<tool id="compute_p-values_correlation_coefficients_featureA_featureB_occurrences_between_two_datasets_using_discrete_wavelet_transfom" name="Compute P-values and Correlation Coefficients for Occurrences of Two Set of Features" version="1.0.0">
  <description>between two datasets using Discrete Wavelet Transfoms</description>
  
  <command interpreter="perl">
  	execute_dwt_cor_aVb_all.pl $inputFile1 $inputFile2 $outputFile1 $outputFile2
  </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"/> 
    <data format="pdf" name="outputFile2"/>
  </outputs>
  	
  <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 correlations 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	
		82			162			158			79				459
		111			196			154			75				459
		98			178			160			79				475
		113			201			170			113				436
		113			173			147			95				446
		107			150			155			84				436
		106			166			175			96				448
		113			176			135			106				514
		113			170			152			87				450
		95			152			167			93				467
		91			171			169			118				426
		84			139			160			100				459
		92			154			164			104				440
		100			145			154			98				472
		91			161			152			71				461
		117			164			139			97				463

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
		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

  
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::

	motif1				motif2				1_cor		1_pval		2_cor		2_pval		3_cor		3_pval		4_cor		4_pval
	
	deletionHoptspot		insertionHoptspot		-0.1		0.346		-0.214		0.338		1		0.127		1		0.467
	deletionHoptspot		dnaPolPauseFrameshift		0.167		0.267		-0.214		0.334		1		0.122		1		0.511
	deletionHoptspot		topoisomeraseCleavageSite	0.167		0.277		0.143		0.412		-0.667		0.243		1		0.521
	deletionHoptspot		translinTarget			0		0.505		0.0714		0.441		1		0.124		1		0.518
	insertionHoptspot		dnaPolPauseFrameshift		-0.202		0.238		0.143		0.379		-1		0.122		1		0.517
	insertionHoptspot		topoisomeraseCleavageSite	-0.0336		0.457		0.214		0.29		0.667		0.252		1		0.503
	insertionHoptspot		translinTarget			0.0672		0.389		0.429		0.186		-1		0.119		1		0.506
	dnaPolPauseFrameshift		topoisomeraseCleavageSite	-0.353		0.101		0.357		0.228		0		0.612		-1		0.49
	dnaPolPauseFrameshift		translinTarget			-0.151		0.303		-0.571		0.09		-0.333		0.37		-1		1
	topoisomeraseCleavageSite	translinTarget			-0.37		0.077		-0.222		0.297		0.667		0.234		-1		0.471

The second output file:

.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_1.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_2.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_3.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_4.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_5.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_6.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_7.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_8.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_9.png
.. image:: ${static_path}/operation_icons/dwt_cor_aVb_all_10.png


  </help>  
  
</tool>