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Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.

ABSTRACT: While the methods available for single-cell ATAC-seq analysis are well optimized for clustering cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration across scATAC-seq data sets by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. We additionally integrate our analysis with scRNA-seq data to identify orthogonal evidence for transcriptional regulators predicted by scATAC-seq analysis. Using publicly available scATAC-seq data, we find patterns that accurately characterize cell types both within and across data sets. Furthermore, we demonstrate that these patterns are both consistent with current biological understanding and reflective of novel regulatory biology.

SUBMITTER: Erbe R

PROVIDER: S-EPMC7337516 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.

Erbe Rossin R Kessler Michael D MD Favorov Alexander V AV Easwaran Hariharan H Gaykalova Daria A DA Fertig Elana J EJ

Nucleic acids research 20200701 12

While the methods available for single-cell ATAC-seq analysis are well optimized for clustering cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration across scATAC-seq data sets by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. We additionally integrate our ...[more]

PMID: 32392348

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Project description:Background:There is a growing interest in the role of chromatin in acquiring and maintaining cell identity. Despite the ever-growing availability of genome-wide gene expression data, understanding how transcription programs are established and regulated to define cell identity remains a puzzle. An important mechanism of gene regulation is the binding of transcription factors (TFs) to specific DNA sequence motifs across the genome. However, these sequences are hindered by the packaging of DNA to chromatin. Thus, the accessibility of these loci for TF binding is highly regulated and determines where and when TFs bind. We present a workflow for measuring chromatin accessibility in Arabidopsis thaliana and define organ-specific regulatory sites and binding motifs of TFs at these sites. Results:We coupled the recently described isolation of nuclei tagged in specific cell types (INTACT) and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) as a genome-wide strategy to uncover accessible regulatory sites in Arabidopsis based on their accessibility to nuclease digestion. By applying this pipeline in Arabidopsis roots, we revealed 41,419 accessible sites, of which approximately half are found in gene promoters and contain the H3K4me3 active histone mark. The root-unique accessible sites from this group are enriched for root processes. Interestingly, most of the root-unique accessible sites are found in nongenic regions but are correlated with root-specific expression of distant genes. Importantly, these gene-distant sites are enriched for binding motifs of TFs important for root development as well as motifs for TFs that may play a role as novel transcriptional regulators in roots, suggesting that these accessible loci are functional novel gene-distant regulatory elements. Conclusions:By coupling INTACT with ATAC-seq methods, we present a feasible pipeline to profile accessible chromatin in plants. We also introduce a rapid measure of the experiment quality. We find that chromatin accessibility at promoter regions is strongly associated with transcription and active histone marks. However, root-specific chromatin accessibility is primarily found at intergenic regions, suggesting their predominance in defining organ identity possibly via long-range chromatin interactions. This workflow can be rapidly applied to study the regulatory landscape in other cell types, plant species and conditions.

Dataset Information

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.

Publications

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.

Similar Datasets

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