Patterns from
Gene
Expression
- What is PaGE?
- Publications, Errata Corrige and Notes
- Download the software
- View the HTML user guides
- View the pdf technical manual
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.
If G0 is used as a reference sample type, the
patterns attached by PaGE to these tags might look like
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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.
* 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
If you have questions on the program or its usage or if you want to report any bugs, please contact: ggrant@pcbi.upenn.edu.
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).
Last updated: September
30, 2004
If you have questions or comments
contact: ggrant@pcbi.upenn.edu