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scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment

https://doi.org/10.1101/2022.06.20.496916

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jamanetworkopen.2021.37257openalex-snapshot

Global Percentage of Asymptomatic SARS-CoV-2 Infections Among the Tested Population and Individuals With Confirmed COVID-19 Diagnosis

inferredcites →https://doi.org/10.1016/j.cell.2018.11.043openalex-snapshot

Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma

inferredcites →https://doi.org/10.1016/j.cell.2020.08.001openalex-snapshot

Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment

inferredcites →https://doi.org/10.1016/j.cell.2021.01.053openalex-snapshot

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas

inferredcites →https://doi.org/10.1016/j.cell.2021.04.048openalex-snapshot

Integrated analysis of multimodal single-cell data

inferredcites →https://doi.org/10.1016/j.cell.2022.01.012openalex-snapshot

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

inferredcites →https://doi.org/10.1016/j.cpc.2010.04.018openalex-snapshot

NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations

inferredcites →https://doi.org/10.1016/j.immuni.2020.12.002openalex-snapshot

PD-1-Expressing SARS-CoV-2-Specific CD8+ T Cells Are Not Exhausted, but Functional in Patients with COVID-19

inferredcites →https://doi.org/10.1038/nbt.4314openalex-snapshot

Dimensionality reduction for visualizing single-cell data using UMAP

inferredcites →https://doi.org/10.1038/s41551-021-00704-1openalex-snapshot

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images

inferredcites →https://doi.org/10.1038/s41587-020-00803-5openalex-snapshot

Quantifying the effect of experimental perturbations at single-cell resolution

inferredcites →https://doi.org/10.1038/s41587-021-01033-zopenalex-snapshot

Differential abundance testing on single-cell data using k-nearest neighbor graphs

inferredcites →https://doi.org/10.1038/s41591-020-0944-yopenalex-snapshot

A single-cell atlas of the peripheral immune response in patients with severe COVID-19

inferredcites →https://doi.org/10.1038/s41591-021-01329-2openalex-snapshot

Single-cell multi-omics analysis of the immune response in COVID-19

inferredcites →https://doi.org/10.1073/pnas.2100293118openalex-snapshot

Detection of differentially abundant cell subpopulations in scRNA-seq data

inferredcites →https://doi.org/10.1073/pnas.2109123118openalex-snapshot

High-dimensional profiling reveals phenotypic heterogeneity and disease-specific alterations of granulocytes in COVID-19

inferredcites →https://doi.org/10.1126/science.abc8511openalex-snapshot

Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

inferredcites →https://doi.org/10.1126/sciimmunol.abd1554openalex-snapshot

Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19

inferredcites →https://doi.org/10.1126/sciimmunol.abe4782openalex-snapshot

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 + T cells

inferredcites →https://doi.org/10.1145/1150402.1150454openalex-snapshot

Supervised probabilistic principal component analysis

inferredcites →https://doi.org/10.1214/06-ba104openalex-snapshot

Variational inference for Dirichlet process mixtures

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.patter.2025.101297openalex-snapshot

cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies

inferred← citeshttps://doi.org/10.1093/bib/bbae673openalex-snapshot

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics

inferred← citeshttps://doi.org/10.1093/bioinformatics/btae067openalex-snapshot

Phenotype prediction from single-cell RNA-seq data using attention-based neural networks

inferred← citeshttps://doi.org/10.1101/2023.12.22.573159openalex-snapshot

OmniClustify XMBD : Uncover putative cell states within multiple single-cell omics datasets

inferred← citeshttps://doi.org/10.1101/2024.07.29.605625openalex-snapshot

Weakly supervised learning uncovers phenotypic signatures in single-cell data

inferred← citeshttps://doi.org/10.1186/s13073-026-01598-xopenalex-snapshot

Exploring phenotype-related single-cells through attention-enhanced representation learning

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