scholarly journals APEC: an accesson-based method for single-cell chromatin accessibility analysis

2019 ◽  
Author(s):  
Bin Li ◽  
Young Li ◽  
Kun Li ◽  
Lianbang Zhu ◽  
Qiaoni Yu ◽  
...  
Keyword(s):  
Single Cell ◽  
Chromatin Accessibility ◽  
Developmental Trajectory ◽  
Data Sets ◽  
Gene Expressions ◽  
Sequencing Technologies ◽  
Cell Clustering ◽  
Public Data ◽  
Accessibility Pattern ◽  
Analytical Tools

ABSTRACTThe development of sequencing technologies has promoted the survey of genome-wide chromatin accessibility at single-cell resolution; however, comprehensive analysis of single-cell epigenomic profiles remains a challenge. Here, we introduce an accessibility pattern-based epigenomic clustering (APEC) method, which classifies each individual cell by groups of accessible regions with synergistic signal patterns termed “accessons”. By integrating with other analytical tools, this python-based APEC package greatly improves the accuracy of unsupervised single-cell clustering for many different public data sets. APEC also predicts gene expressions, identifies significant differential enriched motifs, discovers super enhancers, and projects pseudotime trajectories. Furthermore, we adopted a fluorescent tagmentation-based single-cell ATAC-seq technique (ftATAC-seq) to investigated the per cell regulome dynamics of mouse thymocytes. Associated with ftATAC-seq, APEC revealed a detailed epigenomic heterogeneity of thymocytes, characterized the developmental trajectory and predicted the regulators that control the stages of maturation process. Overall, this work illustrates a powerful approach to study single-cell epigenomic heterogeneity and regulome dynamics.

scholarly journals A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Shaliu Fu ◽  
Shuguang Wang ◽  
Chenyu Zhu ◽  
Bin Duan ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Element ◽  
Chromatin Accessibility ◽  
Generative Model ◽  
Developmental Trajectory ◽  
Differential Analysis ◽  
Sequencing Data ◽  
Cell Clustering ◽  
Cell Groups ◽  
Latent Representations

AbstractHere, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.

scholarly journals Developmental trajectory of prehematopoietic stem cell formation from endothelium

Blood ◽  
2020 ◽  
Vol 136 (7) ◽  
pp. 845-856 ◽  
Author(s):  
Qin Zhu ◽  
Peng Gao ◽  
Joanna Tober ◽  
Laura Bennett ◽  
Changya Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Fetal Liver ◽  
Developmental Trajectories ◽  
Cell Formation ◽  
Intermediate Stage ◽  
Chromatin Accessibility ◽  
Small Population ◽  
Developmental Trajectory ◽  
Mammalian Embryo ◽  
Hematopoietic Stem

Abstract Hematopoietic stem and progenitor cells (HSPCs) in the bone marrow are derived from a small population of hemogenic endothelial (HE) cells located in the major arteries of the mammalian embryo. HE cells undergo an endothelial to hematopoietic cell transition, giving rise to HSPCs that accumulate in intra-arterial clusters (IAC) before colonizing the fetal liver. To examine the cell and molecular transitions between endothelial (E), HE, and IAC cells, and the heterogeneity of HSPCs within IACs, we profiled ∼40 000 cells from the caudal arteries (dorsal aorta, umbilical, vitelline) of 9.5 days post coitus (dpc) to 11.5 dpc mouse embryos by single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing. We identified a continuous developmental trajectory from E to HE to IAC cells, with identifiable intermediate stages. The intermediate stage most proximal to HE, which we term pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. A distal candidate Runx1 enhancer exhibits high chromatin accessibility specifically in pre-HE cells at the bottleneck, but loses accessibility thereafter. Distinct developmental trajectories within IAC cells result in 2 populations of CD45+ HSPCs; an initial wave of lymphomyeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs). This multiomics single-cell atlas significantly expands our understanding of pre-HSC ontogeny.

scholarly journals Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

2019 ◽  
Author(s):  
Shiquan Sun ◽  
Jiaqiang Zhu ◽  
Ying Ma ◽  
Xiang Zhou
Keyword(s):  
Data Analysis ◽  
Dimensionality Reduction ◽  
Single Cell ◽  
Comprehensive Evaluation ◽  
Computational Cost ◽  
Noise Removal ◽  
Data Sets ◽  
Vast Number ◽  
Cell Clustering ◽  
Reduction Methods

