scholarly journals Biological classification with RNA-Seq data: Can alternative splicing enhance machine learning classifier?

2017 ◽  
Author(s):  
Nathan T. Johnson ◽  
Andi Dhroso ◽  
Katelyn J. Hughes ◽  
Dmitry Korkin
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Data Analysis ◽  
Alternative Splicing ◽  
Supervised Learning ◽  
Classification Problem ◽  
Expression Data ◽  
Rna Seq ◽  
Biological Classification ◽  
Classification Tasks

AbstractThe extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data.In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis.

scholarly journals IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

2019 ◽  
Vol 15 (2) ◽  
pp. e1006792 ◽  
Author(s):  
Brandon Monier ◽  
Adam McDermaid ◽  
Cankun Wang ◽  
Jing Zhao ◽  
Allison Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Expression Data Analysis ◽  
Interpretation System

scholarly journals Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Jaewoong Lee ◽  
Sungmin Cho ◽  
Seong-Eui Hong ◽  
Dain Kang ◽  
Hayoung Choi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Differential Diagnosis ◽  
Acute Leukemia ◽  
Gene Expression Data ◽  
Gene Fusion ◽  
Integrative Analysis ◽  
Expression Data ◽  
Rna Seq ◽  
Disease Category

BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation.

scholarly journals Current State-of-the-Art of Clustering Methods for Gene Expression Data with RNA-Seq

2020 ◽  
Author(s):  
Ismail Jamail ◽  
Ahmed Moussa
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Expression Profile ◽  
Clustering Algorithms ◽  
Expression Data ◽  
Rna Seq ◽  
Clustering Methods ◽  
Clustering And Classification

Latest developments in high-throughput cDNA sequencing (RNA-seq) have revolutionized gene expression profiling. This analysis aims to compare the expression levels of multiple genes between two or more samples, under specific circumstances or in a specific cell to give a global picture of cellular function. Thanks to these advances, gene expression data are being generated in large throughput. One of the primary data analysis tasks for gene expression studies involves data-mining techniques such as clustering and classification. Clustering, which is an unsupervised learning technique, has been widely used as a computational tool to facilitate our understanding of gene functions and regulations involved in a biological process. Cluster analysis aims to group the large number of genes present in a sample of gene expression profile data, such that similar or related genes are in same clusters, and different or unrelated genes are in distinct ones. Classification on the other hand can be used for grouping samples based on their expression profile. There are many clustering and classification algorithms that can be applied in gene expression experiments, the most widely used are hierarchical clustering, k-means clustering and model-based clustering that depend on a model to sort out the number of clusters. Depending on the data structure, a fitting clustering method must be used. In this chapter, we present a state of art of clustering algorithms and statistical approaches for grouping similar gene expression profiles that can be applied to RNA-seq data analysis and software tools dedicated to these methods. In addition, we discuss challenges in cluster analysis, and compare the performance of height commonly used clustering methods on four different public datasets from recount2.

scholarly journals Biomarker Screening And Prediction Model Construction of Esophageal Carcinoma Based On Bioinformatics

2021 ◽  
Author(s):  
Yanzhou Zhang ◽  
Qing Zhu ◽  
Xiufeng Cao ◽  
Bin Ni
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Differentially Expressed Genes ◽  
Cross Validation ◽  
Prediction Models ◽  
Differentially Expressed ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Modules ◽  
Fold Cross Validation

