scholarly journals Generalized and scalable trajectory inference in single-cell omics data with VIA

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shobana V. Stassen ◽  
Gwinky G. K. Yip ◽  
Kenneth K. Y. Wong ◽  
Joshua W. K. Ho ◽  
Kevin K. Tsia
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Omics Data ◽  
High Cell ◽  
Data Types ◽  
Cell Fates ◽  
Fine Grained ◽  
Sheer Size ◽  
Cell Data ◽  
Omic Data

AbstractInferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

scholarly journals VIA: Generalized and scalable trajectory inference in single-cell omics data

2021 ◽  
Author(s):  
Shobana V. Stassen ◽  
Gwinky G. K. Yip ◽  
Kenneth K. Y. Wong ◽  
Joshua W. K. Ho ◽  
Kevin K. Tsia
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Omics Data ◽  
High Cell ◽  
Data Types ◽  
Cell Fates ◽  
Fine Grained ◽  
Sheer Size ◽  
Cell Data ◽  
Omic Data

AbstractInferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g. cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

scholarly journals Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo

Science ◽  
2018 ◽  
Vol 360 (6392) ◽  
pp. 981-987 ◽  
Author(s):  
Daniel E. Wagner ◽  
Caleb Weinreb ◽  
Zach M. Collins ◽  
James A. Briggs ◽  
Sean G. Megason ◽  
...  
Keyword(s):  
Single Cell ◽  
Zebrafish Embryo ◽  
Cell Lineage ◽  
Vertebrate Development ◽  
Cell Mapping ◽  
Cell Fates ◽  
Web Based ◽  
A Cell ◽  
Cell Data ◽  
Germ Layer Formation

High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here we applied single-cell RNA sequencing to >92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell-state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by developing a transposon-based barcoding approach (TracerSeq) for reconstructing single-cell lineage histories. Clonally related cells were often restricted by the state landscape, including a case in which two independent lineages converge on similar fates. Cell fates remained restricted to this landscape in embryos lacking the chordin gene. We provide web-based resources for further analysis of the single-cell data.

scholarly journals scDIOR: single cell RNA-seq data IO software

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Huijian Feng ◽  
Lihui Lin ◽  
Jiekai Chen
Keyword(s):  
Single Cell ◽  
Programming Languages ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Rapid Development ◽  
Data Transformation ◽  
Rna Seq ◽  
Data Types ◽  
User Friendly ◽  
Cell Data

Abstract Background Single-cell RNA sequencing is becoming a powerful tool to identify cell states, reconstruct developmental trajectories, and deconvolute spatial expression. The rapid development of computational methods promotes the insight of heterogeneous single-cell data. An increasing number of tools have been provided for biological analysts, of which two programming languages- R and Python are widely used among researchers. R and Python are complementary, as many methods are implemented specifically in R or Python. However, the different platforms immediately caused the data sharing and transformation problem, especially for Scanpy, Seurat, and SingleCellExperiemnt. Currently, there is no efficient and user-friendly software to perform data transformation of single-cell omics between platforms, which makes users spend unbearable time on data Input and Output (IO), significantly reducing the efficiency of data analysis. Results We developed scDIOR for single-cell data transformation between platforms of R and Python based on Hierarchical Data Format Version 5 (HDF5). We have created a data IO ecosystem between three R packages (Seurat, SingleCellExperiment, Monocle) and a Python package (Scanpy). Importantly, scDIOR accommodates a variety of data types across programming languages and platforms in an ultrafast way, including single-cell RNA-seq and spatial resolved transcriptomics data, using only a few codes in IDE or command line interface. For large scale datasets, users can partially load the needed information, e.g., cell annotation without the gene expression matrices. scDIOR connects the analytical tasks of different platforms, which makes it easy to compare the performance of algorithms between them. Conclusions scDIOR contains two modules, dior in R and diopy in Python. scDIOR is a versatile and user-friendly tool that implements single-cell data transformation between R and Python rapidly and stably. The software is freely accessible at https://github.com/JiekaiLab/scDIOR.

scholarly journals BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

2020 ◽  
Author(s):  
Kevin E. Wu ◽  
Kathryn E. Yost ◽  
Howard Y. Chang ◽  
James Zou
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Exploration ◽  
Grand Challenge ◽  
Fine Grained ◽  
Powerful Approach ◽  
Technical Innovations ◽  
Cell Data ◽  
Human And Mouse

AbstractSimultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility – for example generating paired measurements of scRNA-seq and scATAC-seq – wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cell’s scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

scholarly journals Ethics as Methods: Doing Ethics in the Era of Big Data Research—Introduction

2018 ◽  
Vol 4 (3) ◽  
pp. 205630511878450 ◽  
Author(s):  
Annette N Markham ◽  
Katrin Tiidenberg ◽  
Andrew Herman
Keyword(s):  
Big Data ◽  
Data Science ◽  
New Materialism ◽  
Grand Narrative ◽  
Feminist Ethics ◽  
Special Issue ◽  
Data Set ◽  
Fine Grained ◽  
Sheer Size ◽  
Theoretical Paradigms

This is an introduction to the special issue of “Ethics as Methods: Doing Ethics in the Era of Big Data Research.” Building on a variety of theoretical paradigms (i.e., critical theory, [new] materialism, feminist ethics, theory of cultural techniques) and frameworks (i.e., contextual integrity, deflationary perspective, ethics of care), the Special Issue contributes specific cases and fine-grained conceptual distinctions to ongoing discussions about the ethics in data-driven research. In the second decade of the 21st century, a grand narrative is emerging that posits knowledge derived from data analytics as true, because of the objective qualities of data, their means of collection and analysis, and the sheer size of the data set. The by-product of this grand narrative is that the qualitative aspects of behavior and experience that form the data are diminished, and the human is removed from the process of analysis. This situates data science as a process of analysis performed by the tool, which obscures human decisions in the process. The scholars involved in this Special Issue problematize the assumptions and trends in big data research and point out the crisis in accountability that emerges from using such data to make societal interventions. Our collaborators offer a range of answers to the question of how to configure ethics through a methodological framework in the context of the prevalence of big data, neural networks, and automated, algorithmic governance of much of human socia(bi)lity

scholarly journals Eleven grand challenges in single-cell data science

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
David Lähnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J. McCarthy ◽  
Stephanie C. Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Grand Challenges ◽  
Cell Data

scholarly journals 12 Grand Challenges in Single-Cell Data Science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.

scholarly journals 12 Grand challenges in single-cell data science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szcureck ◽  
Davis McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

scholarly journals Feature selection revisited in the single-cell era

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Pengyi Yang ◽  
Hao Huang ◽  
Chunlei Liu
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Feature Selection Method ◽  
Future Research ◽  
Data Types ◽  
Future Directions ◽  
Recent Developments ◽  
Feature Selection Techniques ◽  
High Dimensional Datasets ◽  
Cell Data

AbstractRecent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.

scholarly journals Software for the integration of multi-omics experiments in Bioconductor

2017 ◽  
Author(s):  
Marcel Ramos ◽  
Lucas Schiffer ◽  
Angela Re ◽  
Rimsha Azhar ◽  
Azfar Basunia ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Science ◽  
Data Representation ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Omics Data ◽  
Data Types ◽  
Design Data ◽  
High Throughput Data ◽  
Cancer Genome Atlas

ABSTRACTMulti-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide all of the multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.

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