Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG

Duren, Zhana; Chang, Fengge; Naqing, Fnu; Xin, Jingxue; Li, Qiao; Wong, Wing Hung

doi:10.1186/s13059-022-02682-2

Cited by 32 publications

(33 citation statements)

References 57 publications

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“…The top 20% of cells enriched in either topic (see methods) was selected and we performed differential gene expression, transcription factor enrichment using the CistromeDB ChIP-seq datasets and transcription factor-associated accessibility analysis using ChromVAR ( 42 ) (Figure 6E , Supplementary Figure S10D, E, Table S4 ). To ensure regulatory differences between topics segregated without a priori knowledge of topics, we applied two orthogonal ATAC + RNA joint embedding dimensionality approaches, scREG ( 45 ) and Amateur ( 46 , 47 ). Both analyses demonstrate the same clear separation of cell states enriched for FOXM1 and ESR1 topics ( Supplementary Figure S10F ).…”

Section: Resultsmentioning

confidence: 99%

“…Additional joint-embedding strategies were performed for comparison to our topic modeling method. We used scREG ( 45 ), a reduction method focused on cis-regulatory networks between peak-gene pairs, and Amateur ( 46 , 47 ), a deep learning methodology using an autoencoder to define a reduced space of latent features ( Supplementary Figure S10F ). For scREG (v0.1.0), we modified data input functions to suit our preexisting Seurat Objects.…”

Section: Methodsmentioning

confidence: 99%

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Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer

Örs

Chitsazan

Doe

et al. 2022

Nucleic Acids Research

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Breast cancers are known to be driven by the transcription factor estrogen receptor and its ligand estrogen. While the receptor's cis-binding elements are known to vary between tumors, heterogeneity of hormone signaling at a single-cell level is unknown. In this study, we systematically tracked estrogen response across time at a single-cell level in multiple cell line and organoid models. To accurately model these changes, we developed a computational tool (TITAN) that quantifies signaling gradients in single-cell datasets. Using this approach, we found that gene expression response to estrogen is non-uniform, with distinct cell groups expressing divergent transcriptional networks. Pathway analysis suggested the two most distinct signatures are driven separately by ER and FOXM1. We observed that FOXM1 was indeed activated by phosphorylation upon estrogen stimulation and silencing of FOXM1 attenuated the relevant gene signature. Analysis of scRNA-seq data from patient samples confirmed the existence of these divergent cell groups, with the FOXM1 signature predominantly found in ER negative cells. Further, multi-omic single-cell experiments indicated that the different cell groups have distinct chromatin accessibility states. Our results provide a comprehensive insight into ER biology at the single-cell level and potential therapeutic strategies to mitigate resistance to therapy.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer

Örs

Chitsazan

Doe

et al. 2022

Nucleic Acids Research

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“…First, because of the inherent sparsity of single-nucleus ATACseq data, we tested a zero-inflated negative binomial (ZINB) model, allowing to independently account for the zero component of a peak-gene link. Second, we also tested a new method – scREG – that is reported to outperform the simple Pearson R model on CD14 monocytes peak-gene link predictions based on eQTL data 11,12 . Of note, scREG output link scores for peak-gene within each cell-type.…”

Section: Resultsmentioning

confidence: 99%

“…As described above, scREG calculates peak-gene link scores per cell-type 12 . We repeated the analyses of the Epimap predictions with the Z-scores, Pearson R and ZINB methods but focusing on single cell-type.…”

Section: The Raw Pearson R Coefficients And/or Physical Distance Prov...mentioning

confidence: 99%

Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes

Leblanc

Lettre

2022

Preprint

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Most variants identified by genome-wide association studies (GWAS) are located in non-coding regions of the genome. While largely untested functionally, it is assumed that most of these GWAS variants modulate the activity of enhancers. Epigenomic profiling, including ATACseq, is one of the main tools used to define enhancers. Because enhancers are overwhelmingly cell-type specific, inference of their activity is greatly limited in complex tissues that include multiple cell-types. Multiomic assays that probe in the same nucleus both the open chromatin landscape and gene expression levels enable the study of correlations (links) between these two modalities. Current best practices to infer the regulatory effect of candidate cis-regulatory elements (cCREs) in multiomic data involve removing biases associated with peak coverage and GC content by generating null distributions of matched ATACseq peaks drawn from different chromosomes. This is done under the assumption that the tested cis- and the matched trans-ATACseq peaks are uncorrelated. This strategy has been broadly adopted by popular single-nucleus multiomic workflows such as Signac. Here, we uncovered limitations and confounders of this approach. We found a strong loss of power to detect a regulatory effect for cCREs with high read counts in the dominant cell-type. We showed that this is largely due to cell-type-specific trans-ATACseq peak correlations creating bimodal null distributions. We tested alternative models and concluded that physical distance and/or the raw Pearson correlation coefficients are the best predictors for peak-gene links when compared to predictions from Epimap (e.g. CD14 area under the curve [AUC] = 0.51 with the method implemented in Signac vs 0.71 with the Pearson correlation coefficients) or validation by CRISPR perturbations (AUC = 0.63 vs 0.73).

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“…One fundamental question regarding the abundant single cell data is how to distinguish different cell types in a heterogeneous cell population based on the measured molecular signatures. A variety of computational approaches have been developed to decipher the heterogeneity across cell types based on transcriptome, methylome, and chromatin accessibility [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Deep generative modeling and clustering of single cell Hi-C data

Zeng

Zhang

et al. 2022

Preprint

Self Cite

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Deciphering 3D genome conformation is important for understanding gene regulation and cellular function at a spatial level. The recent advances of single cell Hi-C technologies have enabled the profiling of the 3D architecture of DNA within individual cell, which allows us to study the cell-to-cell variability of 3D chromatin organization. Computational approaches are in urgent need to comprehensively analyze the sparse and heterogeneous single cell Hi-C data. Here, we proposed scDEC-Hi-C, a new framework for single cell Hi-C analysis with deep generative neural networks. scDEC-Hi-C outperforms existing methods in terms of single cell Hi-C data clustering and imputation. Moreover, the generative power of scDEC-Hi-C could help unveil the heterogeneity of chromatin architecture across different cell types. We expect that scDEC-Hi-C could shed light on deepening our understanding of the complex mechanism underlying the formation of chromatin contacts. scDEC-Hi-C is freely available at https://github.com/kimmo1019/scDEC-Hi-C.Key pointsscDEC-Hi-C provides an end-to-end framework based on autoencoder and deep generative model to comprehensively analyze single cell Hi-C data, including low-dimensional embedding and clustering.Through a series of experiments including single cell Hi-C data clustering and structural difference identification, scDEC-Hi-C demonstrates suprioir performance over existing methods.In the downstream analysis of chromatin loops from single cell Hi-C data, scDEC-Hi-C is capable of significantly enhancing the ability for identifying single cell chromatin loops by data imputation.

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Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG

Cited by 32 publications

References 57 publications

Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer

Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer

Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes

Deep generative modeling and clustering of single cell Hi-C data

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