# HG changeset patch
# User iuc
# Date 1715865434 0
# Node ID 00a6721e1f8162a9bf81a51dd45855bc07de8b78
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/snapatac2 commit be132b56781bede5dc6e020aa80ca315546666cd
diff -r 000000000000 -r 00a6721e1f81 macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Thu May 16 13:17:14 2024 +0000
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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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+
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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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diff -r 000000000000 -r 00a6721e1f81 preprocessing.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/preprocessing.xml Thu May 16 13:17:14 2024 +0000
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+ and integration
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+ macros.xml
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+ method['method'] == 'pp.make_fragment_file'
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+ method['method'] != 'pp.make_fragment_file'
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+ advanced_common['show_log']
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+ `__
+
+Import data fragment file` and compute basic QC metrics, using `pp.import_data`
+===============================================================================
+
+Import data fragment files and compute basic QC metrics.
+
+A fragment refers to the sequence data originating from a distinct location in the genome. In single-ended sequencing, one read equates to a fragment. However, in paired-ended sequencing, a fragment is defined by a pair of reads. This function is designed to handle, store, and process input files with fragment data, further yielding a range of basic Quality Control (QC) metrics. These metrics include the total number of unique fragments, duplication rates, and the percentage of mitochondrial DNA detected.
+
+How fragments are stored is dependent on the sequencing approach utilized. For single-ended sequencing, fragments are found in `.obsm['fragment_single']`. In contrast, for paired-ended sequencing, they are located in `.obsm['fragment_paired']`.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Generate cell by bin count matrix, using `pp.add_tile_matrix`
+=============================================================
+
+Generate cell by bin count matrix.
+
+This function is used to generate and add a cell by bin count matrix to the AnnData object.
+
+`import_data` must be ran first in order to use this function.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Generate cell by gene activity matrix, using `pp.make_gene_matrix`
+==================================================================
+
+Generate cell by gene activity matrix.
+
+Generate cell by gene activity matrix by counting the TN5 insertions in gene body regions. The result will be stored in a new file and a new AnnData object will be created.
+
+`import_data` must be ran first in order to use this function.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells`
+============================================================================================
+
+Filter cell outliers based on counts and numbers of genes expressed. For instance, only keep cells with at least `min_counts` counts or `min_ts`` TSS enrichment scores. This is to filter measurement outliers, i.e. “unreliable” observations.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Perform feature selection, using `pp.select_features`
+=====================================================
+
+Perform feature selection by selecting the most accessibile features across all cells unless `max_iter` > 1
+
+More details on the `SnapATAC2 documentation
+`__
+
+Compute probability of being a doublet using the scrublet algorithm, using `pp.scrublet`
+========================================================================================
+
+Compute probability of being a doublet using the scrublet algorithm.
+
+This function identifies doublets by generating simulated doublets using randomly pairing chromatin accessibility profiles of individual cells. The simulated doublets are then embedded alongside the original cells using the spectral embedding algorithm in this package. A k-nearest-neighbor classifier is trained to distinguish between the simulated doublets and the authentic cells. This trained classifier produces a “doublet score” for each cell. The doublet scores are then converted into probabilities using a Gaussian mixture model.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Remove doublets according to the doublet probability or doublet score, using `pp.filter_doublets`
+=================================================================================================
+
+Remove doublets according to the doublet probability or doublet score.
+
+The user can choose to remove doublets by either the doublet probability or the doublet score. `scrublet` must be ran first in order to use this function.
+
+More details on the `SnapATAC2 documentation
+`__
+
+A modified MNN-Correct algorithm based on cluster centroid, using `pp.mnc_correct`
+==================================================================================
+
+A modified MNN-Correct algorithm based on cluster centroid.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Use harmonypy to integrate different experiments,using `pp.harmony`
+===================================================================
+
+Use harmonypy to integrate different experiments.
+
+Harmony is an algorithm for integrating single-cell data from multiple experiments. This function uses the python port of Harmony, `harmonypy`, to integrate single-cell data stored in an AnnData object. This function should be run after performing dimension reduction.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Use Scanorama to integrate different experiments, using `pp.scanorama_integrate`
+========================================================================================
+
+Use Scanorama to integrate different experiments.
+
+Scanorama is an algorithm for integrating single-cell data from multiple experiments stored in an AnnData object. This function should be run after performing `tl.spectral` but before computing the neighbor graph.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Compute the fragment size distribution of the dataset, using `metrics.frag_size_distr`
+======================================================================================
+
+Compute the fragment size distribution of the dataset.
+
+This function computes the fragment size distribution of the dataset. Note that it does not operate at the single-cell level. The result is stored in a vector where each element represents the number of fragments and the index represents the fragment length. The first posision of the vector is reserved for fragments with size larger than the `max_recorded_size` parameter. `import_data` must be ran first in order to use this function.
+
+More details on the `SnapATAC2 documentation
+`__
+
+Compute the TSS enrichment score (TSSe) for each cell, using `metrics.tsse`
+===========================================================================
+
+Compute the TSS enrichment score (TSSe) for each cell.
+
+`import_data` must be ran first in order to use this function.
+
+More details on the `SnapATAC2 documentation
+`__
+
+ ]]>
+
+