  While single-cell RNA-seq enables the investigation of the celltype effect on
the transcriptome, the pure tissue environmental effect has not been well
investigated. The bias in the combination of tissue and celltype in the body
made it difficult to evaluate the effect of pure tissue environment by omics
data mining. It is important to prevent statistical confounding among discrete
variables such as celltype, tissue, and other categorical variables when
evaluating the effects of these variables. We propose a novel method to
enumerate suitable analysis units of variables for estimating the effects of
tissue environment by extending the maximal biclique enumeration problem for
bipartite graphs to $k$-partite hypergraphs. We applied the proposed method to
a large mouse single-cell transcriptome dataset of Tabala Muris Senis to
evaluate pure tissue environmental effects on gene expression. Data Mining
using the proposed method revealed pure tissue environment effects on gene
expression and its age-related change among adipose sub-tissues. The method
proposed in this study helps evaluations of the effects of discrete variables
in exploratory data mining of large-scale genomics datasets.
