Discriminant Analysis and Normalization Methods for Next-Generation Sequencing Data

Zhou, Yan; Wang, Junhui; Zhao, Yichuan; Tong, Tiejun

doi:10.1007/978-3-319-99389-8_18

Cited by 3 publications

(3 citation statements)

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“…Existing methods for classifying microarray data [ 9 – 11 ] cannot be applied to RNA-Seq data due to potential discrete distributions, such as Poisson and Negative Binomial [ 12 , 13 ]. Thus, several statistical methods have been proposed for RNA-Seq data analysis [ 14 ]. Normalization is the first step in RNA-Seq data analysis to eliminate variations caused by different numbers of reads from different samples.…”

Section: Introductionmentioning

confidence: 99%

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework

Feng,

Wang,

Jin

et al. 2024

PLoS ONE

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Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

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Section: Introductionmentioning

confidence: 99%

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework

Feng,

Wang,

Jin

et al. 2024

PLoS ONE

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“…For the classification of RNA-seq data, several statistical methods have been developed [ 20 , 21 ], in particular for the bulk RNA-seq experiments. Poisson and negative binomial distributions are two most commonly used distributions to model the discrete RNA-seq data.…”

Section: Introductionmentioning

confidence: 99%

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data

et al. 2022

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Background Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated. Results We propose a deep learning classifier (scDLC) for large sample scRNA-seq data, based on the long short-term memory recurrent neural networks (LSTMs). Our new scDLC does not require a prior knowledge on the data distribution, but instead, it takes into account the dependency of the most outstanding feature genes in the LSTMs model. LSTMs is a special recurrent neural network, which can learn long-term dependencies of a sequence. Conclusions Simulation studies show that our new scDLC performs consistently better than the existing methods in a wide range of settings with large sample sizes. Four real scRNA-seq datasets are also analyzed, and they coincide with the simulation results that our new scDLC always performs the best. The code named “scDLC” is publicly available at https://github.com/scDLC-code/code.

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“…Zhou et al 18 proposed a zero‐inflated Poisson logistic discriminant analysis (ZIPLDA) for RNA‐seq data in the presence of excess zeros. Tan et al 19 and Zhou et al 20 also discussed the issue on RNA‐seq data classification.…”

Section: Introductionmentioning

confidence: 99%

Category encoding method to select feature genes for the classification of bulk and single‐cell RNA‐seq data

Zhou

Zhang

et al. 2021

Statistics in Medicine

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Bulk and single-cell RNA-seq (scRNA-seq) data are being used as alternatives to traditional technology in biology and medicine research. These data are used, for example, for the detection of differentially expressed (DE) genes. Several statistical methods have been developed for the classification of bulk and single-cell RNA-seq data. These feature genes are vitally important for the classification of bulk and single-cell RNA-seq data. The majority of genes are not DE and they are thus irrelevant for class distinction. To improve the classification performance and save the computation time, removal of irrelevant genes is necessary. Removal will aid the detection of the important feature genes. Widely used schemes in the literature, such as the BSS/WSS (BW) method, assume that data are normally distributed and may not be suitable for bulk and single-cell RNA-seq data. In this article, a category encoding (CAEN) method is proposed to select feature genes for bulk and single-cell RNA-seq data classification. This novel method encodes categories by employing the rank of sequence samples for each gene in each class. Correlation coefficients are considered for gene and class with the rank of sample and a new rank of category. The highest gene correlation coefficients are considered feature genes, which are the most effective for classifying bulk and single-cell RNA-seq dataset. The sure screening method was also established for rank consistency properties of the proposed CAEN method.Simulation studies show that the classifier using the proposed CAEN method performs better than, or at least as well as, the existing methods in most settings.Existing real datasets were analyzed, with the results demonstrating superior performance of the proposed method over current competitors. The application has been coded into an R package named "CAEN" to facilitate wide use.

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Discriminant Analysis and Normalization Methods for Next-Generation Sequencing Data

Cited by 3 publications

References 50 publications

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework

TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data

Category encoding method to select feature genes for the classification of bulk and single‐cell RNA‐seq data

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