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Ghosh M, Sen S, Sarkar R, Maulik U. Quantum squirrel inspired algorithm for gene selection in methylation and expression data of prostate cancer. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107221] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Mallik S, Zhao Z. Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise. Sci Rep 2020; 10:22164. [PMID: 33335112 PMCID: PMC7747741 DOI: 10.1038/s41598-020-78463-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 11/23/2020] [Indexed: 12/20/2022] Open
Abstract
There have been numerous genetic and epigenetic datasets generated for the study of complex disease including neurodegenerative disease. However, analysis of such data often suffers from detecting the outliers of the samples, which subsequently affects the extraction of the true biological signals involved in the disease. To address this critical issue, we developed a novel framework for identifying methylation signatures using consecutive adaptation of a well-known outlier detection algorithm, density based clustering of applications with reducing noise (DBSCAN) followed by hierarchical clustering. We applied the framework to two representative neurodegenerative diseases, Alzheimer's disease (AD) and Down syndrome (DS), using DNA methylation datasets from public sources (Gene Expression Omnibus, GEO accession ID: GSE74486). We first applied DBSCAN algorithm to eliminate outliers, and then used Limma statistical method to determine differentially methylated genes. Next, hierarchical clustering technique was applied to detect gene modules. Our analysis identified a methylation signature comprising 21 genes for AD and a methylation signature comprising 89 genes for DS, respectively. Our evaluation indicated that these two signatures could lead to high classification accuracy values (92% and 70%) for these two diseases. In summary, this framework will be useful to better detect outlier-free genetic and epigenetic signatures in various complex diseases and their developmental stages.
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Affiliation(s)
- Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Zhang E, Zhang M, Shi C, Sun L, Shan L, Zhang H, Song Y. An overview of advances in multi-omics analysis in prostate cancer. Life Sci 2020; 260:118376. [PMID: 32898525 DOI: 10.1016/j.lfs.2020.118376] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/21/2020] [Accepted: 08/31/2020] [Indexed: 02/09/2023]
Abstract
Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.
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Affiliation(s)
- Enchong Zhang
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, People's Republic of China
| | - Mo Zhang
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, People's Republic of China
| | - Changlong Shi
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, People's Republic of China
| | - Li Sun
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, People's Republic of China
| | - Liping Shan
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, People's Republic of China
| | - Hui Zhang
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, People's Republic of China.
| | - Yongsheng Song
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, People's Republic of China.
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Mallik S, Bandyopadhyay S. WeCoMXP: Weighted Connectivity Measure Integrating Co-Methylation, Co-Expression and Protein-Protein Interactions for Gene-Module Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:690-703. [PMID: 30183644 DOI: 10.1109/tcbb.2018.2868348] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The identification of modules (groups of several tightly interconnected genes) in gene interaction network is an essential task for better understanding of the architecture of the whole network. In this article, we develop a novel weighted connectivity measure integrating co-methylation, co-expression, and protein-protein interactions (called WeCoMXP) to detect gene-modules for multi-omics dataset. The proposed measure goes beyond the fundamental degree centrality measure through considering some formulation of higher-order connections. Thereafter, we apply the average linkage clustering method using the corresponding dissimilarity (distance) values of WeCoMXP scores, and utilize a dynamic tree cut method for identifying some gene-modules. We validate the modules through literature search, KEGG pathway, and gene-ontology analyses on the genes representing the modules. Furthermore, the top 10 TFs/miRNAs that are connected with the maximum number of gene-modules and that regulate/target the maximum number of genes from these connected gene-modules, are identified. Moreover, our proposed method provides a better performance than the existing methods in terms of several cluster-validity indices in maximum times.
