Multi-omics integration in the age of million single-cell data

2021 ◽  
Author(s):  
Zhen Miao ◽  
Benjamin D. Humphreys ◽  
Andrew P. McMahon ◽  
Junhyong Kim
Keyword(s):  
Single Cell ◽  
Omics Integration ◽  
Cell Data

scholarly journals Multi-omics integration and regulatory inference for unpaired single-cell data with a graph-linked unified embedding framework

2021 ◽  
Author(s):  
Zhi-Jie Cao ◽  
Ge Gao
Keyword(s):  
Single Cell ◽  
Modular Design ◽  
Superior Performance ◽  
Integration Model ◽  
Computational Framework ◽  
Atlas Construction ◽  
Omics Integration ◽  
Feature Spaces ◽  
Challenging Tasks ◽  
Cell Data

With the ever-increasing amount of single-cell multi-omics data accumulated during the past years, effective and efficient computational integration is becoming a serious challenge. One major obstacle of unpaired multi-omics integration is the feature discrepancies among omics layers. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which utilizes accessible prior knowledge about regulatory interactions to bridge the gaps between feature spaces. Systematic benchmarks demonstrated that GLUE is accurate, robust and scalable. We further employed GLUE for various challenging tasks, including triple-omics integration, model-based regulatory inference and multi-omics human cell atlas construction (over millions of cells) and found that GLUE achieved superior performance for each task. As a generalizable framework, GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE for the community.

Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

2021 ◽  
Author(s):  
Jordan W. Squair ◽  
Michael A. Skinnider ◽  
Matthieu Gautier ◽  
Leonard J. Foster ◽  
Grégoire Courtine
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Data

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Integrated analysis of multimodal single-cell data

Cell ◽  
2021 ◽  
Author(s):  
Yuhan Hao ◽  
Stephanie Hao ◽  
Erica Andersen-Nissen ◽  
William M. Mauck ◽  
Shiwei Zheng ◽  
...  
Keyword(s):  
Single Cell ◽  
Integrated Analysis ◽  
Cell Data

scholarly journals High-throughput single cell data analysis – A tutorial

2021 ◽  
pp. 338872
Author(s):  
Gerjen H. Tinnevelt ◽  
Kristiaan Wouters ◽  
Geert J. Postma ◽  
Rita Folcarelli ◽  
Jeroen J. Jansen
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Throughput ◽  
Cell Data

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

Grids from bands, or bands from grids? An examination of the effects of single unit contamination on grid cell firing fields

2016 ◽  
Vol 115 (2) ◽  
pp. 992-1002 ◽  
Author(s):  
Z. Navratilova ◽  
K. B. Godfrey ◽  
B. L. McNaughton
Keyword(s):  
Single Cell ◽  
Grid Cell ◽  
Cell Isolation ◽  
Recording Technology ◽  
Firing Patterns ◽  
Limiting Step ◽  
Isolation Methods ◽  
Single Cell Isolation ◽  
Cell Firing ◽  
Cell Data

Neural recording technology is improving rapidly, allowing for the detection of spikes from hundreds of cells simultaneously. The limiting step in multielectrode electrophysiology continues to be single cell isolation. However, this step is crucial to the interpretation of data from putative single neurons. We present here, in simulation, an illustration of possibly erroneous conclusions that may be reached when poorly isolated single cell data are analyzed. Grid cells are neurons recorded in rodents, and bats, that spike in equally spaced locations in a hexagonal pattern. One theory states that grid firing patterns arise from a combination of band firing patterns. However, we show here that summing the grid firing patterns of two poorly resolved neurons can result in spurious band-like patterns. Thus, evidence of neurons spiking in band patterns must undergo extreme scrutiny before it is accepted. Toward this aim, we discuss single cell isolation methods and metrics.

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