scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning DOI
Yingxin Lin,

Tung-Yu Wu,

Sheng Wan

et al.

Nature Biotechnology, Journal Year: 2022, Volume and Issue: 40(5), P. 703 - 710

Published: Jan. 20, 2022

Language: Английский

Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID DOI
Akira Cortal, Loredana Martignetti, Emmanuelle Six

et al.

Nature Biotechnology, Journal Year: 2021, Volume and Issue: 39(9), P. 1095 - 1102

Published: April 29, 2021

Language: Английский

Citations

131
MultiVI: deep generative model for the integration of multimodal data DOI Creative Commons
Tal Ashuach, Mariano I. Gabitto,

Rohan V. Koodli

et al.

Nature Methods, Journal Year: 2023, Volume and Issue: 20(8), P. 1222 - 1231

Published: June 29, 2023

Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, probabilistic model analyze such multiomic data leverage it enhance single-modality datasets. MultiVI creates joint representation that allows an analysis all modalities included in input data, even for which one or more are missing. It is available at scvi-tools.org .

Language: Английский

Citations

128
Pan-cancer proteomic map of 949 human cell lines DOI Creative Commons
Emanuel Gonçalves, Rebecca C. Poulos, Zhaoxiang Cai

et al.

Cancer Cell, Journal Year: 2022, Volume and Issue: 40(8), P. 835 - 849.e8

Published: July 14, 2022

The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted identification new cancer biomarkers. Here, proteomes 949 cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence cell-type post-transcriptional modifications. Integrating multi-omics, drug response, CRISPR-Cas9 gene essentiality screens with deep learning-based pipeline reveals thousands protein biomarkers vulnerabilities that not significant at transcript level. power predict response is very similar Further, random downsampling only 1,500 proteins limited impact on predictive power, consistent networks being highly connected co-regulated. This pan-cancer map (ProCan-DepMapSanger) comprehensive resource available https://cellmodelpassports.sanger.ac.uk.

Language: Английский

Citations

123
Profiling epigenetic age in single cells DOI
Alexandre Trapp, Csaba Kerepesi, Vadim N. Gladyshev

et al.

Nature Aging, Journal Year: 2021, Volume and Issue: 1(12), P. 1189 - 1201

Published: Dec. 9, 2021

Language: Английский

Citations

121
scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning DOI
Yingxin Lin,

Tung-Yu Wu,

Sheng Wan

et al.

Nature Biotechnology, Journal Year: 2022, Volume and Issue: 40(5), P. 703 - 710

Published: Jan. 20, 2022

Language: Английский

Citations

112