comparison filter.xml @ 15:aa0059118fb9 draft default tip

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit c21958f44b81d740191999fb6015d5ae69538ee0

14:d636ce5cde16

    total_counts=$method.total_counts,
    #end if
    random_state=$method.random_state,
    replace=$method.replace,
    copy=False)
#end if

@CMD_anndata_write_outputs@
]]></configfile>
    </configfiles>

                <option value="pp.filter_genes">Filter genes based on number of cells or counts, using 'pp.filter_genes'</option>
                <option value="tl.filter_rank_genes_groups">Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories, using 'tl.filter_rank_genes_groups'</option>
                <option value="pp.highly_variable_genes">Annotate (and filter) highly variable genes, using 'pp.highly_variable_genes'</option>
                <option value="pp.subsample">Subsample to a fraction of the number of observations, using 'pp.subsample'</option>
                <option value="pp.downsample_counts">Downsample counts from count matrix, using 'pp.downsample_counts'</option>
            </param>
            <when value="pp.filter_cells">
                <conditional name="filter">
                    <param argument="filter" type="select" label="Filter">
                        <option value="min_counts">Minimum number of counts</option>

                <param argument="counts_per_cell" type="integer" min="0" optional="true" label="Target total counts per cell" help="If a cell has more than ‘counts_per_cell’, it will be downsampled to this number. Resulting counts can be specified on a per cell basis by passing an array."/>
                <param argument="total_counts" type="integer" min="0" optional="true" label="Target total counts" help="If the count matrix has more than total_counts it will be downsampled to have this number."/>
                <param argument="random_state" type="integer" value="0" label="Random seed to change subsampling"/>
                <param argument="replace" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Sample the counts with replacement?"/>
            </when>
        </conditional>
        <expand macro="inputs_common_advanced"/>
    </inputs>
    <outputs>
        <expand macro="anndata_outputs"/>
    </outputs>
    <tests>
        <test expect_num_outputs="2">
            <!-- test 1 -->
            <param name="adata" value="krumsiek11.h5ad" />

                    <has_text_matching expression="replace=False"/>
                </assert_contents>
            </output>
            <output name="anndata_out" file="pp.downsample_counts.random-randint.h5ad" ftype="h5ad" compare="sim_size" delta="10000000" delta_frac="0.5"/>
        </test>
    </tests>
    <help><![CDATA[

Filter cells outliers based on counts and numbers of genes expressed (`pp.filter_cells`)
========================================================================================

==========================================

Downsample counts so that each cell has no more than `target_counts`. Cells with fewer counts than `target_counts` are unaffected by this. This
has been implemented by M. D. Luecken.

More details on the `scanpy documentation
<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.downsample_counts.html>`__


    ]]></help>

15:aa0059118fb9

    total_counts=$method.total_counts,
    #end if
    random_state=$method.random_state,
    replace=$method.replace,
    copy=False)

#else if $method.method == "filter_marker"

#if $method.layer_selection.use_raw == 'False':
    adata.X = adata.layers['$method.layer_selection.layer']
#end if

def check_marker(adata, group, gene, thresh_mean, thresh_frac, groupby):
    filtered_data = adata[adata.obs[groupby] == group, adata.var_names == gene]
    mean_expression = np.mean(filtered_data.X)
    frac_cell_mean_expression = len(filtered_data.X[filtered_data.X > mean_expression]) / filtered_data.n_obs
    if ( mean_expression > thresh_mean and frac_cell_mean_expression >= thresh_frac ):
        return(True)
    return(False)

header='infer'

#if $method.header == 'not_included':
    header=None
#end if

marker_list={key: list(value.values()) for key, value in pd.read_csv('$method.markerfile', sep='\t', index_col=0, header=header).to_dict(orient='index').items()}

for key, value in marker_list.items():
    marker_list[key] = [x for x in value if check_marker(adata, key, x, $method.thresh_mean, $method.thresh_frac, '$method.groupby')]

# Find the maximum length of lists
max_len = max(len(lst) for lst in marker_list.values())

# Fill smaller lists with empty values
for key, value in marker_list.items():
    marker_list[key] = value + [''] * (max_len - len(value))

df = pd.DataFrame(marker_list).T
df.to_csv('marker.tsv', sep='\t', index=True)
#end if

@CMD_anndata_write_outputs@
]]></configfile>
    </configfiles>

                <option value="pp.filter_genes">Filter genes based on number of cells or counts, using 'pp.filter_genes'</option>
                <option value="tl.filter_rank_genes_groups">Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories, using 'tl.filter_rank_genes_groups'</option>
                <option value="pp.highly_variable_genes">Annotate (and filter) highly variable genes, using 'pp.highly_variable_genes'</option>
                <option value="pp.subsample">Subsample to a fraction of the number of observations, using 'pp.subsample'</option>
                <option value="pp.downsample_counts">Downsample counts from count matrix, using 'pp.downsample_counts'</option>
                <option value="filter_marker">Filter markers from count matrix and marker list</option>
            </param>
            <when value="pp.filter_cells">
                <conditional name="filter">
                    <param argument="filter" type="select" label="Filter">
                        <option value="min_counts">Minimum number of counts</option>

