The rapid increase of genome-wide datasets on gene expression, chromatin states, and transcription factor (TF) binding locations offers an exciting opportunity to interpret the information encoded in genomes and epigenomes. This task can be challenging as it requires joint modeling of context-specific activation of cis-regulatory elements (REs) and the effects on transcription of associated regulatory factors. To meet this challenge, we propose a statistical approach based on paired expression and chromatin accessibility (PECA) data across diverse cellular contexts. In our approach, we model (i) the localization to REs of chromatin regulators (CRs) based on their interaction with sequence-specific TFs, (ii) the activation of REs due to CRs that are localized to them, and (iii) the effect of TFs bound to activated REs on the transcription of target genes (TGs). The transcriptional regulatory network inferred by PECA provides a detailed view of how trans-and cis-regulatory elements work together to affect gene expression in a context-specific manner. We illustrate the feasibility of this approach by analyzing paired expression and accessibility data from the mouse Encyclopedia of DNA Elements (ENCODE) and explore various applications of the resulting model. gene regulation | transcription factor | regulatory element | chromatin regulator | chromatin activity E ver since the emergence of high-throughput gene expression experiments (1), computational biologists have been interested in the inference of gene regulatory relationships from gene expression data across diverse cellular contexts corresponding to diverse cell types and experimental conditions ( Fig. 1, red boxes). However, progress has been hindered by the fact that gene expression measurements provide little information on underlying regulatory mechanisms such as transcription factor binding and chromatin modification. To fill this gap, chromatin immunoprecipitationbased methods (2, 3) have been developed for the genome-wide mapping of transcriptional regulator binding locations and the detection of epigenetic marks characteristic of specific chromatin states. For example, by performing thousands of ChIP-seq experiments, the Encyclopedia of DNA Elements (ENCODE) consortium has generated such data for many chromatin marks and transcriptional regulators on a small number of cell lines (Fig. 1, green boxes). However, because a large number of transcriptional regulators and chromatin marks have to be analyzed one by one, it is unlikely that such comprehensive data will become available for many other cell lines. For most cellular contexts, the desired data will remain missing in the foreseeable future (Fig. 1, gray boxes).On the other hand, it is known that many of the protein-DNA interactions important for gene regulation occur in regulatory elements (REs) such as enhancers and insulators, which compose only a small portion of the noncoding sequences in a genome. The REs active in gene regulation in a given cellular state tend to have an open chromatin struc...

SignificanceBiological samples are often heterogeneous mixtures of different types of cells. Suppose we have two single-cell datasets, each providing information on a different cellular feature and generated on a different sample from this mixture. Then, the clustering of cells in the two samples should be coupled as both clusterings are reflecting the underlying cell types in the same mixture. This “coupled clustering” problem is a new problem not covered by existing clustering methods. In this paper, we develop an approach for its solution based on the coupling of two nonnegative matrix factorizations. The method should be useful for integrative single-cell genomics analysis tasks such as the joint analysis of single-cell RNA-sequencing and single-cell ATAC-sequencing data.

In both Turner syndrome (TS) and Klinefelter syndrome (KS) copy number aberrations of the X chromosome lead to various developmental symptoms. We report a comparative analysis of TS vs. KS regarding differences at the genomic network level measured in primary samples by analyzing gene expression, DNA methylation, and chromatin conformation. X-chromosome inactivation (XCI) silences transcription from one X chromosome in female mammals, on which most genes are inactive, and some genes escape from XCI. In TS, almost all differentially expressed escape genes are down-regulated but most differentially expressed inactive genes are up-regulated. In KS, differentially expressed escape genes are up-regulated while the majority of inactive genes appear unchanged. Interestingly, 94 differentially expressed genes (DEGs) overlapped between TS and female and KS and male comparisons; and these almost uniformly display expression changes into opposite directions. DEGs on the X chromosome and the autosomes are coexpressed in both syndromes, indicating that there are molecular ripple effects of the changes in X chromosome dosage. Six potential candidate genes (RPS4X,SEPT6,NKRF,CX0rf57,NAA10, andFLNA) for KS are identified on Xq, as well as candidate central genes on Xp for TS. Only promoters of inactive genes are differentially methylated in both syndromes while escape gene promoters remain unchanged. The intrachromosomal contact map of the X chromosome in TS exhibits the structure of an active X chromosome. The discovery of shared DEGs indicates the existence of common molecular mechanisms for gene regulation in TS and KS that transmit the gene dosage changes to the transcriptome.

