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Stathopoulou KM, Georgakopoulos S, Tasoulis S, Plagianakos VP. Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation. Health Inf Sci Syst 2024; 12:14. [PMID: 38435719 PMCID: PMC10904690 DOI: 10.1007/s13755-023-00265-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 12/05/2023] [Indexed: 03/05/2024] Open
Abstract
Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.
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Affiliation(s)
- Kalliopi-Maria Stathopoulou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, 35100 Lamia, Greece
| | | | - Sotiris Tasoulis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, 35100 Lamia, Greece
| | - Vassilis P. Plagianakos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, 35100 Lamia, Greece
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Kermani N, Versi A, Gay A, Vlasma J, Jayalatha AKS, Koppelman GH, Nawijn M, Faiz A, van den Berge M, Adcock IM, Chung KF. Gene signatures in U-BIOPRED severe asthma for molecular phenotyping and precision medicine: time for clinical use. Expert Rev Respir Med 2023; 17:965-971. [PMID: 37997709 DOI: 10.1080/17476348.2023.2278606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
INTRODUCTION The use and generation of gene signatures have been established as a method to define molecular endotypes in complex diseases such as severe asthma. Bioinformatic approaches have now been applied to large omics datasets to define the various co-existing inflammatory and cellular functional pathways driving or characterizing a particular molecular endotype. AREAS COVERED Molecular phenotypes and endotypes of Type 2 inflammatory pathways and also of non-Type 2 inflammatory pathways, such as IL-6 trans-signaling, IL-17 activation, and IL-22 activation, have been defined in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes dataset. There has also been the identification of the role of mast cell activation and of macrophage dysfunction in various phenotypes of severe asthma. EXPERT OPINION Phenotyping on the basis of clinical treatable traits is not sufficient for understanding of mechanisms driving the disease in severe asthma. It is time to consider whether certain patients with severe asthma, such as those non-responsive to current therapies, including Type 2 biologics, would be better served using an approach of molecular endotyping using gene signatures for management purposes rather than the current sole reliance on blood eosinophil counts or exhaled nitric oxide measurements.
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Affiliation(s)
- Nazanin Kermani
- National Heart & Lung Institute & Data Science Institute, Imperial College London, London, UK
| | - Ali Versi
- National Heart & Lung Institute & Data Science Institute, Imperial College London, London, UK
| | - Aurore Gay
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Centre Groningen, Groningen, The Netherlands
| | - Jelmer Vlasma
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Centre Groningen, Groningen, The Netherlands
| | | | - Gerard H Koppelman
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Centre Groningen, Groningen, The Netherlands
- Beatrix Children's Hospital, University of Groningen, University Medical Center Groningen, Groningen, Groningen, the Netherlands
| | - Martijn Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Centre Groningen, Groningen, The Netherlands
| | - Alen Faiz
- School of Life Sciences, Respiratory Bioinformatics and Molecular Biology, University of Technology Sydney, Sydney, Australia
| | - Maarten van den Berge
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Centre Groningen, Groningen, The Netherlands
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ian M Adcock
- National Heart & Lung Institute & Data Science Institute, Imperial College London, London, UK
| | - Kian Fan Chung
- National Heart & Lung Institute & Data Science Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospital, London, UK
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Bhadra T, Mallik S, Hasan N, Zhao Z. Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer. BMC Bioinformatics 2022; 23:153. [PMID: 35484501 PMCID: PMC9052461 DOI: 10.1186/s12859-022-04678-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND As many complex omics data have been generated during the last two decades, dimensionality reduction problem has been a challenging issue in better mining such data. The omics data typically consists of many features. Accordingly, many feature selection algorithms have been developed. The performance of those feature selection methods often varies by specific data, making the discovery and interpretation of results challenging. METHODS AND RESULTS In this study, we performed a comprehensive comparative study of five widely used supervised feature selection methods (mRMR, INMIFS, DFS, SVM-RFE-CBR and VWMRmR) for multi-omics datasets. Specifically, we used five representative datasets: gene expression (Exp), exon