v 16

grandapp/management/commands/load_tissueSampleInfoThy_data.py

@@ -48,4 +48,5 @@ def handle(self, *args, **options):
             tissuesamplethy.link        = row['link']
             tissuesamplethy.size        = row['size']
             tissuesamplethy.cleanname   = row['cleanname']
+            tissuesamplethy.decsex      = row['decoded_sex']
             tissuesamplethy.save()

grandapp/management/commands/load_tissuelanding_data.py

@@ -44,7 +44,9 @@ def handle(self, *args, **options):
             tissuelanding.ppi          = row['ppi']
             tissuelanding.ppiLink      = row['ppiLink']
             tissuelanding.motif        = row['motif']
+            tissuelanding.motif2        = row['motif2']
             tissuelanding.motifDesc    = row['motifDesc']
+            tissuelanding.motifDesc2    = row['motifDesc2']
             tissuelanding.expression   = row['expression']
             tissuelanding.expLink      = row['expLink']
             tissuelanding.tfs          = row['tfs']

grandapp/migrations/0249_auto_20240816_1511.py

@@ -0,0 +1,25 @@
+# Generated by Django 3.2.5 on 2024-08-16 15:11
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('grandapp', '0248_tissuelanding_cardtissue'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='tissuelanding',
+            name='motif2',
+            field=models.URLField(default=''),
+            preserve_default=False,
+        ),
+        migrations.AddField(
+            model_name='tissuelanding',
+            name='motifDesc2',
+            field=models.CharField(default='', max_length=200),
+            preserve_default=False,
+        ),
+    ]

grandapp/migrations/0250_tissuesamplethy_decsex.py

@@ -0,0 +1,19 @@
+# Generated by Django 3.2.5 on 2024-08-16 20:01
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('grandapp', '0249_auto_20240816_1511'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='tissuesamplethy',
+            name='decsex',
+            field=models.CharField(default='', max_length=600),
+            preserve_default=False,
+        ),
+    ]

grandapp/models.py

@@ -286,7 +286,9 @@ class Tissuelanding(models.Model):
     ppi       = models.URLField()
     ppiLink   = models.URLField()
     motif     = models.URLField()
+    motif2     = models.URLField()
     motifDesc = models.CharField(max_length=200)
+    motifDesc2 = models.CharField(max_length=200)
     expression= models.URLField()
     expLink   = models.URLField()
     tfs       = models.IntegerField()
@@ -344,6 +346,7 @@ class Tissuesamplethy(models.Model):
     smgnsdtc    = models.CharField(max_length=600)
     cleanname   = models.CharField(max_length=600)
     proxage     = models.CharField(max_length=600)
+    decsex      = models.CharField(max_length=600)
 
 class Tissuesample(models.Model):
     sampleid    = models.CharField(max_length=600)

grandapp/templates/geobrca.html

@@ -77,7 +77,7 @@ <h5 class="card-title">Variable description</h5>
                 <tbody>
                  {% for tissue in geosample %}
                   <tr>
-                    <td>{{ tissue.sample }}</td>
+                    <td><a href="/networks/aggregate/{{ tissue.sampleclean }}" style="text-decoration:none">{{ tissue.sample }}</a></td>
                     <td>{{ tissue.gender }}</td>
                     <td>{{ tissue.subtype }}</td>
                     <td>{{ tissue.mutation }}</td>

