CaSTLe – Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments-Reference-Cited by-同舟云学术

CaSTLe – Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments

Published:2018-10-10 Issue:10 Volume:13 Page:e0205499
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Lieberman Yuval^ORCID,Rokach Lior,Shay Tal

Funder

Israel Science Foundation

Broad-Israel Science Foundation

National Institutes of Health

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference31 articles.

1. Defining cell types and states with single-cell genomics;C Trapnell;Genome Research,2015

2. JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets;H Ner-Gaon;J Immunol,2017

3. Challenges and emerging directions in single-cell analysis;G-C Yuan;Genome Biol,2017

4. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets;EZ Macosko;Cell,2015

5. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index;L Jiang;Genome Biol,2016

Cited by 92 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial intelligence in cell annotation for high-resolution RNA sequencing data;TrAC Trends in Analytical Chemistry;2024-09

2. PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data;Bioinformatics;2024-06-26

3. Acinar to β-like cell conversion through inhibition of focal adhesion kinase;Nature Communications;2024-05-03

4. AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics;Nature Communications;2024-05-03

5. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets;Cell Reports Medicine;2024-05