--- a/inspect.xml	Thu Dec 05 07:13:18 2019 -0500
+++ b/inspect.xml	Thu Dec 12 09:27:38 2019 -0500
@@ -135,7 +135,6 @@
 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=$group,
@@ -199,7 +198,7 @@
     tol=$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 = {}
@@ -256,30 +255,30 @@
         <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.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_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.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.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.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="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.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="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.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>
@@ -292,11 +291,11 @@
             <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'>
@@ -426,13 +425,11 @@
                             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">
@@ -598,7 +595,6 @@
                     <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"/>
@@ -608,7 +604,6 @@
                 <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>
@@ -634,7 +629,6 @@
                     <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"/>
@@ -650,7 +644,6 @@
                 <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>
@@ -686,7 +679,6 @@
                     <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"/>
@@ -702,7 +694,6 @@
                 <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>
@@ -797,18 +788,18 @@
 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)
-- total_{expr_type}_{qc_var} (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}_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} - 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} - 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)