VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories

2021 ◽  
Author(s):  
Lyla Atta ◽  
Arpan Sahoo ◽  
Jean Fan
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Supplementary Information ◽  
Intermediate Cell ◽  
Velocity Analysis ◽  
Cell State ◽  
Genome Wide ◽  
Transcriptomics Data ◽  
Reliable Representation ◽  
State Changes

Abstract Motivation Single-cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single-cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity analysis as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding and other 2D embeddings derived from the observed single-cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. Results We developed VeloViz to create RNA velocity-informed 2D and 3D embeddings from single-cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By considering the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Availability and implementation Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bioconductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Datasets used can be found on Zenodo (https://doi.org/10.5281/zenodo.4632471). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals VeloViz: RNA-velocity informed 2D embeddings for visualizing cellular trajectories

2021 ◽  
Author(s):  
Lyla Atta ◽  
Jean Fan
Keyword(s):  
Single Cell ◽  
Principal Components ◽  
R Package ◽  
Velocity Analysis ◽  
Cell State ◽  
Link Type ◽  
Transcriptomics Data ◽  
Reliable Representation ◽  
State Changes

0AbstractRNA velocity analysis can predict cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with 2D embeddings derived from principal components, t-distributed stochastic neighbor embedding, among others. However, these 2D embeddings can yield different representations of the underlying trajectories, hindering the interpretation of cell state changes. To address this challenge, we developed VeloViz to create RNA-velocity-informed 2D embeddings. We show that by taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help ensure a more reliable representation of underlying cellular trajectories. VeloViz is available as an R package at https://github.com/JEFworks-Lab/veloviz.

scholarly journals cgCorrect: A method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics

2016 ◽  
Author(s):  
Thomas Blasi ◽  
Florian Buettner ◽  
Michael K. Strasser ◽  
Carsten Marr ◽  
Fabian J. Theis
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Cell Growth ◽  
Single Cell ◽  
Cell Size ◽  
Computational Analysis ◽  
Simulated Data ◽  
Supplementary Information ◽  
Mrna Transcript ◽  
Transcriptomics Data

AbstractMotivation: Accessing gene expression at the single cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remained obscured in traditional population based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue are differences in cell size, which introduce additional variability into the data, for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers.Results: We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell growth corrected mRNA transcript number given the measured, cell size dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportional to the cell’s volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time PCR data from mouse blood stem and progenitor cells. We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.Availability: A Matlab implementation of cgCorrect is available at http://icb.helmholtz-muenchen.de/cgCorrectSupplementary information: Supplementary information are available online. The simulated data set is available at http://icb.helmholtz-muenchen.de/cgCorrect

scholarly journals SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints

2020 ◽  
Author(s):  
Jianhao Peng ◽  
Ullas V. Chembazhi ◽  
Sushant Bangru ◽  
Ian M. Traniello ◽  
Auinash Kalsotra ◽  
...  
Keyword(s):  
Single Cell ◽  
Network Inference ◽  
Regional Analysis ◽  
Supplementary Information ◽  
Inference Method ◽  
Gene Regulatory Network Inference ◽  
Inference Problem ◽  
Cell State ◽  
Gene Regulatory ◽  
Inference Methods

AbstractMotivationWith the use of single-cell RNA sequencing (scRNA-Seq) technologies, it is now possible to acquire gene expression data for each individual cell in samples containing up to millions of cells. These cells can be further grouped into different states along an inferred cell differentiation path, which are potentially characterized by similar, but distinct enough, gene regulatory networks (GRNs). Hence, it would be desirable for scRNA-Seq GRN inference methods to capture the GRN dynamics across cell states. However, current GRN inference methods produce a unique GRN per input dataset (or independent GRNs per cell state), failing to capture these regulatory dynamics.ResultsWe propose a novel single-cell GRN inference method, named SimiC, that jointly infers the GRNs corresponding to each state. SimiC models the GRN inference problem as a LASSO optimization problem with an added similarity constraint, on the GRNs associated to contiguous cell states, that captures the inter-cell-state homogeneity. We show on a mouse hepatocyte single-cell data generated after partial hepatectomy that, contrary to previous GRN methods for scRNA-Seq data, SimiC is able to capture the transcription factor (TF) dynamics across liver regeneration, as well as the cell-level behavior for the regulatory program of each TF across cell states. In addition, on a honey bee scRNA-Seq experiment, SimiC is able to capture the increased heterogeneity of cells on whole-brain tissue with respect to a regional analysis tissue, and the TFs associated specifically to each sequenced tissue.AvailabilitySimiC is written in Python and includes an R API. It can be downloaded from https://github.com/jianhao2016/[email protected], [email protected] informationSupplementary data are available at the code repository.

scholarly journals scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

2018 ◽  
Vol 34 (12) ◽  
pp. 2077-2086 ◽  
Author(s):  
Suoqin Jin ◽  
Adam L MacLean ◽  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Single Cell ◽  
Transition Probabilities ◽  
Energy Landscape ◽  
Control Cell ◽  
Robust Inference ◽  
Supplementary Information ◽  
Myoblast Differentiation ◽  
State Transitions ◽  
Marker Genes ◽  
Cell State