grandapp/templates/home.html

@@ -3,7 +3,7 @@
 <header class="jumbotron jumbotron-fluid">
   <div class="container">
       <div class="display-2 mb-4"></div>
-      <p class="lead" align="center">GRAND is a collection of gene regulatory networks derived from <a href="/tissues" class="text-decoration-none">human tissues</a>, <a href="/cancers" class="text-decoration-none">cancer</a>, <a href="/cell" class="text-decoration-none">cell lines</a>, and <a href="/drugs" class="text-decoration-none">small molecule</a> drugs. These networks are bipartite graphs linking either transcription factors (TFs) or miRNAs to their target genes. GRAND includes an integrated webserver to test for <a href='/disease' class="text-decoration-none">enrichment</a> of a list of TFs in a set of libraries as well as a tool (CLUEreg) to <a href="/analysis" class="text-decoration-none">find compounds</a> that reverse/exacerbate a gene or TF regulatory profile.<br> Publication: Ben Guebila, Lopes-Ramos et al. <a href="https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab778/6368528" style="text-decoration:none;" target=”_blank”>NAR, 2022.</a> Current version: <a href="https://github.com/QuackenbushLab/grand/releases/tag/1.6.0" style="text-decoration:none;" target=”_blank”>1.6.0</a>. Last update: 08/06/2024.<br>
+      <p class="lead" align="center">GRAND is a collection of gene regulatory networks derived from <a href="/tissues" class="text-decoration-none">human tissues</a>, <a href="/cancers" class="text-decoration-none">cancer</a>, <a href="/cell" class="text-decoration-none">cell lines</a>, and <a href="/drugs" class="text-decoration-none">small molecule</a> drugs. These networks are bipartite graphs linking either transcription factors (TFs) or miRNAs to their target genes. GRAND includes an integrated webserver to test for <a href='/disease' class="text-decoration-none">enrichment</a> of a list of TFs in a set of libraries as well as a tool (CLUEreg) to <a href="/analysis" class="text-decoration-none">find compounds</a> that reverse/exacerbate a gene or TF regulatory profile.<br> Publication: Ben Guebila, Lopes-Ramos et al. <a href="https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab778/6368528" style="text-decoration:none;" target=”_blank”>NAR, 2022.</a> Current version: <a href="https://github.com/QuackenbushLab/grand/releases/tag/1.6.0" style="text-decoration:none;" target=”_blank”>1.6.0</a>. Last update: 09/21/2024.<br>
     </p>      
     </div>
 </header>

grandapp/templates/networksagg.html

@@ -161,7 +161,7 @@ <h5><span data-step="2" data-intro="Since these are genome-scale network, we can
                                   data-bs-animation="false"
                                   data-bs-placement="top"
                                   data-bs-html="true"
-                                  data-bs-content="Draws motif prior edges as dashed lines.">
+                                  data-bs-content="Draws motif prior edges as dashed lines. For sex-specific networks, motif prior edges are specific to males and females.">
                                 Prior edges</a></small>
                               </div>
                             </div>
@@ -188,7 +188,7 @@ <h5><span class="badge bg-secondary">Node scaling</span></h5>
                             data-bs-animation="false"
                             data-bs-placement="top"
                             data-bs-html="true"
-                            data-bs-content="Scale node size by average expression across samples for aggregate networks and by sample for single-sample networks.">
+                            data-bs-content="Scale node size by average expression across samples for aggregate networks; for single-sample networks, node size is also the average across all samples as well; for sex-specific networks, node size is the average across males if patient is male and across females if patient is female.">
                           expression</a></small>
                         </div>
                         <div style="float: left;">

grandapp/templates/tissueslanding.html

@@ -56,7 +56,7 @@ <h5 class="card-title">Tool description</h5>
                   <p class="card-text"><a href="https://netzoo.github.io/zooanimals/panda/" style="text-decoration:none" target="_blank">PANDA</a> reconstructs a gene regulatory network using TF PPI, TF DNA binding motif as regulation prior, and gene expression samples.
                                 <a href="https://netzoo.github.io/zooanimals/puma/" style="text-decoration:none" target="_blank">PUMA</a> reconstructs a gene regulatory network with miRNA as regulators using gene expression samples and miRNA predicted targets by <a href="http://cbio.mskcc.org/microrna_data/manual.html" style="text-decoration:none" target="_blank">miRanda</a> or <a href="http://www.targetscan.org/vert_72/" style="text-decoration:none" target="_blank">TargetScan</a> as regulation priors. The Regulator-regulator interaction matrix is set to the identity matrix in the algorithm.
               {% if name == 'yes' %} <a href="https://netzoo.github.io/zooanimals/lioness/" style="text-decoration:none" target="_blank">LIONESS</a> reconstructs patient-specific <a href="https://netzoo.github.io/zooanimals/panda/" style="text-decoration:none" target="_blank">PANDA</a> networks for each gene expression sample. 
-              <a href="https://netzoo.github.io/zooanimals/ss/bonobo/" style="text-decoration:none" target="_blank">BONOBO</a> is a single-sample network inference method, which can be used to estimate patient-specific co-expression matrices.
+              <p><a href="https://netzoo.github.io/zooanimals/ss/bonobo/" style="text-decoration:none" target="_blank">BONOBO</a> is a single-sample network inference method, which can be used to estimate patient-specific co-expression matrices, which are then fed as input for PANDA to build patient-specific GRNs. To adapt BONOBO-PANDA networks for patient's sex, and in addition to sample-specific co-expression, PANDA's motif prior networks are constructed for each sex (accounting for <a href="https://genome.cshlp.org/content/early/2024/08/10/gr.279117.124.abstract" style="text-decoration:none" target="_blank">sex chromosomes</a>), while the third input (PPI networks) are generic for all patients.
               To download sample-specific networks, you can check <a href="#cardtissue" style="text-decoration:none">the phenotypic information</a> and select the networks by
                                   clinical variables or download all the samples in a single file. {% else %} Check <a href="#cardtissue" style="text-decoration:none" >the phenotypic information</a> for the clinical variables of the samples that were used to reconstruct the network. {% endif %}</p>
                   <table id="tissuestable2" class="table table-hover" width="100%">
@@ -137,7 +137,7 @@ <h5 class="card-title">Tool description</h5>
                           {% else %} 
                               <td><a href="{{ tissue.ppi }}"><i class="fas fa-download"></i></a> <a href="{{ tissue.ppiLink }}" target="_blank"><i class="fas fa-link"></i></a></td>
                           {% endif %}
-                          <td><a href="{{ tissue.motif }}" class="text-decoration-none">{{ tissue.motifDesc }}</a></td>
+                          <td><a href="{{ tissue.motif }}" class="text-decoration-none">{{ tissue.motifDesc }}</a>{% if tissue.tool == 'BONOBO-PANDA' %}, <a href="{{ tissue.motif2 }}" class="text-decoration-none">{{ tissue.motifDesc2 }}</a>{% endif %}</td>
                           <td><a href="{{ tissue.expression }}"><i class="fas fa-download"></i></a> <a href="{{ tissue.expLink }}" target="_blank"><i class="fas fa-link"></i></a></td>
                           <td>{{ tissue.reg }}</td>
                           <td>{{ tissue.tfs }}</td>
@@ -408,7 +408,9 @@ <h5 class="card-title">Variable description</h5>
     </div>
   </div>
 </div>
+{% if slug2 == 'Thyroid' %}
     {% include "bonobothy.html" %}
+{% endif %}
 </div>
 </div>
 