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Mallik S, Zhao Z. Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles. Genes (Basel) 2019; 10:E611. [PMID: 31412637 PMCID: PMC6723724 DOI: 10.3390/genes10080611] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 07/30/2019] [Accepted: 08/07/2019] [Indexed: 02/06/2023] Open
Abstract
Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expression of genes at single-cell resolution in complex disease or tissue. While many methods have been developed to detect cell clusters from the scRNA-seq data, this task currently remains a main challenge. We proposed a multi-objective optimization-based fuzzy clustering approach for detecting cell clusters from scRNA-seq data. First, we conducted initial filtering and SCnorm normalization. We considered various case studies by selecting different cluster numbers ( c l = 2 to a user-defined number), and applied fuzzy c-means clustering algorithm individually. From each case, we evaluated the scores of four cluster validity index measures, Partition Entropy ( P E ), Partition Coefficient ( P C ), Modified Partition Coefficient ( M P C ), and Fuzzy Silhouette Index ( F S I ). Next, we set the first measure as minimization objective (↓) and the remaining three as maximization objectives (↑), and then applied a multi-objective decision-making technique, TOPSIS, to identify the best optimal solution. The best optimal solution (case study) that had the highest TOPSIS score was selected as the final optimal clustering. Finally, we obtained differentially expressed genes (DEGs) using Limma through the comparison of expression of the samples between each resultant cluster and the remaining clusters. We applied our approach to a scRNA-seq dataset for the rare intestinal cell type in mice [GEO ID: GSE62270, 23,630 features (genes) and 288 cells]. The optimal cluster result (TOPSIS optimal score= 0.858) comprised two clusters, one with 115 cells and the other 91 cells. The evaluated scores of the four cluster validity indices, F S I , P E , P C , and M P C for the optimized fuzzy clustering were 0.482, 0.578, 0.607, and 0.215, respectively. The Limma analysis identified 1240 DEGs (cluster 1 vs. cluster 2). The top ten gene markers were Rps21, Slc5a1, Crip1, Rpl15, Rpl3, Rpl27a, Khk, Rps3a1, Aldob and Rps17. In this list, Khk (encoding ketohexokinase) is a novel marker for the rare intestinal cell type. In summary, this method is useful to detect cell clusters from scRNA-seq data.
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Affiliation(s)
- Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
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Diseases and their clinical heterogeneity – Are we ignoring the SNiPers and micRomaNAgers? An illustration using Beta-thalassemia clinical spectrum and fetal hemoglobin levels. Genomics 2019; 111:67-75. [DOI: 10.1016/j.ygeno.2018.01.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 12/18/2017] [Accepted: 01/03/2018] [Indexed: 12/18/2022]
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Mallik S, Zhao Z. Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm. BMC SYSTEMS BIOLOGY 2018; 12:126. [PMID: 30577846 PMCID: PMC6302366 DOI: 10.1186/s12918-018-0650-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Gene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures. Methods In this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. We first performed pre-filtering steps and normalization, then utilized the empirical Bayes test in Limma package to identify the differentially expressed genes (DEGs). Next, we used a multi-objective optimization technique, “Multi-objective optimization for collecting cluster alternatives” (MOCCA in R package) on these DEGs to find Pareto-optimal cluster size, and then applied k-means clustering to the RNA-seq data based on the optimal cluster size. The best cluster was obtained through computing the average Spearman’s Correlation Score among all the genes in pair-wise manner belonging to the module. The best cluster is treated as the signature for the respective disease or cellular condition. Results We applied our framework to a cervical cancer RNA-seq dataset, which included 253 squamous cell carcinoma (SCC) samples and 22 adenocarcinoma (ADENO) samples. We identified a total of 582 DEGs by Limma analysis of SCC versus ADENO samples. Among them, 260 are up-regulated genes and 322 are down-regulated genes. Using MOCCA, we obtained seven Pareto-optimal clusters. The best cluster has a total of 35 DEGs consisting of all-upregulated genes. For validation, we ran PAMR (prediction analysis for microarrays) classifier on the selected best cluster, and assessed the classification performance. Our evaluation, measured by sensitivity, specificity, precision, and accuracy, showed high confidence. Conclusions Our framework identified a multi-objective based cluster that is treated as a signature that can classify the disease and control group of samples with higher classification performance (accuracy 0.935) for the corresponding disease. Our method is useful to find signature for any RNA-seq or microarray data.
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Affiliation(s)
- Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA. .,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, 37232, TN, USA.
