annotate execute_dwt_cor_aVa_perClass.xml @ 2:b87bbe6bc044 draft default tip

"planemo upload for repository https://github.com/galaxyproject/tools-devteam/tree/master/tools/dwt_cor_ava_perclass commit f929353ffb0623f2218d7dec459c7da62f3b0d24"

<tool id="compute_p-values_correlation_coefficients_feature_occurrences_between_two_datasets_using_discrete_wavelet_transfom" name="Compute P-values and Correlation Coefficients for Feature Occurrences" version="1.0.1">
  <description>between two datasets using Discrete Wavelet Transfoms</description>
  <requirements>
    <requirement type="package" version="1.7.5">r-waveslim</requirement>
    <requirement type="package" version="4.6.8">r-wavethresh</requirement>
  </requirements>
  <command detect_errors="exit_code">
    Rscript --vanilla '$__tool_directory__/execute_dwt_cor_aVa_perClass.R'
      '$inputFile1'
      '$inputFile2'
      '$outputFile2'
      '$outputFile1'
  </command>
  <inputs>
    <param format="tabular" name="inputFile1" type="data" label="Select the first input file"/>
  <param format="tabular" name="inputFile2" type="data" label="Select the second input file"/>
  </inputs>
  <outputs>
    <data format="tabular" name="outputFile1" label="${tool.name} on ${on_string}: statistics"/>
    <data format="pdf" name="outputFile2" label="${tool.name} on ${on_string}: pdf"/>
  </outputs>
  <tests>
    <test>
      <param ftype="tabular" name="inputFile1" value="in1.tsv"/>
      <param ftype="tabular" name="inputFile2" value="in2.tsv"/>
      <output name="outputFile1" ftype="tabular">
        <assert_contents><has_line_matching expression="^translinTarget.*"/></assert_contents>
        <assert_contents><has_line_matching expression="^deletionHoptspot.*" /></assert_contents>
      </output>
      <output name="outputFile2" ftype="pdf" file="out2.pdf" compare="sim_size"/>
    </test>
  </tests>
  <help>
.. class:: infomark

**What it does**

This program generates plots and computes table matrix of coefficient correlations and p-values at multiple scales for the correlation between the occurrences of features in one dataset and their occurrences in another using multiscale wavelet analysis technique.

The program assumes that the user has two sets of DNA sequences, S1 and S1, each of which consists of one or more sequences of equal length. Each sequence in each set is divided into the same number of multiple intervals n such that n = 2^k, where k is a positive integer and  k >= 1. Thus, n could be any value of the set {2, 4, 8, 16, 32, 64, 128, ...}. k represents the number of scales.

The program has two input files obtained as follows:

For a given set of features, say motifs, the user counts the number of occurrences of each feature in each interval of each sequence in S1 and S1, and builds two tabular files representing the count results in each interval of S1 and S1. These are the input files of the program.

The program gives two output files:

- The first output file is a TABULAR format file representing the coefficient correlations and p-values for each feature at each scale.
- The second output file is a PDF file consisting of as many figures as the number of features, such that each figure represents the values of the coefficient correlation for that feature at every scale.

-----

.. class:: warningmark

**Note**

In order to obtain empirical p-values, a random perumtation test is implemented by the program, which results in the fact that the program gives slightly different results each time it is run on the same input file.

-----

**Example**

Counting the occurrences of 5 features (motifs) in 16 intervals (one line per interval) of the DNA sequences in S1 gives the following tabular file::

	deletionHoptspot	insertionHoptspot	dnaPolPauseFrameshift	topoisomeraseCleavageSite	translinTarget	
		269			366			330			238				1129
		239			328			327			283				1188
		254			351			358			297				1151
		262			371			355			256				1107
		254			361			352			234				1192
		265			354			367			240				1182
		255			359			333			235				1217
		271			389			387			272				1241
		240			305			341			249				1159
		272			351			337			257				1169
		275			351			337			233				1158
		305			331			361			253				1172
		277			341			343			253				1113
		266			362			355			267				1162
		235			326			329			241				1230
		254			335			360			251				1172

And counting the occurrences of 5 features (motifs) in 16 intervals (one line per interval) of the DNA sequences in S2 gives the following tabular file::

	deletionHoptspot	insertionHoptspot	dnaPolPauseFrameshift	topoisomeraseCleavageSite	translinTarget
		104			146			142			113				478
		89			146			151			94				495
		100			176			151			88				435
		96			163			128			114				468
		99			138			144			91				513
		112			126			162			106				468
		86			127			145			83				491
		104			145			171			110				496
		91			121			147			104				469
		103			141			145			98				458
		92			134			142			117				468
		97			146			145			107				471
		115			121			136			109				470
		113			135			138			101				491
		111			150			138			102				451
		94			128			151			138				481


We notice that the number of scales here is 4 because 16 = 2^4. Running the program on the above input files gives the following output:

The first output file::

	motif				1_cor		1_pval		2_cor		2_pval		3_cor		3_pval		4_cor		4_pval
	
	deletionHoptspot		0.4		0.072		0.143		0.394		-0.667		0.244		1		0.491
	insertionHoptspot		0.343		0.082		-0.0714		0.446		-1		0.12		1		0.502
	dnaPolPauseFrameshift		0.617		0.004		-0.5		0.13		0.667		0.234		1		0.506
	topoisomeraseCleavageSite	-0.183		0.242		-0.286		0.256		0.333		0.353		-1		0.489
	translinTarget			0.0167		0.503		-0.0714		0.469		1		0.136		1		0.485

The second output file:

.. image:: dwt_cor_aVa_1.png
.. image:: dwt_cor_aVa_2.png
.. image:: dwt_cor_aVa_3.png
.. image:: dwt_cor_aVa_4.png
.. image:: dwt_cor_aVa_5.png

  </help>

</tool>