Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types

9 Nov 2018

Abstract excerpt

Advances in single-cell biology have enabled measurements of >40 protein features on millions of immune cells within clinical samples. However, the data analysis steps following cell population identification are susceptible to bias, time-consuming, and challenging to compare across studies. Here, an ensemble of unsupervised tools was developed to evaluate four essential types of immune cell information,...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Immunotherapy and Biomarkers
Share to Single-cell and spatial transcriptomics
Share to T-cell and B-cell Immunology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 30413431
DOI: 10.1158/2326-6066.cir-17-0692

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor TypesPMID 30413431Read ↗

A Single-Cell Tumor Immune Atlas for Precision OncologySemantic proximity Read ↗

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapySemantic proximity Read ↗

Evolution and modulation of antigen-specific T cell responses in melanoma patientsSemantic proximity Read ↗

Single-cell characterization and machine learning-based classification reveal the transcriptional identity and antigen-experienced features of peripheral CD4⁺CD8⁺ double-positive T cellsSemantic proximity Read ↗

Single-Cell Analysis of the Pan-Cancer Immune Microenvironment and scTIME PortalSemantic proximity Read ↗

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligenceSemantic proximity Read ↗

Pan-cancer tumor classification by a holistic tumor microenvironment atlasSemantic proximity Read ↗

Tissue-resident memory T cell signatures from single-cell analysis associated with better melanoma prognosisSemantic proximity Read ↗

Robust identification of perturbed cell types in single-cell RNA-seq dataSemantic proximity Read ↗

Multiomics and spatial mapping characterizes human CD8 + T cell states in cancerSemantic proximity Read ↗

Dissecting cellular states of infiltrating microenvironment cells in melanoma by integrating single-cell and bulk transcriptome analysis.Semantic proximity Read ↗

CD8+ T cell states in human cancer: insights from single-cell analysisSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.