Transitioning single-cell genomics into the clinic

J Lim, V Chin, K Fairfax, C Moutinho, D Suan… - Nature Reviews …, 2023 - nature.com
The use of genomics is firmly established in clinical practice, resulting in innovations across
a wide range of disciplines such as genetic screening, rare disease diagnosis and …

Toward a consensus view of mammalian adipocyte stem and progenitor cell heterogeneity

R Ferrero, P Rainer, B Deplancke - Trends in cell biology, 2020 - cell.com
White adipose tissue (WAT) is a cellularly heterogeneous endocrine organ that not only
serves as an energy reservoir, but also actively participates in metabolic homeostasis …

CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data

CZI Cell Science Program, S Abdulla… - Nucleic Acids …, 2025 - academic.oup.com
Hundreds of millions of single cells have been analyzed using high-throughput
transcriptomic methods. The cumulative knowledge within these datasets provides an …

gEAR: Gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

J Orvis, B Gottfried, J Kancherla, RS Adkins, Y Song… - Nature …, 2021 - nature.com
To the Editor—Biologists are important stakeholders in genomic data, both as data
generators and as users of genomic data resources. Tools to efficiently visualize and …

ShinyCell: simple and sharable visualization of single-cell gene expression data

JF Ouyang, US Kamaraj, EY Cao… - Bioinformatics, 2021 - academic.oup.com
Motivation As the generation of complex single-cell RNA sequencing datasets becomes
more commonplace it is the responsibility of researchers to provide access to these data in a …

UCSC Cell Browser: visualize your single-cell data

ML Speir, A Bhaduri, NS Markov, P Moreno… - …, 2021 - academic.oup.com
As the use of single-cell technologies has grown, so has the need for tools to explore these
large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to …

Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

L Xia, C Lee, JJ Li - Nature Communications, 2024 - nature.com
Abstract Two-dimensional (2D) embedding methods are crucial for single-cell data
visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) …

siVAE: interpretable deep generative models for single-cell transcriptomes

Y Choi, R Li, G Quon - Genome biology, 2023 - Springer
Neural networks such as variational autoencoders (VAE) perform dimensionality reduction
for the visualization and analysis of genomic data, but are limited in their interpretability: it is …

Single-cell omics: experimental workflow, data analyses and applications

F Sun, H Li, D Sun, S Fu, L Gu, X Shao, Q Wang… - Science China Life …, 2024 - Springer
Cells are the fundamental units of biological systems and exhibit unique development
trajectories and molecular features. Our exploration of how the genomes orchestrate the …

The current landscape of single-cell transcriptomics for cancer immunotherapy

P Guruprasad, YG Lee, KH Kim, M Ruella - Journal of Experimental …, 2020 - rupress.org
Immunotherapies such as immune checkpoint blockade and adoptive cell transfer have
revolutionized cancer treatment, but further progress is hindered by our limited …