Package: GEInter 0.3.2

Xing Qin

GEInter: Robust Gene-Environment Interaction Analysis

Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).

Authors:Mengyun Wu [aut], Xing Qin [aut, cre], Shuangge Ma [aut]

GEInter_0.3.2.tar.gz
GEInter_0.3.2.tar.gz(r-4.5-noble)GEInter_0.3.2.tar.gz(r-4.4-noble)
GEInter_0.3.2.tgz(r-4.4-emscripten)GEInter_0.3.2.tgz(r-4.3-emscripten)
GEInter.pdf |GEInter.html✨
GEInter/json (API)
NEWS

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

Peer review:

Datasets:
  • HNSCC - A data frame containing the TCGA head and neck squamous cell carcinoma (HNSCC) data.
  • Rob_data - A matrix containing the simulated data for 'RobSBoosting' and 'Miss.boosting' methods

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

28 exports 0.00 score 74 dependencies 245 downloads

Last updated 2 years agofrom:3d392711bf. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-linuxOKSep 03 2024

Exports:ARAugmented.databic.BLMCPbic.PTRegBLMCPcoef.bic.BLMCPcoef.bic.PTRegcoef.BLMCPcoef.PTRegcoef.RobSBoostingMiss.boostingplot.bic.BLMCPplot.bic.PTRegplot.BLMCPplot.Miss.boostingplot.PTRegplot.RobSBoostingpredict.bic.BLMCPpredict.bic.PTRegpredict.BLMCPpredict.Miss.boostingpredict.PTRegpredict.RobSBoostingPTRegQPCorr.matrixQPCorr.pvalRobSBoostingsimulated_data

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmemoisemgcvmimemunsellmvtnormnlmennetpcaPPpillarpkgconfigplyrquantregR6rappdirsRColorBrewerRcppreshape2rlangrmarkdownrpartrstudioapisassscalesSparseMstringistringrsurvivaltibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
The covariance matrix with an autoregressive (AR) structure among variablesAR
Accommodating missingness in environmental measurements in gene-environment interaction analysisAugmented.data
BIC for BLMCPbic.BLMCP
BIC for PTRegbic.PTReg
Accommodating missingness in environmental measurements in gene-environment interaction analysis: penalized estimation and selectionBLMCP
Extract coefficients from a "bic.BLMCP" objectcoef.bic.BLMCP
Extract coefficients from a "bic.PTReg" objectcoef.bic.PTReg
Extract coefficients from a "BLMCP" objectcoef.BLMCP
Extract coefficients from a "PTReg" objectcoef.PTReg
Extract coefficients from a "RobSBoosting" objectcoef.RobSBoosting
A data frame containing the TCGA head and neck squamous cell carcinoma (HNSCC) data.HNSCC
Robust gene-environment interaction analysis approach via sparse boosting, where the missingness in environmental measurements is effectively accommodated using multiple imputation approachMiss.boosting
Plot coefficients from a "bic.BLMCP" objectplot.bic.BLMCP
Plot coefficients from a "bic.PTReg" objectplot.bic.PTReg
Plot coefficients from a "BLMCP" objectplot.BLMCP
Plot coefficients from a "Miss.boosting" objectplot.Miss.boosting
Plot coefficients from a "PTReg" objectplot.PTReg
Plot coefficients from a "RobSBoosting" objectplot.RobSBoosting
Make predictions from a "bic.BLMCP" object.predict.bic.BLMCP
Make predictions from a "bic.PTReg" objectpredict.bic.PTReg
Make predictions from a "BLMCP" objectpredict.BLMCP
Make predictions from a "Miss.boosting" objectpredict.Miss.boosting
Make predictions from a "PTReg" objectpredict.PTReg
Make predictions from a "RobSBoosting" objectpredict.RobSBoosting
Robust gene-environment interaction analysis using penalized trimmed regressionPTReg
Robust identification of gene-environment interactions using a quantile partial correlation approachQPCorr.matrix
P-values of the "QPCorr.matrix" obtained using a permutation approachQPCorr.pval
A matrix containing the simulated data for 'RobSBoosting' and 'Miss.boosting' methodsRob_data
Robust semiparametric gene-environment interaction analysis using sparse boostingRobSBoosting
Simulated data for generating responsesimulated_data