scholarly journals Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space

2021 ◽  
Author(s):  
Lei Xiong ◽  
Kang Tian ◽  
Yuzhe Li ◽  
Qiangfeng Cliff Zhang
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Experimental Conditions ◽  
Multiple Data ◽  
Mouse Tissues ◽  
Heterogeneous Datasets ◽  
Cell Data ◽  
Human And Mouse ◽  
Biological Differences

Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities. However, experimental conditions often confound biological variations when comparing data from different samples. For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space. We demonstrate that SCALEX accurately and efficiently integrates heterogenous single-cell data using multiple benchmarks. It outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We demonstrate the advantages of SCALEX by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19, which were assembled from multiple data sources and can keep growing through the inclusion of new incoming data. Analyses based on these atlases revealed the complex cellular landscapes of human and mouse tissues and identified multiple peripheral immune subtypes associated with COVID-19 disease severity.

scholarly journals Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space

2021 ◽  
Author(s):  
Lei Xiong ◽  
Kang Tian ◽  
Yuzhe Li ◽  
Qiangfeng Zhang
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Experimental Conditions ◽  
Multiple Data ◽  
Mouse Tissues ◽  
Heterogeneous Datasets ◽  
Cell Data ◽  
Human And Mouse ◽  
Biological Differences

Abstract Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities. However, experimental conditions often confound biological variations when comparing data from different samples. For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space. We demonstrate that SCALEX accurately and efficiently integrates heterogenous single-cell data using multiple benchmarks. It outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations while r,etaining true biological differences. We demonstrate the advantages of SCALEX by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19, which were assembled from multiple data sources and can keep growing through the inclusion of new incoming data. Analyses based on these atlases revealed the complex cellular landscapes of human and mouse tissues and identified multiple peripheral immune subtypes associated with COVID-19 disease severity.

scholarly journals Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Dustin J Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cost Constraints ◽  
Mouse Tissues

Abstract RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

scholarly journals propeller: testing for differences in cell type proportions in single cell data

2021 ◽  
Author(s):  
Belinda Phipson ◽  
Choon Boon Sim ◽  
Enzo R. Porrello ◽  
Alex W Hewitt ◽  
Joseph Powell ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
R Package ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Type Composition ◽  
Type Composition ◽  
Biological Replication ◽  
Cell Data ◽  
Different Sources

Single cell RNA Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. To date, there are more than a thousand software packages that have been developed to analyse scRNA-seq data. These focus predominantly on visualization, dimensionality reduction and cell type identification. Single cell technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments which has not been possible to address with bulk RNA-seq data is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportions estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. We present propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. The propeller method is publicly available in the open source speckle R package (https://github.com/Oshlack/speckle).

scholarly journals Automated population identification and sorting algorithms for high-dimensional single-cell data

2016 ◽  
Author(s):  
Benedict Anchang ◽  
Sylvia K. Plevritis
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Intracellular Signaling ◽  
Expert Knowledge ◽  
Single Cells ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Type Specific ◽  
Cell Subpopulations ◽  
Cell Data

AbstractCell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.

scholarly journals IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq

2020 ◽  
Vol 48 (W1) ◽  
pp. W275-W286 ◽  
Author(s):  
Anjun Ma ◽  
Cankun Wang ◽  
Yuzhou Chang ◽  
Faith H Brennan ◽  
Adam McDermaid ◽  
...  
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Complex Diseases ◽  
Specific Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Cell Type ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Human And Mouse

Abstract A group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development. IRIS3 is freely accessible from https://bmbl.bmi.osumc.edu/iris3/ with no login requirement.

scholarly journals scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network

2021 ◽  
Author(s):  
Xin Shao ◽  
Haihong Yang ◽  
Xiang Zhuang ◽  
Jie Liao ◽  
Penghui Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Single Cell ◽  
High Performance ◽  
Weighted Graph ◽  
Transcriptome Profiling ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Mouse Tissues

Abstract Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes or RNA-seq profiles, resulting in poor extrapolation. Still, the accurate cell-type annotation for single-cell transcriptomic data remains a great challenge. Here, we introduce scDeepSort (https://github.com/ZJUFanLab/scDeepSort), a pre-trained cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network (GNN). Using human and mouse scRNA-seq data resources, we demonstrate the high performance and robustness of scDeepSort in labeling 764 741 cells involving 56 human and 32 mouse tissues. Significantly, scDeepSort outperformed other known methods in annotating 76 external test datasets, reaching an 83.79% accuracy across 265 489 cells in humans and mice. Moreover, we demonstrate the universality of scDeepSort using more challenging datasets and using references from different scRNA-seq technology. Above all, scDeepSort is the first attempt to annotate cell types of scRNA-seq data with a pre-trained GNN model, which can realize the accurate cell-type annotation without additional references, i.e. markers or RNA-seq profiles.

