Wang, Tao Graph-Assisted Inverse Regression for Count Data and Its Application to Sequencing Data <p>Multivariate count data, such as sequencing reads in genomics, are often connected to a clinical phenotype of interest. We develop a flexible framework for dimension reduction in regression, with predictors that are correlated counts, by modeling the conditional distribution of the predictors, given the response, using a pairwise Poisson graphical model. This new framework, called network-based inverse regression for counts, allows us to derive a sufficient reduction of the predictors, while adjusting for the dependence structure among them. We propose a regularized criterion for estimating both the reduction structure and the network structure. The estimation algorithm can be implemented efficiently on a parallel computer. We also introduce an adaptive version and a sparse variant of the proposed procedure. The methods are evaluated on simulated data and are applied to a gut microbiome sequencing dataset. <a href="https://doi.org/10.1080/10618600.2019.1705309" target="_blank">Supplementary materials</a> for this article are available online.</p> Conditional independence model;Distributed computing;Model-based inverse regression;Neighborhood selection;Sufficient dimension reduction 2020-01-29
    https://tandf.figshare.com/articles/dataset/Graph-assisted_inverse_regression_for_count_data_and_its_application_to_sequencing_data/11409933
10.6084/m9.figshare.11409933.v2