--- a/create_seurat.xml	Wed Sep 11 10:21:11 2024 +0000
+++ b/create_seurat.xml	Tue Nov 05 11:54:55 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[
@@ -128,7 +129,7 @@
             pattern = '$method.percent_mt.pattern',
         )
         #end if
-        
+
         #if $method.input_type.citeseq.citeseq == 'true'
             citeseq<-read.table(
                 'citeseq.tab',
@@ -136,7 +137,7 @@
                 row.names = 1,
                 sep = "\t"
             )
-        
+
             seurat_obj[['ADT']]<-CreateAssay5Object(counts = citeseq)
         #end if
     #end if
@@ -446,19 +447,19 @@
 Seurat
 ======
 
-Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. 
+Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data.
 
 Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.
 
-Creating a Seurat Object 
+Creating a Seurat Object
 ========================
 
-Seurat objects can be created from single cell data in matrix market or tab-delimited table formats, using the Read10X or read.table functions followed by CreateSeuratObject. 
+Seurat objects can be created from single cell data in matrix market or tab-delimited table formats, using the Read10X or read.table functions followed by CreateSeuratObject.
 The input should be a single cell matrix with cells as rows and genes as columns.
 
 Both RNA-seq and combined RNA and CITE-seq data can be used as inputs.
 
-Read10X 
+Read10X
 ========
 
 Load sparse data matrices provided by 10X genomics.
@@ -469,7 +470,7 @@
 read.table
 ==========
 
-Read a tab-delimited tsv or tabular file into an RDS file as a table. 
+Read a tab-delimited tsv or tabular file into an RDS file as a table.
 
 More details on the `R documentation
 <https://www.rdocumentation.org/packages/utils/versions/3.6.2/topics/read.table>`__
@@ -477,28 +478,28 @@
 CreateSeuratObject
 ==================
 
-Create a Seurat Object from raw data in RDS format. 
+Create a Seurat Object from raw data in RDS format.
 
 names.field
 
-For the initial identity class for each cell, choose this field from the cell's name. 
+For the initial identity class for each cell, choose this field from the cell's name.
 E.g. If your cells are named as BARCODE_CLUSTER_CELLTYPE in the input matrix, set names.field to 3 to set the initial identities to CELLTYPE.
 
 names.delim
 
-For the initial identity class for each cell, choose this delimiter from the cell's column name. 
+For the initial identity class for each cell, choose this delimiter from the cell's column name.
 E.g. If your cells are named as BARCODE-CLUSTER-CELLTYPE, set this to “-” to separate the cell name into its component parts for picking the relevant field.
 
 meta.data
 
-Additional cell-level metadata to add to the Seurat object. Should be a data.frame where the rows are cell names and the columns are additional metadata fields. 
+Additional cell-level metadata to add to the Seurat object. Should be a data.frame where the rows are cell names and the columns are additional metadata fields.
 Row names in the metadata need to match the column names of the counts matrix.
 
 Filtering can also be performed on:
 
-min.cells = only include features/genes detected in at least this many cells 
+min.cells = only include features/genes detected in at least this many cells
 
-min.features = only include cells where at least this many features are detected 
+min.features = only include cells where at least this many features are detected
 
 Some QC metrics are added when creating a Seurat Object (nCount_RNA and nFeature_RNA).
 Mito percentage can optionally be calculated - it will be based on gene names starting with "MT-". If this pattern does not work for your gene names then you can use the separate 'Calculate QC Metrics' function instead.
@@ -509,7 +510,7 @@
 Calculate QC Metrics
 ====================
 
-Calculate the percentage of all the counts belonging to a subset of the possible features for each cell. This is useful when trying to compute the percentage of transcripts that map to mitochondrial genes for example. 
+Calculate the percentage of all the counts belonging to a subset of the possible features for each cell. This is useful when trying to compute the percentage of transcripts that map to mitochondrial genes for example.
 The calculation here is simply the column sum of the matrix present in the counts slot for features belonging to the set divided by the column sum for all features times 100.
 
 Feature sets can be defined by entering a list of genes or using a shared pattern in the gene names, such as "^MT-" or "^RP[LS]" for human mitochondrial or ribosomal genes.
@@ -520,7 +521,7 @@
 Filter Cells
 ============
 
-Filter cells based on QC metrics. 
+Filter cells based on QC metrics.
 
 nFeature_RNA = number of unique genes identified in the cell
 

--- a/macros.xml	Wed Sep 11 10:21:11 2024 +0000
+++ b/macros.xml	Tue Nov 05 11:54:55 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">