Eun-Kyung Lee, Sung Gon Yi, Yoojin Jung, Taesung Park
Odds ratio based multivactor-dimensionality reduction method for detecting gene-gene interactions

Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions

Eun-Kyung Lee, Sung Gon Yi, Yoojin Jung, Junghyun Namkung, Taesung Park

Eun-Kyung Lee, Sung Gon Yi, Yoojin Jung, Junghyun Namkung,
Taesung Park
Odds ratio based multifactor-dimensionality reduction
method for detecting gene-gene interactions

Connects to Google Cloud API for Label Detection
c(person("Mohamed", "Soudy", email = "MohmedSoudy2009@gmail.com", role=c("aut", "cre")),person("Yasmine", "Afify", email = "yasmine.afify@cis.asu.edu.eg", role = "aut"),person("Nagwa", "Badr", email = "Nagwabadr@cis.asu.edu.eg", role = "aut"))
Mohamed Soudy [aut, cre],
Yasmine Afify [aut],
Nagwa Badr [aut]
Connects to Google cloud vision <https://cloud.google.com/vision> to perform label detection and repurpose this feature for image classification.
utils , stats, googleAuthR, googleCloudVisionR

Identification of Differentially Expressed Genes
José María Martínez Otzeta, Concepción Arenas, Basilio Sierra, Itziar Irigoien
José María Martínez Otzeta <josemaria.martinezo@ehu.eus>
Automated discovery of differentially expressed genes. The method (called ORdensity) is composed of two phases: discovering potential differentially expressed genes and recognizing differentially expressed genes. It makes use of a permutation resampling procedure to build outlying and density indexes. References: a) Irigoien, I. and Arenas, C. (2018). "Identification of differentially expressed genes by means of outlier detection". <doi:10.1186/s12859-018-2318-8>. b) Martínez-Otzeta, J. M., Irigoien, I., Sierra, B., and Arenas, C. (2020). "ORdensity: user-friendly R package to identify differentially expressed genes". <doi:10.1186/s12859-020-3463-4>.
cluster, distances, Rfast, plyr, methods, foreach, doRNG,
parallel, doParallel

Ordered Subset Analysis for Case-Control Studies
Xuejun Qin, Elizabeth R. Hauser, Silke Schmidt,(Center for
Human Genetics, Duke University)
Genetic heterogeneity can reduce the power for complex
disease gene mapping since only a fraction of the cases in the
collected dataset may carry a specific disease susceptibility
allele. The Ordered Subset Analysis for Case-Control data
(OSACC) program was designed to evaluate evidence for
association in the presence of genetic heterogeneity. For a
more detailed description of the method and results of an
extensive simulation study based on different models of genetic
heterogeneity. Please see Qin et. al.(2010), Ordered Subset
Analysis for Case-Control Studies, Genetic Epidemiology 34(5),
407-417.