Related references
Note: Only part of the references are listed.Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models
Chenling Xu et al.
MOLECULAR SYSTEMS BIOLOGY (2021)
BBKNN: fast batch alignment of single cell transcriptomes
Krzysztof Polanski et al.
BIOINFORMATICS (2020)
Decoding the development of the human hippocampus
Suijuan Zhong et al.
NATURE (2020)
A cell atlas of human thymic development defines T cell repertoire formation
Jong-Eun Park et al.
SCIENCE (2020)
Embryonic endothelial evolution towards first hematopoietic stem cells revealed by single-cell transcriptomic and functional analyses
Siyuan Hou et al.
CELL RESEARCH (2020)
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
Zhi-Jie Cao et al.
NATURE COMMUNICATIONS (2020)
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Hoa Thi Nhu Tran et al.
GENOME BIOLOGY (2020)
A comparison of single-cell trajectory inference methods
Wouter Saelens et al.
NATURE BIOTECHNOLOGY (2019)
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Brian Hie et al.
NATURE BIOTECHNOLOGY (2019)
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets
Yingxin Lin et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Comprehensive Integration of Single-Cell Data
Tim Stuart et al.
CELL (2019)
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Joshua D. Welch et al.
CELL (2019)
Tracing the first hematopoietic stem cell generation in human embryo by single-cell RNA sequencing
Yang Zeng et al.
CELL RESEARCH (2019)
Fast, sensitive and accurate integration of single-cell data with Harmony
Ilya Korsunsky et al.
NATURE METHODS (2019)
Dimensionality reduction for visualizing single-cell data using UMAP
Etienne Becht et al.
NATURE BIOTECHNOLOGY (2019)
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Kelly Street et al.
BMC GENOMICS (2018)
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Laleh Haghverdi et al.
NATURE BIOTECHNOLOGY (2018)
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
Josip S. Herman et al.
NATURE METHODS (2018)
Single-cell transcriptomes identify human islet cell signatures and reveal cell-type specific expression changes in type 2 diabetes
Nathan Lawlor et al.
GENOME RESEARCH (2017)
Scaling single-cell genomics from phenomenology to mechanism
Amos Tanay et al.
NATURE (2017)
A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation
Sonia Nestorowa et al.
BLOOD (2016)
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Karthik Shekhar et al.
CELL (2016)
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
Asa Segerstolpe et al.
CELL METABOLISM (2016)
De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data
Dominic Grun et al.
CELL STEM CELL (2016)
A Single-Cell Transcriptome Atlas of the Human Pancreas
Mauro J. Muraro et al.
CELL SYSTEMS (2016)
Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Franziska Paul et al.
CELL (2015)
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
Greg Finak et al.
GENOME BIOLOGY (2015)
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Barbara Treutlein et al.
NATURE (2014)
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Guangchuang Yu et al.
OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY (2012)
Fast unfolding of communities in large networks
Vincent D. Blondel et al.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2008)