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

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

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

10.21203/rs.3.rs-355014/v1 ◽

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009464 ◽

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.

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

International Journal of Computational Science and Engineering ◽

10.1504/ijcse.2021.10041500 ◽

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

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Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

10.1101/786285 ◽

2019 ◽

Cited By ~ 4

Author(s):

Marcus Alvarez ◽

Elior Rahmani ◽

Brandon Jew ◽

Kristina M. Garske ◽

Zong Miao ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Supervised Machine Learning ◽

Data Sets ◽

Rna Seq ◽

Novel Approach ◽

Single Nucleus ◽

Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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

International Journal of Computational Science and Engineering ◽

10.1504/ijcse.2021.118098 ◽

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

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Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data

BMC Bioinformatics ◽

10.1186/s12859-020-03797-8 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Chunxiang Wang ◽

Xin Gao ◽

Juntao Liu

Keyword(s):

Single Cell ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Data Preprocessing ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Preprocessing Method ◽

Cell Clustering ◽

Cell Gene Expression

Abstract Background Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.

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Gene Selection for Single-Cell RNA-Seq Data Based on Information Gain and Genetic Algorithm

2018 14th International Conference on Computational Intelligence and Security (CIS) ◽

10.1109/cis2018.2018.00021 ◽

2018 ◽

Author(s):

Jie Zhang ◽

Junhong Feng

Keyword(s):

Genetic Algorithm ◽

Single Cell ◽

Gene Selection ◽

Information Gain ◽

Rna Seq ◽

Selection For

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SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz801 ◽

2019 ◽

Vol 36 (6) ◽

pp. 1779-1784 ◽

Cited By ~ 1

Author(s):

Chuanqi Wang ◽

Jun Li

Keyword(s):

Neural Network ◽

Single Cell ◽

Count Data ◽

Deep Neural Network ◽

Sequencing Depth ◽

Supplementary Information ◽

Neural Network Classifier ◽

Rna Seq ◽

Scale Invariant ◽

Downstream Analysis

Abstract Motivation Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly. Results We call an analysis method ‘scale-invariant’ (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. Availability and implementation This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. Supplementary information Supplementary data are available at Bioinformatics online.

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