Using the Package
From within R, install from the Sage internal CRAN server:
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source('http://sage.fhcrc.org/CRAN.R'); pkgInstall("SageBionetworksCoex")
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Load the library:
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library(SageBionetworksCoex)
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For guidance on using the package:
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?SageBionetworksCoex
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Table of Contents
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Goals
1) Make the Sage coexpression software runnable by any data analyst in R.
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Dataset | # Probes | # Samples | Sage Time | Sage Space | Package Time | Package Space | Sage beta | Package beta | Gene trees same, independent beta? | Gene trees same, same beta? | Module difference****, independent beta | Module difference****, same beta | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Female mouse liver | 3600 | 135 | --- | --- | --- | --- | 6.5 | 6.5 | TRUE | TRUE | 3.7% | 3.7% | |
Cranio | 2534 | 249 | --- | --- | --- | --- | 4.0 | 4.5 | FALSE | TRUE | 44% | 0.9% | |
Methylation, top 5K genes | 5000 | 555 | --- | --- | --- | --- | 8.5 | 8.5 | TRUE | TRUE | 0 | 0 | |
Colon cancer, top 5K genes | 5000 | 322 | --- | --- | --- | --- | 3 | 3.5 | FALSE | TRUE | 11% | 0.5% | |
Human liver cohort, top 5K genes | 5000 | 427 | --- | --- | --- | --- | 11 | 11 | TRUE | TRUE | 1.0% | 1.0% | |
PARC* | 18,392 | 960 | 5h:55m | 83.9 GB | 1h:40m | 71 GB | 8 | 7.5 | FALSE | FALSE | 4.7% | 0.6% | |
Methylation (full set)* | 27,578 | 555 | 24h:45m | 180 GB | 6h:38m | 196 GB | 8 | 11.5 | FALSE | FALSE | 14% | 0.2% | |
Colon cancer, top 40K genes* | 40,000 | 322 | Out of memory** | --- | Out of memory after 16h:12m** | 276 GB | --- | --- | --- | --- | --- | --- | |
Human liver cohort*** | 40,102 | 427 | --- | --- | Out of memory after 16h:46m** | 276 5h:13m | 313 GB | --- | --- | --- | --- | --- | --- |
* These were run on an Amazon Elastic Compute Cloud (EC2) "High-Memory Quadruple Extra Large" unix server, having 68GB of RAM.
** Note: UCLA-WGCNA package also runs out of memory. (An alternative is to use the WGCNA preprocessing step of K-means decomposition, which has been shown to work with >50K genes.)
*** Run on Sage Bionetworks' "Belltown" Unix server, having 256GB RAM.
**** http://florence.acadiau.ca/collab/hugh_public/index.php?title=R:compare_partitions
Note: We skip the performance evaluation for the small data sets.
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