An iteration normalization and test method for differential expression analysis of RNA-seq data

BACKGROUND: Next generation sequencing technologies are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key to analyzing massive and complex sequencing data. In order to derive gene expression measures and compare these measures across samples or libraries, we first need to normalize read counts to adjust for varying sample sequencing depths and other potentially technical effects. RESULTS: In this paper, we develop a normalization method based on iterating median of M-values (IMM) for detecting the differentially expressed (DE) genes. Compared to a previous approach TMM, the IMM method improves the accuracy of DE detection. Simulation studies show that the IMM method outperforms other methods for the sample normalization. We also look into the real data and find that the genes detected by IMM but not by TMM are much more accurate than the genes detected by TMM but not by IMM. What’s more, we discovered that gene UNC5C is highly associated with kidney cancer and so on

Combining conditional and unconditional moment restrictions with missing responses

AbstractMany statistical models, e.g. regression models, can be viewed as conditional moment restrictions when distributional assumptions on the error term are not assumed. For such models, several estimators that achieve the semiparametric efficiency bound have been proposed. However, in many studies, auxiliary information is available as unconditional moment restrictions. Meanwhile, we also consider the presence of missing responses. We propose the combined empirical likelihood (CEL) estimator to incorporate such auxiliary information to improve the estimation efficiency of the conditional moment restriction models. We show that, when assuming responses are strongly ignorable missing at random, the CEL estimator achieves better efficiency than the previous estimators due to utilization of the auxiliary information. Based on the asymptotic property of the CEL estimator, we also develop Wilks’ type tests and corresponding confidence regions for the model parameter and the mean response. Since kernel smoothing is used, the CEL method may have difficulty for problems with high dimensional covariates. In such situations, we propose an instrumental variable-based empirical likelihood (IVEL) method to handle this problem. The merit of the CEL and IVEL are further illustrated through simulation studies

Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

<p>Abstract</p> <p>Background</p> <p>Time-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time. Peddada <it>et al</it>. <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference. This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important measure when clustering noisy microarray data.</p> <p>Results</p> <p>We propose a computationally efficient information criterion-based clustering algorithm, called ORICC, that also takes account of the ordering in time-course microarray experiments by embedding the order-restricted inference into a model selection framework. Genes are assigned to the profile which they best match determined by a newly proposed information criterion for order-restricted inference. In addition, we also developed a bootstrap procedure to assess ORICC's clustering reliability for every gene. Simulation studies show that the ORICC method is robust, always gives better clustering accuracy than Peddada's method and saves hundreds of times computational time. Under some scenarios, its accuracy is also better than some other existing clustering methods for short time-course microarray data, such as STEM <abbrgrp><abbr bid="B2">2</abbr></abbrgrp> and Wang <it>et al</it>. <abbrgrp><abbr bid="B3">3</abbr></abbrgrp>. It is also computationally much faster than Wang <it>et al</it>. <abbrgrp><abbr bid="B3">3</abbr></abbrgrp>.</p> <p>Conclusion</p> <p>Our ORICC algorithm, which takes advantage of the temporal ordering in time-course microarray experiments, provides good clustering accuracy and is meanwhile much faster than Peddada's method. Moreover, the clustering reliability for each gene can also be assessed, which is unavailable in Peddada's method. In a real data example, the ORICC algorithm identifies new and interesting genes that previous analyses failed to reveal.</p

Tumor suppressor gene RBM5 delivered by attenuated Salmonella inhibits lung adenocarcinoma through diverse apoptotic signaling pathways

