limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

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“Bioinformatics” 특집을 내면서

New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

/pdf/voom-precision-weights-unlock-linear-model-analysis-tools-5bgw8lj2b8.pdf

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

We revisit the problem of interval estimation of a binomial proportion. The erratic behavior of the coverage probability of the stan- d ardWaldconfid ence interval has previously been remarkedon in the literature (Blyth andStill, Agresti andCoull, Santner andothers). We begin by showing that the chaotic coverage properties of the Waldinter- val are far more persistent than is appreciated. Furthermore, common textbook prescriptions regarding its safety are misleading and defective in several respects andcannot be trusted . This leads us to consideration of alternative intervals. A number of natural alternatives are presented, each with its motivation and con- text. Each interval is examinedfor its coverage probability andits length. Basedon this analysis, we recommendthe Wilson interval or the equal- tailedJeffreys prior interval for small n andthe interval suggestedin Agresti andCoull for larger n. We also provide an additional frequentist justification for use of the Jeffreys interval.

/pdf/interval-estimation-for-a-binomial-proportion-3t1vha8pn0.pdf

Interval Estimation for a Binomial Proportion

This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large. The estimated factors are shown to be consistent, even in the presence of time variation in the factor model.

/pdf/forecasting-using-principal-components-from-a-large-number-2t04tfbbw7.pdf

Forecasting Using Principal Components From a Large Number of Predictors

Combining information across genes in the statistical analysis of microarray data is desirable because of the relatively small number of data points obtained for each individual gene. Here we develop an estimator of the error variance that can borrow information across genes using the James-Stein shrinkage concept. A new test statistic (FS) is constructed using this estimator. The new statistic is compared with other statistics used to test for differential expression: the gene-specific F test (F1), the pooled-variance F statistic (F3), a hybrid statistic (F2) that uses the average of the individual and pooled variances, the regularized t-statistic, the posterior odds statistic B, and the SAM t-test. The FS-test shows best or nearly best power for detecting differentially expressed genes over a wide range of simulated data in which the variance components associated with individual genes are either homogeneous or heterogeneous. Thus FS provides a powerful and robust approach to test differential expression of genes that utilizes information not available in individual gene testing approaches and does not suffer from biases of the pooled variance approach.

/pdf/improved-statistical-tests-for-differential-gene-expression-3agrca0uqz.pdf

Improved statistical tests for differential gene expression by shrinking variance components estimates.

The artificial neural network (ANN) is becoming a very popular model for engineering and scientific applications. Inspired by brain architecture, artificial neural networks represent a class of nonlinear models capable of learning from data. Neural networks have been applied in many areas, including pattern matching, classification, prediction, and process control. This article focuses on the construction of prediction intervals. Previous statistical theory for constructing confidence intervals for the parameters (or the weights in an ANN), is inappropriate, because the parameters are unidentifiable. We show in this article that the problem disappears in prediction. We then construct asymptotically valid prediction intervals and also show how to use the prediction intervals to choose the number of nodes in the network. We then apply the theory to an example for predicting the electrical load.

Prediction Intervals for Artificial Neural Networks

The appropriateness of a statistical analysis for evaluating a model depends on the model's purpose. A common purpose for models in agricultural research and environmental management is accurate prediction. In this context, correlation and linear regression are frequently used to test or compare models, including tests of intercept a = 0 and slope b = 1, but unfortunately such results are related only obliquely to the specific matter of predictive success. The mean squared deviation (MSD) between model predictions X and measured values Y thus been proposed as a directly relevant measure of predictive success, with MSD partitioned into three components to gain further insight into model performance. This paper proposes a different and better partitioning of MSD: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). These MSD components are distinct and additive, they have straightforward geometric and analysis of variance (ANOVA) interpretations, and they relate transparently to regression parameters. Our MSD components are illustrated using several models for wheat (Triticum aestivum L.) yield. The MSD statistic and its components nicely complement correlation and linear regression in evaluating the predictive accuracy of models.

Model Evaluation by Comparison of Model-Based Predictions and Measured Values

In an earlier article, an intuitively appealing method for estimating the number of true null hypotheses in a multiple test situation was proposed. That article presented an iterative algorithm that relies on a histogram of observed p values to obtain the estimator. We characterize the limit of that iterative algorithm and show that the estimator can be computed directly without iteration. We compare the performance of the histogram-based estimator with other procedures for estimating the number of true null hypotheses from a collection of observed p values and find that the histogram-based estimator performs well in settings similar to those encountered in microarray data analysis. We demonstrate the approach using p values from a large microarray experiment aimed at uncovering molecular mechanisms of barley resistance to a fungal pathogen.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1215&context=stat_las_pubs

Estimating the number of true null hypotheses from a histogram of p values

This article deals with the construction of confidence intervals when the components of the location parameter $\mu$ of the random variable $\mathbf{X}$, which is elliptically symmetrically distributed, are subject to order restrictions. Several domination results are proved by studying the derivative of the coverage probability of the confidence intervals centered at the improved point estimators. Consequently, we strengthen the previously known results regarding the simple ordering and obtain several new results for other general forms of order restrictions, including the simple tree ordering, the umbrella ordering, the simple and the double loop ordering and some combination of these. These domination results are obtained under the assumption that $\Sigma$ is a diagonal matrix. When $\Sigma$ is nondiagonal, some new intervals are introduced which dominate the standard intervals centered at the unrestricted maximum likelihood estimator for various types of order restrictions. Similar results are obtained for scale parameters as well. Contrary to the location problems, in case of the scale parameters satisfying the simple ordering we find that the restricted maximum likelihood estimator of the largest parameter fails to universally dominate the unrestricted maximum likelihood estimator. A similar negative result is noted for simple tree order restriction.

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J. T. Gene Hwang

Papers

Improved statistical tests for differential gene expression by shrinking variance components estimates.

Prediction Intervals for Artificial Neural Networks

Model Evaluation by Comparison of Model-Based Predictions and Measured Values

Estimating the number of true null hypotheses from a histogram of p values

Confidence Interval Estimation Subject to Order Restrictions