scanpy_inspect: inspect.xml comparison

comparison inspect.xml @ 3:cc0deb593fc8 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 3b41d687ff30583540d055f6995de00530cca81d"

author	iuc
date	Thu, 12 Dec 2019 09:27:38 -0500
parents	7d22964a8639
children	08192eebb47d

comparison

equal deleted inserted replaced

-:7d22964a8639
+:cc0deb593fc8
 #else if $method.method == 'tl.rank_genes_groups'
 sc.tl.rank_genes_groups(
 adata=adata,
 groupby='$method.groupby',
-use_raw=$method.use_raw,
 #if str($method.groups) != ''
 #set $group=[x.strip() for x in str($method.groups).split(',')]
 groups=$group,
 #end if
 #if $method.ref.rest == 'rest'
 multi_class='$method.tl_rank_genes_groups_method.solver.multi_class',
 #end if
 tol=$method.tl_rank_genes_groups_method.tol,
 C=$method.tl_rank_genes_groups_method.c,
 #end if
-only_positive=$method.only_positive)
+use_raw=$method.use_raw)
 #else if $method.method == "tl.marker_gene_overlap"
 reference_markers = {}
 #for $i, $s in enumerate($method.reference_markers)
 #set $list=[x.strip() for x in str($s.values).split(',')]
 </configfiles>
 <inputs>
 <expand macro="inputs_anndata"/>
 <conditional name="method">
 <param argument="method" type="select" label="Method used for inspecting">
-<option value="pp.calculate_qc_metrics">Calculate quality control metrics, using `pp.calculate_qc_metrics`</option>
+<option value="pp.calculate_qc_metrics">Calculate quality control metrics, using 'pp.calculate_qc_metrics'</option>
-<option value="pp.neighbors">Compute a neighborhood graph of observations, using `pp.neighbors`</option>
+<option value="pp.neighbors">Compute a neighborhood graph of observations, using 'pp.neighbors'</option>
-<option value="tl.score_genes">Score a set of genes, using `tl.score_genes`</option>
+<option value="tl.score_genes">Score a set of genes, using 'tl.score_genes'</option>
-<option value="tl.score_genes_cell_cycle">Score cell cycle genes, using `tl.score_genes_cell_cycle`</option>
+<option value="tl.score_genes_cell_cycle">Score cell cycle genes, using 'tl.score_genes_cell_cycle'</option>
-<option value="tl.rank_genes_groups">Rank genes for characterizing groups, using `tl.rank_genes_groups`</option>
+<option value="tl.rank_genes_groups">Rank genes for characterizing groups, using 'tl.rank_genes_groups'</option>
-<!--<option value="tl.marker_gene_overlap">Calculate an overlap score between data-deriven marker genes and provided markers, using `tl.marker_gene_overlap`</option>-->
+<!--<option value="tl.marker_gene_overlap">Calculate an overlap score between data-deriven marker genes and provided markers, using 'tl.marker_gene_overlap'</option>-->
-<option value="pp.log1p">Logarithmize the data matrix, using `pp.log1p`</option>
+<option value="pp.log1p">Logarithmize the data matrix, using 'pp.log1p'</option>
-<option value="pp.scale">Scale data to unit variance and zero mean, using `pp.scale`</option>
+<option value="pp.scale">Scale data to unit variance and zero mean, using 'pp.scale'</option>
-<option value="pp.sqrt">Square root the data matrix, using `pp.sqrt`</option>
+<option value="pp.sqrt">Square root the data matrix, using 'pp.sqrt'</option>
 </param>
 <when value="pp.calculate_qc_metrics">
 <param argument="expr_type" type="text" value="counts" label="Name of kind of values in X"/>
 <param argument="var_type" type="text" value="genes" label="The kind of thing the variables are"/>
-<param argument="qc_vars" type="text" value="" label="Keys for boolean columns of `.var` which identify variables you could want to control for"
+<param argument="qc_vars" type="text" value="" label="Keys for boolean columns of '.var' which identify variables you could want to control for"
 help="Keys separated by a comma"/>
 <param argument="percent_top" type="text" value="" label="Proportions of top genes to cover"
-help=" Values (integers) are considered 1-indexed, `50` finds cumulative proportion to the 50th most expressed genes. Values separated by a comma.
+help=" Values (integers) are considered 1-indexed, '50' finds cumulative proportion to the 50th most expressed genes. Values separated by a comma.
 If empty don't calculate"/>
 </when>
 <when value="pp.neighbors">
-<param argument="n_neighbors" type="integer" min="0" value="15" label="The size of local neighborhood (in terms of number of neighboring data points) used for manifold approximation" help="Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100. If `knn` is `True`, number of nearest neighbors to be searched. If `knn` is `False`, a Gaussian kernel width is set to the distance of the `n_neighbors` neighbor."/>
