tools relating to preparing data for Paradigm and post-processing Paradigm results Running PARADIGM via five3 Website
PARADIGM is a factor graph method for inferring biological pathway activity from gene expression and gene copy number sample data. More information about PARADIGM is available at:
- http://bioinformatics.oxfordjournals.org/content/26/12/i237
- http://bioinformatics.oxfordjournals.org/content/29/13/i62
- pathway file
- Here at UCSC, we use our superpathway. The current version is
superpathway_4.0.no_chemicals.tab
. You could create your own pathway file by following the format described at https://dna.five3genomics.com/paradigm/help.
- Here at UCSC, we use our superpathway. The current version is
- evidence files (expression data & copy number data)
- The sample data are provided in tab-delimited 2-D matrix format.
- samples in the columns
- genes in the rows
- The gene names must match the gene names in the pathway file (usually HUGO symbol).
- The sample names must be common across data files as well as in the same order.
- use
commonColumnOrder.py
- use
- Are the features (genes) required to be common across data files as well as in the same order?
- use
commonRowOrder.py
- use
- normalizing expression data
- If no match normal data is available, normalize against the cohort itself using a gene-wise, mean-center method.
Rscript normalize.r mRNA_matrix.tab mean_shift row > mRNA_matrix.meanshifted.tab
- If normal data is available, then normalize against the normal data.
- (Do we have a script for doing this?)
- If no match normal data is available, normalize against the cohort itself using a gene-wise, mean-center method.
- Is there any normalization requirement for copy number data?
- The sample data are provided in tab-delimited 2-D matrix format.
- starting parameters and training parameters?
Submit the job via website at http://paradigm.five3genomics.com/.
(What to do with the results?)
(modulation filter)
(constituent pathways most enriched for changed activity level)