ABSTRACTBackgroundDimensionality reduction (DR) is an indispensable analytic component for many areas of single cell RNA sequencing (scRNAseq) data analysis. Proper DR can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of DR in scRNAseq analysis and the vast number of DR methods developed for scRNAseq studies, however, few comprehensive comparison studies have been performed to evaluate the effectiveness of different DR methods in scRNAseq.ResultsHere, we aim to fill this critical knowledge gap by providing a comparative evaluation of a variety of commonly used DR methods for scRNAseq studies. Specifically, we compared 18 different DR methods on 30 publicly available scRNAseq data sets that cover a range of sequencing techniques and sample sizes. We evaluated the performance of different DR methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix, and for cell clustering and lineage reconstruction in terms of their accuracy and robustness. We also evaluated the computational scalability of different DR methods by recording their computational cost.ConclusionsBased on the comprehensive evaluation results, we provide important guidelines for choosing DR methods for scRNAseq data analysis. We also provide all analysis scripts used in the present study atwww.xzlab.org/reproduce.html. Together, we hope that our results will serve as an important practical reference for practitioners to choose DR methods in the field of scRNAseq analysis.

scholarly journals Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning

2021 ◽  
Author(s):  
Wolfgang Kopp ◽  
Altuna Akalin ◽  
Uwe Ohler
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Learning Strategy ◽  
Single Cells ◽  
Simulated Data ◽  
Chromatin Accessibility ◽  
Noise Model ◽  
Data Sets ◽  
Batch Correction ◽  
Regulatory Processes

Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens of thousands of single cells, shedding light on gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality of the resulting data continue to pose challenges for its computational analysis, and specifically the integration of data from different sources. We have developed a dedicated computational approach, a variational auto-encoder using a noise model specifically designed for single-cell ATAC-seq data, which facilitates simultaneous dimensionality reduction and batch correction via an adversarial learning strategy. We showcase both its individual advantages on carefully chosen real and simulated data sets, as well as the benefits for detailed cell type characterization via integrating multiple complex datasets.

scholarly journals Single-cell epigenomics maps the continuous regulatory landscape of human hematopoietic differentiation

2017 ◽  
Author(s):  
Jason D Buenrostro ◽  
M Ryan Corces ◽  
Beijing Wu ◽  
Alicia N Schep ◽  
Caleb A Lareau ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Developmental Trajectory ◽  
Hematopoietic Differentiation ◽  
Ensemble Averaging ◽  
Regulatory Variation ◽  
Multipotent Cells ◽  
Normal Human

AbstractNormal human hematopoiesis involves cellular differentiation of multipotent cells into progressively more lineage-restricted states. While epigenomic landscapes of this process have been explored in immunophenotypically-defined populations, the single-cell regulatory variation that defines hematopoietic differentiation has been hidden by ensemble averaging. We generated single-cell chromatin accessibility landscapes across 8 populations of immunophenotypically-defined human hematopoietic cell types. Using bulk chromatin accessibility profiles to scaffold our single-cell data analysis, we constructed an epigenomic landscape of human hematopoiesis and characterized epigenomic heterogeneity within phenotypically sorted populations to find epigenomic lineage-bias toward different developmental branches in multipotent stem cell states. We identify and isolate sub-populations within classically-defined granulocyte-macrophage progenitors (GMPs) and use ATAC-seq and RNA-seq to confirm that GMPs are epigenomically and transcriptomically heterogeneous. Furthermore, we identified transcription factors andcis-regulatory elements linked to changes in chromatin accessibility within cellular populations and across a continuous myeloid developmental trajectory, and observe relatively simple TF motif dynamics give rise to a broad diversity of accessibility dynamics at cis-regulatory elements. Overall, this work provides a template for exploration of complex regulatory dynamics in primary human tissues at the ultimate level of granular specificity – the single cell.One Sentence SummarySingle cell chromatin accessibility reveals a high-resolution, continuous landscape of regulatory variation in human hematopoiesis.

scholarly journals ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis

2020 ◽  
Author(s):  
Jeffrey M. Granja ◽  
M. Ryan Corces ◽  
Sarah E. Pierce ◽  
S. Tansu Bagdatli ◽  
Hani Choudhry ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Biological Meaning ◽  
Cell Clustering ◽  
Gene Regulatory ◽  
Regulatory Landscapes ◽  
Scalable Software

ABSTRACTThe advent of large-scale single-cell chromatin accessibility profiling has accelerated our ability to map gene regulatory landscapes, but has outpaced the development of robust, scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; www.ArchRProject.com) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses including doublet removal, single-cell clustering and cell type identification, robust peak set generation, cellular trajectory identification, DNA element to gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility, and multi-omic integration with scRNA-seq. Enabling the analysis of over 1.2 million single cells within 8 hours on a standard Unix laptop, ArchR is a comprehensive analytical suite for end-to-end analysis of single-cell chromatin accessibility data that will accelerate the understanding of gene regulation at the resolution of individual cells.

scholarly journals CReSCENT: CanceR Single Cell ExpressioN Toolkit

2020 ◽  
Author(s):  
Suluxan Mohanraj ◽  
J. Javier Díaz-Mejía ◽  
Martin D. Pham ◽  
Hillary Elrick ◽  
Mia Husić ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Biology ◽  
High Performance ◽  
Data Sets ◽  
User Expertise ◽  
Execution Engine ◽  
Public Data ◽  
Single Cell Rna Sequencing ◽  
Cell Expression ◽  
Performance Computing

ABSTRACTCReSCENTCanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

scholarly journals Correlation of gene expressions between nucleus and cytoplasm reflects single-cell physiology

2017 ◽  
Author(s):  
Mahmoud N. Abdelmoez ◽  
Kei Iida ◽  
Yusuke Oguchi ◽  
Hidekazu Nishikii ◽  
Ryuji Yokokawa ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Splicing ◽  
Single Cells ◽  
Physiological State ◽  
Microfluidic System ◽  
Leukemic Cells ◽  
Cell Physiology ◽  
Rna Seq ◽  
Gene Expressions ◽  
Sequencing Technologies

BackgroundEukaryotes transcribe RNAs in nuclei and transport them to the cytoplasm through multiple steps of post-transcriptional regulation. Existing single-cell sequencing technologies, however, are unable to analyse nuclear (nuc) and cytoplasmic (cyt) RNAs separately and simultaneously. Hence, there remain challenges to discern correlation, localisation, and translocation between them.ResultsHere we report a microfluidic system that physically separates nucRNA and cytRNA from a single cell and enables single-cell integrated nucRNA and cytRNA-sequencing (SINC-seq). SINC-seq constructs two individual RNA-seq libraries, nucRNA and cytRNA per cell, quantifies gene expression in the subcellular compartments and combines them to create a novel single-cell RNA-seq data enabled by our system, which we here term in-silico single cell.ConclusionsLeveraging SINC-seq, we discovered three distinct natures of correlation among cytRNA and nucRNA that reflected the physiological state of single cells: The cell-cycle-related genes displayed highly correlated expression pattern with minor differences; RNA splicing genes showed lower nucRNA-to-cytRNA correlation, suggesting a retained intron may be implicated in inhibited mRNA transport; A chemical perturbation, sodium butyrate treatment, transiently distorted the correlation along differentiating human leukemic cells to erythroid cells. These data uniquely provide insights into the regulatory network of mRNA from nucleus toward cytoplasm at the single cell level.

scholarly journals Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

2017 ◽  
Author(s):  
Joshua D. Welch ◽  
Alexander J. Hartemink ◽  
Jan F. Prins
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Epigenetic Modification ◽  
Single Cells ◽  
Embryonic Stem ◽  
Genomic Data ◽  
Chromatin Accessibility ◽  
Data Sets ◽  
Cell Transcriptome ◽  
Dynamic Interplay

AbstractSingle cell genomic techniques promise to yield key insights into the dynamic interplay between gene expression and epigenetic modification. However, the experimental difficulty of performing multiple measurements on the same cell currently limits efforts to combine multiple genomic data sets into a united picture of single cell variation. We show that it is possible to construct cell trajectories, reflecting the changes that occur in a sequential biological process, from single cell ATAC-seq, bisulfite sequencing, and ChIP-seq data. In addition, we present an approach called MATCHER that computationally circumvents the experimental difficulties inherent in performing multiple genomic measurements on a single cell by inferring correspondence between single cell transcriptomic and epigenetic measurements performed on different cells of the same type. MATCHER works by first learning a separate manifold for the trajectory of each kind of genomic data, then aligning the manifolds to infer a shared trajectory in which cells measured using different techniques are directly comparable. Using scM&T-seq data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondence information. We also used MATCHER to infer correlations among gene expression, chromatin accessibility, and histone modifications in single mouse embryonic stem cells. These results reveal the dynamic interplay between epigenetic changes and gene expression underlying the transition from pluripotency to differentiation priming. Our work is a first step toward a united picture of heterogeneous transcriptomic and epigenetic states in single cells.

scholarly journals Efficient and scalable integration of single-cell data using domain-adversarial and variational approximation

2021 ◽  
Author(s):  
Jialu Hu ◽  
Yuanke Zhong ◽  
Xuequn Shang
Keyword(s):  
Single Cell ◽  
Latent Variable ◽  
Real Data ◽  
Variational Approximation ◽  
Data Sets ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Non Linear ◽  
Post Hoc ◽  
Cell Data

Single-cell data provides us new ways of discovering biological truth at the level of individual cells, such as identification of cellular sub-populations and cell development. With the development of single-cell sequencing technologies, a key analytical challenge is to integrate these data sets to uncover biological insights. Here, we developed a domain-adversarial and variational approximation framework, DAVAE, to integrate multiple single-cell data across samples, technologies and modalities without any post hoc data processing. We fit normalized gene expression into a non-linear model, which transforms a latent variable of a lower-dimension into expression space with a non-linear function, a KL regularizier and a domain-adversarial regularizer. Results on five real data integration applications demonstrated the effectiveness and scalability of DAVAE in batch-effect removing, transfer learning, and cell type predictions for multiple single-cell data sets across samples, technologies and modalities. DAVAE was implemented in the toolkit package scbean in the pypi repository, and the source code can be also freely accessible at https://github.com/jhu99/scbean.

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