Learning single-cell chromatin accessibility profiles using meta-analytic marker genes

Kawaguchi, Risa Karakida; Tang, Ziqi; Fischer, Stephan; Rajesh, Chandana; Tripathy, Rohit; Koo, Peter K.; Gillis, Jesse

doi:10.1093/bib/bbac541

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…Sequence-based deep learning models have shown major advances to delineate which sequence patterns in enhancer regions are important for cell type specific chromatin accessibility or gene expression ( 6, 7 ). They have contributed substantially to identify TFBS specific to mammalian interneurons ( 8, 9 ), fly brain cell types ( 10 ), mouse liver cells ( 11 ), and mouse embryonic stem cells ( 12 ). Furthermore, deep learning models have been applied to predict chromatin accessibility across mammalian brain cell types ( 13, 14 ), to compare enhancer codes of melanocytes across species ( 15 ), and to identify potential enhancer regions linked to the evolution of neocortex expansion and vocal learning ( 8, 16 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Hecker,

Kempynck,

Mauduit

et al. 2024

Preprint

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Combinations of transcription factors govern the identity of cell types, which is reflected by enhancer codes in cis-regulatory genomic regions. Cell type-specific enhancer codes at nucleotide-level resolution have not yet been characterized for the mammalian neocortex. It is currently unknown whether these codes are conserved in other vertebrate brains, and whether they are informative to resolve homology relationships for species that lack a neocortex such as birds. To compare enhancer codes of cell types from the mammalian neocortex with those from the bird pallium, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. We then trained deep learning models to characterize cell type-specific enhancer codes for the human, mouse, and chicken telencephalon. We devised three metrics that exploit enhancer codes to compare cell types between species. Based on these metrics, non-neuronal and GABAergic cell types show a high degree of regulatory similarity across vertebrates. Proposed homologies between mammalian neocortical and avian pallial excitatory neurons are still debated. Our enhancer code based comparison shows that excitatory neurons of the mammalian neocortex and the avian pallium exhibit a higher degree of divergence than other cell types. In contrast to existing evolutionary models, the mammalian deep layer excitatory neurons are most similar to mesopallial neurons; and mammalian upper layer neurons to hyper- and nidopallial neurons based on their enhancer codes. In addition to characterizing the enhancer codes in the mammalian and avian telencephalon, and revealing unexpected correspondences between cell types of the mammalian neocortex and the chicken pallium, we present generally applicable deep learning approaches to characterize and compare cell types across species via the genomic regulatory code.

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Section: Introductionmentioning

confidence: 99%

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Hecker,

Kempynck,

Mauduit

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It provides the ability to examine the openness of chromatin regions in the nucleus at the single-cell level [17], which is unavailable in single-cell RNA-sequencing data. This enhances our comprehension of the epigenetic state, cell-type heterogeneity and cell state [20, 21]. However, scATAC-seq data has extreme sparsity than scRNA-seq data [18, 22, 23], which also increases the difficulty of analysis based on scATAC-seq data.…”

Section: Introductionmentioning

confidence: 99%

A Cell Cycle-aware Network for Data Integration and Label Transferring of Single-cell RNA-seq and ATAC-seq

Liu,

Ma,

Wen

et al. 2024

Preprint

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In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity and confounding factors. As we know, cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it's not clear how it will work on the integrated single-cell multi-omics data. Here, we developed a Cell Cycle-Aware Network (CCAN) to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the out-standing performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

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A Cell Cycle‐Aware Network for Data Integration and Label Transferring of Single‐Cell RNA‐Seq and ATAC‐Seq

Liu,

Ma,

Wen

et al. 2024

Advanced Science

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In recent years, the integration of single‐cell multi‐omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non‐single omics perspective, but it still suffers many challenges, such as omics‐variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single‐cell RNA‐seq data, but it is not clear how it will work on the integrated single‐cell multi‐omics data. Here, a cell cycle‐aware network (CCAN) is developed to remove cell cycle effects from the integrated single‐cell multi‐omics data while keeping the cell type‐specific variations. This is the first computational model to study the cell‐cycle effects in the integration of single‐cell multi‐omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA‐seq datasets from different protocols, integrating paired and unpaired scRNA‐seq and scATAC‐seq data, accurately transferring cell type labels from scRNA‐seq to scATAC‐seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

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Learning single-cell chromatin accessibility profiles using meta-analytic marker genes

Cited by 3 publications

References 58 publications

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

A Cell Cycle-aware Network for Data Integration and Label Transferring of Single-cell RNA-seq and ATAC-seq

A Cell Cycle‐Aware Network for Data Integration and Label Transferring of Single‐Cell RNA‐Seq and ATAC‐Seq

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