Graphical Abstract Highlights d Epigenome landscape maps reveal key transitions during epidermal lineage commitment d Network modeling identifies master regulators of lineage initiation and maturation d TFAP2C drives chromatin dynamics during initiation and primes p63-dependent maturation d Crosstalk between TFAP2C and p63 drives epigenetic transitions during differentiation

Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.

When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this "coupled clustering" problem as an optimization problem, and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single cell RNA-seq and single cell ATAC-seq data.Key words: coupled clustering, NMF, single cell genomic data Significance StatementsBiological samples are often heterogeneous mixtures of different types of cells. Suppose we have two single cell data sets, each providing information on a different cellular feature and generated on a different sample from this mixture. Then, the clustering of cells in the two samples should be coupled . CC-BY-NC-ND 4.0 International license It is made available under awas not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/312348 doi: bioRxiv preprint first posted online May. 2, 2018; as both clusterings are reflecting the underlying cell types in the same mixture. This "coupled clustering" problem is a new problem not covered by existing clustering methods. In this paper we develop an approach for its solution based the coupling of two nonnegative matrix factorizations. The method should be useful for integrative single cell genomics analysis tasks such as the joint analysis of single cell RNA-seq and single cell ATAC-seq data.

Time course experiment is a widely used design in the study of cellular processes such as differentiation or response to stimuli. In this paper, we propose TimeReg (Time Course Regulatory Analysis) as a method for the analysis of gene regulatory networks based on paired gene expression and chromatin accessibility data from the time course. TimeReg can be used to prioritize regulatory elements, to extract core regulatory modules at each time point, to identify key regulators driving changes of the cellular state, and to causally connect the modules across different time points. We applied the method to analyze paired chromatin accessibility and gene expression data from retinoic acid (RA) induced mouse embryonic stem cells (mESC) differentiation experiment. The analysis identified 57,048 novel regulatory elements, regulating cerebellar development, synapse assembly and hindbrain morphogenesis, which substantially extended our knowledge of cis-regulatory elements during the differentiation. Using single cell RNA-seq data, we showed that the core regulatory modules can reflect the properties of different subpopulations of cells. Finally, the driver regulators are shown to be important in clarifying the relations between modules across adjacent time points. As a second example, our method on Ascl1 induced direct reprogramming from fibroblast to neuron time-course data identified Id1/2 as driver regulators of early stage of reprogramming.

High-altitude adaptation of Tibetans represents a remarkable case of natural selection during recent human evolution. Previous genome-wide scans found many non-coding variants under selection, suggesting a pressing need to understand the functional role of non-coding regulatory elements (REs). Here, we generate time courses of paired ATAC-seq and RNA-seq data on cultured HUVECs under hypoxic and normoxic conditions. We further develop a variant interpretation methodology (vPECA) to identify active selected REs (ASREs) and associated regulatory network. We discover three causal SNPs of EPAS1, the key adaptive gene for Tibetans. These SNPs decrease the accessibility of ASREs with weakened binding strength of relevant TFs, and cooperatively down-regulate EPAS1 expression. We further construct the downstream network of EPAS1, elucidating its roles in hypoxic response and angiogenesis. Collectively, we provide a systematic approach to interpret phenotype-associated noncoding variants in proper cell types and relevant dynamic conditions, to model their impact on gene regulation.