BACKGROUND: RBM5 (RNA-binding motif protein 5, also named H37/LUCA-15) gene from chromosome 3p21.3 has been demonstrated to be a tumor suppressor. Current researches in vitro confirm that RBM5 can suppress the growth of lung adenocarcinoma cells by inducing apoptosis. There is still no effective model in vivo, however, that thoroughly investigates the effect and molecular mechanism of RBM5 on lung adenocarcinoma. METHOD: We established the transplanted tumor model on BALB/c nude mice using the A549 cell line. The mice were treated with the recombinant plasmids carried by attenuated Salmonella to induce the overexpression of RBM5 in tumor tissues. RBM5 overexpression was confirmed by immunohistochemistry staining. H&E staining was performed to observe the histological performance on plasmids-treated A549 xenografts. Apoptosis was assessed by TUNEL staining with a TUNEL detection kit. Apoptosis-regulated genes were detected by Western blot. RESULTS: We successful established the lung adenocarcinoma animal model in vivo. The growth of tumor xenografts was significantly retarded on the mice treated with pcDNA3.1-RBM5 carried by attenuated Salmonella compared to that on mice treated with pcDNA3.1. Overexpression of RBM5 enhanced the apoptosis in tumor xenografts. Furthermore, the expression of Bcl-2 protein was decreased significantly, while the expression of BAX, TNF-α, cleaved caspase-3, cleaved caspase-8, cleaved caspase-9 and cleaved PARP proteins was significantly increased in the pcDNA3.1-RBM5-treated mice as compared to that in the control mice. CONCLUSIONS: In this study, we established a novel animal model to determine RBM5 function in vivo, and concluded that RBM5 inhibited tumor growth in mice by inducing apoptosis. The study suggests that although RBM5’s involvement in the death receptor-mediated apoptotic pathway is still to be investigated, RBM5-mediated growth suppression, at least in part, employs regulation of the mitochondrial apoptotic pathways

Active Discriminative Dictionary Learning for Weather Recognition

Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN), we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method

Comparative DNA methylome analysis of endometrial carcinoma reveals complex and distinct deregulation of cancer promoters and enhancers

BACKGROUND: Aberrant DNA methylation is a hallmark of many cancers. Classically there are two types of endometrial cancer, endometrioid adenocarcinoma (EAC), or Type I, and uterine papillary serous carcinoma (UPSC), or Type II. However, the whole genome DNA methylation changes in these two classical types of endometrial cancer is still unknown. RESULTS: Here we described complete genome-wide DNA methylome maps of EAC, UPSC, and normal endometrium by applying a combined strategy of methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation-sensitive restriction enzyme digestion sequencing (MRE-seq). We discovered distinct genome-wide DNA methylation patterns in EAC and UPSC: 27,009 and 15,676 recurrent differentially methylated regions (DMRs) were identified respectively, compared with normal endometrium. Over 80% of DMRs were in intergenic and intronic regions. The majority of these DMRs were not interrogated on the commonly used Infinium 450K array platform. Large-scale demethylation of chromosome X was detected in UPSC, accompanied by decreased XIST expression. Importantly, we discovered that the majority of the DMRs harbored promoter or enhancer functions and are specifically associated with genes related to uterine development and disease. Among these, abnormal methylation of transposable elements (TEs) may provide a novel mechanism to deregulate normal endometrium-specific enhancers derived from specific TEs. CONCLUSIONS: DNA methylation changes are an important signature of endometrial cancer and regulate gene expression by affecting not only proximal promoters but also distal enhancers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-868) contains supplementary material, which is available to authorized users

Analytical test on effectiveness of MCDF operations

Modified conjugate directional filtering (MCDF) is a method proposed by Guo and Watson recently for digital data and image processing. By using MCDF, directionally filtered results in conjugate directions can be not only merged into one image that shows the maximum linear features in the two conjugate directions, but also further manipulated by a number of predefined generic MCDF operations for different purposes. Although a number of cases have been used to test the usefulness of several proposed MCDF operations, and the results are ‘visually’ better than some conventional methods, however, no quantified analytical results on its effectiveness have been obtained. This has been the major obstacle on the decision whether it is worth developing a usable MCDF system. This paper firstly outlines a FFT-based analytical design for conducting the tests, and then presents the results of applying this analytical design to the analysis of MCDF(add1) operation for an image of digital terrain model in central Australia. The test verifies that the MCDF(add1) operation indeed overcomes the two weaknesses of using the conventional directional filtering in image processing, i.e., separation in presentation of processed results in different directions, and significant loss in low-frequency components. Therefore, the MCDF method is worth for further development