The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Association of covariance to detect differential co-expression (ACDC) is a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. The default method (ACDC) identifies modules using Partition and the modACDC method allows users to supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) or Genome-wide Complex Trait Analysis (GCTA).
The manuscript for ACDC can be found here.
You can install modACDC directly from CRAN with:
Or you can install the development, GitHub version with:
# install.packages("remotes")
remotes::install_github("USCbiostats/ACDC")OSCA is a suite of C++ functions that provides an estimate of the percent of variance in an external phenotype that can be explained by an omics profile, akin to heritability estimates in GWAS. Here, we make calls to OSCA’s Omics Restricted Maximum Likelihood (OREML) method.
In order to use the OSCA functions, the user must specify the absolute path to the OSCA software, which can be downloaded from the Yang Lab website here.
GCTA is a suite of C++ functions that provides an estimate of the heritability of a trait. Here, we make calls to GCTA’s Genomics REstricted Maximum Likelihood (GREML) method.
In order to use the GCTA functions, the user must specify the absolute path to the GCTA software, which can be downloaded from the Yang Lab website here.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.