Kernel Mixed Model for Transcriptome Association Study
Journal of Computational Biology
We introduce the python software package Kernel Mixed Model (KMM), which allows users to incorporate the network structure into transcriptome-wide association studies (TWASs). Our software is based on the association algorithm KMM, which is a method that enables the incorporation of the network structure as the kernels of the linear mixed model for TWAS. The implementation of the algorithm aims to offer users simple access to the algorithm through a one-line command. Furthermore, to improve the computing efficiency in case when the interaction network is sparse, we also provide the flexibility of computing with the sparse counterpart of the matrices offered in Python, which reduces both the computation operations and the memory required.
gene-set prioritization, linear mixed model, transcriptome association
Wang, H., Lopez, O., Xing, E.P. and Wu, W., "Kernel Mixed Model for Transcriptome Association Study", Journal of Computational Biology, vol. 29(12), p. 1353-1356, Dec 2022, doi:10.1089/cmb.2022.0280