Mapping single-cell data to reference atlases by transfer learning

[1]  M. Lotfollahi,et al.  Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data , 2021, bioRxiv.

[2]  Konrad U. Förstner,et al.  6th European Congress of Immunology, 1-4 September 2021, Virtual meeting. , 2021, European journal of immunology.

[3]  Aaron M. Streets,et al.  scvi-tools: a library for deep probabilistic analysis of single-cell omics data , 2021, bioRxiv.

[4]  Fabian J Theis,et al.  Compositional perturbation autoencoder for single-cell response modeling , 2021 .

[5]  Aaron M. Streets,et al.  Joint probabilistic modeling of single-cell multi-omic data with totalVI , 2021, Nature Methods.

[6]  Michael I. Jordan,et al.  Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models , 2021, Molecular systems biology.

[7]  Fabian J Theis,et al.  Conditional out-of-distribution generation for unpaired data using transfer VAE. , 2020, Bioinformatics.

[8]  Helio T. Navarro,et al.  Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia , 2020, Nature.

[9]  Camille M. Moore,et al.  Airspace Macrophages and Monocytes Exist in Transcriptionally Distinct Subsets in Healthy Adults. , 2020, American journal of respiratory and critical care medicine.

[10]  Raphael Gottardo,et al.  Integrated analysis of multimodal single-cell data , 2020, Cell.

[11]  Aaron J. Wilk,et al.  A single-cell atlas of the peripheral immune response in patients with severe COVID-19 , 2020, Nature Medicine.

[12]  M Dugas,et al.  Benchmarking atlas-level data integration in single-cell genomics , 2020, Nature Methods.

[13]  I. Amit,et al.  Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19 , 2020, Nature Medicine.

[14]  Hongyang Wang,et al.  Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing , 2020, Cell Discovery.

[15]  David S. Fischer,et al.  Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells , 2020, bioRxiv.

[16]  Matthias Heinig,et al.  Cells and gene expression programs in the adult human heart , 2020, bioRxiv.

[17]  Dan Zhang,et al.  Construction of a human cell landscape at single-cell level , 2020, Nature.

[18]  Ricardo J. Miragaia,et al.  scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation , 2019, Genome Biology.

[19]  Fabian J Theis,et al.  Generalizing RNA velocity to transient cell states through dynamical modeling , 2019, Nature Biotechnology.

[20]  Rixin Wang,et al.  Cross-Domain Fault Diagnosis Using Knowledge Transfer Strategy: A Review , 2019, IEEE Access.

[21]  Irving L. Weissman,et al.  A molecular cell atlas of the human lung from single cell RNA sequencing , 2019, Nature.

[22]  Mohammad Lotfollahi,et al.  scGen predicts single-cell perturbation responses , 2019, Nature Methods.

[23]  David Lopez-Paz,et al.  Invariant Risk Minimization , 2019, ArXiv.

[24]  A. Shilatifard,et al.  Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis , 2019, American journal of respiratory and critical care medicine.

[25]  Jingshu Wang,et al.  Surface protein imputation from single cell transcriptomes by deep neural networks , 2019, Nature Communications.

[26]  Evan Z. Macosko,et al.  Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity , 2019, Cell.

[27]  Angela Oliveira Pisco,et al.  A Single Cell Transcriptomic Atlas Characterizes Aging Tissues in the Mouse , 2019, bioRxiv.

[28]  Thomas Wolf,et al.  Transfer Learning in Natural Language Processing , 2019, NAACL.

[29]  Samuel Demharter,et al.  Joint analysis of heterogeneous single-cell RNA-seq dataset collections , 2019, Nature Methods.

[30]  Jun Cheng,et al.  The Kipoi repository accelerates community exchange and reuse of predictive models for genomics , 2019, Nature Biotechnology.

[31]  Marcel J. T. Reinders,et al.  A comparison of automatic cell identification methods for single-cell RNA sequencing data , 2019, Genome Biology.

[32]  Ajit Singh,et al.  Machine Learning With Python , 2019 .

[33]  Bonnie Berger,et al.  Efficient integration of heterogeneous single-cell transcriptomes using Scanorama , 2019, Nature Biotechnology.

