# HG changeset patch
# User iuc
# Date 1726050097 0
# Node ID 94f1b9c7286f08b27bf864d2e52ad5876fde31b5
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat_v5 commit a9214c07b0cc929a51fd92a369bb89c675b6c88d
diff -r 000000000000 -r 94f1b9c7286f macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Wed Sep 11 10:21:37 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 94f1b9c7286f neighbors_clusters_markers.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/neighbors_clusters_markers.xml Wed Sep 11 10:21:37 2024 +0000
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+ - Neighbors and Markers
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+ macros.xml
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+ `__
+
+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.
+
+Takes as input two dimensional reductions, one computed for each modality.
+
+More details on the `seurat documentation
+`__
+
+FindClusters
+============
+
+Identify clusters of cells by a shared nearest neighbor (SNN) modularity optimization based clustering algorithm.
+
+First calculate k-nearest neighbors and construct the SNN graph. Then optimize the modularity function to determine clusters.
+
+More details on the `seurat documentation
+`__
+
+
+FindAllMarkers
+==============
+
+Find markers (differentially expressed genes) for each of the identity classes in a dataset
+
+Outputs a matrix containing a ranked list of putative markers, and associated statistics (p-values, ROC score, etc.)
+
+Methods:
+
+"wilcox" : Identifies differentially expressed genes between two groups of cells using a Wilcoxon Rank Sum test (default); will use a fast implementation by Presto if installed
+
+"wilcox_limma" : Identifies differentially expressed genes between two groups of cells using the limma implementation of the Wilcoxon Rank Sum test; set this option to reproduce results from Seurat v4
+
+"bimod" : Likelihood-ratio test for single cell gene expression, (McDavid et al., Bioinformatics, 2013)
+
+"roc" : Identifies 'markers' of gene expression using ROC analysis. For each gene, evaluates (using AUC) a classifier built on that gene alone, to classify between two groups of cells. An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. Each of the cells in cells.1 exhibit a higher level than each of the cells in cells.2). An AUC value of 0 also means there is perfect classification, but in the other direction. A value of 0.5 implies that the gene has no predictive power to classify the two groups. Returns a 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially expressed genes.
+
+"t" : Identify differentially expressed genes between two groups of cells using Student's t-test.
+
+"negbinom" : Identifies differentially expressed genes between two groups of cells using a negative binomial generalized linear model. Use only for UMI-based datasets
+
+"poisson" : Identifies differentially expressed genes between two groups of cells using a poisson generalized linear model. Use only for UMI-based datasets
+
+"LR" : Uses a logistic regression framework to determine differentially expressed genes. Constructs a logistic regression model predicting group membership based on each feature individually and compares this to a null model with a likelihood ratio test.
+
+"MAST" : Identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST package to run the DE testing.
+
+"DESeq2" : Identifies differentially expressed genes between two groups of cells based on a model using DESeq2 which uses a negative binomial distribution (Love et al, Genome Biology, 2014).This test does not support pre-filtering of genes based on average difference (or percent detection rate) between cell groups. However, genes may be pre-filtered based on their minimum detection rate (min.pct) across both cell groups.
+
+More details on the `seurat documentation
+`__
+
+FindMarkers
+===========
+
+Find markers (differentially expressed genes) for identity classes (clusters) or groups of cells
+
+Outputs a data.frame with a ranked list of putative markers as rows, and associated statistics as columns (p-values, ROC score, etc., depending on the test used (test.use)).
+
+Methods - as for FindAllMarkers
+
+More details on the `seurat documentation
+`__
+
+FindConservedMarkers
+====================
+
+Finds markers that are conserved between the groups
+
+Uses metap::minimump as meta.method.
+
+More details on the `seurat documentation
+`__
+
+ ]]>
+
+
\ No newline at end of file