expression (ExpExon), DNA methylation (hMethyl27), copy number variation (Gistic2), and pathway activity dataset (Paradigm IPLs) from a multi-omics study of acute myeloid leukemia (LAML) from The Cancer Genome Atlas (TCGA). The different feature subsets selected by the aforesaid five different feature selection algorithms are assessed using three evaluation criteria: (1) classification accuracy (Acc), (2) representation entropy (RE) and (3) redundancy rate (RR). Four different classifiers, viz., C4.5, NaiveBayes, KNN, and AdaBoost, were used to measure the classification accuary (Acc) for each selected feature subset. The VWMRmR algorithm obtains the best Acc for three datasets (ExpExon, hMethyl27 and Paradigm IPLs). The VWMRmR algorithm offers the best RR (obtained using normalized mutual information) for three datasets (Exp, Gistic2 and Paradigm IPLs), while it gives the best RR (obtained using Pearson correlation coefficient) for two datasets (Gistic2 and Paradigm IPLs). It also obtains the best RE for three datasets (Exp, Gistic2 and Paradigm IPLs). Overall, the VWMRmR algorithm yields best performance for all three evaluation criteria for majority of the datasets. In addition, we identified signature genes using supervised learning collected from the overlapped top feature set among five feature selection methods. We obtained a 7-gene signature (ZMIZ1, ENG, FGFR1, PAWR, KRT17, MPO and LAT2) for EXP, a 9-gene signature for ExpExon, a 7-gene signature for hMethyl27, one single-gene signature (PIK3CG) for Gistic2 and a 3-gene signature for Paradigm IPLs. CONCLUSION We performed a comprehensive comparison of the performance evaluation of five well-known feature selection methods for mining features from various high-dimensional datasets. We identified signature genes using supervised learning for the specific omic data for the disease. The study will help incorporate higher order dependencies among features.
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Affiliation(s)
- Tapas Bhadra
- Department of Computer Science and Engineering, Aliah University, Kolkata, West Bengal, 700160, India
| | - Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Neaj Hasan
- Department of Computer Science and Engineering, Aliah University, Kolkata, West Bengal, 700160, 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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Mandal M, Sahoo SK, Patra P, Mallik S, Zhao Z. In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells. BMC Bioinformatics 2020; 21:499. [PMID: 33371879 PMCID: PMC7768647 DOI: 10.1186/s12859-020-03849-z] [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: 10/25/2020] [Accepted: 10/27/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Cancer stem cells (CSCs) have features such as the ability to self-renew, differentiate into defined progenies and initiate the tumor growth. Treatments of cancer include drugs, chemotherapy and radiotherapy or a combination. However, treatment of cancer by various therapeutic strategies often fail. One possible reason is that the nature of CSCs, which has stem-like properties, make it more dynamic and complex and may cause the therapeutic resistance. Another limitation is the side effects associated with the treatment of chemotherapy or radiotherapy. To explore better or alternative treatment options the current study aims to investigate the natural drug-like molecules that can be used as CSC-targeted therapy. Among various natural products, anticancer potential of phenolics is well established. We collected the 21 phytochemicals from phenolic group and their interacting CSC genes from the publicly available databases. Then a bipartite graph is constructed from the collected CSC genes along with their interacting phytochemicals from phenolic group as other. The bipartite graph is then transformed into weighted bipartite graph by considering the interaction strength between the phenolics and the CSC genes. The CSC genes are also weighted by two scores, namely, DSI (Disease Specificity Index) and DPI (Disease Pleiotropy Index). For each gene, its DSI score reflects the specific relationship with the disease and DPI score reflects the association with multiple diseases. Finally, a ranking technique is developed based on PageRank (PR) algorithm for ranking the phenolics. RESULTS We collected 21 phytochemicals from phenolic group and 1118 CSC genes. The top ranked phenolics were evaluated by their molecular and pharmacokinetics properties and disease association networks. We selected top five ranked phenolics (Resveratrol, Curcumin, Quercetin, Epigallocatechin Gallate, and Genistein) for further examination of their oral bioavailability through molecular properties, drug likeness through pharmacokinetic properties, and associated network with CSC genes. CONCLUSION Our PR ranking based approach is useful to rank the phenolics that are associated with CSC genes. Our results suggested some phenolics are potential molecules for CSC-related cancer treatment.
