scholarly journals Representation learning of RNA velocity reveals robust cell transitions

2021 ◽  
Author(s):  
Chen Qiao ◽  
Yuanhua Huang
Keyword(s):  
Cellular Differentiation ◽  
Representation Learning ◽  
Biological Processes ◽  
Dimensional Representation ◽  
Promising Technique ◽  
Technical Noise ◽  
Wide Range ◽  
Cell Transcriptome ◽  
Low Dimensional ◽  
Single Cell Transcriptome

RNA velocity is a promising technique to reveal transient cellular dynamics among a heterogeneous cell population and quantify their transitions from single-cell transcriptome experiments. However, the cell transitions estimated from high dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present VeloAE, a tailored representation learning method to learn a low-dimensional representation of RNA velocity on which cell transitions can be robustly estimated. From various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture the expected cellular differentiation in different biological systems. VeloAE therefore enhances the usefulness of RNA velocity for studying a wide range of biological processes.

scholarly journals Representation learning of RNA velocity reveals robust cell transitions

2021 ◽  
Vol 118 (49) ◽  
pp. e2105859118
Author(s):  
Chen Qiao ◽  
Yuanhua Huang
Keyword(s):  
Cellular Differentiation ◽  
Representation Learning ◽  
Biological Processes ◽  
Dimensional Representation ◽  
Promising Technique ◽  
Technical Noise ◽  
Wide Range ◽  
Cell Transcriptome ◽  
Low Dimensional ◽  
Single Cell Transcriptome

RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present Velocity Autoencoder (VeloAE), a tailored representation learning method, to learn a low-dimensional representation of RNA velocity on which cellular transitions can be robustly estimated. On various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture expected cellular differentiation in different biological systems. VeloAE, therefore, enhances the usefulness of RNA velocity for studying a wide range of biological processes.

scholarly journals Graph representation learning: a survey

2020 ◽  
Vol 9 ◽  
Author(s):  
Fenxiao Chen ◽  
Yun-Cheng Wang ◽  
Bin Wang ◽  
C.-C. Jay Kuo
Keyword(s):  
Graph Embedding ◽  
Large Data ◽  
Representation Learning ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Data ◽  
Graph Properties ◽  
Wide Range ◽  
Regular Lattices ◽  
Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

scholarly journals Building the mega single-cell transcriptome ocular meta-atlas

GigaScience ◽  
2021 ◽  
Vol 10 (10) ◽  
Author(s):  
Vinay S Swamy ◽  
Temesgen D Fufa ◽  
Robert B Hufnagel ◽  
David M McGaughey
Keyword(s):  
Single Cell ◽  
Biological Effects ◽  
R Package ◽  
Technical Noise ◽  
Batch Correction ◽  
Workflow System ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Compare And Contrast ◽  
Cluster Purity

Abstract Background: The development of highly scalable single-cell transcriptome technology has resulted in the creation of thousands of datasets, >30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable because this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects because there are substantial batch effects from computational processing, single-cell technology utilized, and the natural biological variation. While many single-cell transcriptome-specific batch correction methods purport to remove the technical noise, it is difficult to ascertain which method functions best. Results: We developed a lightweight R package (scPOP, single-cell Pick Optimal Parameters) that brings in batch integration methods and uses a simple heuristic to balance batch merging and cell type/cluster purity. We use this package along with a Snakefile-based workflow system to demonstrate how to optimally merge 766,615 cells from 33 retina datsets and 3 species to create a massive ocular single-cell transcriptome meta-atlas. Conclusions: This provides a model for how to efficiently create meta-atlases for tissues and cells of interest.

scholarly journals A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells

2019 ◽  
Author(s):  
Adam Gayoso ◽  
Romain Lopez ◽  
Zoë Steier ◽  
Jeffrey Regier ◽  
Aaron Streets ◽  
...  
Keyword(s):  
Single Cells ◽  
Surface Protein ◽  
Random Variable ◽  
Protein Quantification ◽  
Variational Inference ◽  
Stochastic Variational Inference ◽  
Cell Transcriptome ◽  
Downstream Analysis ◽  
Low Dimensional ◽  
Single Cell Transcriptome

Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) combines unbiased single-cell transcriptome measurements with surface protein quantification comparable to flow cytometry, the gold standard for cell type identification. However, current analysis pipelines cannot address the two primary challenges of CITE-seq data: combining both modalities in a shared latent space that harnesses the power of the paired measurements, and handling the technical artifacts of the protein measurement, which is obscured by non-negligible background noise. Here we present Total Variational Inference (totalVI), a fully probabilistic end-to-end framework for normalizing and analyzing CITE-seq data, based on a hierarchical Bayesian model. In totalVI, the mRNA and protein measurements for each cell are generated from a low-dimensional latent random variable unique to that cell, representing its cellular state. totalVI uses deep neural networks to specify conditional distributions. By leveraging advances in stochastic variational inference, it scales easily to millions of cells. Explicit modeling of nuisance factors enables totalVI to produce denoised data in both domains, as well as a batch-corrected latent representation of cells for downstream analysis tasks.

scholarly journals Exosomes: Versatile Nano Mediators of Immune Regulation

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1557 ◽  
Author(s):  
Li ◽  
Wang ◽  
Peng ◽  
Huyan ◽  
Cacalano
Keyword(s):  
Cellular Differentiation ◽  
Vaccine Development ◽  
Cell Communication ◽  
Diagnostic Tools ◽  
Biological Processes ◽  
Pathological Conditions ◽  
Wide Range ◽  
Almost All ◽  
Novel Drug

One of many types of extracellular vesicles (EVs), exosomes are nanovesicle structures that are released by almost all living cells that can perform a wide range of critical biological functions. Exosomes play important roles in both normal and pathological conditions by regulating cell-cell communication in cancer, angiogenesis, cellular differentiation, osteogenesis, and inflammation. Exosomes are stable in vivo and they can regulate biological processes by transferring lipids, proteins, nucleic acids, and even entire signaling pathways through the circulation to cells at distal sites. Recent advances in the identification, production, and purification of exosomes have created opportunities to exploit these structures as novel drug delivery systems, modulators of cell signaling, mediators of antigen presentation, as well as biological targeting agents and diagnostic tools in cancer therapy. This review will examine the functions of immunocyte-derived exosomes and their roles in the immune response under physiological and pathological conditions. The use of immunocyte exosomes in immunotherapy and vaccine development is discussed.

scholarly journals Multi-View Network Representation Learning Algorithm Research

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 62 ◽  
Author(s):  
Zhonglin Ye ◽  
Haixing Zhao ◽  
Ke Zhang ◽  
Yu Zhu
Keyword(s):  
Learning Algorithm ◽  
Representation Learning ◽  
Research Field ◽  
Dimensional Representation ◽  
Multi Scale ◽  
Network Representation ◽  
Excellent Research ◽  
Network Feature ◽  
The Matrix ◽  
Low Dimensional

Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional representation space. In contrast to existing approaches, MVNR explicitly encodes higher order information using k-step networks. In addition, we introduce the matrix forest index as a kind of network feature, which can be applied to balance the representation weights of different network views. We also research the relevance amongst MVNR and several excellent research achievements, including DeepWalk, node2vec and GraRep and so forth. We conduct our experiment on several real-world citation datasets and demonstrate that MVNR outperforms some new approaches using neural matrix factorization. Specifically, we demonstrate the efficiency of MVNR on network classification, visualization and link prediction tasks.

scholarly journals Building the Mega Single Cell Transcriptome Ocular Meta-Atlas

2021 ◽  
Author(s):  
Vinay S Swamy ◽  
Temesgen D Fufa ◽  
Robert B Hufnagel ◽  
David M McGaughey
Keyword(s):  
Single Cell ◽  
Biological Effects ◽  
R Package ◽  
Technical Noise ◽  
Batch Correction ◽  
Workflow System ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Compare And Contrast ◽  
Cluster Purity

The development of highly scalable single cell transcriptome technology has resulted in the creation of thousands of datasets, over 30 in the retina alone. Analyzing the transcriptomes between different projects is highly desirable as this would allow for better assessment of which biological effects are consistent across independent studies. However it is difficult to compare and contrast data across different projects as there are substantial batch effects from computational processing, single cell technology utilized, and the natural biological variation. While many single cell transcriptome specific batch correction methods purport to remove the technical noise it is difficult to ascertain which method functions works best. We developed a lightweight R package (scPOP) that brings in batch integration methods and uses a simple heuristic to balance batch merging and celltype/cluster purity. We use this package along with a Snakefile based workflow system to demonstrate how to optimally merge 766,615 cells from 34 retina datsets and three species to create a massive ocular single cell transcriptome meta-atlas. This provides a model how to efficiently create meta-atlases for tissues and cells of interest.

Sign in / Sign up

Export Citation Format

Share Document