This package implements a computationally efficient method for fitting linear mixed model. It is based on the OLS fitting and moment-matching and is 30 ~ 60 times faster than the standard LMER fitting algorithm (e.g., \code{lme4}. One application of this method is to combine the gene expression deconvolution analysis and downstream differential expression analysis into one step. To this end, we include a hypothesis testing framework to identity significant interactions between the clinical variables, gene expressions, and cell types. In addition, we implemented several discriminant scores based the FastMix model, so that the users may apply a wide range of machine learning algorithms on these scores to build predictive models.
Package details |
|
---|---|
Author | Hao Sun, Xing Qiu |
Maintainer | Hao Sun <terrysun0302@gmail.com> |
License | GPL-3 |
Version | 0.2.5 |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.