Package: CaDrA 1.10.0

Reina Chau

CaDrA: Candidate Driver Analysis

Performs both stepwise and backward heuristic search for candidate (epi)genetic drivers based on a binary multi-omics dataset. CaDrA's main objective is to identify features which, together, are significantly skewed or enriched pertaining to a given vector of continuous scores (e.g. sample-specific scores representing a phenotypic readout of interest, such as protein expression, pathway activity, etc.), based on the union occurence (i.e. logical OR) of the events.

Authors:Reina Chau [aut, cre], Katia Bulekova [aut], Vinay Kartha [aut], Stefano Monti [aut]

CaDrA_1.10.0.tar.gz
CaDrA_1.10.0.zip(r-4.7-x86_64)CaDrA_1.10.0.zip(r-4.6-x86_64)CaDrA_1.10.0.zip(r-4.5-x86_64)
CaDrA_1.10.0.tgz(r-4.6-x86_64)CaDrA_1.10.0.tgz(r-4.6-arm64)CaDrA_1.10.0.tgz(r-4.5-x86_64)CaDrA_1.10.0.tgz(r-4.5-arm64)
CaDrA_1.10.0.tar.gz(r-4.7-arm64)CaDrA_1.10.0.tar.gz(r-4.7-x86_64)CaDrA_1.10.0.tar.gz(r-4.6-arm64)CaDrA_1.10.0.tar.gz(r-4.6-x86_64)
CaDrA_1.10.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CaDrA/json (API)

# Install 'CaDrA' in R:
install.packages('CaDrA', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/montilab/cadra/issues

Datasets:

On BioConductor:CaDrA-1.11.0(bioc 3.24)CaDrA-1.10.0(bioc 3.23)

microarrayrnaseqgeneexpressionsoftwarefeatureextraction

6.81 score 24 stars 10 scripts 272 downloads 9 exports 58 dependencies

Last updated from:17d98f67f3 (on RELEASE_3_23). Checks:1 NOTE, 13 OK. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksNOTE212
linux-devel-arm64OK385
linux-devel-x86_64OK391
source / vignettesOK361
linux-release-arm64OK384
linux-release-x86_64OK461
macos-release-arm64OK279
macos-release-x86_64OK548
macos-oldrel-arm64OK319
macos-oldrel-x86_64OK653
windows-devel-x86_64OK433
windows-release-x86_64OK420
windows-oldrel-x86_64OK383
wasm-releaseOK153

Exports:CaDrAcalc_rowscorecandidate_searchgenerate_permutationsmeta_plotpermutation_plotprefilter_datatopn_besttopn_plot

Dependencies:abindBiobaseBiocGenericsbitopscaToolsclicodetoolscpp11DelayedArraydigestdoParallelfarverforeachgenericsGenomicRangesggplot2gluegplotsgtablegtoolsIRangesisobanditeratorsKernSmoothknnmilabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmisc3dplyrppcorR.cacheR.methodsS3R.ooR.utilsR6RColorBrewerRcppreshape2rlangS4ArraysS4VectorsS7scalesSeqinfoSparseArraystringistringrSummarizedExperimentvctrsviridisLitewithrXVector

Scoring Functions
Load packages | Load required datasets | Heatmap of simulated feature set | Search for a subset of genomic features that are likely associated with a functional response of interest using each of the scoring methods | 1. Kolmogorov-Smirnov Scoring Method | 2. Wilcoxon Rank-Sum Scoring Method | 3. Conditional Mutual Information Scoring Method from REVEALER | 4. K-Nearest Neighbor Mutual Information Estimator from knnmi package | 5. Correlation Scoring Method | 6. Custom - An User Defined Scoring Method | SessionInfo
Rendered fromscoring_functions.Rmdusingknitr::rmarkdown

Last update: 2024-09-21
Started: 2022-07-10

How to run CaDrA within a Docker Environment
Software requirements | Build Docker image of CaDrA | (1) Clone this repository | (2) Navigate to CaDrA folder where Dockerfile is stored and build its Docker image. | (3) After the build is completed, check if the image is built successfully | Run CaDrA container with its built image | Run CaDrA on RStudio Server hosted within a Docker environment
Rendered fromdocker.Rmdusingknitr::rmarkdown

Last update: 2024-03-14
Started: 2023-12-12

Permutation-Based Testing
Load packages | Load required datasets | Find a subset of features that maximally associated with a given outcome of interest | Visualize best meta-features result | Perform permutation-based testing | Visualize permutation result | SessionInfo
Rendered frompermutation_based_testing.Rmdusingknitr::rmarkdown

Last update: 2024-03-14
Started: 2022-07-10