scholarly journals Optimal transport analysis reveals trajectories in steady-state systems

2021 ◽  
Vol 17 (12) ◽  
pp. e1009466
Author(s):  
Stephen Zhang ◽  
Anton Afanassiev ◽  
Laura Greenstreet ◽  
Tetsuya Matsumoto ◽  
Geoffrey Schiebinger
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Single Cell Analysis ◽  
Simulated Data ◽  
Unified Approach ◽  
Transport Analysis ◽  
Time Courses ◽  
Cell Trajectories ◽  
Cell Data ◽  
Natural Way

Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore, optimal transport provides a unified approach to inferring trajectories that is applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington’s epigenetic landscape. We implement StationaryOT as a software package and demonstrate its efficacy in applications to simulated data as well as single-cell data from Arabidopsis thaliana root development.

scholarly journals Optimal transport analysis reveals trajectories in steady-state systems

2021 ◽  
Author(s):  
Stephen Zhang ◽  
Anton Afanassiev ◽  
Laura Greenstreet ◽  
Tetsuya Matsumoto ◽  
Geoffrey Schiebinger
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Single Cell Analysis ◽  
Simulated Data ◽  
Unified Approach ◽  
Transport Analysis ◽  
Time Courses ◽  
Cell Trajectories ◽  
Cell Data ◽  
Natural Way

AbstractUnderstanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore optimal transport provides a unified approach to inferring trajectories, applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington’s epigenetic landscape. We implemented StationaryOT as a software package and demonstrate its efficacy when applied to simulated data as well as single-cell data from Arabidopsis thaliana root development.

scholarly journals Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming

Cell ◽  
2019 ◽  
Vol 176 (4) ◽  
pp. 928-943.e22 ◽  
Author(s):  
Geoffrey Schiebinger ◽  
Jian Shu ◽  
Marcin Tabaka ◽  
Brian Cleary ◽  
Vidya Subramanian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Optimal Transport ◽  
Developmental Trajectories ◽  
Cell Gene Expression ◽  
Transport Analysis ◽  
Cell Gene

scholarly journals CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

2019 ◽  
Author(s):  
Thomas D. Sherman ◽  
Tiger Gao ◽  
Elana J. Fertig
Keyword(s):  
Single Cell ◽  
Data Structures ◽  
Computational Efficiency ◽  
Matrix Factorization ◽  
Single Cell Analysis ◽  
Sparse Data ◽  
Data Sets ◽  
Cell Analysis ◽  
Gradient Based ◽  
Cell Data

AbstractMotivationBayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis.ResultsWe upgraded CoGAPS in Version 3 to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This software includes a new parallelization framework that is designed around the sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data. Altogether, these updates to CoGAPS enhance the efficiency of the algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.AvailabilityCoGAPS is available as a Bioconductor package and the source code is provided at github.com/FertigLab/CoGAPS. All efficiency updates to enable single-cell analysis available as of version [email protected]

scholarly journals Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes

2018 ◽  
Author(s):  
Karren Dai Yang ◽  
Karthik Damodaran ◽  
Saradha Venkatchalapathy ◽  
Ali C. Soylemezoglu ◽  
G.V. Shivashankar ◽  
...  
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Chromatin Condensation ◽  
Breast Cancer Cell Lines ◽  
Biological Processes ◽  
Proof Of Concept ◽  
Tissue Samples ◽  
Early Biomarkers ◽  
Latent Space ◽  
Cell Trajectories

AbstractAlthough we can increasingly image and measure biological processes at single-cell resolution, most assays can only take snapshots from a population of cells in time. Here we describe ImageAEOT, which combines an AutoEncoder, to map single-cell Images from different cell populations to a common latent space, with the framework of Optimal Transport to infer cellular trajectories. As a proof-of-concept, we apply ImageAEOT to nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Importantly, ImageAEOT can infer the trajectory of a particular cell from one snapshot in time and identify the changing features to provide early biomarkers for developmental and disease progression.

scholarly journals EPCO-12. SINGLE CELL PROFILING OF LONG NON-CODING RNAS IN GLIOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii71-ii71
Author(s):  
Bharati Mehani ◽  
Hye-Jung Chung ◽  
Russell Bandle ◽  
Sarah Young ◽  
Michael Kelly ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Glioma Cells ◽  
Specific Expression ◽  
Lncrna Expression ◽  
Tumor Tissues ◽  
Single Cell Profiling ◽  
Non Coding Rnas ◽  
And Migration ◽  
Cell Data

Abstract Non-coding RNAs have critical functions across biological processes that regulate glioma initiation and progression, and deregulated expression of long non-coding RNAs (lncRNAs) have been implicated in the onset and progression of malignancies. The majority of these transcripts exhibit tissue- and cancer-specific expression but little has been investigated at the single-cell level. We performed single cell RNA Sequencing (10x Genomics) for 9 IDH-wild-type glioblastomas from 7 patients. In total 66,825 cells dissociated from tumor tissues and not sorted were included in this analysis which encompassed 41,989 mean sequencing reads and 2,619 median coding genes per cell. Single cell analysis of lncRNAs in captured 190 median lncRNAs per cell and demonstrated a distinct lncRNA expression profile for glioma cells compared to the non-tumor cells with SOX2-OT significantly upregulated (2X) in glioma cells. Consistent with this finding, SOX2-OT is known to be overexpressed in a variety of cancers and has been previously implicated in glioma proliferation and migration. We then examined patterns of lncRNA expression in GBM expression subtypes. Subtype correlation indicated overexpression of RMST (classical subtype), PCED1B-AS1 (mesenchymal) and LINC00689 (proneural) lncRNAs in these expression subtypes. Consistent with these findings, upregulation of each of these 3 lncRNAs have previously been implicated on pro-tumorigenic effects, including in glioma. Examination of an independent published single cell GBM dataset also validated PCED1B-AS1 in the mesenchymal subtype. Comparison with bulk tumor GBM profiles (IDHwt TCGA GBM dataset) also showed correlations with the expression of RMST, PCED1B-AS1 and LINC00689 lncRNAs in the classical, mesenchymal and proneural subtypes respectively. Overall, these results indicate lncRNA expression can be determined in 10x-generated glioma single cell data and may reveal additional insights about cellular state and glioma biology.

