CNS on Single-Cell Analysis

12 minute read

Published:

A track of CNS papers mainly related to single-cell analysis and machine learning.

Contents

2023, 2022, 2021, 2020, 2019

2023

Feb

  • Biologically informed deep learning to query gene programs in single-cell atlases. Nature Cell Biology

Jan

  • Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA. Nature Communications
  • Decision level integration of unimodal and multimodal single cell data with scTriangulate. Nature Communications
  • scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nature Communications
  • Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications
  • Gene expression model inference from snapshot RNA data using Bayesian non-parametrics. Nature Computational Science
  • Transformer for one stop interpretable cell type annotation. Nature Communications
  • Robust single-cell matching and multimodal analysis using shared and distinct features. Nature Methods

2022

Dec

  • Nonnegative spatial factorization applied to spatial genomics. Nature Methods
  • Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications
  • Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. Nature Communications
  • Evaluating deep learning for predicting epigenomic profiles. Nature Machine Intelligence
  • Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease. Nature Communications
  • A unified computational framework for single-cell data integration with optimal transport. Nature Communications

Nov

  • Leveraging data-driven self-consistency for high-fidelity gene expression recovery. Nature Communications
  • A flexible cross-platform single-cell data processing pipeline. Nature Communications
  • Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nature Methods
  • Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer. Nature Communications

Oct

  • Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data. Nature Communications
  • De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution. Nature Communications
  • A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation. Nature Machine Intelligence
  • Annotation of spatially resolved single-cell data with STELLAR. Nature Methods
  • Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. Nature Communications
  • Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction. Nature Biotechnology
  • Deep learning of cross-species single-cell landscapes identifies conserved regulatory program. Nature Genetics

Sep

  • scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nature Machine Intelligence
  • Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity. Nature Communications
  • ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nature Methods
  • Deep neural networks with controlled variable selection for the identification of putative causal genetic variants. Nature Machine Intelligence
  • devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data. Nature Communications
  • MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nature Methods

Aug

  • Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale. Nature Machine Intelligence
  • Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data. Nature Communications
  • scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. Nature Methods

Jun

  • Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM. Nature Computational Science
  • Forest Fire Clustering for single-cell sequencing combines iterative label propagation with parallelized Monte Carlo simulations. Nature Communications

May

  • Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets. Nature Computational Science
  • Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nature Biotechnology

Apr

  • DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology
  • Membrane marker selection for segmenting single cell spatial proteomics data. Nature Communications
  • Interactive single-cell data analysis using Cellar. Nature Communications
  • Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. Nature Machine Intelligence
  • A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data. Nature Communications
  • Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nature Communications

Mar

  • Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nature Biotechnology
  • Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro. Nature Biotechnology
  • Spatial charting of single-cell transcriptomes in tissues. Nature Biotechnology
  • Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nature Communications

Feb

  • Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning. Nature Machine Intelligence
  • Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. Nature Machine Intelligence
  • UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization. Nature Communications
  • Inferring protein expression changes from mRNA in Alzheimer’s dementia using deep neural networks. Nature Communications

Jan

  • A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data. Nature Computational Science
  • Temporal modelling using single-cell transcriptomics. Nature Reviews Genetics
  • Interpreting neural networks for biological sequences by learning stochastic masks. Nature Machine Intelligence
  • scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq. Nature Biotechnology
  • Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO. Nature Methods
  • CellRank for directed single-cell fate mapping. Nature Methods
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2021

Dec

  • Benchmarking atlas-level data integration in single-cell genomics. Nature Methods

Nov

  • scCODA is a Bayesian model for compositional single-cell data analysis. Nature Communications
  • Navigating the pitfalls of applying machine learning in genomics. Nature Reviews Genetics
  • Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen. Nature Communications

Oct

  • Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nature Methods
  • SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nature Methods
  • ClusterMap for multi-scale clustering analysis of spatial gene expression. Nature Communications
  • Efficient and precise single-cell reference atlas mapping with Symphony. Nature Communications
  • DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data. Nature Communications

Sep

  • VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. Nature Communications
  • Generalized and scalable trajectory inference in single-cell omics data with VIA. Nature Communications
  • Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data. Nature Communications
  • EpiScanpy: integrated single-cell epigenomic analysis. Nature Communications

Aug

  • Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology
  • Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data. Nature Communications

