Abstract
High-performance Next-Generation Sequencing (NGS) has become a widely used technology to characterize case-control comparison studies for RNA transcripts, such as mRNAs and small non-coding RNAs. The first step in the analysis strategies is map** NGS reads against a reference database and a critical issue emerges in this phase: the problem of multireads. In this paper we present a novel approach to represent and quantify read map** ambiguities through the use of fuzzy sets and possibility theory. The aim of this work is to obtain a list of candidate differential expression events, providing a description of the uncertainty of the results due to multiread presence. In a preliminary experiment on HeLa cells, the method correctly detected the possibility of false positiveness, while on a case-control study of human endobronchial biopsies, the method identified 11 genes with possible different expression, four of them with an uncertain fold change. This last result was confirmed by FDR adjusted Fisher’s test, while DESeq2 did not provide significant differences between case and control.
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Notes
- 1.
\(A'=A-1\) if \(A>0\), otherwise \(A'=A=0\).
- 2.
A standard application of the extension principle to the fold change results in a fuzzy set with a complex membership function, which requires complex computations without any real benefits.
- 3.
Given two expressions \(e_1\) and \(e_2\) of a gene in two samples, the MA-plot places the gene on a plane (M, A) where \(M=\log _2 ({e_1}/{e_2})\) (the fold change) and \(A = (1/2)\log _2(e_1e_2)\) (average intensity).
- 4.
The centroid is computed with the constraint of falling inside the interval [B, C].
- 5.
The boundaries are estimated as hyperbolas, with their parameters fitted on the dataset; their horizontal asymptotes represent a limit fold change value under which differential expression loses significance.
- 6.
The possibility measure between two fuzzy sets \(F_1\) and \(F_2\) is defined as \(\varPi (F_1, F_2)=\max _x \min \{F_1(x), F_2(x)\}\).
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Acknowledgments
We thank Dr. Flavio Licciulli, Dr. Mariano Caratozzolo and Dr. Flaviana Marzano for their suggestions and help with NGS data elaboration. A.C. is supported by Progetto MICROMAP PON01_02589.
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Consiglio, A., Mencar, C., Grillo, G., Liuni, S. (2016). Managing NGS Differential Expression Uncertainty with Fuzzy Sets. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_4
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DOI: https://doi.org/10.1007/978-3-319-44332-4_4
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