Evidence map›Paper›PMID 35942344›Full record

ArticleSTAR protocols2022

Protocol to estimate cell type proportions from bulk RNA-seq using DAISM-DNN

Yating Lin, Shangze Wu, Xu Xiao, Jingbo Zhao, Minshu Wang, Haojun Li, Kejia Wang, Minwei Zhang, Frank Zheng, Wenxian Yang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in STAR protocols, 2022. 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

13 authors.

Yating LinSchool of Informatics, Xiamen University, Xiamen 361005, China.
Shangze WuSchool of Informatics, Xiamen University, Xiamen 361005, China.
Xu XiaoSchool of Informatics, Xiamen University, Xiamen 361005, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.
Jingbo ZhaoAmoy Diagnostics, Xiamen 361000, China.
Minshu WangNational Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China; School of Medicine, Xiamen University, Xiamen 361102, China.
Haojun LiSchool of Informatics, Xiamen University, Xiamen 361005, China.
Kejia WangSchool of Medicine, Xiamen University, Xiamen 361102, China.
Minwei ZhangDepartment of Critical Care Medicine, The First Affiliated Hospital of Xiamen University, Xiamen 361003, China.
Frank ZhengAmoy Diagnostics, Xiamen 361000, China.
Wenxian YangAginome Scientific, Xiamen 361005, China.
Lei ZhangSchool of Life Science, Xiamen University, Xiamen 361102, China. Electronic address: zhanglei@xmu.edu.cn.
Jiahuai HanResearch Unit of Cellular Stress of CAMS, Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, Xiamen 361102, China. Electronic address: jhan@xmu.edu.cn.
Rongshan YuSchool of Informatics, Xiamen University, Xiamen 361005, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China; Aginome Scientific, Xiamen 361005, China. Electronic address: rsyu@xmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational protocols for cell type deconvolution from bulk RNA-seq data have been used to understand cellular heterogeneity in disease-related samples, but their performance can be impacted by batch effect among datasets. Here, we present a DAISM-DNN protocol to achieve robust cell type proportion estimation on the target dataset. We describe the preparation of calibrated samples from human blood samples. We then detail steps to train a dataset-specific deep neural network (DNN) model and cell type proportion estimation using the trained model. For complete details on the use and execution of this protocol, please refer to Lin et al. (2022).

Indexed as

Neural Networks, ComputerHumansRNA-SeqBioinformaticsCell BiologyFlow Cytometry/Mass CytometryGene ExpressionImmunologyRNAseqSequence analysis

Identifiers

PMID35942344
PMCPMC9356155

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.