Publications, Errata Corrige and Notes
Note: A Java version of PaGE is available now. Please check it out.
What is PaGE?
PaGE is free downloadable software for microarray analisys. PaGE can be used to produce sets of differentially expressed genes with confidence measures attached. These lists are generated the False Discovery Rate method of controlling the false positives.
But PaGE is more than a differential expression analysis tool. PaGE is a tool to attach descriptive , dependable, and easily interpretable expression patterns to genes across multiple conditions, each represented by a set of replicated array experiments.
The input consists of (replicated) intensities from a collection of array experiments from two or more conditions (or from a collection of direct comparisons on 2-channel arrays). The output consists of patterns, one for each row identifier in the data file.
One condition is used as a reference to which the other types are compared. The length of a pattern equals the number of non-reference sample types. The symbols in the patterns are integers, where positive integers represent up-regulation as compared to the reference sample type and negative integers represent down-regulation.
The patterns are based on the false discovery rates for each position in the pattern, so that the number of positive and negative symbols that appear in each position of the pattern is as descriptive as the data variability allows.
The patterns generated are easily interpretable in that integers are used to represent different levels of up- or down-regulation as compared to the reference sample type.
To illustrate this, the following table gives an excerpt of
data for four of the gene tags in a given of hybridization experiment
and four sample types. There are three replicates for sample types
G0 and G2 and two replicates for sample types G1
and G3. As they are these data are hard to peruse for information.
| gene tag | G0 I | G0 II | G0 III | G1 I | G1 II | G2 I | G2 II | G2 III | G3 I | G3 II |
| 1 | 0.0114 | 0.0328 | 0.0151 | 0.0060 | 0.0236 | 0.0436 | 0.5640 | 0.8920 | 0.0639 | 0.2490 |
| 2 | 0.0050 | 0.0131 | 0.0061 | 0.0041 | 0.0364 | 0.0296 | 0.8830 | 0.7000 | 0.0199 | 0.1050 |
| 3 | 0.0629 | 0.2340 | 0.0431 | 0.2270 | 0.2120 | 0.0105 | 0.1400 | 0.0243 | 0.0117 | 0.0907 |
| 4 | 0.0250 | 0.0600 | 0.0264 | 0.1500 | 0.2660 | 0.0134 | 0.1860 | 0.0851 | 0.0172 | 0.0112 |
| gene tag | G1 | G2 | G3 |
| 1 | 0 | 7 | 2 |
| 2 | 0 | 8 | 1 |
| 3 | 2 | -1 | -1 |
| 4 | 3 | 1 | 0 |
this is an easily interpretable set of patterns. For example gene tag 3 is detected as up-regulated 2 levels in sample type G1 and down-regulated one level in sample types G2 and G3, as compared to sample type G0.
Publications, Errata Corrige and Notes
* Grant G.R., Liu J., Stoeckert C.J.Jr. (2005) A practical false discovery rate approach to identifying patterns of differential expression in microarray data, Bioinformatics, Vol 21 no 11, 2684-2690.
| Note: There is a typo in this publication on page 2686 in formula for muk(i+1) (the second to last displayed forumla on the page). On the right hand side, the first muk(i) should be mu-tildek(1) (i replaced by 1). |
* Grant G.R., Liu J., Stoeckert C.J.Jr. The technical manual for PaGE 5.1.
* Grant G.R., Manduchi E., Stoeckert C.J. Jr. Using non-parametric methods in the context of multiple testing to identify differentially expressed genes. Methods of microarray data analysis, editors S.M. Lin and K.F. Johnson, Kluwer Academic Publishers (Boston, 2002): 37-55. (Winner of the best presentation award CAMDA'00).
* Manduchi E., Grant G.R., McKenzie S.E., Overton G.C., Surrey S., Stoeckert C.J. Jr. (2000) Generation of patterns from gene expression data by assigning confidence to differentially expressed genes, Bioinformatics, 16(8): 685-698.
Errata Corrige and Notes to the above original paper
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To download the perl version of the PaGE 5.1 software click here. This is the latest stable release of PaGE and presents several improvements as compared to the previous release (4.0). Besides bug fixes, it offers a more informative and richer output and at the same time it is easier to use as it has less mandatory options. |
If you have questions on the program or its usage or if you want to report any bugs, please contact: [email protected].
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Make sure you have the clang version of Python and not the apple's version which is compiled with llvm-gcc.
brew install pythonFor more information, please see http://pragmaticstartup.wordpress.com/2012/12/25/error-command-llvm-gcc-4-2-failed-with-exit-status-1/
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Install command line tools with the latest version of Xcode. You can download the Xcode here. To install the command line tools:
- Start Xcode on the Mac.
- Choose Preferences from the Xcode menu.
- In the General panel, click Downloads.
- On the Downloads window, choose the Components tab.
- Click the Install button next to Command Line Tools.
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Install pip.
sudo easy_install pip -
Install required dependencies.
Install gfortran:
brew install gfortranInstall numpy, scipy, yaml and setuptools:
pip install numpy sudo pip install scipy sudo pip install pyyaml pip install setuptoolsInstall hdf5:
brew install hdf5(if that doesn't work, try: )
brew tap homebrew/science brew install hdf5Then, you can install h5py:
sudo pip install h5pyInstall redis-server and libpng:
brew install redis brew install libpngInstall freetype. You can download it here. Then:
cd path/to/freetype-2.5.2 ./configure make make install sudo ln -s /usr/local/include/freetype2/ /usr/include/freetypeFinally, install matplotlib:
sudo pip install matplotlib -
Git clone and Install PADE
git clone [email protected]:itmat/pade.git cd pade/ sudo python setup.py install