Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data

Yuan, Fei; Pan, Xiaoyong; Zeng, Tao; Zhang, Yuhang; Chen, Lei; Gan, Zijun; Huang, Tao; Cai, Yi

doi:10.3389/fbioe.2020.00350

Cited by 9 publications

(10 citation statements)

References 69 publications

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“…Boruta feature filtering ( Kursa and Rudnicki, 2010 ; Pan et al, 2020 ; Yuan et al, 2020 ) is usually used to rapidly select all relevant features to the target labels on the basis of a random forest (RF) classifier. In brief, the calculation of Boruta includes the following steps: (1) shuffled data are created by shuffling the feature values of copies of original data; (2) RF can be trained on the original and shuffled data to measure the feature importance, and the Z score is calculated for each feature by standardizing its importance score from the RF; (3) one original feature is tagged as important when its Z score is greater than the maximum Z score of shadow features; otherwise, it is tagged as unimportant; (4) the above processes are repeated until all features are tagged as important or not.…”

Section: Methodsmentioning

confidence: 99%

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Zhang

Zeng

et al. 2021

Front. Cell Dev. Biol.

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The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

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Section: Methodsmentioning

confidence: 99%

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Zhang

Zeng

et al. 2021

Front. Cell Dev. Biol.

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“…The Boruta method can find out the relevant features and significantly reduce the number of features based on ensemble learning of random forest classifiers. Boruta is a widely used method and has been proven to be an effective method to find all relevant features ( Pan et al, 2020 ; Yuan et al, 2020 ; Zhang et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL

Ying

et al. 2021

Front. Cell Dev. Biol.

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Acute lymphoblastic leukemia (ALL) as a common cancer is a heterogeneous disease which is mainly divided into BCP-ALL and T-ALL, accounting for 80–85% and 15–20%, respectively. There are many differences between BCP-ALL and T-ALL, including prognosis, treatment, drug screening, gene research and so on. In this study, starting with methylation and gene expression data, we analyzed the molecular differences between BCP-ALL and T-ALL and identified the multi-omics signatures using Boruta and Monte Carlo feature selection methods. There were 7 expression signature genes (CD3D, VPREB3, HLA-DRA, PAX5, BLNK, GALNT6, SLC4A8) and 168 methylation sites corresponding to 175 methylation signature genes. The overall accuracy, accuracy of BCP-ALL, accuracy of T-ALL of the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) classifier using these signatures evaluated with 10-fold cross validation repeated 3 times were 0.973, 0.990, and 0.933, respectively. Two overlapped genes between 175 methylation signature genes and 7 expression signature genes were CD3D and VPREB3. The network analysis of the methylation and expression signature genes suggested that their common gene, CD3D, was not only different on both methylation and expression levels, but also played a key regulatory role as hub on the network. Our results provided insights of understanding the underlying molecular mechanisms of ALL and facilitated more precision diagnosis and treatment of ALL.

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“…Here, we employed Boruta ( Kursa and Rudnicki, 2010 ) method to quickly select relevant features with particular class labels (e.g., cancer types or non-cancer class). This method has been applied to deal with different biological and medical problems ( Pan et al, 2020 ; Yuan et al, 2020 ; Zhang et al, 2021a ).…”

Section: Methodsmentioning

confidence: 99%

Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes

Yuan

Chen

et al. 2021

Front. Genet.

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Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.

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Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data

Cited by 9 publications

References 69 publications

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Multi-Omics Analysis of Acute Lymphoblastic Leukemia Identified the Methylation and Expression Differences Between BCP-ALL and T-ALL

Identifying the Signatures and Rules of Circulating Extracellular MicroRNA for Distinguishing Cancer Subtypes

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