A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments

2020 ◽  
Author(s):  
Archit Verma ◽  
Barbara Engelhardt
Keyword(s):  
Single Cell ◽  
Latent Variable ◽  
Environmental Variability ◽  
Simulated Data ◽  
Joint Analysis ◽  
Variable Model ◽  
Manifold Alignment ◽  
Multiple Data Sets ◽  
Sequencing Platforms ◽  
Low Dimensional

Joint analysis of multiple single cell RNA-sequencing (scRNA-seq) data is confounded by technical batch effects across experiments, biological or environmental variability across cells, and different capture processes across sequencing platforms. Manifold alignment is a principled, effective tool for integrating multiple data sets and controlling for confounding factors. We demonstrate that the semi-supervised t-distributed Gaussian process latent variable model (sstGPLVM), which projects the data onto a mixture of fixed and latent dimensions, can learn a unified low-dimensional embedding for multiple single cell experiments with minimal assumptions. We show the efficacy of the model as compared with state-of-the-art methods for single cell data integration on simulated data, pancreas cells from four sequencing technologies, induced pluripotent stem cells from male and female donors, and mouse brain cells from both spatial seqFISH+ and traditional scRNA-seq.Code and data is available at https://github.com/architverma1/sc-manifold-alignment

scholarly journals A robust nonlinear low-dimensional manifold for single cell RNA-seq data

2018 ◽  
Author(s):  
Archit Verma ◽  
Barbara E. Engelhardt
Keyword(s):  
Gaussian Process ◽  
Dimension Reduction ◽  
Single Cell ◽  
Latent Variable ◽  
Developmental Trajectories ◽  
Latent Variable Model ◽  
Variable Model ◽  
Sequencing Technologies ◽  
Heavy Tailed ◽  
Low Dimensional

AbstractModern developments in single cell sequencing technologies enable broad insights into cellular state. Single cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden understanding of cell heterogeneity in tissues and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single cell data. However, methods have yet to be developed for unfiltered and unnormalized count data. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data. Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student’s t-distribution to estimate a manifold that is robust to technical and biological noise. We compare our approach to common dimension reduction tools to highlight our model’s ability to enable important downstream tasks, including clustering and inferring cell developmental trajectories, on available experimental data. We show that our robust nonlinear manifold is well suited for raw, unfiltered gene counts from high throughput sequencing technologies for visualization and exploration of cell states.

Bayesian semiparametric latent variable model with DP prior for joint analysis: Implementation with nimble

2019 ◽  
Vol 20 (1) ◽  
pp. 71-95
Author(s):  
Zhihua Ma ◽  
Guanghui Chen
Keyword(s):  
Dirichlet Process ◽  
Latent Variable ◽  
Model Comparison ◽  
Marginal Likelihood ◽  
Deviance Information Criterion ◽  
Simulated Data ◽  
Latent Variable Model ◽  
Survey Study ◽  
Joint Analysis ◽  
Variable Model

Multiple responses of mixed types are naturally encountered in a variety of data analysis problems, which should be jointly analysed to achieve higher efficiency gains. As an efficient approach for joint modelling, the latent variable model induces dependence among the mixed outcomes through a shared latent variable. Generally, the latent variable is assumed to be normal, which is not that flexible and realistic in practice. This tutorial article demonstrates how to jointly analyse mixed continuous and ordinal responses using a semiparametric latent variable model by allowing the latent variable to follow a Dirichlet process (DP) prior, and illustrates how to implement Bayesian inference through a powerful R package nimble. Two model comparison criteria, deviance information criterion (DIC) and logarithm of the pseudo-marginal likelihood (LPML), are employed for model selection. Simulated data and data from a social survey study are used for illustrating the proposed method with nimble. An extension of DP prior to DP mixtures prior is introduced as well.

