A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data

Puliparambil, Bhavithry Sen; Tomal, Jabed H.; Yan, Yan

doi:10.3390/biology11101495

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…The features are further filtered by referring to a binary expression score. Sen Puliparambil et al [ 32 ] introduced a method to select a set of discriminative genes using multiple penalization models. Other embedded-based gene selection methods incorporate models such as logistics regression (LR) [ 33 ], autoencoder [ 34 ], or deep learning [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data

Ng,

Tan,

Ong

2023

PLoS ONE

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Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.

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Section: Introductionmentioning

confidence: 99%

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data

Ng,

Tan,

Ong

2023

PLoS ONE

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Analysis of Leaf cover on Raspberry Fruits Based on Hyperspectral Techniques Combined with Machine Learning Models

Chen,

Wang,

et al. 2024

Preprint

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The aim of this study is to explore the potential application of hyperspectral technology in detecting the problem of fruit cover in the orchard. Three types of hyperspectral data were collected using a hyperspectral instrument to cover raspberry fruits with leaves. Machine learning models were used to classify and regress covered and uncovered fruits. The results show that hyperspectral technology can effectively differentiate fruits under different cover conditions, with spectral intensity data performing better in addressing cover issues. Random forest (RF) and multilayer perceptron (MLP) models demonstrated high accuracy in classification analysis, with MLP achieving a ROC AUC value of 0.99 on full-band data. Regression analysis also revealed a significant correlation between degree of coverage and spectral features, highlighting in particular the high explanatory power of light intensity data in predicting degree of coverage. This study not only confirms the application value of hyperspectral technology in precision agriculture, but also provides new technical support for intelligent orchard management and automated harvesting. Future research will focus on improving the generalisation ability of the models, integrating multi-source data to further improve the accuracy of coverage detection, and exploring the development of real-time monitoring and automatic control systems to achieve comprehensive intelligence in orchard management.

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A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data

Cited by 2 publications

References 57 publications

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data

Analysis of Leaf cover on Raspberry Fruits Based on Hyperspectral Techniques Combined with Machine Learning Models

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