+<param argument="n_neighbors" type="integer" min="0" value="15" label="The size of local neighborhood (in terms of number of neighboring data points) used for manifold approximation" help="Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100. If 'knn' is 'True', number of nearest neighbors to be searched. If 'knn' is 'False', a Gaussian kernel width is set to the distance of the 'n_neighbors' neighbor."/>
 <param argument="n_pcs" type="integer" min="0" value="" optional="true" label="Number of PCs to use" help=""/>
 <param argument="use_rep" type="text" value="" optional="true" label="Indicated representation to use" help="If not set, the representation is chosen automatically: for n_vars below 50, X is used, otherwise X_pca (uns) is used. If X_pca is not present, it's computed with default parameter"/>
-<param argument="knn" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Use a hard threshold to restrict the number of neighbors to n_neighbors?" help="If true, it considers a knn graph. Otherwise, it uses a Gaussian Kernel to assign low weights to neighbors more distant than the `n_neighbors` nearest neighbor."/>
+<param argument="knn" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Use a hard threshold to restrict the number of neighbors to n_neighbors?" help="If true, it considers a knn graph. Otherwise, it uses a Gaussian Kernel to assign low weights to neighbors more distant than the 'n_neighbors' nearest neighbor."/>
 <param argument="random_state" type="integer" value="0" label="Numpy random seed" help=""/>
 <param name="pp_neighbors_method" argument="method" type="select" label="Method for computing connectivities" help="">
 <option value="umap">umap (McInnes et al, 2018)</option>
 <option value="gauss">gauss: Gauss kernel following (Coifman et al 2005) with adaptive width (Haghverdi et al 2016)</option>
 </param>
 </param>
 </when>
 <when value="tl.score_genes">
 <param argument="gene_list" type="text" value="" label="The list of gene names used for score calculation" help="Genes separated by a comma"/>
 <param argument="ctrl_size" type="integer" value="50" label="Number of reference genes to be sampled"
-help="If `len(gene_list)` is not too low, you can set `ctrl_size=len(gene_list)`."/>
+help="If 'len(gene_list)' is not too low, you can set 'ctrl_size=len(gene_list)'."/>
 <param argument="gene_pool" type="text" value="" optional="true" label="Genes for sampling the reference set"
 help="Default is all genes. Genes separated by a comma"/>
 <expand macro="score_genes_params"/>
-<param argument="score_name" type="text" value="score" label="Name of the field to be added in `.obs`" help=""/>
+<param argument="score_name" type="text" value="score" label="Name of the field to be added in '.obs'" help=""/>
 </when>
 <when value="tl.score_genes_cell_cycle">
 <conditional name='s_genes'>
 <param name="format" type="select" label="Format for the list of genes associated with S phase">
 <option value="file">File</option>
 <param argument="tol" type="float" value="1e-4" label="Tolerance for stopping criteria" help=""/>
 <param argument="c" type="float" value="1.0" label="Inverse of regularization strength"
 help="It must be a positive float. Like in support vector machines, smaller values specify stronger regularization."/>
 </when>
 </conditional>
-<param argument="only_positive" type="boolean" truevalue="True" falsevalue="False" checked="true"
-label="Only consider positive differences?" help=""/>
 </when>
 <!--<when value="tl.marker_gene_overlap">
 <repeat name="reference_markers" title="Marker genes">
 <param name="key" type="text" value="" label="Cell identity name" help=""/>
-<param name="values" type="text" value="" label="List of genes" help="Comma-separated names from `var`"/>
+<param name="values" type="text" value="" label="List of genes" help="Comma-separated names from 'var'"/>
 </repeat>
 <param argument="key" type="text" value="rank_genes_groups" label="Key in adata.uns where the rank_genes_groups output is stored"/>
 <conditional name="overlap">
 <param argument="method" type="select" label="Method to calculate marker gene overlap">
 <option value="overlap_count">overlap_count: Intersection of the gene set</option>
 <param name="n_genes" value="100"/>
 <conditional name="tl_rank_genes_groups_method">
 <param name="method" value="t-test_overestim_var"/>
 <param name="corr_method" value="benjamini-hochberg"/>
 </conditional>
