comparison plotFingerprint.xml @ 10:314348664620 draft

planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 0eeb2e1a184186ba5ac044136e71b943e2af0f09

9:ef2bcce0be62

            #if $output.showOutputSettings == "yes"
                --plotFileFormat $output.outFileFormat
                #if $output.saveRawCounts:
                    --outRawCounts '$outFileRawCounts'
                #end if
            #else
                --plotFileFormat 'png'
            #end if

            #if str($region).strip() != '':

                --binSize '$advancedOpt.binSize'
                --numberOfSamples '$advancedOpt.numberOfSamples'

                $advancedOpt.ignoreDuplicates
                $advancedOpt.skipZeros

                #if $advancedOpt.minMappingQuality:
                --minMappingQuality '$advancedOpt.minMappingQuality'
                #end if

                #if $advancedOpt.plotTitle and str($advancedOpt.plotTitle.value) != "":
                    --plotTitle '$advancedOpt.plotTitle'
                #end if
                @ADVANCED_OPTS_READ_PROCESSING@

            </param>
            <when value="no" />
            <when value="yes">
                <expand macro="input_image_file_format" />
                <param name="saveRawCounts" type="boolean" label="Save the bin counts" help="(--outRawCounts)"/>
            </when>
        </conditional>
    </inputs>
    <outputs>
        <expand macro="output_image_file_format" />
        <data format="tabular" name="outFileRawCounts" label="${tool.name} on ${on_string}: bin counts">
            <filter>
            ((
                output['showOutputSettings'] == 'yes' and
                output['saveRawCounts'] is True
            ))
            </filter>
        </data>
    </outputs>
    <tests>

        <test>
            <param name="bamfiles" value="bowtie2 test1.bam,bowtie2 test1.bam" ftype="bam" />
            <param name="showAdvancedOpt" value="yes" />
            <param name="showOutputSettings" value="yes" />
            <param name="saveRawCounts" value="True" />
            <param name="plotTitle" value="Test Fingerprint Plot" />
            <output name="outFileName" file="plotFingerprint_result2.png" ftype="png" compare="sim_size" />
            <output name="outFileRawCounts" file="plotFingerprint_result2.tabular" ftype="tabular" />
        </test>
    </tests>
    <help>
<![CDATA[

Output
------

The default output is a diagnostic plot (see below for an example and further down for some background information).

Optionally, you can obtain the table of raw counts that was used to generate the plot.

.. image:: $PATH_TO_IMAGES/plotFingerprint_output.png
  :width: 600
  :height: 395

-----

Theoretical Background
----------------------

The tool first samples indexed BAM files and counts all reads overlapping a window (bin) of the specified length.
These counts are then sorted according to their rank (the bin with the highest number of reads has the highest rank)
and the cumulative sum of read counts are plotted. An ideal input (control) with a uniform distribution of reads alignments
result in a diagonal line. A very specific and strong ChIP enrichment, on the other hand, would result in a large portion
of reads accumulating in just a few bins and the resulting plot showing a steep rise toward the right-most edge. Such results are
most commonly seen with transcription factors.


.. image:: $PATH_TO_IMAGES/QC_fingerprint.png

10:314348664620

            #if $output.showOutputSettings == "yes"
                --plotFileFormat $output.outFileFormat
                #if $output.saveRawCounts:
                    --outRawCounts '$outFileRawCounts'
                #end if
                #if $output.saveQualityMetrics:
                    --outQualityMetrics '$outFileQualityMetrics'
                    #if $output.JSDsample:
                        #if "'{}'".format($output.JSDsample.display_name) in $labels:
                            #set JSDidx = $labels.index("'{}'".format($output.JSDsample.display_name))
                            --JSDsample "./${JSDidx}.bam"
                        #end if
                    #end if
                #end if
            #else
                --plotFileFormat 'png'
            #end if

            #if str($region).strip() != '':

                --binSize '$advancedOpt.binSize'
                --numberOfSamples '$advancedOpt.numberOfSamples'

                $advancedOpt.ignoreDuplicates
                $advancedOpt.skipZeros

                #if $advancedOpt.plotTitle and str($advancedOpt.plotTitle.value) != "":
                    --plotTitle '$advancedOpt.plotTitle'
                #end if
                @ADVANCED_OPTS_READ_PROCESSING@

            </param>
            <when value="no" />
            <when value="yes">
                <expand macro="input_image_file_format" />
                <param name="saveRawCounts" type="boolean" label="Save the bin counts" help="(--outRawCounts)"/>
                <param name="saveQualityMetrics" type="boolean" label="Save quality metrics"
                   help="The file will have one row per input BAM file and columns containing a variety of QC metrics. For the full description of all metrics and their interpretation, please see our online documentation: http://deeptools.readthedocs.io/en/latest/content/feature/plotFingerprint_QC_metrics.html. (--outQualityMetrics)"/>
                <param name="JSDsample" type="data" format="bam"
                   label="Jensen-Shannon distance reference sample"
                   help="If output metrics should be saved, then use this sample as the reference for computing the Jensen-Shannon distance and CHANCE metrics of all other samples. Normally this is an input sample. If you don't specify this, then these metrics will not be computed. This is only applicable if quality metrics are being saved. (--JSDsample)" />
            </when>
        </conditional>
    </inputs>
    <outputs>
        <expand macro="output_image_file_format" />
        <data format="tabular" name="outFileRawCounts" label="${tool.name} on ${on_string}: bin counts">
            <filter>
            ((
                output['showOutputSettings'] == 'yes' and
                output['saveRawCounts'] is True
            ))
            </filter>
        </data>
        <data format="tabular" name="outFileQualityMetrics" label="${tool.name} on ${on_string}: quality metrics">
            <filter>
            ((
                output['showOutputSettings'] == 'yes' and
                output['saveQualityMetrics'] is True
            ))
            </filter>
        </data>
    </outputs>
    <tests>

        <test>
            <param name="bamfiles" value="bowtie2 test1.bam,bowtie2 test1.bam" ftype="bam" />
            <param name="showAdvancedOpt" value="yes" />
            <param name="showOutputSettings" value="yes" />
            <param name="saveRawCounts" value="True" />
            <param name="saveQualityMetrics" value="True" />
            <param name="plotTitle" value="Test Fingerprint Plot" />
            <param name="JSDsample" value="bowtie2 test1.bam" ftype="bam" />
            <output name="outFileName" file="plotFingerprint_result2.png" ftype="png" compare="sim_size" />
            <output name="outFileRawCounts" file="plotFingerprint_result2.tabular" ftype="tabular" />
            <output name="outFileQualityMetrics" file="plotFingerprint_quality_metrics.tabular" ftype="tabular" />
        </test>
    </tests>
    <help>
<![CDATA[

Output
------

The default output is a diagnostic plot (see below for an example and further down for some background information).

Optionally, you can obtain the table of raw values that were used to generate the plot.

.. image:: $PATH_TO_IMAGES/plotFingerprint_output.png
  :width: 600
  :height: 395

-----

Theoretical Background
----------------------

The tool first samples indexed BAM files and sums the per-base coverage of reads/fragments overlapping a window (bin) of the specified length.
These values are then sorted according to their rank (the bin with the highest number of reads has the highest rank)
and the cumulative sum plotted. An ideal input (control) with a uniform distribution of reads alignments
and infinite sequencing depth will result in a diagonal line. A very specific and strong ChIP enrichment, on the other hand, would result in a large portion
of reads accumulating in just a few bins and the resulting plot showing a steep rise toward the right-most edge. Such results are
most commonly seen with transcription factors.


.. image:: $PATH_TO_IMAGES/QC_fingerprint.png