Bayesian joint analysis using a semiparametric latent variable model with non-ignorable missing covariates for CHNS data

2020 ◽  
pp. 1471082X1989668
Author(s):  
Zhihua Ma ◽  
Guanghui Chen
Keyword(s):  
Missing Data ◽  
Latent Variable ◽  
Marginal Likelihood ◽  
Latent Variable Model ◽  
Joint Analysis ◽  
Missing Covariates ◽  
Model Framework ◽  
Variable Model ◽  
Local Sensitivity ◽  
Mechanism Model

Motivated by the China Health and Nutrition Survey (CHNS) data, a semiparametric latent variable model with a Dirichlet process (DP) mixtures prior on the latent variable is proposed to jointly analyse mixed binary and continuous responses. Non-ignorable missing covariates are considered through a selection model framework where a missing covariate model and a missing data mechanism model are included. The logarithm of the pseudo-marginal likelihood (LPML) is applied for selecting the priors, and the deviance information criterion measure focusing on the missing data mechanism model only is used for selecting different missing data mechanisms. A Bayesian index of local sensitivity to non-ignorability (ISNI) is extended to explore the local sensitivity of the parameters in our model. A simulation study is carried out to examine the empirical performance of the proposed methodology. Finally, the proposed model and the ISNI index are applied to analyse the CHNS data in the motivating example.

scholarly journals A Flow-Based Deep Latent Variable Model for Speech Spectrogram Modeling and Enhancement

2020 ◽  
Author(s):  
Aditya Arie Nugraha ◽  
Kouhei Sekiguchi ◽  
Kazuyoshi Yoshii
Keyword(s):  
Speech Enhancement ◽  
Latent Variables ◽  
Latent Variable ◽  
Generative Models ◽  
Latent Variable Model ◽  
Variable Model ◽  
Variational Autoencoder ◽  
Latent Representations ◽  
Low Dimensional ◽  
Better Than

This paper describes a deep latent variable model of speech power spectrograms and its application to semi-supervised speech enhancement with a deep speech prior. By integrating two major deep generative models, a variational autoencoder (VAE) and a normalizing flow (NF), in a mutually-beneficial manner, we formulate a flexible latent variable model called the NF-VAE that can extract low-dimensional latent representations from high-dimensional observations, akin to the VAE, and does not need to explicitly represent the distribution of the observations, akin to the NF. In this paper, we consider a variant of NF called the generative flow (GF a.k.a. Glow) and formulate a latent variable model called the GF-VAE. We experimentally show that the proposed GF-VAE is better than the standard VAE at capturing fine-structured harmonics of speech spectrograms, especially in the high-frequency range. A similar finding is also obtained when the GF-VAE and the VAE are used to generate speech spectrograms from latent variables randomly sampled from the standard Gaussian distribution. Lastly, when these models are used as speech priors for statistical multichannel speech enhancement, the GF-VAE outperforms the VAE and the GF.

scholarly journals A tractable latent variable model for nonlinear dimensionality reduction

2020 ◽  
Vol 117 (27) ◽  
pp. 15403-15408
Author(s):  
Lawrence K. Saul
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Linear Equations ◽  
Three Dimensional ◽  
Coarse Graining ◽  
Graph Laplacian ◽  
Latent Variable Model ◽  
Nonlinear Dimensionality Reduction ◽  
Variable Model ◽  
Low Dimensional

We propose a latent variable model to discover faithful low-dimensional representations of high-dimensional data. The model computes a low-dimensional embedding that aims to preserve neighborhood relationships encoded by a sparse graph. The model both leverages and extends current leading approaches to this problem. Like t-distributed Stochastic Neighborhood Embedding, the model can produce two- and three-dimensional embeddings for visualization, but it can also learn higher-dimensional embeddings for other uses. Like LargeVis and Uniform Manifold Approximation and Projection, the model produces embeddings by balancing two goals—pulling nearby examples closer together and pushing distant examples further apart. Unlike these approaches, however, the latent variables in our model provide additional structure that can be exploited for learning. We derive an Expectation–Maximization procedure with closed-form updates that monotonically improve the model’s likelihood: In this procedure, embeddings are iteratively adapted by solving sparse, diagonally dominant systems of linear equations that arise from a discrete graph Laplacian. For large problems, we also develop an approximate coarse-graining procedure that avoids the need for negative sampling of nonadjacent nodes in the graph. We demonstrate the model’s effectiveness on datasets of images and text.