scholarly journals GranatumX: A community engaging and flexible software environment for single-cell analysis

2018 ◽  
Author(s):  
Xun Zhu ◽  
Breck Yunits ◽  
Thomas Wolfgruber ◽  
Yu Liu ◽  
Qianhui Huang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Programming Languages ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Software Environment ◽  
Software Developers ◽  
Software Ecosystem ◽  
Graphical Environment ◽  
Cell Data

AbstractWe present GranatumX, the next-generation software environment for single-cell data analysis. It enables biologists access to the latest single-cell bioinformatics methods in a graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named “Gboxes”, that wrap around bioinformatics tools written in various programming languages. GranatumX can be run in the cloud or private servers, and generate reproducible results. It is expected to become a community-engaging, flexible, and evolving software ecosystem for scRNA-Seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at: http://garmiregroup.org/granatumx/app

scholarly journals Poincaré Maps for Analyzing Complex Hierarchies in Single-Cell Data

2019 ◽  
Author(s):  
Anna Klimovskaia ◽  
David Lopez-Paz ◽  
Léon Bottou ◽  
Maximilian Nickel
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Hyperbolic Geometry ◽  
Continuous Extension ◽  
Two Dimensions ◽  
Biological Processes ◽  
Poincaré Maps ◽  
Poincare Maps ◽  
Cell Trajectories ◽  
Cell Data

AbstractThe need to understand cell developmental processes spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry, a suboptimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method that harness the power of hyperbolic geometry into the realm of single-cell data analysis. Often understood as a continuous extension of trees, hyperbolic geometry enables the embedding of complex hierarchical data in only two dimensions while preserving the pairwise distances between points in the hierarchy. This enables direct exploratory analysis and the use of our embeddings in a wide variety of downstream data analysis tasks, such as visualization, clustering, lineage detection and pseudo-time inference. When compared to existing methods —unable to address all these important tasks using a single embedding— Poincaré maps produce state-of-the-art two-dimensional representations of cell trajectories on multiple scRNAseq datasets. More specifically, we demonstrate that Poincaré maps allow in a straightforward manner to formulate new hypotheses about biological processes unbeknown to prior methods.Significance statementThe discovery of hierarchies in biological processes is central to developmental biology. We propose Poincaré maps, a new method based on hyperbolic geometry to discover continuous hierarchies from pairwise similarities. We demonstrate the efficacy of our method on multiple single-cell datasets on tasks such as visualization, clustering, lineage identification, and pseudo-time inference.

scholarly journals singlecellVR: Interactive Visualization of Single-Cell Data in Virtual Reality

2021 ◽  
Vol 12 ◽  
Author(s):  
David F. Stein ◽  
Huidong Chen ◽  
Michael E. Vinyard ◽  
Qian Qin ◽  
Rebecca D. Combs ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Single Cell ◽  
Data Visualization ◽  
Web Application ◽  
Single Cell Analysis ◽  
Data Conversion ◽  
Complex Data ◽  
Proteomic Data ◽  
Cell Assays ◽  
Cell Data

Single-cell assays have transformed our ability to model heterogeneity within cell populations. As these assays have advanced in their ability to measure various aspects of molecular processes in cells, computational methods to analyze and meaningfully visualize such data have required matched innovation. Independently, Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data and shows promise for adaptation to challenges in single-cell data visualization. However, adopting VR for single-cell data visualization has thus far been hindered by expensive prerequisite hardware or advanced data preprocessing skills. To address current shortcomings, we present singlecellVR, a user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, ∼$8). singlecellVR can visualize data from a variety of sequencing-based technologies including transcriptomic, epigenomic, and proteomic data as well as combinations thereof. Analysis modalities supported include approaches to clustering as well as trajectory inference and visualization of dynamical changes discovered through modelling RNA velocity. We provide a companion software package, scvr to streamline data conversion from the most widely-adopted single-cell analysis tools as well as a growing database of pre-analyzed datasets to which users can contribute.

scholarly journals De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments

2019 ◽  
Vol 2 (4) ◽  
pp. e201900443 ◽  
Author(s):  
Jun Woo ◽  
Boris J. Winterhoff ◽  
Timothy K. Starr ◽  
Constantin Aliferis ◽  
Jinhua Wang
Keyword(s):  
Single Cell ◽  
Model Comparison ◽  
De Novo ◽  
Nonnegative Matrix ◽  
Simulated Data ◽  
Cell Type ◽  
Pancreatic Cell ◽  
Bayesian Model Comparison ◽  
Cellular Microenvironments ◽  
Cell Data

Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.

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