-<param name="only_positive" value="true"/>
 </conditional>
 <assert_stdout>
 <has_text_matching expression="sc.tl.rank_genes_groups"/>
 <has_text_matching expression="groupby='cell_type'"/>
 <has_text_matching expression="use_raw=True"/>
 <has_text_matching expression="reference='rest'"/>
 <has_text_matching expression="n_genes=100"/>
 <has_text_matching expression="method='t-test_overestim_var'"/>
 <has_text_matching expression="corr_method='benjamini-hochberg'"/>
-<has_text_matching expression="only_positive=True"/>
 </assert_stdout>
 <output name="anndata_out" file="tl.rank_genes_groups.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/>
 </test>
 <test>
 <!-- test 7 -->
 <param name="multi_class" value="auto"/>
 </conditional>
 <param name="tol" value="1e-4"/>
 <param name="c" value="1.0"/>
 </conditional>
-<param name="only_positive" value="true"/>
 </conditional>
 <assert_stdout>
 <has_text_matching expression="sc.tl.rank_genes_groups"/>
 <has_text_matching expression="groupby='louvain'"/>
 <has_text_matching expression="use_raw=True"/>
 <has_text_matching expression="fit_intercept=True"/>
 <has_text_matching expression="max_iter=100"/>
 <has_text_matching expression="multi_class='auto'"/>
 <has_text_matching expression="tol=0.0001"/>
 <has_text_matching expression="C=1.0"/>
-<has_text_matching expression="only_positive=True"/>
 </assert_stdout>
 <output name="anndata_out" file="tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad" ftype="h5ad" compare="sim_size">
 <assert_contents>
 <has_h5_keys keys="X, obs, obsm, raw.X, raw.var, uns, var" />
 </assert_contents>
 </conditional>
 </conditional>
 <param name="tol" value="1e-4"/>
 <param name="c" value="1.0"/>
 </conditional>
-<param name="only_positive" value="true"/>
 </conditional>
 <assert_stdout>
 <has_text_matching expression="sc.tl.rank_genes_groups"/>
 <has_text_matching expression="groupby='louvain'"/>
 <has_text_matching expression="use_raw=True"/>
 <has_text_matching expression="dual=False"/>
 <has_text_matching expression="fit_intercept=True"/>
 <has_text_matching expression="intercept_scaling=1.0"/>
 <has_text_matching expression="tol=0.0001"/>
 <has_text_matching expression="C=1.0"/>
-<has_text_matching expression="only_positive=True"/>
 </assert_stdout>
 <output name="anndata_out" file="tl.rank_genes_groups.liblinear.krumsiek11.h5ad" ftype="h5ad" compare="sim_size">
 <assert_contents>
 <has_h5_keys keys="X, obs, obsm, raw.X, raw.var, uns, var" />
 </assert_contents>
 ===================================================================
 Calculates a number of qc metrics for an AnnData object, largely based on calculateQCMetrics from scater.
 Currently is most efficient on a sparse CSR or dense matrix.
-It updates the observation level metrics:
+It updates the observation level metrics with
 - total_{var_type}_by_{expr_type} (e.g. "total_genes_by_counts", number of genes with positive counts in a cell)
 - total_{expr_type} (e.g. "total_counts", total number of counts for a cell)
-- pct_{expr_type}_in_top_{n}_{var_type} (e.g. "pct_counts_in_top_50_genes", cumulative percentage of counts for 50 most expressed genes in a cell)
+- pct_{expr_type}_in_top_{n}_{var_type} - for n in percent_top (e.g. "pct_counts_in_top_50_genes", cumulative percentage of counts for 50 most expressed genes in a cell)
-- total_{expr_type}_{qc_var} (e.g. "total_counts_mito", total number of counts for variabes in qc_vars )
+- total_{expr_type}_{qc_var} - for qc_var in qc_vars (e.g. "total_counts_mito", total number of counts for variabes in qc_vars)
-- pct_{expr_type}_{qc_var} (e.g. "pct_counts_mito", proportion of total counts for a cell which are mitochondrial)
+- pct_{expr_type}_{qc_var} - for qc_var in qc_vars (e.g. "pct_counts_mito", proportion of total counts for a cell which are mitochondrial)
 And also the variable level metrics:
 - total_{expr_type} (e.g. "total_counts", sum of counts for a gene)
-- mean_{expr_type} (e.g. "mean counts", mean expression over all cells.
+- mean_{expr_type} (e.g. "mean counts", mean expression over all cells)
 - n_cells_by_{expr_type} (e.g. "n_cells_by_counts", number of cells this expression is measured in)
 - pct_dropout_by_{expr_type} (e.g. "pct_dropout_by_counts", percentage of cells this feature does not appear in)
 More details on the `scanpy documentation
 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.calculate_qc_metrics.html>`__

Mercurial > repos > iuc > scanpy_inspect

comparison inspect.xml @ 3:cc0deb593fc8 draft