YADA - Reference Free Deconvolution of RNA Sequencing Data

Dani Livne; Tom Snir; Sol Efroni

doi:10.2174/0115748936304034240405034414

ISSN: 1574-8936
E-ISSN: 2212-392X

YADA - Reference Free Deconvolution of RNA Sequencing Data
Authors: Dani Livne¹, Tom Snir¹ and Sol Efroni¹
View Affiliations Hide Affiliations

¹ Department of Life Sciences, Bar Ilan University, Ramat Gan, Israel
Source: Current Bioinformatics, Volume 20, Issue 3, Mar 2025, p. 266 - 275
DOI: https://doi.org/10.2174/0115748936304034240405034414
- Received: 12 Jan 2024
- Accepted: 20 Mar 2024
- Available online: 05 Jun 2024

Abstract

Introduction

We present YADA, a cellular content deconvolution algorithm for estimating cell type proportions in heterogeneous cell mixtures based on gene expression data. YADA utilizes curated gene signatures of cell type-specific marker genes, either obtained intrinsically from pure cell type expression matrices or provided by the user.

Methods

YADA implements an accessible and extensible deconvolution framework uniquely capable of handling marker genes alone as inputs. Adoption barriers are lowered significantly by relying solely on literature-supported cell type-specific signatures rather than full transcriptomic profiles from purified isolates. However, flexible inputs do not necessitate sacrificing rigor - predictions match metrics of current methodologies through an integrated optimization scheme balancing multiple inference algorithms. Efficiency optimizations via compiled runtimes enable rapid execution. Packaging as an importable Python toolkit promotes community enhancement while retaining codebase extensibility.

Results

Validation studies demonstrate that YADA matches or exceeds the performance of current deconvolution methods on benchmark datasets. To demonstrate the utility and enable immediate usage, we provide an online Jupyter Notebook implementation coupled with tutorials.

Conclusion

YADA provides an accurate, efficient, and extensible Python-based toolkit for cellular deconvolution analysis of heterogeneous gene expression data.

Article metrics loading...

/content/journals/cbio/10.2174/0115748936304034240405034414

2024-06-05

2025-03-25

From This Site

/content/journals/cbio/10.2174/0115748936304034240405034414

dcterms_title,dcterms_subject,pub_keyword

-contentType:Contributor -contentType:Concept -contentType:Institution

10

5

Full text loading...

