A novel unsupervised batch removal framework, called iMAP, based on neural networks.

Learning disentangled representations of single-cell data for high-quality generation

Simultaneous deep generative modeling and clustering of single cell genomic data

Iterative transfer learning with neural network improves clustering and cell type classification in single-cell RNA-seq analysis

MultiMAP for integration of single cell multi-omics

An unsupervised scRNA-seq analysis workflow with graph attention networks

Dataset distribution for Seurat

Accompanying code for the tutorial: Annotating single cell transcriptomic maps using automated and manual methods

Reference mapping for single-cell genomics

R package for integrating and analyzing multiple single-cell datasets

Single-cell trajectory alignment with Trajan

Scissor package

R package implementation of Milo for testing for differential abundance in KNN graphs

Quasilinear data representations for single-cell omics data analysis

Geometry-preserving random sampling

Accurate and fast cell marker gene identification with COSG

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings

Neighborhood contrastive clustering with ancillary mask estimation for scRNA-seq data

Individual level Differential Expression Analysis for Single cells

Inclusive and efficient quantification of labeling and splicing RNAs for time-resolved metabolic labeling based scRNA-seq experiments

Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses

trajectory inference

single-cell graph attentional clustering