# HG changeset patch
# User iuc
# Date 1726050038 0
# Node ID c3170652bd98c499c1bbc85a175564fbaf373e5e
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat_v5 commit a9214c07b0cc929a51fd92a369bb89c675b6c88d
diff -r 000000000000 -r c3170652bd98 macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Wed Sep 11 10:20:38 2024 +0000
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+ 5.0
+ 0
+ 23.0
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+ 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
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+ 10.1038/s41587-023-01767-y
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+ /dev/null | grep -v -i "WARNING: ")
+ ]]>
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+ ]
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+ $hidden_output &&
+Rscript '$script_file' >> $hidden_output
+ ]]>
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+ advanced_common['show_log']
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+ method['method'] != 'Inspect'
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+ method['method'] == 'FindVariableFeatures' or method['method'] == 'SCTransform'
+ method['output_topN']['output_topN'] == 'true'
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+ method['method'] == 'FindAllMarkers' or method['method'] == 'FindMarkers' or method['method'] == 'FindConservedMarkers'
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+ method['method'] == 'RunPCA' and method['print_pcs']['print_pcs'] == 'true'
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+ method['method'] == 'Inspect' and method['inspect']['inspect'] != 'General'
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+ method['method'] == 'Inspect' and method['inspect']['inspect'] == 'General'
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+ plot_format == 'png'
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+ plot_format == 'pdf'
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+ plot_format == 'svg'
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+ plot_format == 'jpeg'
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+ plot_format == 'tex'
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+ plot_format == 'tiff'
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+ plot_format == 'eps'
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+ ^[\w\-.]+$
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+ ^[A-Za-z_]+$
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+ ^[\w\-., ]+$
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+ ^[\w[:punct:]]+$
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+ ^[\w[:punct:]]+$
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diff -r 000000000000 -r c3170652bd98 normalize_select_features_scale.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/normalize_select_features_scale.xml Wed Sep 11 10:20:38 2024 +0000
@@ -0,0 +1,466 @@
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+ - Normalize, Find Variable Features, Scale and Regress
+
+ macros.xml
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+ `__
+
+FindVariableFeatures
+====================
+
+Identify features that are outliers on a 'mean variability plot'.
+
+Methods:
+
+“vst”: First, fits a line to the relationship of log(variance) and log(mean) using local polynomial regression (loess). Then standardizes the feature values using the observed mean and expected variance (given by the fitted line). Feature variance is then calculated on the standardized values after clipping to a maximum (see clip.max parameter).
+
+“mean.var.plot” (mvp): First, uses a function to calculate average expression (mean.function, using FastExpMean) and dispersion (dispersion.function, using FastLogVMR) for each feature. Next, divides features into num.bin (deafult 20) bins based on their average expression, and calculates z-scores for dispersion within each bin. The purpose of this is to identify variable features while controlling for the strong relationship between variability and average expression
+
+“dispersion” (disp): selects the genes with the highest dispersion values
+
+More details on the `seurat documentation
+`__
+
+Scale and regress the data with ScaleData
+=========================================
+
+Scale and center features in the dataset.
+
+If variables are provided in vars.to.regress, they are individually regressed against each feature, and the resulting residuals are then scaled and centered.
+
+More details on the `seurat documentation
+`__
+
+SCTransform
+===========
+
+Use this function as an alternative to the NormalizeData, FindVariableFeatures, ScaleData workflow.
+
+Results are saved in a new assay (named SCT by default) with counts being (corrected) counts, data being log1p(counts), scale.data being pearson residuals; sctransform::vst intermediate results are saved in misc slot of new assay.
+
+More details on the `seurat documentation
+`__
+
+ ]]>
+
+
\ No newline at end of file