Repository 'seurat_clustering'
hg clone https://toolshed.g2.bx.psu.edu/repos/iuc/seurat_clustering

Changeset 1:51eb02d9b17a (2024-11-05)
Previous changeset 0:94f1b9c7286f (2024-09-11)
Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat_v5 commit 566984b588e88225f0b3f2dae88c6fd084315e7c
modified:
macros.xml
neighbors_clusters_markers.xml
b
diff -r 94f1b9c7286f -r 51eb02d9b17a macros.xml
--- a/macros.xml Wed Sep 11 10:21:37 2024 +0000
+++ b/macros.xml Tue Nov 05 11:54:58 2024 +0000
[
@@ -2,6 +2,11 @@
     <token name="@TOOL_VERSION@">5.0</token>
     <token name="@VERSION_SUFFIX@">0</token>
     <token name="@PROFILE@">23.0</token>
+    <xml name="bio_tools">
+        <xrefs>
+            <xref type="bio.tools">seurat</xref>
+        </xrefs>
+      </xml>
     <xml name="requirements">
         <requirements>
             <requirement type="package" version="@TOOL_VERSION@">r-seurat</requirement>
@@ -141,7 +146,7 @@
         </data>
     </xml>
     <token name="@CMD_inspect_rds_outputs@"><![CDATA[
-write.table(inspect, 'inspect_out.tab', sep="\t", col.names = col.names, row.names = row.names, quote = FALSE)    
+write.table(inspect, 'inspect_out.tab', sep="\t", col.names = col.names, row.names = row.names, quote = FALSE)
     ]]>
     </token>
     <xml name="plot_out">
b
diff -r 94f1b9c7286f -r 51eb02d9b17a neighbors_clusters_markers.xml
--- a/neighbors_clusters_markers.xml Wed Sep 11 10:21:37 2024 +0000
+++ b/neighbors_clusters_markers.xml Tue Nov 05 11:54:58 2024 +0000
[
@@ -3,6 +3,7 @@
     <macros>
         <import>macros.xml</import>
     </macros>
+    <expand macro="bio_tools"/>
     <expand macro="requirements"/>
     <expand macro="version_command"/>
     <command detect_errors="exit_code"><![CDATA[
@@ -754,7 +755,7 @@
 FindMultiModalNeighbors
 =======================
 
-This function will construct a weighted nearest neighbor (WNN) graph for two modalities (e.g. RNA-seq and CITE-seq). For each cell, we identify the nearest neighbors based on a weighted combination of two modalities. 
+This function will construct a weighted nearest neighbor (WNN) graph for two modalities (e.g. RNA-seq and CITE-seq). For each cell, we identify the nearest neighbors based on a weighted combination of two modalities.
 
 Takes as input two dimensional reductions, one computed for each modality.