grandapp/views.py

@@ -148,7 +148,7 @@ def networksagg(request,slug):
         edges['dispval'] =1
         nodes=nodes.to_json(orient='records')
         edges=edges.to_json(orient='records')
-        object_key,ssagg,categorynet,regnetdisp,backpage,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14=mapObjectkey(slug)
+        object_key,ssagg,categorynet,regnetdisp,backpage,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14,mid,fid=mapObjectkey(slug)
         # Targeting form 
         formtar  = TarForm({'topbottomtar':'Largest','nedgestar':100,'topbottomtartf':'Largest','nedgestartf':100,'tfgeneseltar':'nosel'})
         clueform = ClueForm({'tfgeneselclue':'by gene'})
@@ -173,24 +173,30 @@ def networksagg(request,slug):
             if (slug[0:3]=='ACH') | (slug=='mirnadragon') | (slugsplit=='bonobo'):
                 form.fields['edgetargeting'].widget.attrs['disabled']    = 'disabled'
             print('The number of edges is',nedges)
-            object_key,ssagg,categorynet,regnetdisp,backpage,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14=mapObjectkey(slug)
-            print('hey')
+            object_key,ssagg,categorynet,regnetdisp,backpage,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14,mid,fid=mapObjectkey(slug)
             print(object_key)
+            print(dt)
             df=fetchNetwork(object_key)
             if dt=='dtt':
                 tftar  = df.sum(axis=1) 
                 genetar= df.sum(axis=0)
             if dt=='dee':
-                object_key,ew1,ew2,ew3,ew4,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14=mapObjectkey(slug,modality='expression')
+                object_key,ew1,ew2,ew3,ew4,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14,mid,fid=mapObjectkey(slug,modality='expression')
+                print(attr2)
                 print(object_key)
-                deDf=fetchNetwork(object_key)
+                if attr2=='MALE':
+                    deDf=fetchNetwork(object_key,sexind=mid)
+                elif attr2=='FEMALE':
+                    deDf=fetchNetwork(object_key,sexind=fid)
+                else:
+                    deDf=fetchNetwork(object_key)
                 deDfmean = deDf.values.mean()
                 deDf = deDf.mean(axis=1)
             df.index.name='TF'
             df,found,ngwas,ngenesfound=selectgenes(df,tfgenesel,geneform,tfform,goform,gwasform)
             df=df.stack().reset_index().rename(columns={'TF':'source','level_1':'target', 0:'value'})
             if edgetargeting == 'on':
-                object_key,ew1,ew2,ew3,ew4,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14=mapObjectkey(slug,modality='motif')
+                object_key,ew1,ew2,ew3,ew4,attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14,mid,fid=mapObjectkey(slug,modality='motif',sex=attr2)
                 motif=fetchNetwork(object_key,how='motif')
                 motif=motif[motif['value'] >0]
                 df = pd.merge(df, motif, how ='left', on =['source', 'target'])
@@ -2296,7 +2302,7 @@ def selectgenes(df,tfgenesel,geneform,tfform,goform,gwasform):
             print('no intersection')
     return df, found, ngwas, ngenesfound
 
