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

https://doi.org/10.1101/532895
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Domain-Adversarial Training of Neural Networks
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Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
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Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
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Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
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Single-Cell Genomics Unveils Critical Regulators of Th17 Cell Pathogenicity
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Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
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Molecular Architecture of the Mouse Nervous System
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Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
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Comprehensive Integration of Single-Cell Data
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A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
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A Single-Cell Transcriptome Atlas of the Human Pancreas
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Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
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Single cells make big data: New challenges and opportunities in transcriptomics
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VASC: Dimension Reduction and Visualization of Single-Cell RNA-Seq Data by Deep Variational Autoencoder
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Comparative Analysis of Single-Cell RNA Sequencing Methods
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The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas
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Scaling single-cell genomics from phenomenology to mechanism
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Population snapshots predict early haematopoietic and erythroid hierarchies
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Revealing the vectors of cellular identity with single-cell genomics
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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
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Boosting the power of single-cell analysis
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Dimensionality reduction for visualizing single-cell data using UMAP
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Massively parallel digital transcriptional profiling of single cells
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Systems biology of vaccination for seasonal influenza in humans
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The Immunological Genome Project: networks of gene expression in immune cells
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Normalizing single-cell RNA sequencing data: challenges and opportunities
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Simultaneous epitope and transcriptome measurement in single cells
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Bias, robustness and scalability in single-cell differential expression analysis
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scmap: projection of single-cell RNA-seq data across data sets
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Full-length RNA-seq from single cells using Smart-seq2
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Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells
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A general and flexible method for signal extraction from single-cell RNA-seq data
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Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
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Single-cell RNA-seq denoising using a deep count autoencoder
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Simulating multiple faceted variability in single cell RNA sequencing
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Functional interpretation of single cell similarity maps
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Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis
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Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
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Deep generative models of genetic variation capture the effects of mutations
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Deep generative modeling for single-cell transcriptomics
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Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing
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Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells
inferredcites →https://doi.org/10.1093/bioinformatics/btaa293openalex-snapshot
scVAE: variational auto-encoders for single-cell gene expression data
inferredcites →https://doi.org/10.1093/bioinformatics/btp616openalex-snapshot
edgeR : a Bioconductor package for differential expression analysis of digital gene expression data
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Removal of batch effects using distribution-matching residual networks
inferredcites →https://doi.org/10.1093/bioinformatics/btx435openalex-snapshot
powsimR: power analysis for bulk and single cell RNA-seq experiments
inferredcites →https://doi.org/10.1093/bioinformatics/bty1044openalex-snapshot
M3Drop: dropout-based feature selection for scRNASeq
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Adjusting batch effects in microarray expression data using empirical Bayes methods
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Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses
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limma powers differential expression analyses for RNA-sequencing and microarray studies
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Expression Atlas: gene and protein expression across multiple studies and organisms
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Exploring Single-Cell Data with Deep Multitasking Neural Networks
inferredcites →https://doi.org/10.1101/456129openalex-snapshot
Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data
inferredcites →https://doi.org/10.1101/gr.155697.113openalex-snapshot
Defining cell-type specificity at the transcriptional level in human disease
inferredcites →https://doi.org/10.1109/cvpr.2018.00916openalex-snapshot
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
inferredcites →https://doi.org/10.1109/iccv.2013.116openalex-snapshot
Frustratingly Easy NBNN Domain Adaptation
inferredcites →https://doi.org/10.1109/iccv.2013.274openalex-snapshot
Transfer Feature Learning with Joint Distribution Adaptation
inferredcites →https://doi.org/10.1109/iccv.2017.244openalex-snapshot
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
inferredcites →https://doi.org/10.1126/science.1247651openalex-snapshot
Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types
inferredcites →https://doi.org/10.1126/science.1254257openalex-snapshot
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
inferredcites →https://doi.org/10.1126/science.aaw3381openalex-snapshot
Lineage tracing on transcriptional landscapes links state to fate during differentiation
inferredcites →https://doi.org/10.1142/9789813279827_0033openalex-snapshot
Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics
inferredcites →https://doi.org/10.1146/annurev-statistics-031017-100307openalex-snapshot
Onp-Values and Bayes Factors
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The human reticulocyte transcriptome
inferredcites →https://doi.org/10.1172/jci24176openalex-snapshot
Chronic lymphocytic leukemia cells induce changes in gene expression of CD4 and CD8 T cells
inferredcites →https://doi.org/10.1186/s12859-016-1175-6openalex-snapshot
CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data
inferredcites →https://doi.org/10.1186/s12859-016-1176-5openalex-snapshot
FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
inferredcites →https://doi.org/10.1186/s13059-016-0938-8openalex-snapshot
CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq
inferredcites →https://doi.org/10.1609/aaai.v30i1.10306openalex-snapshot
Return of Frustratingly Easy Domain Adaptation
inferredcites →https://doi.org/10.21105/joss.00861openalex-snapshot
UMAP: Uniform Manifold Approximation and Projection
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A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
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Bayes Factors
inferredcites →https://doi.org/10.48550/arxiv.1406.5298openalex-snapshot
Semi-Supervised Learning with Deep Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1711.03213openalex-snapshot
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
inferredcites →https://doi.org/10.48550/arxiv.1803.00385openalex-snapshot
MAGAN: Aligning Biological Manifolds
inferredcites →https://doi.org/10.48550/arxiv.1805.08672openalex-snapshot
Information Constraints on Auto-Encoding Variational Bayes
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A Deep Generative Model for Semi-Supervised Classification with Noisy Labels
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A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
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The Human Cell Atlas

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Technology for Studying Multi-omics
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Functional interpretation of single cell similarity maps
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Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
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Dental cell type atlas reveals stem and differentiated cell types in mouse and human teeth
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Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces
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Biologically informed deep learning to query gene programs in single-cell atlases
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MARS: discovering novel cell types across heterogeneous single-cell experiments
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Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis
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Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis
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scPretrain: multi-task self-supervised learning for cell-type classification
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Candida albicans exhibits distinct cytoprotective responses to anti-fungal drugs that facilitate the evolution of drug resistance
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Query to reference single-cell integration with transfer learning
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Mucosal vaccination with cyclic-di-nucleotide adjuvants induces effective T cell homing and IL-17 dependent protection against M. tuberculosis infection
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TabVI: Leveraging Lightweight Transformer Architectures to Learn Biologically Meaningful Cellular Representations
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Coexpression enables multi-study cellular trajectories of development and disease
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Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics
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Exploring Sex Differences in Type 2 Diabetes via a Male-Dominant Beta-Cell Cluster from Single-Cell Pancreatic Sequencing of Public Datasets
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