Topic 147

seq github bioinformatics com https scrna single datasets clustering types batch rna available cell data methods sequencing method unsupervised present type supervised biological package dataset cells integration identification framework computational technologies simulated bulk robust across analysis algorithms annotation accuracy batches algorithm tool real approach existing enables cluster challenging introduce dimensionality clusters software technical art information resolution at accurately classification challenges leverages step such improves meaningful sample subpopulations cite heterogeneity alignment multiple multi advances identifies genes expression modalities integrate code omics apply outperforms simultaneously learns annotate marker nearest python learning seurat correction feature visualization dimensional sets accurate profiles demonstrate atac performance

88 items. Top items listed below.

BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching 147 43 13 4

scLM: automatic detection of consensus gene clusters across multiple single-cell datasets 147 43 13 4

Alignment of single-cell RNA-seq samples without over-correction using kernel density matching 147 43 13 4

Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data 147 43 13 4

Network-based imputation of dropouts in single-cell RNA sequencing data 147 43 13 4

Supervised Adversarial Alignment of scRNA-seq Data 147 43 26 4

Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis 147 43 26 4

JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation 147 13 4

mbkmeans: fast clustering for single cell data using mini-batch k-means 147 43 13 4

ILoReg enables high-resolution cell population identification from single-cell RNA-seq data 147 13 4

scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data 147 13 4

Integrating scRNA-seq data of multiple donors increases cell-type identification accuracy 147 13 4

A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments 147 43 13 4

INSCT: Integrating millions of single cells using batch-aware triplet neural networks 147 43 13 4

HieRFIT: Hierarchical Random Forest for Information Transfer 147 13 4

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution 147 43 13 4

Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data 147 43 13 4

Non-negative Independent Factor Analysis for single cell RNA-seq 147 43 13 4

sc-REnF: An entropy guided robust feature selection for clustering of single-cell rna-seq data 147 43 13 4

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench 147 13 4

Deep feature extraction of single-cell transcriptomes by generative adversarial network 147 43 13 4

Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces 147 13 4

STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data 147 43 13 4

Probabilistic gene expression signatures identify cell-types from single cell RNA-seq data 43 4

FEATS: Feature selection based clustering of single-cell RNA-seq data 147 13 4

FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test 147 43 13 4

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets 147 43 4

Phenotype-guided subpopulation identification from single-cell sequencing data 147 43 4

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement 43 26 4

Deep learning of gene interactions from single cell time-course expression data 147 13 4

Improving replicability in single-cell RNA-Seq cell type discovery with Dune 147 43 4

SCSA: a cell type annotation tool for single-cell RNA-seq data 147 43 4

Cluster similarity spectrum integration of single-cell genomics data 147 43 4

Unsupervised Topological Alignment for Single-Cell Multi-Omics Integration 147 13 4

Detection of differential RNA modifications from direct RNA sequencing of human cell lines 147 13 4

CellMeSH: Probabilistic Cell-Type Identification Using Indexed Literature 147 43 13 4

Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models 43 4

Cell-ID: gene signature extraction and cell identity recognition at individual cell level 147 13 4

Robust decomposition of cell type mixtures in spatial transcriptomics 147 43 13 4

Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network 43 26 4

Gromov-Wasserstein optimal transport to align single-cell multi-omics data 147 13 4

SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection 147 43 4

Reference-free Cell-type Annotation for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural Network 147 43 13 4

Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring 43 4

scASK: A novel ensemble framework for classifying cell types based on single-cell RNA-seq data 147 13 7 4

pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single-cell RNA-seq preprocessing tools 147 13 4

A novel single-cell based method for breast cancer prognosis 43 26 4

Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data from CITE-seq 147 13 4

Joint Inference of Clonal Structure using Single-cell Genome and Transcriptome Sequencing Data 147 43 13 4

FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms 43 4

Evaluation of Cell Type Annotation R Packages on Single Cell RNA-seq Data 147 43 13 4

Fast and interpretable scRNA-seq data analysis 147 43 26 4

Bayesian non-parametric clustering of single-cell mutation profiles 147 13 4

Hierarchical progressive learning of cell identities in single-cell data 147 13 4

A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification 147 13 4

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution 43 4

A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset 43 4

On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data 43 4

TWO-SIGMA: a novel TWO-component SInGle cell Model-based Association method for single-cell RNA-seq data 147 43 13 4

A statistical nonparametric method for identifying consistently important features across samples 147 13 4

Automated quality control and cell identification of droplet-based single-cell data using dropkick 147 43 4

Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data 147 43 4

A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell Transcriptomics 147 43 4

Subpopulation identification for single-cell RNA-sequencing data using functional data analysis 43 4

Capybara: A computational tool to measure cell identity and fate transitions 147 13 4

Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data 147 43 13 4

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles 147 13 4

Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN 147 43 4

cFIT: Integration and transfer learning of single cell transcriptomes, illustrated by fetal brain cell development 43 4

PieParty: Visualizing cells from scRNA-seq data as pie charts 147 43 13 4

Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells 147 43 4

iCellR: Combined Coverage Correction and Principal Component Alignment for Batch Alignment in Single-Cell Sequencing Analysis 147 43 4

SCRAPP: A tool to assess the diversity of microbial samples from phylogenetic placements 162 13 4

Identifying signaling genes in spatial single cell expression data 147 13 4

Single-cell ChIP-seq imputation with SIMPA by leveraging bulk ENCODE data 147 13 4

BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data 43 4

scIGANs: single-cell RNA-seq imputation using generative adversarial networks 43 4

Comprehensive benchmarking of computational deconvolution of transcriptomics data 43 4

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments 147 13 4

MONET: Multi-omic patient module detection by omic selection 147 43 4

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics 147 43 13 4

Iterative point set registration for aligning scRNA-seq data 43 26 4

scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution 147 13 4

An analytical framework for interpretable and generalizable 'quasilinear' single-cell data analysis 147 13 4

Simultaneous deep generative modeling and clustering of single cell genomic data 43 26 4

AtacWorks: A deep convolutional neural network toolkit for epigenomics 147 13 4

Inference of multiple trajectories in single cell RNA-seq data from RNA velocity 43 4

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data 13 4

New gene association measures by joint network embedding of multiple gene expression datasets 43 4

Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks 43 4

Scedar: a scalable Python package for single-cell RNA-seq exploratory data analysis 43 13 4

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets 147 43 4

Detection of differentially abundant cell subpopulations discriminates biological states in scRNA-seq data 43 4

Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data 175 43 9 4

ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis 147 43 31 4

Dincta: Data INtegration and Cell Type Annotation of Single Cell Transcriptomes 147 43 4

Discovering novel cell types across heterogeneous single-cell experiments 147 43 26 4

Dimension reduction and denoising of single-cell RNA sequencing data in the presence of observed confounding variables 147 43 4

GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data 13 4

Unifying single-cell annotations based on the Cell Ontology 147 43 4