Dataset Information

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data.

ABSTRACT: RNA sequencing (RNA-seq) has become a widely used technology for analyzing global gene-expression changes during certain biological processes. It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data also displays underdispersion characteristics that cannot be adequately captured by general RNA-seq analysis methods. Based on a double Poisson model capable of capturing all data characteristics, we developed a new RNA-seq analysis method (DREAMSeq). Comparison of DREAMSeq with five other frequently used RNA-seq analysis methods using simulated datasets showed that its performance was comparable to or exceeded that of other methods in terms of type I error rate, statistical power, receiver operating characteristics (ROC) curve, area under the ROC curve, precision-recall curve, and the ability to detect the number of differentially expressed genes, especially in situations involving underdispersion. These results were validated by quantitative real-time polymerase chain reaction using a real Foxtail dataset. Our findings demonstrated DREAMSeq as a reliable, robust, and powerful new method for RNA-seq data mining. The DREAMSeq R package is available at http://tanglab.hebtu.edu.cn/tanglab/Home/DREAMSeq.

SUBMITTER: Gao Z

PROVIDER: S-EPMC6284200 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data.

Gao Zhihua Z Zhao Zhiying Z Tang Wenqiang W

Frontiers in genetics 20181130

RNA sequencing (RNA-seq) has become a widely used technology for analyzing global gene-expression changes during certain biological processes. It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data ...[more]

PMID: 30559761

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Project description:BackgroundIdentifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data.ResultsIn this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization.ConclusionsSimulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB .

Project description:BackgroundA limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets.ResultsWe implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obtained from The Cancer Genome Atlas database. DEGnext uses biologically validated data along with logarithmic fold change values to classify differentially expressed genes (DEGs) as UR and DR genes. We applied transfer learning to our model to leverage the knowledge of trained feature maps to untrained cancer datasets. DEGnext's results were competitive (ROC scores between 88 and 99[Formula: see text]) with those of five traditional machine learning methods: Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and XGBoost. DEGnext was robust and effective in terms of transferring learned feature maps to facilitate classification of unseen datasets. Additionally, we validated that the predicted DEGs from DEGnext were mapped to significant Gene Ontology terms and pathways related to cancer.ConclusionsDEGnext can classify DEGs into UR and DR genes from RNA-seq cancer datasets with high performance. This type of analysis, using biologically relevant fine-tuning data, may aid in the exploration of potential biomarkers and can be adapted for other disease datasets.

Dataset Information

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data.

Publications

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data.

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