Motivation: ChIP-seq experiments that are aimed at detecting DNA-protein interactions require biological replication to draw inferential conclusions, however there is no current consensus on how to analyze ChIP-seq data with biological replicates. Very few methodologies exist for the joint analysis of replicated ChIP-seq data, with approaches ranging from combining the results of analyzing replicates individually to joint modeling of all replicates. Combining the results of individual replicates analyzed separately can lead to reduced peak classification performance compared to joint modeling. Currently available methods for joint analysis may fail to control the false discovery rate at the nominal level. Results: We propose BinQuasi, a peak caller for replicated ChIP-seq data, that jointly models biological replicates using a generalized linear model framework and employs a one-sided quasi-likelihood ratio test to detect peaks. When applied to simulated data and real datasets, BinQuasi performs favorably compared to existing methods, including better control of false discovery rate than existing joint modeling approaches. BinQuasi offers a flexible approach to joint modeling of replicated ChIP-seq data which is preferable to combining the results of replicates analyzed individually. Availability and implementation: Source code is freely available for download at https://cran.r-project.org/package=BinQuasi, implemented in R. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Transposon insertion sequencing (Tn-seq) is an emerging technology that combines transposon mutagenesis with next-generation sequencing technologies for the identification of genes related to bacterial survival. The resulting data from Tn-seq experiments consist of sequence reads mapped to millions of potential transposon insertion sites and a large portion of insertion sites have zero mapped reads. Novel statistical method for Tn-seq data analysis is needed to infer functions of genes on bacterial growth. RESULTS: In this article, we propose a zero-inflated Poisson model for analyzing the Tn-seq data that are high-dimensional and with an excess of zeros. Maximum likelihood estimates of model parameters are obtained using an expectation-maximization (EM) algorithm, and pseudogenes are utilized to construct appropriate statistical tests for the transposon insertion tolerance of normal genes of interest. We propose a multiple testing procedure that categorizes genes into each of the three states, hypo-tolerant, tolerant and hyper-tolerant, while controlling the false discovery rate. We evaluate the proposed method with simulation studies and apply the proposed method to a real Tn-seq data from an experiment that studied the bacterial pathogen, Campylobacter jejuniAvailability and implementation: We provide R code for implementing our proposed method at http://github.com/ffliu/TnSeq A user's guide with example data analysis is also available there. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments. RESULTS: In this paper, we propose a procedure for sample size calculation while controlling FDR for RNA-seq experimental design. Our procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. We derive a method that approximates the average power across the differentially expressed genes, and then calculate the sample size to achieve a desired average power while controlling FDR. Simulation results demonstrate that the actual power of several popularly applied tests for differential expression is achieved and is close to the desired power for RNA-seq data with sample size calculated based on our method. CONCLUSIONS: Our proposed method provides an efficient algorithm to calculate sample size while controlling FDR for RNA-seq experimental design. We also provide an R package ssizeRNA that implements our proposed method and can be downloaded from the Comprehensive R Archive Network ( http://cran.r-project.org ).

MOTIVATION: Asymmetry is frequently observed in the empirical distribution of test statistics that results from the analysis of gene expression experiments. This asymmetry indicates an asymmetry in the distribution of effect sizes. A common method for identifying differentially expressed (DE) genes in a gene expression experiment while controlling false discovery rate (FDR) is Storey's q-value method. This method ranks genes based solely on the P-values from each gene in the experiment. RESULTS: We propose a method that alters and improves upon the q-value method by taking the sign of the test statistics, in addition to the P-values, into account. Through two simulation studies (one involving independent normal data and one involving microarray data), we show that the proposed method, when compared with the traditional q-value method, generally provides a better ranking for genes as well as a higher number of truly DE genes declared to be DE, while still adequately controlling FDR. We illustrate the proposed method by analyzing two microarray datasets, one from an experiment of thale cress seedlings and the other from an experiment of maize leaves. AVAILABILITY AND IMPLEMENTATION: The R code and data files for the proposed method and examples are available at Bioinformatics online.

MOTIVATION: RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis. RESULTS: In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. AVAILABILITY AND IMPLEMENTATION: An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org