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Sen S, Maulik U. Recent advancement toward significant association between disordered transcripts and virus-infected diseases: a survey. Brief Funct Genomics 2018; 17:458-470. [DOI: 10.1093/bfgp/ely021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Sagnik Sen
- Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, India
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, India
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Bandyopadhyay S, Mallik S. Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:673-687. [PMID: 28114033 DOI: 10.1109/tcbb.2016.2636207] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers ( s) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., ) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support ( ) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential s. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.
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Mallik S, Zhao Z. ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis. Genes (Basel) 2017; 9:E7. [PMID: 29283433 PMCID: PMC5793160 DOI: 10.3390/genes9010007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 12/12/2017] [Accepted: 12/12/2017] [Indexed: 01/18/2023] Open
Abstract
For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.
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Affiliation(s)
- Saurav Mallik
- Department of Computer Science & Engineering, Aliah University, Newtown, WB-700156, India.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
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Mallik S, Zhao Z. Towards integrated oncogenic marker recognition through mutual information-based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles. QUANTITATIVE BIOLOGY 2017; 5:302-327. [PMID: 30221015 DOI: 10.1007/s40484-017-0119-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Background Marker detection is an important task in complex disease studies. Here we provide an association rule mining (ARM) based approach for identifying integrated markers through mutual information (MI) based statistically significant feature extraction, and apply it to acute myeloid leukemia (AML) and prostate carcinoma (PC) gene expression and methylation profiles. Methods We first collect the genes having both expression and methylation values in AML as well as PC. Next, we run Jarque-Bera normality test on the expression/methylation data to divide the whole dataset into two parts: one that ollows normal distribution and the other that does not follow normal distribution. Thus, we have now four parts of the dataset: normally distributed expression data, normally distributed methylation data, non-normally distributed expression data, and non-normally distributed methylated data. A feature-extraction technique, "mRMR" is then utilized on each part. This results in a list of top-ranked genes. Next, we apply Welch t-test (parametric test) and Shrink t-test (non-parametric test) on the expression/methylation data for the top selected normally distributed genes and non-normally distributed genes, respectively. We then use a recent weighted ARM method, "RANWAR" to combine all/specific resultant genes to generate top oncogenic rules along with respective integrated markers. Finally, we perform literature search as well as KEGG pathway and Gene-Ontology (GO) analyses using Enrichr database for in silico validation of the prioritized oncogenes as the markers and labeling the markers as existing or novel. Results The novel markers of AML are {ABCB11↑∪KRT17↓} (i.e., ABCB11 as up-regulated, & KRT17 as down-regulated), and {AP1S1-∪KRT17↓∪NEIL2-∪DYDC1↓}) (i.e., AP1S1 and NEIL2 both as hypo-methylated, & KRT17 and DYDC1 both as down-regulated). The novel marker of PC is {UBIAD1¶∪APBA2‡∪C4orf31‡} (i.e., UBIAD1 as up-regulated and hypo-methylated, & APBA2 and C4orf31 both as down-regulated and hyper-methylated). Conclusion The identified novel markers might have critical roles in AML as well as PC. The approach can be applied to other complex disease.
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Affiliation(s)
- Saurav Mallik
- Computer Science & Engineering, Aliah University, Newtown, Newtown 700156, India
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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Mallik S, Bhadra T, Maulik U. Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data. IEEE Trans Nanobioscience 2017; 16:3-10. [PMID: 28092570 DOI: 10.1109/tnb.2017.2650217] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution. For each case, we utilize a gene-selection method that provides maximal-relevant, but variable-weighted minimum-redundant genes as top ranked genes. For statistical validation, we apply t-test on both the expression and methylation data consisting of only the normally distributed top ranked genes to determine how many of them are both differentially expressed andmethylated. Similarly, we utilize Limma package for performing non-parametric Empirical Bayes test on both expression and methylation data comprising only the non-normally distributed top ranked genes to identify how many of them are both differentially expressed and methylated. We finally report the top-ranking significant gene-markerswith biological validation. Moreover, our framework improves positive predictive rate and reduces false positive rate in marker identification. In addition, we provide a comparative analysis of our gene-selection method as well as othermethods based on classificationperformances obtained using several well-known classifiers.
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