Mercurial > repos > bgruening > deeptools_plot_pca

comparison plotPCA.xml @ 16:179b2441ae0b draft

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planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 2e8510e4f4015f51f7726de5697ba2de9b4e2f4c
author bgruening
date Wed, 09 Mar 2016 17:35:03 -0500
parents e8b8434f7125
children d209f92da14b
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15:e8b8434f7125 16:179b2441ae0b
10 @BINARY@ 10 @BINARY@
11 --corData "$corData" 11 --corData "$corData"
12 --plotTitle "$plotTitle" 12 --plotTitle "$plotTitle"
13 --plotFile "$outFileName" 13 --plotFile "$outFileName"
14 --plotFileFormat "$outFileFormat" 14 --plotFileFormat "$outFileFormat"
15 #if $outFileNameData
16 --outFileNameData "$output_outFileNameData"
17 #end if
15 ]]> 18 ]]>
16 </command> 19 </command>
17 <inputs> 20 <inputs>
18 <param name="corData" format="deeptools_coverage_matrix" type="data" label="Matrix file from the multiBamSummary or multiBigwigSummary tools"/> 21 <param name="corData" format="deeptools_coverage_matrix" type="data" label="Matrix file from the multiBamSummary or multiBigwigSummary tools"/>
19 <expand macro="input_image_file_format" /> 22 <expand macro="input_image_file_format" />
20 <expand macro="plotTitle" /> 23 <expand macro="plotTitle" />
24 <param argument="--outFileNameData" type="boolean" label="Save the matrix of PCA and eigenvalues underlying the plot."/>
21 </inputs> 25 </inputs>
22 <outputs> 26 <outputs>
23 <expand macro="output_image_file_format_not_nested" /> 27 <expand macro="output_image_file_format_not_nested" />
28 <data format="tabular" name="output_outFileNameData" label="${tool.name} on ${on_string}: PCA matrix">
29 <filter>outFileNameData</filter>
30 </data>
24 </outputs> 31 </outputs>
25 <tests> 32 <tests>
26 <test> 33 <test>
27 <param name="corData" value="multiBamSummary_result2.npz" ftype="deeptools_coverage_matrix" /> 34 <param name="corData" value="multiBamSummary_result2.npz" ftype="deeptools_coverage_matrix" />
28 <param name="plotTitle" value="Test Plot" /> 35 <param name="plotTitle" value="Test Plot" />
29 <param name="outFileFormat" value="png" /> 36 <param name="outFileFormat" value="png" />
30 <output name="outFileName" file="plotPCA_result1.png" ftpye="png" compare="sim_size" delta="4000" /> 37 <output name="outFileName" file="plotPCA_result1.png" ftpye="png" compare="sim_size" delta="4000" />
31 </test> 38 </test>
39 <test>
40 <param name="corData" value="multiBamSummary_result2.npz" ftype="deeptools_coverage_matrix" />
41 <param name="plotTitle" value="Test Plot" />
42 <param name="outFileFormat" value="png" />
43 <param name="outFileNameData" value="True" />
44 <output name="outFileName" file="plotPCA_result2.png" ftpye="png" compare="sim_size" delta="4000" />
45 <output name="output_outFileNameData" file="plotPCA_result2.tabular" ftype="tabular" />
46 </test>
32 </tests> 47 </tests>
33 <help> 48 <help>
34 <![CDATA[ 49 <![CDATA[
35 50
36 What it does 51 What it does
37 --------------- 52 ------------
38 53
39 This tool takes the **default output file of ``multiBamSummary``** or ``multiBigwigSummary`` to perform a principal component analysis (PCA). 54 This tool takes the **default output file** of ``multiBamSummary`` or ``multiBigwigSummary`` to perform a principal component analysis (PCA).
40 55
41 Output 56 Output
42 ------------- 57 ------
43 58
44 The result is a panel of two plots: 59 The result is a panel of two plots:
45 60
46 1. The eigenvalues of the **top two principal components**. 61 1. The eigenvalues of the **top two principal components**.
47 2. The **Scree plot** for the top five principal components where the bars represent the amount of variability explained by the individual factors and the red line traces the amount of variability is explained by the individual components in a cumulative manner 62 2. The **Scree plot** for the top five principal components where the bars represent the amount of variability explained by the individual factors and the red line traces the amount of variability is explained by the individual components in a cumulative manner
48 63
49 Example plot 64 Example plot
50 --------------- 65 ------------
51 66
52 .. image:: $PATH_TO_IMAGES/plotPCA_annotated.png 67 .. image:: $PATH_TO_IMAGES/plotPCA_annotated.png
53 :width: 600 68 :width: 600
54 :height: 315 69 :height: 315
55 70
56 =================== 71 -----
57 72
58 Background 73 Theoretical Background
59 ----------------- 74 ----------------------
60 75
61 Principal component analysis (PCA) can be used, for example, to determine whether **samples display greater variability** between experimental conditions than between replicates of the same treatment. PCA is also useful to identify unexpected patterns, such as those caused by batch effects or outliers. 76 Principal component analysis (PCA) can be used, for example, to determine whether **samples display greater variability** between experimental conditions than between replicates of the same treatment. PCA is also useful to identify unexpected patterns, such as those caused by batch effects or outliers.
62 Principal components represent the directions along which the variation in the data is maximal, so that the information (e.g., read coverage values) from thousands of regions can be represented by just a few dimensions. 77 Principal components represent the directions along which the variation in the data is maximal, so that the information (e.g., read coverage values) from thousands of regions can be represented by just a few dimensions.
63 78
64 PCA is not necessarily meant to identify unknown groupings or clustering; it is up to the researcher to determine the experimental or technical reason underlying the principal components. 79 PCA is not necessarily meant to identify unknown groupings or clustering; it is up to the researcher to determine the experimental or technical reason underlying the principal components.