Evidence map›Paper›PMID 42734751›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

Systematic Analysis of Tumor Microenvironment Using IOBR.

Yiran Fang, Qingcong Luo, Guangchuang Yu, Dongqiang Zeng

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Yiran FangClinical Innovation and Research Centre (CIRC), Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, China.
Qingcong LuoClinical Innovation and Research Centre (CIRC), Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, China.
Guangchuang YuDepartment of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China. gcyu1@smu.edu.cn.
Dongqiang ZengClinical Innovation and Research Centre (CIRC), Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, China. interlaken@smu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Immuno-Oncology Biological Research (IOBR) package is an R-based analysis tool for exploring the tumor microenvironment (TME) and its influence on anti-tumor immunity. Built for high-throughput data-spanning both transcriptomic and genomic profiles-IOBR integrates six analytical modules, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand-receptor interaction analysis, genome-TME interaction assessment, and visualization. In this chapter, we walk through a multi-omics workflow using example datasets, illustrating data preparation, distribution analyses, result interpretation, and graphical output. IOBR is open source and is available at https://github.com/IOBR/IOBR and a detailed GitBook ( https://iobr.github.io/book/ ) offers a complete manual and analysis guide for each function.

Indexed as

Computational BiologyNeoplasmsSoftwareTumor MicroenvironmentGene Expression ProfilingGenomicsHumansMultiomicsTranscriptomeGene signatureIOBRMulti-omicsSingle-cell dataTumor–immune interactionTumor microenvironment

Identifiers

PMID42734751

What OpenQuestion holds

Textmetadata
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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.