
    • 
-
  • 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.
    • 
+
  • anndata – if the expression data is in anndata format you should pass it through this parameter. X should be expression matrix. var is a gene information and obs is a sample information.
    • 
 
  • anndata – anndata
    • 
 
  • geneExp (pandas dataframe) – expression matrix which genes are in the rows and samples are columns
    • 
 
  • geneExpPath (str) – path of expression matrix
    • 
@@ -195,8 +195,7 @@
 
  • species (str) – species of the data you use i.e mouse, human
    • 
 
  • level (str) – which type of data you use including gene, transcript (default: gene)
    • 
 
  • outputPath (str) – path you want to save all you figures and object (default: ‘’, where you rau your script)
    • 
-
  • 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.
    • 
-
  • anndata – anndata
    • 
+
  • anndata (anndata) – if the expression data is in anndata format you should pass it through this parameter. X should be expression matrix. var is a gene information and obs is a sample information.
    • 
 
  • geneExp (pandas dataframe) – expression matrix which genes are in the rows and samples are columns
    • 
 
  • geneExpPath (str) – path of expression matrix
    • 
 
  • sep (str) – separation symbol to use for reading data in geneExpPath properly
    • 
@@ -718,7 +717,7 @@
 
    
 static goodSamplesGenes(datExpr, weights=None, minFraction=0.5, minNSamples=4, minNGenes=4, tol=None, minRelativeWeight=0.1)[source]
    
 
    Checks data for missing entries, entries with weights below a threshold, and zero-variance genes. If necessary, the filtering is iterated.
    
-
    :param datExpr:expression data. A data frame in which columns are genes and rows ar samples.
+
    :param datExpr:expression data. A data frame in which columns are samples and rows are gene.
 :type datExpr: pandas dataframe
 :param weights: optional observation weights in the same format (and dimensions) as datExpr.
 :type weights: pandas dataframe
@@ -876,12 +875,13 @@
 
 
    
 
    
-module_trait_relationships_heatmap(metaData, figsize=None, show=True, file_name='module-traitRelationships')[source]
    
+module_trait_relationships_heatmap(metaData, alternative='two-sided', 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)
      • 
+
    • alternative (str) – Defines the alternative hypothesis for calculating correlation for module-trait relationship. Default is ‘two-sided’. The following options are available: ‘two-sided’: the correlation is nonzero, ‘less’: the correlation is negative (less than zero), ‘greater’: the correlation is positive (greater than zero)
      • 
 
    • 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/suggested_reading.rst b/docs/suggested_reading.rst
index 3d952c3..47a6073 100644
--- a/docs/suggested_reading.rst
+++ b/docs/suggested_reading.rst
@@ -1,5 +1,5 @@
 Suggested Reading
-=========
+=================
 
 If you are unfamiliar with R refrence WGCNA, we suggest reading the original WGCNA publication:
 
diff --git a/setup.py b/setup.py
index a6c74b7..7d21d88 100644
--- a/setup.py
+++ b/setup.py
@@ -3,14 +3,14 @@
 setup(
     name='PyWGCNA',  # the name of your package
     packages=['PyWGCNA'],  # same as above
-    version='2.0.1',  # version number
+    version='2.0.2',  # 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='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/v2.0.1.zip',  # link to the tar.gz file associated with this release
+    download_url='https://github.com/mortazavilab/PyWGCNA/archive/refs/tags/v2.0.2.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