...
Note: We skip the performance evaluation for the small data sets.
The details of the differences summarized in the table can be found here: http://sagebionetworks.jira.com/wiki/display/SCICOMP/Package+Comparison+Details
Goals, Revisited
Goal | How we met it |
|---|---|
Make the Sage coexpression software runnable by any data analyst in R | Created easy to use, documented R package. (TODO: Vignette, training class) |
Clearly explain the methodology underlying the coexpression algorithms. | Included links to literature in the R package. |
Make the Sage coexpression software publicly available. | TBD |
Make the Sage coexpression software perform well, on commonly available hardware. | Used UCLA's accelerated algorithms. Profiled datasets of up to 27,000 genes on inexpensive, high capacity cloud resources. |