comparison edgeR_Differential_Gene_Expression.xml @ 36:185effd01633 draft

Uploaded

35:fab87cecb3e5

		</data>
	</outputs>
	
	<help>
edgeR: Differential Gene(Expression) Analysis

**Overview**

Differential expression analysis of RNA-seq and digital gene expression profiles with biological replication. Uses empirical Bayes estimation and exact tests based on the negative binomial distribution. Also useful for differential signal analysis with other types of genome-scale count data.
Author: Mark Robinson, Davis McCarthy, Yunshun Chen, Aaron Lun &amp; Gordon Smyth

http://www.bioconductor.org/packages/2.12/bioc/html/edgeR.html
http://dx.doi.org/10.1093/bioinformatics/btp616

For every experiment, the algorithm requires a design matrix. This matrix describes which samples belong to which groups.
More details on this are given in the edgeR manual:
http://www.bioconductor.org/packages/2.12/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
and the limma manual.

Because the creation of a design matrix can be complex and time consuming, especially if no GUI is used, this package comes with an alternative tool which can help you with it.
This tool is called *edgeR Design Matrix Creator*.
If the appropriate design matrix (with corresponding links to the files) is given,
the correct contrast ( http://en.wikipedia.org/wiki/Contrast_(statistics) ) has to be given.

If you have for example two groups, with an equal weight, you would like to compare either
"g1~g2" or "normal~cancer".

** Input **

Expression matrix::

  Geneid  "\t" Sample-1 "\t" Sample-2 "\t" Sample-3 "\t" Sample-4 [...] "\n"
  SMURF   "\t"      123 "\t"       21 "\t"    34545 "\t"       98  ...  "\n"
  BRCA1   "\t"      435 "\t"     6655 "\t"       45 "\t"       55  ...  "\n"
  LINK33  "\t"        4 "\t"      645 "\t"      345 "\t"        1  ...  "\n"
  SNORD78 "\t"      498 "\t"       65 "\t"       98 "\t"       27  ...  "\n"
  [...]

Note: Make sure the number of columns in the header is identical to the number of columns in the body.

Design matrix::

  Sample    "\t" Condition "\t" Ethnicity "\t" Patient "\t" Batch "\n"
  Sample-1  "\t"     Tumor "\t"  European "\t"       1 "\t"     1 "\n"
  Sample-2  "\t"    Normal "\t"  European "\t"       1 "\t"     1 "\n"
  Sample-3  "\t"     Tumor "\t"  European "\t"       2 "\t"     1 "\n"

  Sample-9  "\t"     Tumor "\t"     Asian "\t"       5 "\t"     2 "\n"
  Sample-10 "\t"    Normal "\t"     Asian "\t"       5 "\t"     2 "\n"
  Sample-11 "\t"     Tumor "\t"     Asian "\t"       6 "\t"     2 "\n"
  Sample-12 "\t"    Normal "\t"     Asian "\t"       6 "\t"     2 "\n"

Note: Avoid factor names that are (1) numerical, (2) contain mathematical symbols and preferebly only use letters.

Contrast::

  Tumor-Normal
  African-European
  0.5*(Control+Placebo) / Treated


**Installation**

This tool requires no specific configurations. The following dependencies are installed automatically:
 * R
 * Bioconductor
   - limma
   - edgeR

**License**
 - R - GPL-2 & GPL-3
 - limma - GPL (>=2)
 - edgeR - GPL (>=2)

**Contact**

The tool wrapper has been written by Youri Hoogstrate from the Erasmus Medical Center (Rotterdam, Netherlands) on behalf of the Translational Research IT (TraIT) project:
http://www.ctmm.nl/en/programmas/infrastructuren/traitprojecttranslationeleresearch

More tools by the Translational Research IT (TraIT) project can be found in the following repository:
http://toolshed.nbic.nl/

**References**

The test data is coming from:  doi: 10.1093/bioinformatics/btt688.
http://www.ncbi.nlm.nih.gov/pubmed/24319002

	</help>
</tool>

36:185effd01633

		</data>
	</outputs>
	
	<help>
edgeR: Differential Gene(Expression) Analysis
#############################################

Overview
--------
Differential expression analysis of RNA-seq and digital gene expression profiles with biological replication. Uses empirical Bayes estimation and exact tests based on the negative binomial distribution. Also useful for differential signal analysis with other types of genome-scale count data [1].

