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GuidedSparseKmeans

swMATH ID: 38238
Software Authors: Lingsong Meng, Dorina Avram, George Tseng, Zhiguang Huo
Description: Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data. The discovery of disease subtypes is an essential step for developing precision medicine, and disease subtyping via omics data has become a popular approach. While promising, subtypes obtained from current approaches are not necessarily associated with clinical outcomes. With the rich clinical data along with the omics data in modern epidemiology cohorts, it is urgent to develop an outcome-guided clustering algorithm to fully integrate the phenotypic data with the high-dimensional omics data. Hence, we extended a sparse K-means method to an outcome-guided sparse K-means (GuidedSparseKmeans) method, which incorporated a phenotypic variable from the clinical dataset to guide gene selections from the high-dimensional omics data. We demonstrated the superior performance of the GuidedSparseKmeans by comparing with existing clustering methods in simulations and applications of high-dimensional transcriptomic data of breast cancer and Alzheimer’s disease. Our algorithm has been implemented into an R package, which is publicly available on GitHub https://github.com/LingsongMeng/GuidedSparseKmeans
Homepage: https://arxiv.org/abs/2103.09974
Source Code: https://github.com/LingsongMeng/GuidedSparseKmeans
Dependencies: R
Keywords: arXiv_q-bio.QM; Genomics; arXiv_q-bio.GN; arXiv_stat.AP; R package; R; Outcome-guided clustering; sparse K-means; Disease subtype; Phenotypic data; Transcriptomic data
Related Software: GitHub; R
Cited in: 0 Publications

Standard Articles

1 Publication describing the Software Year
Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data
Lingsong Meng, Dorina Avram, George Tseng, Zhiguang Huo
2021