RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

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Ranking near-native candidate protein structures via random forest classification

BMC Bioinformatics ◽

10.1186/s12859-019-3257-8 ◽

2019 ◽

Vol 20 (S25) ◽

Cited By ~ 1

Author(s):

Hongjie Wu ◽

Hongmei Huang ◽

Weizhong Lu ◽

Qiming Fu ◽

Yijie Ding ◽

...

Keyword(s):

Random Forest ◽

Binary Classification ◽

Protein Structures ◽

Three Dimensional ◽

Exact Order ◽

Large Set ◽

Clustering Methods ◽

Native Structure ◽

Random Forest Classification ◽

Forest Classification

Abstract Background In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult. Results To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal. Conclusions In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.

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Machine Learning Techniques for Intrusion Detection

Handbook of Research on Intrusion Detection Systems - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-2242-4.ch003 ◽

2020 ◽

pp. 47-65

Author(s):

Tameem Ahmad ◽

Mohd Asad Anwar ◽

Misbahul Haque

Keyword(s):

Random Forest ◽

Intrusion Detection ◽

False Alarm ◽

False Alarm Rate ◽

Detection Rate ◽

Clustering Algorithms ◽

Training Data ◽

Hybrid Classifier ◽

Random Forest Classification ◽

Forest Classification

This chapter proposes a hybrid classifier technique for network Intrusion Detection System by implementing a method that combines Random Forest classification technique with K-Means and Gaussian Mixture clustering algorithms. Random-forest will build patterns of intrusion over a training data in misuse-detection, while anomaly-detection intrusions will be identiðed by the outlier-detection mechanism. The implementation and simulation of the proposed method for various metrics are carried out under varying threshold values. The effectiveness of the proposed method has been carried out for metrics such as precision, recall, accuracy rate, false alarm rate, and detection rate. The various existing algorithms are analyzed extensively. It is observed experimentally that the proposed method gives superior results compared to the existing simpler classifiers as well as existing hybrid classifier techniques. The proposed hybrid classifier technique outperforms other common existing classifiers with an accuracy of 99.84%, false alarm rate as 0.09% and the detection rate as 99.7%.

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Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN

10.1101/770388 ◽

2019 ◽

Cited By ~ 3

Author(s):

Florian Wagner

Keyword(s):

Single Cell ◽

Mononuclear Cells ◽

Gene Selection ◽

Real Data ◽

Cell Types ◽

Fine Tuning ◽

Rna Seq ◽

Clustering Methods ◽

Cell Type ◽

Peripheral Blood Mononuclear

AbstractClustering of cells by cell type is arguably the most common and repetitive task encountered during the analysis of single-cell RNA-Seq data. However, as popular clustering methods operate largely independently of visualization techniques, the fine-tuning of clustering parameters can be unintuitive and time-consuming. Here, I propose Galapagos, a simple and effective clustering workflow based on t-SNE and DBSCAN that does not require a gene selection step. In practice, Galapagos only involves the fine-tuning of two parameters, which is straightforward, as clustering is performed directly on the t-SNE visualization results. Using peripheral blood mononuclear cells as a model tissue, I validate the effectiveness of Galapagos in different ways. First, I show that Galapagos generates clusters corresponding to all main cell types present. Then, I demonstrate that the t-SNE results are robust to parameter choices and initialization points. Next, I employ a simulation approach to show that clustering with Galapagos is accurate and robust to the high levels of technical noise present. Finally, to demonstrate Galapagos’ accuracy on real data, I compare clustering results to true cell type identities established using CITE-Seq data. In this context, I also provide an example of the primary limitation of Galapagos, namely the difficulty to resolve related cell types in cases where t-SNE fails to clearly separate the cells. Galapagos helps to make clustering scRNA-Seq data more intuitive and reproducible, and can be implemented in most programming languages with only a few lines of code.

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