[34]  Sheng Liu,et al.  Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species. , 2019, Cell systems.

[35]  Li Chen,et al.  A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies , 2019, Nature Communications.

[36]  Yvan Saeys,et al.  A comparison of single-cell trajectory inference methods , 2019, Nature Biotechnology.

[37]  Fabian J Theis,et al.  PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells , 2019, Genome Biology.

[38]  Michael I. Jordan,et al.  Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models , 2019, bioRxiv.

[39]  Fabian J. Theis,et al.  Comprehensive single cell mRNA profiling reveals a detailed roadmap for pancreatic endocrinogenesis , 2019, Development.

[40]  K. Oetjen,et al.  Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry , 2018, bioRxiv.

[41]  Fuchou Tang,et al.  Faculty Opinions recommendation of Single-cell epigenomics: Recording the past and predicting the future. , 2018, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[42]  Fan Zhang,et al.  Fast, sensitive, and accurate integration of single cell data with Harmony , 2018, bioRxiv.

[43]  Christoph Hafemeister,et al.  Comprehensive integration of single cell data , 2018, bioRxiv.

[44]  M. Hemberg,et al.  False signals induced by single-cell imputation , 2018, F1000Research.

[45]  Jingshu Wang,et al.  Data denoising with transfer learning in single-cell transcriptomics , 2019, Nature Methods.

[46]  Michael I. Jordan,et al.  Deep Generative Modeling for Single-cell Transcriptomics , 2018, Nature Methods.

[47]  Lior Rokach,et al.  CaSTLe – Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments , 2018, PloS one.

[48]  James T. Webber,et al.  Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris , 2018, Nature.

[49]  Carlo Colantuoni,et al.  Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species , 2018, bioRxiv.

[50]  Luyi Tian,et al.  Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data , 2018, F1000Research.

[51]  Evan Z. Macosko,et al.  Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain , 2018, Cell.

[52]  H. Woodrow,et al.  : A Review of the , 2018 .

[53]  Michael I. Jordan,et al.  Information Constraints on Auto-Encoding Variational Bayes , 2018, NeurIPS.

[54]  Lars E. Borm,et al.  Molecular Architecture of the Mouse Nervous System , 2018, Cell.

[55]  Laleh Haghverdi,et al.  Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors , 2018, Nature Biotechnology.

[56]  Richard A. Muscat,et al.  Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding , 2018, Science.

[57]  S. Orkin,et al.  Mapping the Mouse Cell Atlas by Microwell-Seq , 2018, Cell.

[58]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[59]  Z. Kira,et al.  Learning to cluster in order to Transfer across domains and tasks , 2017, ICLR.

[60]  Fabian J Theis,et al.  Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells , 2017, bioRxiv.

[61]  O. Stegle,et al.  Single-cell epigenomics: Recording the past and predicting the future , 2017, Science.

[62]  Hannah A. Pliner,et al.  Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.

[63]  H. Swerdlow,et al.  Large-scale simultaneous measurement of epitopes and transcriptomes in single cells , 2017, Nature Methods.

[64]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[65]  N. Hacohen,et al.  Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors , 2017, Science.

[66]  J. George,et al.  Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes , 2017, Genome research.

[67]  Samuel L. Wolock,et al.  A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. , 2016, Cell systems.

[68]  Mauro J. Muraro,et al.  A Single-Cell Transcriptome Atlas of the Human Pancreas , 2016, Cell systems.

[69]  D. M. Smith,et al.  Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes , 2016, Cell metabolism.

[70]  Mauro J. Muraro,et al.  De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data , 2016, Cell stem cell.

[71]  Carl Doersch,et al.  Tutorial on Variational Autoencoders , 2016, ArXiv.

[72]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.

[73]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[74]  Honglak Lee,et al.  Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.

[75]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[76]  Jeff G. Schneider,et al.  Active Transfer Learning under Model Shift , 2014, ICML.

[77]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[78]  Jaime G. Carbonell,et al.  A theory of transfer learning with applications to active learning , 2013, Machine Learning.

[79]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[80]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[81]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[82]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[83]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[84]  Lipo Wang,et al.  Deep Learning Applications in Medical Image Analysis , 2018, IEEE Access.