Generic placeholder image

Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

voomSOM: voom-based Self-Organizing Maps for Clustering RNASequencing Data

Author(s): Ahu Cephe, Necla Koçhan, Gözde Ertürk Zararsız, Vahap Eldem, Erdal Coşgun, Erdem Karabulut and Gökmen Zararsız*

Volume 18, Issue 2, 2023

Published on: 30 December, 2022

Page: [154 - 169] Pages: 16

DOI: 10.2174/1574893618666221205154712


Background: Due to overdispersion in the RNA-Seq data and its discrete structure, clustering samples based on gene expression profiles remains a challenging problem, and several clustering approaches have been developed so far. However, there is no “gold standard” strategy for clustering RNA-Seq data, so alternative approaches are needed.

Objective: In this study, we presented a new clustering approach, which incorporates two powerful methods, i.e., voom and self-organizing maps, into the frequently used clustering algorithms such as kmeans, k-medoid and hierarchical clustering algorithms for RNA-seq data clustering.

Methods: We first filter and normalize the raw RNA-seq count data. Then to transform counts into continuous data, we apply the voom method, which outputs the log-cpm matrix and sample quality weights. After the voom transformation, we apply the SOM algorithm to log-cpm values to get the codebook used in the downstream analysis. Next, we calculate the weighted distance matrices using the sample quality weights obtained from voom transformation and codebooks from the SOM algorithm. Finally, we apply k-means, k-medoid and hierarchical clustering algorithms to cluster samples.

Results: The performances of the presented approach and existing methods are compared over simulated and real datasets. The results show that the new clustering approach performs similarly or better than other methods in the Rand index and adjusted Rand index. Since the voom method accurately models the observed mean-variance relationship of RNA-seq data and SOM is an efficient algorithm for modeling high dimensional data, integrating these two powerful methods into clustering algorithms increases the performance of clustering algorithms in overdispersed RNA-seq data.

Conclusion: The proposed algorithm, voomSOM, is an efficient and novel clustering approach that can be applied to RNA-Seq data clustering problems.

Keywords: Gene expression, class discovery, cancer, clustering, diagnosis, next-generation sequencing, RNA-seq, voomSOM.

