Gene selection and classification combining information gain ratio with fruit fly optimisation algorithm for single-cell RNA-seq data

2021 ◽  
Vol 24 (5) ◽  
pp. 495
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Xiani Yang ◽  
Jianming Liu
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Information Gain ◽  
Fruit Fly ◽  
Rna Seq ◽  
Gain Ratio ◽  
Optimisation Algorithm ◽  
Information Gain Ratio ◽  
Combining Information

scholarly journals sc-REnF:An entropy guided robust feature selection for clustering of single-cell rna-seq data

2020 ◽  
Author(s):  
Snehalika Lall ◽  
Abhik Ghosh ◽  
Sumanta Ray ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Rna Seq ◽  
Technical Noise ◽  
Marker Selection ◽  
Cell Clustering ◽  
Typing Methods ◽  
Original Application ◽  
Downstream Analysis ◽  
Cell Typing

ABSTRACTMany single-cell typing methods require pure clustering of cells, which is susceptible towards the technical noise, and heavily dependent on high quality informative genes selected in the preliminary steps of downstream analysis. Techniques for gene selection in single-cell RNA sequencing (scRNA-seq) data are seemingly simple which casts problems with respect to the resolution of (sub-)types detection, marker selection and ultimately impacts towards cell annotation. We introduce sc-REnF, a novel and robust entropy based feature (gene) selection method, which leverages the landmark advantage of ‘Renyi’ and ‘Tsallis’ entropy achieved in their original application, in single cell clustering. Thereby, gene selection is robust and less sensitive towards the technical noise present in the data, producing a pure clustering of cells, beyond classifying independent and unknown sample with utmost accuracy. The corresponding software is available at: https://github.com/Snehalikalall/sc-REnF

scholarly journals RgCop-A regularized copula based method for gene selection in single cell rna-seq data

2021 ◽  
Vol 17 (10) ◽  
pp. e1009464
Author(s):  
Snehalika Lall ◽  
Sumanta Ray ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Real Life ◽  
Classification Performance ◽  
Rna Seq ◽  
Scale Invariant ◽  
Dependence Measure ◽  
Highly Expressed Genes ◽  
The Stability ◽  
Downstream Analysis

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We raise an objective function by adding a l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop can able to annotate the unknown cells with high accuracy.

scholarly journals sc-REnF: An Entropy Guided Robust Feature Selection for Single-Cell RNA-seq Data

2021 ◽  
Author(s):  
Snehalika Lall ◽  
Abhik Ghosh ◽  
Sumanta Ray ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Small Sample ◽  
Rna Seq ◽  
Homogeneous Grouping ◽  
Cell Clustering ◽  
Selection For ◽  
Downstream Analysis ◽  
Cell Data

Abstract Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. Since single cell data is susceptible to technical noise, the quality of genes selected prior to clustering is of crucial importance in the preliminary steps of downstream analysis. Therefore, interest in robust gene selection has gained considerable attention in recent years. We introduce sc-REnF, (robust entropy based feature (gene) selection method), aiming to leverage the advantages of Rényi and Tsallis> entropies in gene selection for single cell clustering. Experiments demonstrate that with tuned parameter (q), Rényi and Tsallis entropies select genes that improved the clustering results significantly, over the other competing methods. sc-REnF can capture relevancy and redundancy among the features of noisy data extremely well due to its robust objective function. Moreover, the selected features/genes can able to clusters the unknown cells with a high accuracy. Finally, sc-REnF yields good clustering performance in small sample, large feature scRNA-seq data.

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