# HG changeset patch
# User iuc
# Date 1715865357 0
# Node ID af821711b356a23f98f72d1b6536a06b234df301
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/snapatac2 commit be132b56781bede5dc6e020aa80ca315546666cd
diff -r 000000000000 -r af821711b356 dimension_reduction_clustering.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/dimension_reduction_clustering.xml Thu May 16 13:15:57 2024 +0000
@@ -0,0 +1,579 @@
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+ and dimension reduction
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+ macros.xml
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+ advanced_common['show_log']
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+ method['method'] and 'tl.diff_test' in method['method']
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+ `__
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+Compute Umap, using `tl.umap`
+=============================
+
+Compute Umap
+
+More details on the `SnapATAC2 documentation
+`__
+
+Compute a neighborhood graph of observations, using `pp.knn`
+============================================================
+
+Compute a neighborhood graph of observations.
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+Computes a neighborhood graph of observations stored in adata using the method specified by method. The distance metric used is Euclidean.
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+More details on the `SnapATAC2 documentation
+`__
+
+Cluster cells into subgroups, using `tl.leiden`
+===============================================
+
+Cluster cells into subgroups.
+
+Cluster cells using the Leiden algorithm, an improved version of the Louvain algorithm. It has been proposed for single-cell analysis by. This requires having ran `knn`.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Cluster cells into subgroups using the K-means algorithm, using `tl.kmeans`
+===========================================================================
+
+Cluster cells into subgroups using the K-means algorithm, a classical algorithm in data mining.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Cluster cells into subgroups using the DBSCAN algorithm, using `tl.dbscan`
+==========================================================================
+
+Cluster cells into subgroups using the DBSCAN algorithm.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Cluster cells into subgroups using the HDBSCAN algorithm, using `tl.hdbscan`
+============================================================================
+
+Cluster cells into subgroups using the HDBSCAN algorithm.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Aggregate values in adata.X in a row-wise fashion, using `tl.aggregate_X`
+=========================================================================
+
+Aggregate values in adata.X in a row-wise fashion.
+
+Aggregate values in adata.X in a row-wise fashion. This is used to compute RPKM or RPM values stratified by user-provided groupings.
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+More details on the `SnapATAC2 documentation
+`__
+
+Aggregate cells into pseudo-cells, using `tl.aggregate_cells`
+=============================================================
+
+Aggregate cells into pseudo-cells.
+
+Aggregate cells into pseudo-cells by iterative clustering.
+
+More details on the `SnapATAC2 documentation
+`__
+ ]]>
+
+
diff -r 000000000000 -r af821711b356 macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Thu May 16 13:15:57 2024 +0000
@@ -0,0 +1,187 @@
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+ 2.5.3
+ 0
+ 23.0
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+ snapatac2
+ plotly
+ python-kaleido
+ polars
+ pyarrow
+ python-igraph
+ hdbscan
+ harmonypy
+ scanorama
+
+
+
+
+
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+ '$hidden_output' &&
+ python '$script_file' >> '$hidden_output' &&
+ touch 'anndata_info.txt' &&
+ cat 'anndata_info.txt' @CMD_prettify_stdout@
+ ]]>
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+ 10.1038/s41592-023-02139-9
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