Abstract
Single-cell multi-omics technology can be applied to plant cells to characterize gene expression and open chromatin regions in individual cells. In this chapter, we describe a computational pipeline for the analysis of single-cell data to construct gene regulatory networks. The major steps of this pipeline include the following: (1) normalize and integrate scRNA-seq and scATAC-seq data (2) identify cluster maker genes (3) perform motif finding for selected marker genes, and (4) identify regulatory networks with machine learning. The pipeline has been tested using data from the model species Arabidopsis and is generally applicable to other plant and animal species to characterize regulatory networks using single-cell multi-omics data.
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