# HG changeset patch
# User iuc
# Date 1726050071 0
# Node ID d0c26c9430f284dfab2b98b3a71391527b55fd78
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat_v5 commit a9214c07b0cc929a51fd92a369bb89c675b6c88d
diff -r 000000000000 -r d0c26c9430f2 create_seurat.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/create_seurat.xml Wed Sep 11 10:21:11 2024 +0000
@@ -0,0 +1,537 @@
+
+ - Prepare data for the pipeline
+
+ macros.xml
+
+
+
+ $hidden_output &&
+ Rscript '$script_file' >> $hidden_output
+#else:
+@CMD@
+#end if
+ ]]>
+
+ $method.minimum_nFeature_RNA
+ )
+ #end if
+ #if $method.maximum_nFeature_RNA
+ seurat_obj<-subset(
+ seurat_obj,
+ subset = nFeature_RNA < $method.maximum_nFeature_RNA
+ )
+ #end if
+ #if $method.minimum_nCount_RNA
+ seurat_obj<-subset(
+ seurat_obj,
+ subset = nCount_RNA > $method.minimum_nCount_RNA
+ )
+ #end if
+ #if $method.maximum_nCount_RNA
+ seurat_obj<-subset(
+ seurat_obj,
+ subset = nCount_RNA < $method.maximum_nCount_RNA
+ )
+ #end if
+ #if $method.minimum_percent_mt
+ seurat_obj<-subset(
+ seurat_obj,
+ subset = percent.mt> $method.minimum_percent_mt
+ )
+ #end if
+ #if $method.maximum_percent_mt
+ seurat_obj<-subset(
+ seurat_obj,
+ subset = percent.mt < $method.maximum_percent_mt
+ )
+ #end if
+ #if $method.other.other == 'true'
+ #if $method.other.minimum
+ seurat_obj<-subset(
+ seurat_obj,
+ subset = $method.other.other_variable > $method.other.minimum
+ )
+ #end if
+ #if $method.other.maximum
+ seurat_obj<-subset(
+ seurat_obj,
+ subset = $method.other.other_variable < $method.other.maximum
+ )
+ #end if
+ #end if
+
+#end if
+
+@CMD_rds_write_outputs@
+
+]]>
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+ `__
+
+read.table
+==========
+
+Read a tab-delimited tsv or tabular file into an RDS file as a table.
+
+More details on the `R documentation
+`__
+
+CreateSeuratObject
+==================
+
+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.
+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.
+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.
+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.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.
+
+More details on the `seurat documentation
+`__
+
+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.
+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.
+
+More details on the `seurat documentation
+`__
+
+Filter Cells
+============
+
+Filter cells based on QC metrics.
+
+nFeature_RNA = number of unique genes identified in the cell
+
+ncounts_RNA = total number of RNAs found in the cell
+
+percent.mt = percentage of mitochondrial genes in the cell
+
+More details on the `R documentation
+`__
+
+
+ ]]>
+
+
\ No newline at end of file
diff -r 000000000000 -r d0c26c9430f2 macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Wed Sep 11 10:21:11 2024 +0000
@@ -0,0 +1,437 @@
+
+ 5.0
+ 0
+ 23.0
+
+
+ r-seurat
+ fit-sne
+ bioconductor-limma
+ bioconductor-mast
+ bioconductor-deseq2
+ r-svglite
+ r-metap
+ bioconductor-glmGamPoi
+ umap-learn
+ leidenalg
+ r-harmony
+ bioconductor-batchelor
+ numpy
+ pandas
+
+
+
+
+ 10.1038/s41587-023-01767-y
+
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+ /dev/null | grep -v -i "WARNING: ")
+ ]]>
+
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+
+
+ ]
+
+ $hidden_output &&
+Rscript '$script_file' >> $hidden_output
+ ]]>
+
+
+
+
+
+
+ advanced_common['show_log']
+
+
+
+
+ method['method'] != 'Inspect'
+
+
+
+
+
+
+
+ method['method'] == 'FindVariableFeatures' or method['method'] == 'SCTransform'
+ method['output_topN']['output_topN'] == 'true'
+
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+ method['method'] == 'FindAllMarkers' or method['method'] == 'FindMarkers' or method['method'] == 'FindConservedMarkers'
+
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+ method['method'] == 'RunPCA' and method['print_pcs']['print_pcs'] == 'true'
+
+
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+ method['method'] == 'Inspect' and method['inspect']['inspect'] != 'General'
+
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+ method['method'] == 'Inspect' and method['inspect']['inspect'] == 'General'
+
+
+
+
+
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+ plot_format == 'png'
+
+
+ plot_format == 'pdf'
+
+
+ plot_format == 'svg'
+
+
+ plot_format == 'jpeg'
+
+
+ plot_format == 'tex'
+
+
+ plot_format == 'tiff'
+
+
+ plot_format == 'eps'
+
+
+
+
+
+
+ ^[\w\-.]+$
+
+
+ ^[A-Za-z_]+$
+
+
+ ^[\w\-., ]+$
+
+
+ ^[\w[:punct:]]+$
+
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+ ^[\w[:punct:]]+$
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