Abstract Background and objective: Esophageal cancer(ESCA) ranks eleventh in incidence and eighth in mortality among malignant tumors in the world. Due to the paucity of effective early diagnostic approach, a lot of patients have missed the first-rank treatment time frame and were already in the advanced phase at their first diagnosis. The continuous reforming of high-throughput sequencing technologies and analytical techniques has provided novel concepts and approaches for the study of cancer biomarkers in esophageal cancer. The development of cancer is a complex biological process with multi-gene concernment, multi-factor mutual effect and multi-phase development. This process includes the mutations in proto-oncogenes, changes in transcript expression profiles, and abnormalities of protein structure, function, or expression levels. The study of the molecular mechanism of ESCA using high-throughput sequencing technology will lay theoretic foundation for the early diagnosis and targeted therapy of ESCA.Materials and methods: In this study, a search was conducted in tow commonly used public databases, UCSC XENA and GEO, one UCSC XENA RNA-seq data and tow GEO datasets were included in this study. Differential expression analysis was implemented by using limma in R software.Weighted gene co-expression network analysis (WGCNA) was used to analyze the gene transcriptome expression profile consisting of 181 ESCA tissues and 181 normal tissues as controls to construct topology network. We constructed gene modules and searched for gene modules that were closely participant to ESCA, and gene ontology (GO) and KEGG pathway enrichment analysis were implemented to probe into the functions of the DEGs and differentially expressed hub genes in key modules. By combining the consequences of differential gene expression analysis with WGCNA consequences(hub genes), we procured a 30 of differentially expressed genes in module that were closely participant to ESCA. Next, we procured the expression data of these genes from normalized transcriptome expression data to construct ESCA predictive model. Then, ten-fold cross validation combining with machine learning algorithms were used to construct prediction models for ESCA. Finally, we also verified the four screened biomarkers which used to build the predictive model with the GEO data sets.Results: Analysis of differentially expressed genes were conducted by using the limma packages and differentially expressed genes were defined as |log2FC|>1 and adj.P.Val < 0.01. After comparison the results from limma, a total of 15814 genes were up-regulated in ESCA, a total of 6176 gene were down-regulated in ESCA.A total of 7 gene modules were identified from WGCNA, 2 modules of them are strongly corelative with ESCA (Brown module: R2=0.87, Lightcyan module: R2=-0.75, both P <0.001). Brown module is closely related to ESCA.The consequences of WGCNA analysis combined with differentially expressed genes revealed that there were 4419 differentially expressed genes in the brown module which were closely related to ESCA. 30 hub gene were screened by kWithin top 30 from brown module, and all of them are differentially expressed.GO analysis of differetially expressed genes from brown module revealed that these genes are from immunoglobulin complex, “chromosome, centromeric region”, condensed chromosome, “immunoglobulin complex, circulating”, condensed chromosome, centromeric region, and other components, and they participated in biological function such as antigen binding, immunoglobulin receptor binding, ATPase activity, cadherin binding, DNA helicase activity, etc., involved in biological processes such as adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains, mitotic nuclear division, lymphocyte mediated immunity, nuclear division, and DNA replication; KEGG pathway analysis shows the brown module differentially expressed genes are mainly enriched in signal pathways such as cell cycle, pathogenic escherichia coli infection, DNA replication, IL-17 signaling pathway and human T-cell leukemia virus 1 infection. This shed new light on molecular mechanisms of the development of ESCA.Twelve ESCA prediction models constructed from 30 gene expression matrices from 362 subjects by using 10-fold cross-validation combined with machine learning algorithms revealed good prediction performance in validation dataset, among which models from gbm, BoostGLM, C5.0 algorithms revealed higher accuracy than from other algorithms. Although the transparent or semi-transparent models constructed by JRip, PART, and Rpart algorithms have acceptable accuracy in validation dataset, their sensitivity are lower. From a comprehensive perspective, two black box algorithm models including gbm and BoostGLM models are selected as the final model. This study has successfully constructed ESCA prediction models with accuracies higher than 0.97.Finally, three of the four screened biomarkers were validated.Conclusions: In current study, differential expression analysis and WGCNA of ESCA participant RNA-seq data available in public database were used to screen DEGs and genes that were closely participant with ESCA. Consequences from GO and KEGG analysis further revealed the underlying mechanisms of ESCA. Normalized gene expression data was feed to several different machine learning techniques and 10-fold cross validation was used to construct high accuracy ESCA predictive models. Eventually, several ESCA predictive models with accuracy higher than 0.96 in validation group were constructed. At the meantime, three biomarkers(G3BP1, CHEK1 and MOB1A) were screened and validated, in particular, G3BP1 may be a potential therapeutic target, as overall survival analysis have shown it to be an adverse prognostic factor. Current study has lay the basis of applying RNA-seq data in the early genetic diagnosis of ESCA, and a prognostic marker that might contribute to treatment of ESCA.

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