Graphical Abstract
Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 2014; 15(2): R29.
[] [PMID: 24485249]
Mardis ER. Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet 2008; 9(1): 387-402.
[] [PMID: 18576944]
Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev Genet 2009; 10(1): 57-63.
[] [PMID: 19015660]
Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002; 99(10): 6567-72.
[] [PMID: 12011421]
Zhang L, He Y, Wang H, et al. Clustering count-based RNA methylation data using a nonparametric generative model. Curr Bioinform 2018; 14(1): 11-23.
Daxin Jiang , Chun Tang , Aidong Zhang . Cluster analysis for gene expression data: A survey. IEEE Trans Knowl Data Eng 2004; 16(11): 1370-86.
Georgiou DN, Karakasidis TE, Megaritis AC. A short survey on genetic sequences, chou’s pseudo amino acid composition and its combination with fuzzy set theory. Open Bioinform J 2013; 7(1): 41-8.
Nieto JJ, Torres A, Georgiou DN, Karakasidis TE. Fuzzy polynucleotide spaces and metrics. Bull Math Biol 2006; 68(3): 703-25.
[] [PMID: 16794951]
Kerr G, Ruskin HJ, Crane M, Doolan P. Techniques for clustering gene expression data. Comput Biol Med 2008; 38(3): 283-93.
[] [PMID: 18061589]
Georgiou DN, Karakasidis TE, Nieto JJ, Torres A. A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets. J Theor Biol 2010; 267(1): 95-105.
[] [PMID: 20708019]
Jamail I, Moussa A. Current State-of-the-Art of Clustering Methods for Gene Expression Data with RNA-SeqAppl Pattern Recognit. London: Intechopen 2021.
Bugnon LA, Raad J, Merino GA, et al. Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19. Mach Learn Appl 2021; 6: 100150.
[] [PMID: 34939043]
Witten DM. Classification and clustering of sequencing data using a Poisson model. Ann Appl Stat 2011; 5(4): 2493-518.
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010; 11(10): R106.
[] [PMID: 20979621]
Robinson MD, McCarthy DJ, Smyth GK, Edge R. A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010; 26(1): 139-40.
[] [PMID: 19910308]
Si Y, Liu P, Li P, Brutnell TP. Model-based clustering for RNA-seq data. Bioinformatics 2014; 30(2): 197-205.
[] [PMID: 24191069]
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(12): 550.
[] [PMID: 25516281]
Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43(7): e47.
[] [PMID: 25605792]
Zararsiz G, Goksuluk D, Klaus B, et al. voomDDA: Discovery of diagnostic biomarkers and classification of RNA-seq data. PeerJ 2017; 5: e3890.
[] [PMID: 29018623]
Durmuscelebi A. Novel statisical approaches in clustering RNA-sequencing data Erciyes. Erciyes University 2019.
Nan F, Li Y, Jia X, Dong L, Chen Y. Application of improved SOM network in gene data cluster analysis. Measurement 2019; 145: 370-8.
Kohonen T. Exploration of very large databases by self-organizing maps. Proceeding of International Conference on Neural Networks. 12-12 June 1997; Houston, TX, USA:. 1997; pp. PL1-6.
Mokhtar M, Abbas Abdulwahhab A, Mariam Shafie S. Comparison between clustering algorithms for microarray data analysis. IOSR J Comput Eng 2014; 16(1): 22-6.
Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979; 74(368): 829-36.
Liu R, Holik AZ, Su S, et al. Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res 2015; 43(15): e97.
[] [PMID: 25925576]
Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3(1): 1-25.
[] [PMID: 16646809]
Ritchie ME, Diyagama D, Neilson J, et al. Empirical array quality weights in the analysis of microarray data. BMC Bioinformatics 2006; 7(1): 261.
[] [PMID: 16712727]
Ahmad A, Yusof R. A modified kohonen self-organizing map (KSOM) clustering for four categorical data. J Teknol 2016; 78: 6-13.
Vesanto J, Alhoniemi E. Clustering of the self-organizing map. IEEE Trans Neural Netw 2000; 11(3): 586-600.
[] [PMID: 18249787]
Witten D, Tibshirani R, Gu SG, Fire A, Lui WO. Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls. BMC Biol 2010; 8(1): 58.
[] [PMID: 20459774]
Smyth GK. Limma: Linear models for microarray data. Bioinforma Comput Biol Solut Using R Bioconductor 2005; pp. 397-420.
Montgomery SB, Sammeth M, Gutierrez-Arcelus M, et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 2010; 464(7289): 773-7.
[] [PMID: 20220756]
Pickrell JK, Marioni JC, Pai AA, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 2010; 464(7289): 768-72.
[] [PMID: 20220758]
Deng Q, Ramsköld D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 2014; 343(6167): 193-6.
Singh SP, Janjuha S, Chaudhuri S, et al. Machine learning based classification of cells into chronological stages using single-cell transcriptomics. Sci Rep 2018; 8(1): 17156.
[] [PMID: 30464314]
Rand WM. Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 1971; 66(336): 846-50.
Hubert L, Arabie P. Comparing partitions. J Classif 1985; 2(1): 193-218.
Ronan T, Qi Z, Naegle KM. Avoiding common pitfalls when clustering biological data. Sci Signal 2016; 9(432): re6.
[] [PMID: 27303057]
Gao Z, Zhao Z, Tang W. DREAMSeq: An improved method for analyzing differentially expressed genes in RNA-seq data. Front Genet 2018; 9: 588.
[] [PMID: 30559761]
Zhou YH, Xia K, Wright FA. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 2011; 27(19): 2672-8.
[] [PMID: 21810900]
Torkkola K, Mike Gardner R, Kaysser-Kranich T, Ma C. Self-organizing maps in mining gene expression data. Inf Sci 2001; 139(1-2): 79-96.
Nikkilä J, Törönen P, Kaski S, Venna J, Castrén E, Wong G. Analysis and visualization of gene expression data using self-organizing maps. Neural Netw 2002; 15(8-9): 953-66.
[] [PMID: 124166]
Chavez-Alvarez R, Chavoya A, Mendez-Vazquez A. Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases. PLoS One 2014; 9(4): e93233.
[] [PMID: 24699245]

© 2023 Bentham Science Publishers | Privacy Policy