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modACDC

Association of Covariance for Detecting Differential Co-Expression

v2.0.1 · Jan 30, 2024 · MIT + file LICENSE

Description

A series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may 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) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) <doi:10.3389/fmed.2023.1118824>.

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Namespace in Imports field not imported from: ‘tibble’
  All declared Imports should be used.
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dependencies in R code

Namespace in Imports field not imported from: ‘tibble’
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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-fedora-clang

dependencies in R code

Namespace in Imports field not imported from: ‘tibble’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘tibble’
  All declared Imports should be used.

Dependency Network

Dependencies Reverse dependencies CCP data.table doParallel foreach genieclust genio ggplot2 partition tibble tidyr modACDC

Version History

new 2.0.1 Mar 9, 2026