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Classification of RNA-seq Data

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Statistical Analysis of Next Generation Sequencing Data

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

Next-generation sequencing technologies have made it possible to obtain, at a relatively low cost, a detailed snapshot of the RNA transcripts present in a tissue sample. The resulting reads are usually binned by gene, exon, or other region of interest; thus the data typically amount to read counts for tens of thousands of features, on no more than dozens or hundreds of observations. It is often of interest to use these data to develop a classifier in order to assign an observation to one of several pre-defined classes. However, the high dimensionality of the data poses statistical challenges: because there are far more features than observations, many existing classification techniques cannot be directly applied. In recent years, a number of proposals have been made to extend existing classification approaches to the high-dimensional setting. In this chapter, we discuss the use of, and modifications to, logistic regression, linear discriminant analysis, principal components analysis, partial least squares, and the support vector machine in the high-dimensional setting. We illustrate these methods on two RNA-sequencing data sets.

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Notes

  1. 1.

    The training set is the set of observations used to fit the classifier.

  2. 2.

    We thank Liguo Wang for providing us the raw counts for the prostate cancer data set used in [49].

  3. 3.

    In greater detail, for all methods except for Poisson LDA, we divided each observation by the scaling factors discussed in Sect. 11.7. In contrast, in applying Poisson LDA, observations were not divided by the scaling factor—instead, the scaling factor is directly incorporated into (11.14).

  4. 4.

    Briefly, R-fold cross-validation involves splitting the observations in the training set into R sets. Then for r = 1, …, R, we build classifiers for a range of tuning parameters using all observations except those in the rth fold. We then calculate the error e r of each of these classifiers on the observations in the rth fold. Finally, we calculate the cross-validation error as \(\frac{1} {R}\sum _{r=1}^{R}e_{r}\). The tuning parameter value corresponding to the minimum cross-validation error is selected.

  5. 5.

    Proposals have been made for an ℓ 1-penalized SVM that results in a sparse decision rule, but the standard SVM decision rule involves all of the features [100].

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Acknowledgements

D.W. received support for this work from NIH Grant DP5OD009145, NSF CAREER Award DMS-1252624, and a Sloan Foundation Research Fellowship.

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  1. Department of Biostatistics, University of Washington, Seattle, WA, 98195-7232, USA

    Kean Ming Tan, Ashley Petersen & Daniela Witten

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    Somnath Datta

  2. Department of Statistics, Iowa State University, Ames, Iowa, USA

    Dan Nettleton

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Tan, K.M., Petersen, A., Witten, D. (2014). Classification of RNA-seq Data. In: Datta, S., Nettleton, D. (eds) Statistical Analysis of Next Generation Sequencing Data. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-07212-8_11

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