Evidence map›Paper›PMID 36929073›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper.

Chloé B Steen, Bogdan A Luca, Ash A Alizadeh, Andrew J Gentles, Aaron M Newman

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chloé B SteenDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Bogdan A LucaDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Ash A AlizadehDivision of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA.
Andrew J GentlesDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Aaron M NewmanDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.

Funding

Stanford Cancer Immune Monitoring and Analysis Center (CIMAC)U24CA224309 · NCI · STANFORD UNIVERSITY · PI Sean Curtis Bendall, Holden T. Maecker · 2017 to 2026
$19.9M
Spatial-Genomic Integrative Multi-Species Analysis of Lymph Node MetastasisU54CA209971 · NCI · STANFORD UNIVERSITY · PI NOLAN, GARRY P, PLEVRITIS, SYLVIA KATINA · 2016 to 2021
$11.0M
A Genomic Framework for Molecular Risk Prediction & Individualized Lymphoma TherapyR01CA233975 · NCI · STANFORD UNIVERSITY · PI ALIZADEH, ASH ARASH, DIEHN, MAXIMILIAN · 2019 to 2023
$2.8M
Delineating developmental programs driving tumorigenesis in triple-negative breast cancerR01CA255450 · NCI · STANFORD UNIVERSITY · PI NEWMAN, AARON M · 2021 to 2025
$2.5M
Ultrasensitive Quantitation of Circulating Tumor DNAR00CA187192 · NCI · STANFORD UNIVERSITY · PI NEWMAN, AARON M · 2017 to 2019
$747k
NCI NIH HHS R00 CA187192NCI NIH HHS R01 CA233975NCI NIH HHS R01 CA255450NCI NIH HHS U54 CA209971
6 · The paper itself

Abstract

Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

Indexed as

EcosystemHealth StatusGene Expression ProfilingMachine LearningSingle-Cell AnalysisTranscriptomeCell statesEcosystemsEcotypesSingle-cell RNA sequencingSpatial transcriptomicsTissue heterogeneityTranscriptomicsTumor microenvironment

Identifiers

PMID36929073

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.