Jul

  • Modeling gene regulatory networks using neural network architectures. Nature Computational Science
  • Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications. Nature Communications

Jun

  • scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics. Nature Communications
  • The triumphs and limitations of computational methods for scRNA-seq. Nature Methods
  • Integration of millions of transcriptomes using batch-aware triplet neural networks. Nature Machine Intelligence
  • Model-based prediction of spatial gene expression via generative linear mapping. Nature Communications
  • Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology

May

  • Integrated analysis of multimodal single-cell data. Cell
  • Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions. Nature Communications
  • Hierarchical progressive learning of cell identities in single-cell data. Nature Communications
  • Simultaneous deep generative modelling and clustering of single-cell genomic data. Nature Machine Intelligence
  • Computational principles and challenges in single-cell data integration. Nature Biotechnology

Apr

  • Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID. Nature Biotechnology
  • Bayesian inference of gene expression states from single-cell RNA-seq data. Nature Biotechnology
  • Iterative single-cell multi-omic integration using online learning. Nature Biotechnology
  • Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nature Machine Intelligence

Mar

  • Robust integration of multiple single-cell RNA sequencing datasets using a single reference space. Nature Biotechnology
  • Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data. Nature Communications
  • scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nature Communications
  • Deep generative neural network for accurate drug response imputation. Nature Communications
  • Deep learning-based enhancement of epigenomics data with AtacWorks. Nature Communications
  • An automated framework for efficiently designing deep convolutional neural networks in genomics. Nature Machine Intelligence

Feb

  • ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nature Genetics
  • Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nature Methods
  • Fast and precise single-cell data analysis using a hierarchical autoencoder. Nature Communications
  • Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nature Machine Intelligence

Jan

  • Deep neural networks identify sequence context features predictive of transcription factor binding. Nature Machine Intelligence
  • Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nature Communications
  • Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nature Communications
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2020

Dec

  • Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders. Nature Machine Intelligence
  • Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure. Nature Communications

Nov

  • Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data. Nature Communications
  • An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data. Nature Machine Intelligence

Oct

  • Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis. Nature Machine Intelligence
  • A multiresolution framework to characterize single-cell state landscapes. Nature Communications
  • MARS: discovering novel cell types across heterogeneous single-cell experiments. Nature Methods

Sept

  • Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks. Nature Communications

Aug

  • A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nature Communications

Jul

  • VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nature Communications
  • A unified framework for integrative study of heterogeneous gene regulatory mechanisms. Nature Machine Intelligence
  • Deep learning decodes the principles of differential gene expression. Nature Machine Intelligence
  • Deep learning for genomics using Janggu. Nature Communications
  • Searching Large-Scale scRNA-seq Databases via Unbiased Cell Embedding With Cell BLAST. Nature Communications

Jun

  • Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data. Nature Communications

May

  • Putative cell type discovery from single-cell gene expression data. Nature Methods
  • Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nature Methods
  • Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nature Communications

Apr

  • Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nature Communications
  • Large scale active-learning-guided exploration for in vitro protein production optimization. Nature Communications

Mar

  • Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. Nature Methods
  • TooManyCells identifies and visualizes relationships of single-cell clades. Nature Methods
  • Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nature Communications
  • Latent periodic process inference from single-cell RNA-seq data. Nature Communications

Feb

  • Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nature Methods
  • Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder. Nature Communications

Jan

  • Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods
  • Surface protein imputation from single cell transcriptomes by deep neural networks. Nature Communications
  • Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nature Communications
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2019

Dec

  • Orchestrating single-cell analysis with Bioconductor. Nature Methods

Nov

  • Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nature Methods
  • Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods

Oct

  • Exploring single-cell data with deep multitasking neural networks. Nature Methods

Sept

  • Supervised classification enables rapid annotation of cell atlases. Nature Methods
  • Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nature Methods

Aug

  • Data denoising with transfer learning in single-cell transcriptomics. Nature Methods

Jul

  • scGen predicts single-cell perturbation responses. Nature Methods
  • Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nature Methods

Jun

  • Pathway-level information extractor (PLIER) for gene expression data. Nature Methods

May

  • Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nature Methods

Apr

  • cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nature Methods

Mar

  • Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning. Nature Methods
  • Selene: a PyTorch-based deep learning library for sequence data. Nature Methods
  • Deep-learning augmented RNA-seq analysis of transcript splicing. Nature Methods
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