CSKAT

Compute the adjusted score statistic and p-value

Test for overall association between microbiome composition data
and phenotypes via phylogenetic kernels. The phenotype can be univariate
continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>),
survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>),
multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and
structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>).
The package can also use robust regression (unpublished work) and
integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>).
In each case, the microbiome community effect is modeled nonparametrically
through a kernel function, which can incorporate phylogenetic tree information.

Anna Plantinga

MiRKAT

Microbiome Regression-Based Kernel Association Tests

Nehemiah Wilson

Haotian Zheng

Tianying Wang

Xiang Zhan

Michael Wu

Ni Zhao

Jun Chen

CSKAT function

<dl><dt>lmer.obj</dt>
<dd>A fitted lme4 object (model under H0)</dd>
<dt>Ks</dt>
<dd>A kernel matrix or list of kernels, quantifying the similarities between samples.</dd></dl>

Arguments

Nehemiah Wilson, Anna Plantinga, Xiang Zhan, Jun Chen.

Author

Small-sample SKAT for correlated (continuous) data ('c' stands for 'correlated'). Called within GLMM-MiRKAT. — CSKAT

<dl>

<dt>lmer.obj</dt>
<dd>A fitted lme4 object (model under H0)</dd>


<dt>Ks</dt>
<dd>A kernel matrix or list of kernels, quantifying the similarities between samples.</dd>

</dl>

CSKAT: Small-sample SKAT for correlated (continuous) data ('c' stands for 'correlated'). Called within GLMM-MiRKAT.

Description

Usage

Value

Arguments

Author

References