Mixed model approach for identification of chromatin accessibility regulators.

<p>For a TF binding motif, we search for all its instances in the genome. For each cell line, we calculate the accessibility score by counting how many motif instances are found in the open chromatin fraction of the genome. After further normalization, these accessibility scores are compared to gene expression values for all genes via regression (Methods). To account for confounding, we use mixed model regression, where an additional random component is used with the same covariance structure as the gene expression matrix. To be considered a CAR candidate, motif accessibility of a TF must show strong association (low p-value) with the expression of the corresponding TF gene compared to other genes. The gene-level <i>CAR rank</i> of a TF is defined as the rank of its association p-value among the p-values for all genes.