Mercurial > repos > iuc > seurat_clustering
changeset 1:51eb02d9b17a draft default tip
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat_v5 commit 566984b588e88225f0b3f2dae88c6fd084315e7c
| author | iuc |
|---|---|
| date | Tue, 05 Nov 2024 11:54:58 +0000 |
| parents | 94f1b9c7286f |
| children | |
| files | macros.xml neighbors_clusters_markers.xml |
| diffstat | 2 files changed, 8 insertions(+), 2 deletions(-) [+] |
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--- 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">
--- 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.