Topic 43

seq scrna rna single bioinformatics sequencing cell datasets types data expression methods gene cells method biological analysis type clustering heterogeneity computational transcriptome genomics technologies transcriptomics bulk transcriptomic existing profiling enables cellular dataset tissue tissues profiles developed across technical genes samples batch approach framework technology differential each algorithm mouse resolution transcriptomes introduce comprehensive applied individual can heterogeneous present atlas tool such information sample based integrate infer provides downstream multiple tools spatial available analyzing apply allows visualization throughput identification analyses atac states using integrating accurately package signatures profile populations unsupervised different multi trajectories new integration annotation level subpopulations specific novel marker clusters

367 items. Top items listed below.

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

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

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

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

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

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis 43 9 4

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

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

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

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

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

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

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

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

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

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

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

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

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

Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq(scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression Analysis 43 9 4 3

Comprehensive benchmarking of computational deconvolution of transcriptomics data 43 4

Probabilistic index models for testing differential expression in single cell RNA sequencing data 43 4

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

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

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

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

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

Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data 43 4

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

A Systematic Evaluation of Single-cell RNA-sequencing Imputation Methods 43 4

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

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

CytoTalk: De novo construction of signal transduction networks using single-cell RNA-Seq data 43 9 4

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

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines 43 4

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

Combined single-cell gene and isoform expression analysis in haematopoietic stem and progenitor cells 43 9 3

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

Design and power analysis for multi-sample single cell genomics experiments 43 4

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

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions 43 9 4

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier 43 26 4

scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures 43 4

Reconstruction of cell spatial organization based on ligand-receptor mediated self-assembly 43 9 4

Comprehensive evaluation of human brain gene expression deconvolution methods 175 43 9 4

scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses 43 26 4

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

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

Computational approaches towards reducing contamination in single-cell RNA-seq data 43 4

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders 43 9 4

Global computational alignment of tumor and cell line transcriptional profiles 43 14 9 4

SpaGE: Spatial Gene Enhancement using scRNA-seq 43 4

Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level 43 4

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

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

SnapATAC: A Comprehensive Analysis Package for Single Cell ATAC-seq 43 9 4

talklr uncovers ligand-receptor mediated intercellular crosstalk 43 4

Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics 43 4

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

Integrative Spatial Single-cell Analysis with Graph-based Feature Learning 43 4

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

Nabo - a framework to define leukemia-initiating cells and differentiation in single-cell RNA-sequencing data 175 43 4

Model-based prediction of spatial gene expression via generative linear mapping 43 4

The impact of chromatin remodeling on gene expression at the single cell level in Arabidopsis thaliana 43 31 9 3

TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment 43 9 4

Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes 43 4

WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition 43 4

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

Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGER 43 4

Correlation imputation in single cell RNA-seq using auxiliary information and ensemble learning 43 4

Whole-organism mapping of the genetics of gene expression at cellular resolution 19 9 4

Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures 120 43 9

A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples 175 43 4

Comparative analysis of human brain organoids of brainstem and midbrain at single-cell resolution 148 43 9

Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion 43 13 4

Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing 43 9 4

Measuring the Information Obtained from a Single-Cell Sequencing Experiment 43 4

miRNA activity inferred from single cell mRNA expression 43 4

Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis 43 4

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

Establishment of a simplified preparation method for single-nucleus RNA-sequencing and its application to long-term frozen tumor tissues 175 43 4

NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing 43 9 4

Spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution 43 9 4

μCB-seq: Microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells 43 18 4

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

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer 43 26 4

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

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

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

Imputing missing RNA-seq data from DNA methylation by using transfer learning based-deep neural network 105 26 4

Modeling metabolic variation with single-cell expression data 43 9 4

COTAN: Co-expression Table Analysis for scRNA-seq data 43 4

Preprocessing choices affect RNA velocity results for droplet scRNA-seq data 43 4

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

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

CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets 43 9 4

Single cell RNA-Seq analysis identifies molecular mechanisms controlling hypothalamic patterning and differentiation. 43 20 9 3

Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions 43 26 4

BingleSeq: A user-friendly R package for Bulk and Single-cell RNA-Seq Data Analysis 62 43 4

Novel Human Kidney Cell Subsets Identified by Mux-Seq 43 9 4