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Affiliation(s)
- Monalisa Mandal
- Department of School of Computer Science and Engineering, Xavier University, Bhubaneswar, Odisha, 752050, India
| | | | - Priyadarsan Patra
- Department of School of Computer Science and Engineering, Xavier University, Bhubaneswar, Odisha, 752050, India
| | - 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.
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Xue Y, Guo Y, Luo S, Zhou W, Xiang J, Zhu Y, Xiang Z, Shen J. Aberrantly Methylated-Differentially Expressed Genes Identify Novel Atherosclerosis Risk Subtypes. Front Genet 2020; 11:569572. [PMID: 33381146 PMCID: PMC7767999 DOI: 10.3389/fgene.2020.569572] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 11/16/2020] [Indexed: 12/15/2022] Open
Abstract
Increasing evidence has indicated that modulation of epigenetic mechanisms, especially methylation and long-non-coding RNA (lncRNA) regulation, plays a pivotal role in the process of atherosclerosis; however, few studies focused on revealing the epigenetic-related subgroups during atherosclerotic progression using unsupervised clustering analysis. Hence, we aimed to identify the epigenetics-related differentially expressed genes associated with atherosclerosis subtypes and characterize their clinical utility in atherosclerosis. Eighty samples with expression data (GSE40231) and 49 samples with methylation data (GSE46394) from a large artery plaque were downloaded from the GEO database, and aberrantly methylated-differentially expressed (AMDE) genes were identified based on the relationship between methylation and expression. Furthermore, we conducted weighted correlation network analysis (WGCNA) and co-expression analysis to identify the core AMDE genes strongly involved in atherosclerosis. K-means clustering was used to characterize two subtypes of atherosclerosis in GSE40231, and then 29 samples were recognized as validation dataset (GSE28829). In a blood sample cohort (GSE90074), chi-square test and logistic analysis were performed to explore the clinical implication of the K-means clusters. Furthermore, significance analysis of microarrays and prediction analysis of microarrays (PAM) were applied to identify the signature AMDE genes. Moreover, the classification performance of signature AMDE gene-based classifier from PAM was validated in another blood sample cohort (GSE34822). A total of 1,569 AMDE mRNAs and eight AMDE long non-coding RNAs (lncRNAs) were identified by differential analysis. Through the WGCNA and co-expression analysis, 32 AMDE mRNAs and seven AMDE lncRNAs were identified as the core genes involved in atherosclerosis development. Functional analysis revealed that AMDE genes were strongly related to inflammation and axon guidance. In the clinical analysis, the atherosclerotic subtypes were associated with the severity of coronary artery disease and risk of adverse events. Eight genes, including PARP15, SERGEF, PDGFD, MRPL45, UBR1, STAU1, WIZ, and LSM4, were selected as the signature AMDE genes that most significantly differentiated between atherosclerotic subtypes. Ultimately, the area under the curve of signature AMDE gene-based classifier for atherosclerotic subtypes was 0.858 and 0.812 in GSE90074 and GSE34822, respectively. This study identified the AMDE genes (lncRNAs and mRNAs) that could be implemented in clinical clustering to recognize high-risk atherosclerotic patients.
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Affiliation(s)
- Yuzhou Xue
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yongzheng Guo
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Suxin Luo
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Zhou
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Xiang
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuansong Zhu
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhenxian Xiang
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jian Shen
- Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Guo L, Zhang A, Xiong J. Identification of specific microRNA-messenger RNA regulation pairs in four subtypes of breast cancer. IET Syst Biol 2020; 14:120-126. [PMID: 32406376 PMCID: PMC8687302 DOI: 10.1049/iet-syb.2019.0086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 10/04/2019] [Accepted: 12/13/2019] [Indexed: 01/01/2023] Open
Abstract
Four subtypes of breast cancer, luminal A, luminal B, basal-like, human epidermal growth factor receptor-enriched, have been identified based on gene expression profiles of human tumours. The goal of this study is to find whether the same groups' genes would exhibit different networks among the four subtypes. Differential expressed genes between each of the four subtypes and the normal samples were identified. The overlaps between the four groups of differentially expressed genes were used to construct regulations networks for each of the four subtypes. Univariate and multivariate Cox regressions were employed to test the genes in the four regulation networks. This study demonstrated that the common genes in four subtypes showed different regulation. Also, the hsa-miR-182 and decorin pair performs different functions among the four subtypes of breast cancer. The result indicated that heterogeneity of breast cancer is not only reflected in the different expression patterns among different genes, but also in the different regulatory networks of the same group of genes.