                <param argument="counts_per_cell" type="integer" min="0" optional="true" label="Target total counts per cell" help="If a cell has more than ‘counts_per_cell’, it will be downsampled to this number. Resulting counts can be specified on a per cell basis by passing an array."/>
                <param argument="total_counts" type="integer" min="0" optional="true" label="Target total counts" help="If the count matrix has more than total_counts it will be downsampled to have this number."/>
                <param argument="random_state" type="integer" value="0" label="Random seed to change subsampling"/>
                <param argument="replace" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Sample the counts with replacement?"/>
            </when>
            <when value="filter_marker">
                <param argument="markerfile" type="data" format="tabular" label="List of markers" help="This should be a tsv where row = group (e.g. celltypes) and columns = markers."></param>
                <param name="header" type="select" label="Header in the list of markers?">
                    <option value="included">Header incldued</option>
                    <option value="not_included">Header not included</option>
                </param>
                <param argument="thresh_mean" type="float" min="0.0" value="1.0" label="Minimal average count of all cells of a group (e.g., celltype) for a particular marker" help="Increasing the threshold will result in a smaller marker set."/>
                <param argument="thresh_frac" type="float" min="0.0" max="1.0" value="0.1" label="Minimal fractions of cells that has a higher count than the average count of all cells of the group for the marker" help="Increasing this threshold might remove marker outliers."/>
                <conditional name="layer_selection">
                    <param name="use_raw" type="select" label="Use .X of adata to perform the filtering" help="">
                        <option value="True">Yes</option>
                        <option value="False">No</option>
                    </param>
                    <when value="False">
                        <param argument="layer" type="text" value="" label="Key from adata.layers whose value will be used to filter" help="If layers specified then use adata.layers[layer]."/>
                    </when>
                    <when value="True"/>
                </conditional>
                <param argument="groupby" type="text" value="" label="The key of the observation grouping to consider (e.g., celltype)" help="">
                    <expand macro="sanitize_query" />
                </param>
            </when>
        </conditional>
        <expand macro="inputs_common_advanced"/>
    </inputs>
    <outputs>
        <expand macro="anndata_outputs"/>
        <data name="marker_out" format="tabular" from_work_dir="marker.tsv" label="${tool.name} on ${on_string}: Markers">
            <filter>method['method'] == 'filter_marker'</filter>
        </data>
    </outputs>
    <tests>
        <test expect_num_outputs="2">
            <!-- test 1 -->
            <param name="adata" value="krumsiek11.h5ad" />

                    <has_text_matching expression="replace=False"/>
                </assert_contents>
            </output>
            <output name="anndata_out" file="pp.downsample_counts.random-randint.h5ad" ftype="h5ad" compare="sim_size" delta="10000000" delta_frac="0.5"/>
        </test>
        <test expect_num_outputs="3">
            <!-- test 10 -->
            <param name="adata" value="cosg.rank_genes_groups.newton-cg.pbmc68k_highly_reduced_1.h5ad" />
            <conditional name="method">
                <param name="method" value="filter_marker"/>
                <param name="markerfile" value="tl.rank_genes_groups.newton-cg.pbmc68k_highly_reduced_marker_1.tsv"/>
                <param name="thresh_mean" value="1.0"/>
                <param name="thresh_frac" value="0.2"/>
                <param name="layer_selection" value="True"/>
                <param name="groupby" value="bulk_labels"/>
            </conditional>
            <section name="advanced_common">
                <param name="show_log" value="true" />
            </section>
            <output name="hidden_output">
                <assert_contents>
                    <has_text_matching expression="adata, key, x, 1.0, 0.2, 'bulk_labels'"/>
                </assert_contents>
            </output>
            <output name="anndata_out" file="cosg.rank_genes_groups.newton-cg.pbmc68k_highly_reduced_1.h5ad" ftype="h5ad">
                <assert_contents>
                    <has_h5_keys keys="obs, var, uns" />
                </assert_contents>
            </output>
            <output name="marker_out" file="tl.rank_genes_groups.newton-cg.pbmc68k_highly_reduced_marker_filtered_1.tsv" ftype="tabular" compare="sim_size"/>
        </test>
    </tests>
    <help><![CDATA[

Filter cells outliers based on counts and numbers of genes expressed (`pp.filter_cells`)
========================================================================================

==========================================

Downsample counts so that each cell has no more than `target_counts`. Cells with fewer counts than `target_counts` are unaffected by this. This
has been implemented by M. D. Luecken.


Filter marker genes (`filter_marker`)
======================================================================

This option is specific for celltype marker gene detection. You can generate a celltype marker gene file (tsv) with **COSG** provided at Galaxy.

The marker gene file should have as rows celltypes and columns as marker genes. Each celltype can have varying number of marker genes.

A marker gene is returned (retained in the list) if the mean expression of the marker gene is bigger than the threshold of mean expression (thresh_mean) and if the fraction of cells with the marker gene expression is equal or higher than the cell fraction threshold (thresh_frac).

More details on the `scanpy documentation
<https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.downsample_counts.html>`__


    ]]></help>