For every experiment, the algorithm requires a design matrix. This matrix describes which samples belong to which groups.
More details on this are given in the edgeR manual: http://www.bioconductor.org/packages/2.12/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
and the limma manual.

Because the creation of a design matrix can be complex and time consuming, especially if no GUI is used, this package comes with an alternative tool which can help you with it.
This tool is called *edgeR Design Matrix Creator*.
If the appropriate design matrix (with corresponding links to the files) is given,
the correct contrast ( http://en.wikipedia.org/wiki/Contrast_(statistics) ) has to be given.

If you have for example two groups, with an equal weight, you would like to compare either
"g1~g2" or "normal~cancer".

The test function makes use of a MCF7 dataset used in a study that indicates that a higher sequencing depth is not neccesairily more important than a higher amount of replaciates[2].

Input
-----
Expression matrix
^^^^^^^^^^^^^^^^^
::

  Geneid  "\t" Sample-1 "\t" Sample-2 "\t" Sample-3 "\t" Sample-4 [...] "\n"
  SMURF   "\t"      123 "\t"       21 "\t"    34545 "\t"       98  ...  "\n"
  BRCA1   "\t"      435 "\t"     6655 "\t"       45 "\t"       55  ...  "\n"
  LINK33  "\t"        4 "\t"      645 "\t"      345 "\t"        1  ...  "\n"
  SNORD78 "\t"      498 "\t"       65 "\t"       98 "\t"       27  ...  "\n"
  [...]

*Note: Make sure the number of columns in the header is identical to the number of columns in the body.*

Design matrix
^^^^^^^^^^^^^
::

  Sample    "\t" Condition "\t" Ethnicity "\t" Patient "\t" Batch "\n"
  Sample-1  "\t"     Tumor "\t"  European "\t"       1 "\t"     1 "\n"
  Sample-2  "\t"    Normal "\t"  European "\t"       1 "\t"     1 "\n"
  Sample-3  "\t"     Tumor "\t"  European "\t"       2 "\t"     1 "\n"

  Sample-9  "\t"     Tumor "\t"     Asian "\t"       5 "\t"     2 "\n"
  Sample-10 "\t"    Normal "\t"     Asian "\t"       5 "\t"     2 "\n"
  Sample-11 "\t"     Tumor "\t"     Asian "\t"       6 "\t"     2 "\n"
  Sample-12 "\t"    Normal "\t"     Asian "\t"       6 "\t"     2 "\n"

*Note: Avoid factor names that are (1) numerical, (2) contain mathematical symbols and preferebly only use letters.*

Contrast
^^^^^^^^
The contrast represents the biological question. There can be many questions asked, e.g.:

- Tumor-Normal
- African-European
- 0.5*(Control+Placebo) / Treated

Installation
------------

This tool requires no specific configurations. The following dependencies are installed automatically:

- R
- Bioconductor
   - limma

   - edgeR

License
-------
- R
   - GPL-2 & GPL-3
- limma
    - GPL (>=2)
- edgeR
     - GPL (>=2)

References
----------

EdgeR
^^^^^
**[1] edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.**

*Mark D. Robinson, Davis J. McCarthy and Gordon K. Smyth* - Bioinformatics (2010) 26 (1): 139-140.

- http://www.bioconductor.org/packages/2.12/bioc/html/edgeR.html
- http://dx.doi.org/10.1093/bioinformatics/btp616
- http://www.bioconductor.org/packages/release/bioc/html/edgeR.html

Test-data (MCF7)
^^^^^^^^^^^^^^^^
**[2] RNA-seq differential expression studies: more sequence or more replication?**

*Yuwen Liu, Jie Zhou and Kevin P. White* - Bioinformatics (2014) 30 (3): 301-304.

- http://www.ncbi.nlm.nih.gov/pubmed/24319002
- http://dx.doi.org/10.1093/bioinformatics/btt688

Contact
-------
The tool wrapper has been written by Youri Hoogstrate from the Erasmus Medical Center (Rotterdam, Netherlands) on behalf of the Translational Research IT (TraIT) project:
http://www.ctmm.nl/en/programmas/infrastructuren/traitprojecttranslationeleresearch

I would like to thank Hina Riaz - Naz Khan for her helpful contribution.

More tools by the Translational Research IT (TraIT) project can be found in the following repository:
http://testtoolshed.g2.bx.psu.edu/
	</help>
</tool>