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Results Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using ‘single-cell energy’ and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are—in combination—more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. Availability and implementation A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals breakpointR: an R/Bioconductor package to localize strand state changes in Strand-seq data

2019 ◽  
Author(s):  
David Porubsky ◽  
Ashley D Sanders ◽  
Aaron Taudt ◽  
Maria Colomé-Tatché ◽  
Peter M Lansdorp ◽  
...  
Keyword(s):  
Single Cell ◽  
Sister Chromatid Exchanges ◽  
Supplementary Information ◽  
Specific Information ◽  
Bioconductor Package ◽  
Sequencing Data ◽  
Sequencing Technique ◽  
Important Addition ◽  
State Changes ◽  
Chromatid Exchanges

Abstract Motivation Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data. Results Here we introduce breakpointR, an R/Bioconductor package specifically tailored to process and interpret single-cell strand-specific sequencing data obtained from Strand-seq. We developed breakpointR to detect local changes in strand directionality of aligned Strand-seq data, to enable fine-mapping of sister chromatid exchanges, germline inversion and to support global haplotype assembly. Given the broad spectrum of Strand-seq applications we expect breakpointR to be an important addition to currently available tools and extend the accessibility of this novel sequencing technique. Availability and implementation R/Bioconductor package https://bioconductor.org/packages/breakpointR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals scDoc: correcting drop-out events in single-cell RNA-seq data

2020 ◽  
Vol 36 (15) ◽  
pp. 4233-4239
Author(s):  
Di Ran ◽  
Shanshan Zhang ◽  
Nicholas Lytal ◽  
Lingling An
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Drop Out ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Cell Subpopulation ◽  
Rna Seq ◽  
Imputation Methods ◽  
Similarity Estimation ◽  
Differential Expression Detection

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of ‘drop-out’ events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. Results scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. Availability and implementation R code is available at https://github.com/anlingUA/scDoc. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

2019 ◽  
Author(s):  
Chenling Xu ◽  
Romain Lopez ◽  
Edouard Mehlman ◽  
Jeffrey Regier ◽  
Michael I. Jordan ◽  
...  
Keyword(s):  
Single Cell ◽  
Probabilistic Approach ◽  
Cell Types ◽  
Generative Models ◽  
Marker Genes ◽  
Data Sets ◽  
Data Set ◽  
Cell State ◽  
Transcriptomics Data ◽  
Single Data

AbstractAs single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations — for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.

scholarly journals Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships

2018 ◽  
Vol 35 (13) ◽  
pp. 2226-2234 ◽  
Author(s):  
Ameen Eetemadi ◽  
Ilias Tagkopoulos
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Gene Networks ◽  
Supplementary Information ◽  
Genome Wide ◽  
Transcriptomics Data ◽  
Artificial Neural

Abstract Motivation Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications. Results We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations. In its core, the GNN maps existing gene regulatory information in its architecture and it uses cell nodes that have been specifically designed to capture the dependencies and non-linear dynamics that exist in gene networks. These two key features make the GNN architecture capable to capture complex relationships without the need of large training datasets. As a result, GNNs were 40% more accurate on average than competing architectures (MLP, RNN, BiRNN) when compared on hundreds of curated and inferred transcription modules. Our results argue that GNNs can become the architecture of choice when building predictors of gene expression from exponentially growing corpus of genome-wide transcriptomics data. Availability and implementation https://github.com/IBPA/GNN Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SPARSim single cell: a count data simulator for scRNA-seq data

2019 ◽  
Author(s):  
Giacomo Baruzzo ◽  
Ilaria Patuzzi ◽  
Barbara Di Camillo
Keyword(s):  
Single Cell ◽  
Count Data ◽  
Simulated Data ◽  
Real Data ◽  
R Package ◽  
Supplementary Information ◽  
Rna Seq ◽  
Distribution Of Zeros ◽  
New Methods ◽  
Research Fields

Abstract Motivation Single cell RNA-seq (scRNA-seq) count data show many differences compared with bulk RNA-seq count data, making the application of many RNA-seq pre-processing/analysis methods not straightforward or even inappropriate. For this reason, the development of new methods for handling scRNA-seq count data is currently one of the most active research fields in bioinformatics. To help the development of such new methods, the availability of simulated data could play a pivotal role. However, only few scRNA-seq count data simulators are available, often showing poor or not demonstrated similarity with real data. Results In this article we present SPARSim, a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. We demonstrate that SPARSim allows to generate count data that resemble real data in terms of count intensity, variability and sparsity, performing comparably or better than one of the most used scRNA-seq simulator, Splat. In particular, SPARSim simulated count matrices well resemble the distribution of zeros across different expression intensities observed in real count data. Availability and implementation SPARSim R package is freely available at http://sysbiobig.dei.unipd.it/? q=SPARSim and at https://gitlab.com/sysbiobig/sparsim. Supplementary information Supplementary data are available at Bioinformatics online.

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