A Systematic Evaluation of Feature Selection and Classification Algorithms Using Simulated and Real miRNA Sequencing Data.
Sequencing is widely used to discover associations between microRNAs (miRNAs) and diseases. However, the negative binomial distribution (NB) and high dimensionality of data obtained using sequencing can lead to low-power results and low reproducibility. Several statistical learning algorithms have been proposed to address sequencing data, and although evaluation of these methods is essential, such studies are relatively rare. The performance of seven feature selection (FS) algorithms, including baySeq, DESeq, edgeR, the rank sum test, lasso, particle swarm optimistic decision tree, and random forest (RF), was compared by simulation under different conditions based on the difference of the mean, the dispersion parameter of the NB, and the signal to noise ratio. Real data were used to evaluate the performance of RF, logistic regression, and support vector machine. Based on the simulation and real data, we discuss the behaviour of the FS and classification algorithms. The Apriori algorithm identified frequent item sets (mir-133a, mir-133b, mir-183, mir-937, and mir-96) from among the deregulated miRNAs of six datasets from The Cancer Genomics Atlas. Taking these findings altogether and considering computational memory requirements, we propose a strategy that combines edgeR and DESeq for large sample sizes.
Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing, Jiangsu 211166, China.
Sheng
S
Yang
Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing, Jiangsu 211166, China.
Li
L
Guo
Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing, Jiangsu 211166, China.
Fang
F
Shao
Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing, Jiangsu 211166, China.
Yang
Y
Zhao
Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing, Jiangsu 211166, China.
Feng
F
Chen
D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D005260:Female; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D055641:Mathematical Concepts; D035683:MicroRNAs / Q000145:classification / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics; D012334:RNA, Neoplasm / Q000145:classification / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000706:statistics & numerical data
D035683:MicroRNAs; D012334:RNA, Neoplasm