scholarly journals Cell type prioritization in single-cell data

2019 ◽  
Author(s):  
Michael A. Skinnider ◽  
Jordan W. Squair ◽  
Claudia Kathe ◽  
Mark A. Anderson ◽  
Matthieu Gautier ◽  
...  
Keyword(s):  
Single Cell ◽  
Neural Circuits ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
High Dimensional ◽  
Machine Learning Method ◽  
Learning Method ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Data

We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.

scholarly journals An improved zebrafish transcriptome annotation for sensitive and comprehensive detection of cell type-specific genes

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Nathan D Lawson ◽  
Rui Li ◽  
Masahiro Shin ◽  
Ann Grosse ◽  
Onur Yukselen ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Human Disease ◽  
Untranslated Regions ◽  
Rna Seq ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Clustering ◽  
A Genome ◽  
Cell Type Specific

The zebrafish is ideal for studying embryogenesis and is increasingly applied to model human disease. In these contexts, RNA-sequencing (RNA-seq) provides mechanistic insights by identifying transcriptome changes between experimental conditions. Application of RNA-seq relies on accurate transcript annotation for a genome of interest. Here, we find discrepancies in analysis from RNA-seq datasets quantified using Ensembl and RefSeq zebrafish annotations. These issues were due, in part, to variably annotated 3' untranslated regions and thousands of gene models missing from each annotation. Since these discrepancies could compromise downstream analyses and biological reproducibility, we built a more comprehensive zebrafish transcriptome annotation that addresses these deficiencies. Our annotation improves detection of cell type-specific genes in both bulk and single cell RNA-seq datasets, where it also improves resolution of cell clustering. Thus, we demonstrate that our new transcriptome annotation can outperform existing annotations, providing an important resource for zebrafish researchers.

scholarly journals Predicting the impact of sequence motifs on gene regulation using single-cell data

2020 ◽  
Author(s):  
Jacob Hepkema ◽  
Nicholas Keone Lee ◽  
Benjamin J. Stewart ◽  
Siwat Ruangroengkulrith ◽  
Varodom Charoensawan ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Human Kidney ◽  
Sequence Motifs ◽  
Cell Type ◽  
Regulatory Motifs ◽  
Mouse Tissues ◽  
The Impact ◽  
Cell Data ◽  
Proximal Promoters

AbstractBinding of transcription factors (TFs) at proximal promoters and distal enhancers is central to gene regulation. Yet, identification of TF binding sites, also known as regulatory motifs, and quantification of their impact remains challenging. Here we present scover, a convolutional neural network model that can discover putative regulatory motifs along with their cell type-specific importance from single-cell data. Analysis of scRNA-seq data from human kidney shows that ETS, YY1 and NRF1 are the most important motif families for proximal promoters. Using multiple mouse tissues we obtain for the first time a model with cell type resolution which explains 34% of the variance in gene expression. Finally, by applying scover to distal enhancers identified using scATAC-seq from the mouse cerebral cortex we highlight the emergence of layer specific regulatory patterns during development.

scholarly journals findPC: An R package to automatically select number of principal components in single-cell analysis

2021 ◽  
Author(s):  
Haotian Zhuang ◽  
Zhicheng Ji
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Principal Component ◽  
R Package ◽  
Optimal Number ◽  
Rna Seq ◽  
Perpendicular Line ◽  
Mouse Tissues ◽  
Overall Performance ◽  
Human And Mouse

Principal component analysis (PCA) is widely used in analyzing single-cell genomic data. Selecting the optimal number of PCs is a crucial step for downstream analyses. The elbow method is most commonly used for this task, but it requires one to visually inspect the elbow plot and manually choose the elbow point. To address this limitation, we developed six methods to automatically select the optimal number of PCs based on the elbow method. We evaluated the performance of these methods on real single-cell RNA-seq data from multiple human and mouse tissues. The perpendicular line method with 20 PCs has the best overall performance, and its results are highly consistent with the numbers of PCs identified manually. We implemented the six methods in an R package, findPC, that objectively selects the number of PCs and can be easily incorporated into any automatic analysis pipeline.

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