-def fetchNetwork(object_key,how='net'):
+def fetchNetwork(object_key,how='net',sexind=[]):
     client = boto3.client('s3')
     bucket_name = 'granddb'
     csv_obj = client.get_object(Bucket=bucket_name, Key=object_key)
@@ -2307,6 +2313,8 @@ def fetchNetwork(object_key,how='net'):
             df = pd.read_csv(StringIO(csv_string),index_col=0,sep='\t')    
         else:
             df = pd.read_csv(StringIO(csv_string),index_col=0,sep=',')
+            if len(sexind) != 0:
+                df=df.iloc[:,sexind]
     elif how=='motif':
         if (str.split(object_key,'_')[-2] == 'otter') | (str.split(object_key,'/')[-1][0:3]=='GSM'):
             df = pd.read_csv(StringIO(csv_string), sep=',',index_col=0)
@@ -2376,9 +2384,9 @@ def selectgenestar(genetarscore,tfgenesel,geneform,goform,gwasform):
             found='not found'
     return genetarscore, found, ngwas, ngenesfound
 
-def mapObjectkey(slug,modality='network',how=''):
+def mapObjectkey(slug,modality='network',how='',sex=''):
     regnetdisp='Transcription factor'
-    attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14='','','','','','','',''
+    attr1,attr2,attr3,attr4,attr11,attr12,attr13,attr14,fid,mid='','','','','','','','','',''
     if len(slug.split('_')) > 2:
         slugsplit = slug.split('_')[2] 
     else:
@@ -2583,12 +2591,28 @@ def mapObjectkey(slug,modality='network',how=''):
         attr13 = 'Estrogen receptor status'
         attr14 = 'HER2 receptor status'
     elif (slugsplit == 'BONOBO'): # bonobo panda thyroid network
-            print('ho')
-            print(slug[17:])
             object_key = 'tissues/networks/panda_bonobo/THY_PANDA_BONOBO_'+slug[17:].replace('_','-')+'.csv'
             backpage   = 'tissues/Thyroid_tissue'
             ssagg='Single sample'
             categorynet='Tissues'
+            if modality == 'motif':
+                if sex=='MALE':
+                    object_key='tissues/motif/MotifPriorGencode_p5.txt'
+                elif sex=='FEMALE':
+                    object_key='tissues/motif/MotifPriorGencode_p5_female_PARonX.txt'
+            elif modality=='expression':
+                object_key = 'tissues/expression/GTEx_thyroid_allSex.txt'
+            tissuesample   = Tissuesamplethy.objects.get(sampleid=slug[-24:].replace('_','-'))
+            mid=np.array(list(Tissuesamplethy.objects.filter(decsex='MALE').values_list('pk', flat=True)), dtype='int')-1
+            fid=np.array(list(Tissuesamplethy.objects.filter(decsex='FEMALE').values_list('pk', flat=True)), dtype='int')-1
+            attr1  = tissuesample.smts
+            attr2  = tissuesample.decsex
+            attr3  = tissuesample.age
+            attr4  = tissuesample.subjectid
+            attr11 = 'Tissue'
+            attr12 = 'Donor Gender'
+            attr13 = 'Donor age'
+            attr14 = 'Subject'
     elif (str.split(slug,'_')[2] == 'TCGA') | (str.split(slug,'_')[2][0:3] == 'GSM'): # single sample networks
         ssagg='Single sample'
         categorynet='Cancer'
@@ -2668,4 +2692,4 @@ def mapObjectkey(slug,modality='network',how=''):
         categorynet='Cell lines'
         backpage='cell/gm12878'
         object_key = 'data/' + str.split(slug,'_')[0] + '_' + str.split(slug,'_')[1] + '.csv'
-    return object_key, ssagg, categorynet, regnetdisp, backpage, attr1, attr2, attr3, attr4, attr11, attr12, attr13, attr14
+    return object_key, ssagg, categorynet, regnetdisp, backpage, attr1, attr2, attr3, attr4, attr11, attr12, attr13, attr14, mid, fid