Evidence map›Paper›PMID 34214174›Full record

ArticleNucleic acids research2021

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.

Kailin Tang, Xuejie Ji, Mengdi Zhou, Zeliang Deng, Yuwei Huang, Genhui Zheng, Zhiwei Cao

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
5.2field-weighted citation impact, top 3% of its field
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

50 citing papers in PubMed, 74 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Kailin TangDepartment of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.ORCID 0000-0002-0508-6056
Xuejie JiDepartment of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.
Mengdi ZhouDepartment of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.
Zeliang DengDepartment of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.
Yuwei HuangDepartment of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.
Genhui ZhengDepartment of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.
Zhiwei CaoDepartment of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China.ORCID 0000-0003-1165-1928
Tongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

Indexed as

AlgorithmsCluster AnalysisColonic NeoplasmsComputational BiologyDiagnosis, DifferentialGene Expression ProfilingGlioblastomaHumansInternetNeoplasmsOligonucleotide Array Sequence AnalysisReproducibility of ResultsRNA-SeqSensitivity and Specificity

Identifiers

PMID34214174
PMCPMC8464058
OpenAlexW3174811345

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.