scholarly journals Dynamic pseudo-time warping of complex single-cell trajectories

2019 ◽  
Author(s):  
Van Hoan Do ◽  
Mislav Blažević ◽  
Pablo Monteagudo ◽  
Luka Borozan ◽  
Khaled Elbassioni ◽  
...  
Keyword(s):  
Single Cell ◽  
Heuristic Algorithms ◽  
Biological Processes ◽  
Time Warping ◽  
Branching Points ◽  
Consistent Manner ◽  
Dynamic Time ◽  
Different Characteristics ◽  
Cell Trajectories ◽  
Complex Trajectories

AbstractSingle-cell RNA sequencing enables the construction of trajectories describing the dynamic changes in gene expression underlying biological processes such as cell differentiation and development. The comparison of single-cell trajectories under two distinct conditions can illuminate the differences and similarities between the two and can thus be a powerful tool. Recently developed methods for the comparison of trajectories rely on the concept of dynamic time warping (dtw), which was originally proposed for the comparison of two time series. Consequently, these methods are restricted to simple, linear trajectories. Here, we adopt and theoretically link arboreal matchings to dtw and propose an algorithm to compare complex trajectories that more realistically contain branching points that divert cells into different fates. We implement a suite of exact and heuristic algorithms suitable for the comparison of trajectories of different characteristics in our tool Trajan. Trajan automatically pairs similar biological processes between conditions and aligns them in a globally consistent manner. In an alignment of singlecell trajectories describing human muscle differentiation and myogenic reprogramming, Trajan identifies and aligns the core paths without prior information. From Trajan’s alignment, we are able to reproduce recently reported barriers to reprogramming. In a perturbation experiment, we demonstrate the benefits in terms of robustness and accuracy of our model which compares entire trajectories at once, as opposed to a pairwise application of dtw. Trajan is available at https://github.com/canzarlab/Trajan.

scholarly journals Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

2018 ◽  
Author(s):  
Pierre-Cyril Aubin-Frankowski ◽  
Jean-Philippe Vert
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
De Novo ◽  
State Of The Art ◽  
Real Data ◽  
Biological Processes ◽  
Rna Seq ◽  
Cell Cycles ◽  
Linear Differential ◽  
Cell Trajectories

AbstractSingle-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulation networks (GRN) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology. In this work we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual data, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data.

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 A Pseudo-Temporal Causality Approach to Identifying miRNA-mRNA Interactions During Biological Processes

2020 ◽  
Author(s):  
Andres M. Cifuentes-Bernal ◽  
Vu VH Pham ◽  
Xiaomei Li ◽  
Lin Liu ◽  
Jiuyong Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
Cancer Metastasis ◽  
Biological Process ◽  
Biological Processes ◽  
Mesenchymal Transition ◽  
Novel Approach ◽  
Static Information ◽  
Temporal Aspect ◽  
Cell Trajectories

AbstractMotivationmicroRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA-mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process. The recent development of single cell data and the booming of the exploration of cell trajectories using “pseudo-time” concept have inspired us to develop a pseudo-time based method to infer the miRNA-mRNA relationships characterising a biological process by taking into account the temporal aspect of the process.ResultsWe have developed a novel approach, called pseudo-time causality (PTC), to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition (EMT), a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA-mRNA interactions in both single cell and bulk data. The results suggest that utilising the pseudo-temporal information from the data helps reveal the gene regulation in a biological process much better than using the static information.AvailabilityR scripts and datasets can be found at https://github.com/AndresMCB/PTC

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 Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

2020 ◽  
Vol 36 (18) ◽  
pp. 4774-4780 ◽  
Author(s):  
Pierre-Cyril Aubin-Frankowski ◽  
Jean-Philippe Vert
Keyword(s):  
Single Cell ◽  
De Novo ◽  
Real Data ◽  
Supplementary Information ◽  
Biological Processes ◽  
Rna Seq ◽  
Cell Cycles ◽  
Gene Regulatory ◽  
Linear Differential ◽  
Cell Trajectories

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulatory network (GRNs) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology. Results In this work, we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual cells, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data. Availability and implementation The MATLAB code of GRISLI is available at: https://github.com/PCAubin/GRISLI. Supplementary information Supplementary data are available at Bioinformatics online.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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