Using the Package
Get the source from Github and build the package:
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# Install the dependencies, from R:
> install.packages(pkgs=c("WGCNA", "flashClust", "dynamicTreeCut"))
# now, from the command line, clone the Github repository
git clone https://github.com/Sage-Bionetworks/SageBionetworksCoex.git
# again, from the command line, build the package
R CMD INSTALL SageBionetworksCoex
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In R, 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.
Document the analysis steps, package functions, and adjustable parameters. Write start-to-finish vignette(s) having sufficient detail to guide a new analyst through the software, including making parameter settings and other intermediate decisions. Refactoring software to enhance readability and/or to make decision points and adjustable parameters explicit, is within scope for this goal.
2) Clearly explain the methodology underlying the coexpression algorithms.
This includes contrasting the Sage algorithms with the separately published “WGCNA” algorithms and explaining the rationale for the differences and the decision process for choosing which to use.
3) Make the Sage coexpression software publicly available.
The code must be available through a standard R distribution channel (CRAN or Bioconductor). An option is to merge Sage-specific algorithms or steps into WGCNA (if the two are sufficiently overlapping).
4) Make the Sage coexpression software perform well, on commonly available hardware.
Currently, special high capacity hardware is required to run the Sage coexpression software on commonly encountered data set sizes. The goal is to optimize the code to run on commonly available hardware, at a minimum the code should be runnable on the 68GB, quad core servers available through Amazon Web Services. Ideally, the code would be runnable on a “heavy” Sage laptop (8 GB total RAM).
Strategy
The steps for Sage Coexpression are:
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Performance questions: For datasets having >18,000 probes, how much time and space does each algorithm use?
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 45K genes*** | 4045,000 | 322 Out of memory** | --- | --- | Out of memory after 16h:12m** | 276 GB 5h:52 | 368 GB | --- | --- | --- | --- | --- | --- |
Human liver cohort*** | 40,102 | 427 Out of memory** | --- | --- | 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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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: training class) |
Clearly explain the methodology underlying the coexpression algorithms. | Included links to literature in the R package documentation. |
Make the Sage coexpression software publicly available. | TBD (see below) |
Make the Sage coexpression software perform well, on commonly available hardware. | Used UCLA's accelerated algorithms. Accelerated the 'intra-module statistics' computation. Profiled datasets of up to 27,000 genes on inexpensive, high capacity cloud resources. |
Choices for package 'publication' include:
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