Evidence map›Paper›PMID 34456903›Full record

ArticleFrontiers in immunology2021

ImmuCellDB: An Indicative Database of Immune Cell Composition From Different Tissues and Disease Conditions in Mouse and Human.

Ziyi Chen, Han Na, Aiping Wu

Abstract read
In one paragraph

Article in Frontiers in immunology, 2021. 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. Article
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

3 authors.

Ziyi ChenInstitute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Han NaInstitute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Aiping WuInstitute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune cell composition is highly divergent across different tissues and diseases. A comprehensive resource of tissue immune cells across different conditions in mouse and human will thus provide great understanding of the immune microenvironment of many diseases. Recently, computational methods for estimating immune cell abundance from tissue transcriptome data have been developed and are now widely used. Using these computational tools, large-scale estimation of immune cell composition across tissues and conditions should be possible using gene expression data collected from public databases. In total, 266 tissue types and 706 disease types in humans, as well as 143 tissue types and 61 disease types, and 206 genotypes in mouse had been included in a database we have named ImmuCellDB (http://wap-lab.org:3200/ImmuCellDB/). In ImmuCellDB, users can search and browse immune cell proportions based on tissues, disease or genotype in mouse or humans. Additionally, the variation and correlation of immune cell abundance and gene expression level between different conditions can be compared and viewed in this database. We believe that ImmuCellDB provides not only an indicative view of tissue-dependent or disease-dependent immune cell profiles, but also represents an easy way to pre-determine immune cell abundance and gene expression profiles for specific situations.

Indexed as

TranscriptomeAnimalsCellular MicroenvironmentComputational BiologyDatabases, GeneticDisease Models, AnimalGene Expression ProfilingGene Expression RegulationHumansImmune SystemMicedatabasedeconvolutionhumanimmune cellmousetranscriptome

Identifiers

PMID34456903
PMCPMC8387820

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Registered trials

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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.