Latent Variable Model for Joint Analysis of Multiple Repeated Measures and Bivariate Event Times

2001 ◽  
Vol 96 (455) ◽  
pp. 906-914 ◽  
Author(s):  
Wenzheng Huang ◽  
Scott L Zeger ◽  
James C Anthony ◽  
Elizabeth Garrett
Keyword(s):  
Repeated Measures ◽  
Latent Variable ◽  
Latent Variable Model ◽  
Joint Analysis ◽  
Variable Model ◽  
Event Times

scholarly journals MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

2019 ◽  
Author(s):  
Ricard Argelaguet ◽  
Damien Arnol ◽  
Danila Bredikhin ◽  
Yonatan Deloro ◽  
Britta Velten ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Single Cell ◽  
Cell Fate ◽  
Joint Analysis ◽  
Experimental Conditions ◽  
Joint Modelling ◽  
Stochastic Variational Inference ◽  
Technological Advances ◽  
Low Dimensional ◽  
Cell Data

AbstractTechnological advances have enabled the joint analysis of multiple molecular layers at single cell resolution. At the same time, increased experimental throughput has facilitated the study of larger numbers of experimental conditions. While methods for analysing single-cell data that model the resulting structure of either of these dimensions are beginning to emerge, current methods do not account for complex experimental designs that include both multiple views (modalities or assays) and groups (conditions or experiments). Here we present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of structured single cell multi-modal data. MOFA+ builds upon a Bayesian Factor Analysis framework combined with fast GPU-accelerated stochastic variational inference. Similar to existing factor models, MOFA+ allows for interpreting variation in single-cell datasets by pooling information across cells and features to reconstruct a low-dimensional representation of the data. Uniquely, the model supports flexible group-level sparsity constraints that allow joint modelling of variation across multiple groups and views.To illustrate MOFA+, we applied it to single-cell data sets of different scales and designs, demonstrating practical advantages when analyzing datasets with complex group and/or view structure. In a multi-omics analysis of mouse gastrulation this joint modelling reveals coordinated changes between gene expression and epigenetic variation associated with cell fate commitment.

scholarly journals Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

2020 ◽  
Author(s):  
Jonathan Rush ◽  
Philippe Rast ◽  
Scott Michael Hofer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Variable Model ◽  
Alternative Approach ◽  
Scale Scores ◽  
Weighted Model ◽  
The Impact

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

scholarly journals Efficient and precise single-cell reference atlas mapping with Symphony

2020 ◽  
Author(s):  
Joyce B. Kang ◽  
Aparna Nathan ◽  
Nghia Millard ◽  
Laurie Rumker ◽  
D. Branch Moody ◽  
...  
Keyword(s):  
Single Cell ◽  
Fetal Liver ◽  
Surface Protein ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Model Framework ◽  
Pancreatic Cell ◽  
Linear Mixture Model ◽  
Sequencing Platforms ◽  
Low Dimensional

AbstractRecent advances in single-cell technologies and integration algorithms make it possible to construct large, comprehensive reference atlases from multiple datasets encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map new query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony, a novel algorithm for building compressed, integrated reference atlases of ≥106 cells and enabling efficient query mapping within seconds. Based on a linear mixture model framework, Symphony precisely localizes query cells within a low-dimensional reference embedding without the need to reintegrate the reference cells, facilitating the downstream transfer of many types of reference-defined annotations to the query cells. We demonstrate the power of Symphony by (1) mapping a query containing multiple levels of experimental design to predict pancreatic cell types in human and mouse, (2) localizing query cells along a smooth developmental trajectory of human fetal liver hematopoiesis, and (3) harnessing a multimodal CITE-seq reference atlas to infer query surface protein expression in memory T cells. Symphony will enable the sharing of comprehensive integrated reference atlases in a convenient, portable format that powers fast, reproducible querying and downstream analyses.

scholarly journals scGAE: topology-preserving dimensionality reduction for single-cell RNA-seq data using graph autoencoder

2021 ◽  
Author(s):  
Zixiang Luo ◽  
Chenyu Xu ◽  
Zhen Zhang ◽  
Wenfei Jin
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Topological Structure ◽  
Dimensional Space ◽  
Simulated Data ◽  
Oriented Graph ◽  
Developmental Trajectory ◽  
Structure Information ◽  
Low Dimensional ◽  
Cell Graph

ABSTRACTDimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE), a dimensionality reduction method that preserves topological structure in scRNA-seq data. scGAE builds a cell graph and uses a multitask-oriented graph autoencoder to preserve topological structure information and feature information in scRNA-seq data simultaneously. We further extended scGAE for scRNA-seq data visualization, clustering, and trajectory inference. Analyses of simulated data showed that scGAE accurately reconstructs developmental trajectory and separates discrete cell clusters under different scenarios, outperforming recently developed deep learning methods. Furthermore, implementation of scGAE on empirical data showed scGAE provided novel insights into cell developmental lineages and preserved inter-cluster distances.

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