References

AbbasA.R. WolslegelK. SeshasayeeD. ModrusanZ. ClarkH.F. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus.PLoS One200947e609810.1371/journal.pone.0006098 19568420
[Google Scholar]
ClarkeJ. SeoP. ClarkeB. Statistical expression deconvolution from mixed tissue samples.Bioinformatics20102681043104910.1093/bioinformatics/btq097 20202973
[Google Scholar]
GongT. HartmannN. KohaneI.S. Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples.PLoS One2011611e2715610.1371/journal.pone.0027156 22110609
[Google Scholar]
ZhongY. WanY.W. PangK. ChowL.M.L. LiuZ. Digital sorting of complex tissues for cell type-specific gene expression profiles.BMC Bioinformatics20131418910.1186/1471‑2105‑14‑89 23497278
[Google Scholar]
Shen-OrrS.S. GaujouxR. Computational deconvolution: extracting cell type-specific information from heterogeneous samples.Curr. Opin. Immunol.201325557157810.1016/j.coi.2013.09.015 24148234
[Google Scholar]
AranD. HuZ. ButteA.J. xCell: digitally portraying the tissue cellular heterogeneity landscape.Genome Biol.201718122010.1186/s13059‑017‑1349‑1 29141660
[Google Scholar]
ErkkiläT. LehmusvaaraS. RuusuvuoriP. VisakorpiT. ShmulevichI. LähdesmäkiH. Probabilistic analysis of gene expression measurements from heterogeneous tissues.Bioinformatics201026202571257710.1093/bioinformatics/btq406 20631160
[Google Scholar]
GongT. SzustakowskiJ.D. Bioinformatics applications note deconrnaseq : a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data.Bioinformatics201829810831085
[Google Scholar]
GaujouxR. SeoigheC. CellMix: A comprehensive toolbox for gene expression deconvolution.Bioinformatics201329172211221210.1093/bioinformatics/btt351 23825367
[Google Scholar]
NewmanA.M. LiuC.L. GreenM.R. Robust enumeration of cell subsets from tissue expression profiles.Nat. Methods201512545345710.1038/nmeth.3337 25822800
[Google Scholar]
QiaoW. QuonG. CsaszarE. YuM. MorrisQ. ZandstraP.W. PERT: A method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions.PLOS Comput. Biol.2012812e100283810.1371/journal.pcbi.1002838 23284283
[Google Scholar]
LiebnerD.A. HuangK. ParvinJ.D. MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples.Bioinformatics201430568268910.1093/bioinformatics/btt566 24085566
[Google Scholar]
SubramanianA. TamayoP. MoothaV.K. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. USA200510243155451555010.1073/pnas.0506580102 16199517
[Google Scholar]
Czerwinska PhD thesis https://urszulaczerwinska.github.io/UCzPhDThesis/ Accessed 25 Feb 2020.
NewmanA.M. SteenC.B. LiuC.L. Determining cell type abundance and expression from bulk tissues with digital cytometry.Nat. Biotechnol.201937777378210.1038/s41587‑019‑0114‑2 31061481
[Google Scholar]
AlieeH. TheisF.J. AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution.Cell Syst.2021127706715.e410.1016/j.cels.2021.05.006 34293324
[Google Scholar]
MendenK. MaroufM. OllerS. Deep learning–based cell composition analysis from tissue expression profiles.Sci. Adv.2020630eaba261910.1126/sciadv.aba2619 32832661
[Google Scholar]
DongR. YuanG.C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data.Genome Biol.202122114510.1186/s13059‑021‑02362‑7 33971932
[Google Scholar]
Livne DaniYADA Github Repository2024Available from: https://github.com/zurkin1/yada Accessed 2 Mar 2024.
[Google Scholar]
MillerB.F. HuangF. AttaL. SahooA. FanJ. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data.Nat. Commun.2022131233910.1038/s41467‑022‑30033‑z 35487922
[Google Scholar]
KangK. HuangC. LiY. UmbachD.M. LiL. CDSeqR: fast complete deconvolution for gene expression data from bulk tissues.BMC Bioinformatics202122126210.1186/s12859‑021‑04186‑5 34030626
[Google Scholar]
Shen-OrrS.S. TibshiraniR. KhatriP. Cell type–specific gene expression differences in complex tissues.Nat. Methods20107428728910.1038/nmeth.1439 20208531
[Google Scholar]
MonacoG. LeeB. XuW. RNA-seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types.Cell Rep.201926616271640.e710.1016/j.celrep.2019.01.041 30726743
[Google Scholar]
HansenL. PS-IT on PA, 1990 undefined. Neural network ensembles 1990. computer.org.
[Google Scholar]
KunchevaL. Combining pattern classifiers: methods and algorithms200410.1002/0471660264
[Google Scholar]
RacleJ. de JongeK. BaumgaertnerP. SpeiserD.E. GfellerD. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.eLife20176e2647610.7554/eLife.26476 29130882
[Google Scholar]
LiB. SeversonE. PignonJ.C. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.Genome Biol.201617117410.1186/s13059‑016‑1028‑7 27549193
[Google Scholar]
SchelkerM. FeauS. DuJ. Estimation of immune cell content in tumour tissue using single-cell RNA-seq data.Nat. Commun.201781203210.1038/s41467‑017‑02289‑3 29230012
[Google Scholar]
Tumor Deconvolution DREAM Challenge : syn15589870https://www.synapse.org/#!Synapse:syn15589870/wiki/583086
Sautès-FridmanC. LacroixL. ButtardB. SelvesJ. FridmanW.H. ElarouciN. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression.Genome Biol.201617120
[Google Scholar]
FinotelloF. MayerC. PlattnerC. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data.Genome Med.20171134
[Google Scholar]
ZappiaL. PhipsonB. OshlackA. Splatter: simulation of single-cell RNA sequencing data.Genome Biol.201718117410.1186/s13059‑017‑1305‑0 28899397
[Google Scholar]

/content/journals/cbio/10.2174/0115748936304034240405034414

YADA - Reference Free Deconvolution of RNA Sequencing Data

Curr Bioinform 20, 266 (2025); https://doi.org/10.2174/0115748936304034240405034414

/content/journals/cbio/10.2174/0115748936304034240405034414

Data & Media loading...

Article Type: Research Article

Keyword(s): Deconvolution; ensemble; genomics; machine learning; optimization; RNA sequencing

Most Cited Most Cited RSS feed

- A Review of Ensemble Methods in Bioinformatics
  
  Authors: Pengyi Yang, Yee Hwa Yang, Bing B. Zhou and Albert Y. Zomaya
- Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis
  
  Authors: Masahiro Sugimoto, Masato Kawakami, Martin Robert, Tomoyoshi Soga and Masaru Tomita
- Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
  
  Authors: Haoyu Zhang, Quan Zou, Ying Ju, Chenggang Song and Dong Chen
- A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods
  
  Authors: Jun Zhang and Bin Liu
- Molecular Genetic Markers: Discovery, Applications, Data Storage and Visualisation
  
  Authors: Chris Duran, Nikki Appleby, David Edwards and Jacqueline Batley
- A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization
  
  Authors: Wuritu Yang, Xiao-Juan Zhu, Jian Huang, Hui Ding and Hao Lin
- Cancer Diagnosis Through IsomiR Expression with Machine Learning Method
  
  Authors: Zhijun Liao, Dapeng Li, Xinrui Wang, Lisheng Li and Quan Zou
- The Advances and Challenges of Deep Learning Application in Biological Big Data Processing
  
  Authors: Li Peng, Manman Peng, Bo Liao, Guohua Huang, Weibiao Li and Dingfeng Xie
- Gene Expression Profile Classification: A Review
  
  Authors: Musa H. Asyali, Dilek Colak, Omer Demirkaya and Mehmet S. Inan
- Relevance of Molecular Docking Studies in Drug Designing
  
  Authors: Ritu Jakhar, Mehak Dangi, Alka Khichi and Anil K. Chhillar
More Less

YADA - Reference Free Deconvolution of RNA Sequencing Data

Abstract

Most Read This Month

Most Cited Most Cited RSS feed