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Affiliation(s)
- Ling Guo
- College of Electrical Engineering, Northwest University for Nationalities, Lanzhou, 730030, People's Republic of China
| | - Aihua Zhang
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, People's Republic of China.
| | - Jie Xiong
- Department of applied mathematics, Changsha University, Changsha, 410022, People's Republic of China
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Li H, Song M, Yang W, Cao P, Zheng L, Zuo Y. A Comparative Analysis of Single-Cell Transcriptome Identifies Reprogramming Driver Factors for Efficiency Improvement. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 19:1053-1064. [PMID: 32045876 PMCID: PMC7015826 DOI: 10.1016/j.omtn.2019.12.035] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/23/2019] [Accepted: 12/26/2019] [Indexed: 12/11/2022]
Abstract
Terminally differentiated somatic cells can be reprogrammed into a totipotent state through somatic cell nuclear transfer (SCNT). The incomplete reprogramming is the major reason for developmental arrest of SCNT embryos at early stages. In our studies, we found that pathways for autophagy, endocytosis, and apoptosis were incompletely activated in nuclear transfer (NT) 2-cell arrest embryos, whereas extensively inhibited pathways for stem cell pluripotency maintenance, DNA repair, cell cycle, and autophagy may result in NT 4-cell embryos arrest. As for NT normal embryos, a significant shift in expression of developmental transcription factors (TFs) Id1, Pou6f1, Cited1, and Zscan4c was observed. Compared with pluripotent gene Ascl2 being activated only in NT 2-cell, Nanog, Dppa2, and Sall4 had major expression waves in normal development of both NT 2-cell and 4-cell embryos. Additionally, Kdm4b/4d and Kdm5b had been confirmed as key markers in NT 2-cell and 4-cell embryos, respectively. Histone acetylases Kat8, Elp6, and Eid1 were co-activated in NT 2-cell and 4-cell embryos to facilitate normal development. Gadd45a as a key driver functions with Tet1 and Tet2 to improve the efficiency of NT reprogramming. Taken together, our findings provided an important theoretical basis for elucidating the potential molecular mechanisms and identified reprogramming driver factor to improve the efficiency of SCNT reprogramming.
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Affiliation(s)
- Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Mingmin Song
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Wuritu Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Pengbo Cao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China.
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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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Wang K, Liu X, Guo Y, Wu Z, Zhi D, Ruan J, Zhao Z. The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: systems biology on diverse data types. BMC SYSTEMS BIOLOGY 2018; 12:125. [PMID: 30577731 PMCID: PMC6302362 DOI: 10.1186/s12918-018-0648-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Between June 10–12, 2018, the International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held in Los Angeles, California, USA. The conference included 11 scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics and systems biology. Systems biology has been a main theme in ICIBM 2018, with exciting advances presented in many areas of systems biology, covering various different data types such as gene regulation, circular RNAs expression, single-cell RNA-Seq, inter-chromosomal interactions, metabolomics, proteomics and phosphoproteomics. Here, we describe ten high quality papers to be published in BMC Systems Biology.
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Affiliation(s)
- Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. .,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Xiaoming Liu
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.,College of Public Health, University of South Florida, Tampa, FL, 33612, USA
| | - Yan Guo
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Zhijin Wu
- Department of Biostatistics, Brown University, Providence, RI, 02912, USA
| | - Degui Zhi
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, 78249, USA
| | - 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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