MOTIVATION: Meta-analysis methods have been widely used to combine results from multiple clinical or genomic studies to increase statistical powers and ensure robust and accurate conclusions. The adaptively weighted Fisher's method (AW-Fisher), initially developed for omics applications but applicable for general meta-analysis, is an effective approach to combine P-values from K independent studies and to provide better biological interpretability by characterizing which studies contribute to the meta-analysis. Currently, AW-Fisher suffers from the lack of fast P-value computation and variability estimate of AW weights. When the number of studies K is large, the 3K - 1 possible differential expression pattern categories generated by AW-Fisher can become intractable. In this paper, we develop an importance sampling scheme with spline interpolation to increase the accuracy and speed of the P-value calculation. We also apply bootstrapping to construct a variability index for the AW-Fisher weight estimator and a co-membership matrix to categorize (cluster) differentially expressed genes based on their meta-patterns for intuitive biological investigations. RESULTS: The superior performance of the proposed methods is shown in simulations as well as two real omics meta-analysis applications to demonstrate its insightful biological findings. AVAILABILITY AND IMPLEMENTATION: An R package AWFisher (calling C++) is available at Bioconductor and GitHub (https://github.com/Caleb-Huo/AWFisher), and all datasets and programing codes for this paper are available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions. AVAILABILITY AND IMPLEMENTATION: MetaOmics is freely available at https://github.com/metaOmics/metaOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: With the prevalent usage of microarray and massively parallel sequencing, numerous high-throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the high-dimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization. Results: In this article, we combine multiple omics datasets of identical or similar biological hypothesis and introduce two variations of meta-analytic framework of PCA, namely MetaPCA. Regularization is further incorporated to facilitate sparse feature selection in MetaPCA. We apply MetaPCA and sparse MetaPCA to simulations, three transcriptomic meta-analysis studies in yeast cell cycle, prostate cancer, mouse metabolism and a TCGA pan-cancer methylation study. The result shows improved accuracy, robustness and exploratory visualization of the proposed framework. Availability and implementation: An R package MetaPCA is available online. (http://tsenglab.biostat.pitt.edu/software.htm). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Energy awareness of mobile devices with limited battery capacity can be achieved by embedding battery drain rate monitoring capability into the devices. With Li-ion battery, battery drain rate varies with temperature and battery aging, since they affect battery characteristics such as capacity and internal resistance. We develop BattTracker, an algorithm to estimate battery drain rate without knowing the exact capacity and internal resistance by incorporating the concept of effective resistance. BattTracker tracks the instantaneous battery drain rate with up to 0.5 second time granularity. Extensive evaluation with smartphones demonstrates that BattTracker accurately estimates the battery drain rate with less than 5% estimation error, thus enabling energy-aware operation of smartphones with finegrained time granularity. To the best of our knowledge, this is the first effort to estimate the instantaneous battery drain rate by considering both temperature and aging effects on the battery characteristics.

MOTIVATION: DNA methylation is an epigenetic modification with important roles in many biological processes and diseases. Bisulfite sequencing (BS-seq) has emerged recently as the technology of choice to profile DNA methylation because of its accuracy, genome coverage and higher resolution. Current statistical methods to identify differential methylation mainly focus on comparing two treatment groups. With an increasing number of experiments performed under a general and multiple-factor design, particularly in reduced representation bisulfite sequencing, there is a need to develop more flexible, powerful and computationally efficient methods. RESULTS: We present a novel statistical model to detect differentially methylated loci from BS-seq data under general experimental design, based on a beta-binomial regression model with 'arcsine' link function. Parameter estimation is based on transformed data with generalized least square approach without relying on iterative algorithm. Simulation and real data analyses demonstrate that our method is accurate, powerful, robust and computationally efficient. AVAILABILITY AND IMPLEMENTATION: It is available as Bioconductor package DSS. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-Path TCP (MPTCP) has attracted much attention as a promising technology to improve throughput performance of wireless devices that support multi-homed heterogeneous networks. Although MPTCP provides significant increase in network capacity, it may suffer from poor delay performance since the delay tends to be aligned with the worst-performing path: packets delivered through a short-delay subflow have to wait in the reordering buffer for packets being transmitted over a long-delay subflow. In this paper, we investigate the application-level delay performance of streaming traffic over MPTCP, and develop an analytical framework to take into account non-negligible network queuing delay and the interplay of congestion control between multiple subflows. We design a simple threshold-based subflow traffic allocation scheme that aims to minimize user-level delay and develop a receiver-centric traffic splitting control (R-TSC) that can be tuned to user preferences. The client-side R-TSC solution facilitates incremental deployment of low-delay streaming service over MPTCP. Through simulation and testbed experiments using commercial LTE and WiFi networks, we demonstrate significant performance gains over the standard MPTCP protocol.

This paper presents a multipath multimedia transport protocol (MPMTP), which exploits path diversity over heterogeneous wireless networks. The goal of MPMTP is to provide a seamless high-quality video streaming service by using multiple wireless networks simultaneously. In MPMTP, systematic Raptor codes are adopted to mitigate video quality degradation caused by wireless channel errors as well as to alleviate a head-of-line blocking problem in multipath environments, and their encoding parameters such as code rate, symbol size, and the number of source symbols are determined on the fly by considering the wireless channel state, Raptor encoding and decoding complexity, and receiver buffer occupancy. Furthermore, MPMTP performs packet scheduling considering not only wireless network conditions but also packet payload characteristics for smooth video playback. The proposed MPMTP is fully implemented in a Linux kernel and examined over real wireless network environments.

We propose an adaptive Interest (or content request message) adjustment scheme as an additional enhancement to the CCN (Content-Centric Networking) flow control algorithm described in [11], where the one-to-one message exchange nature of CCN was re-architected to enable more effective one-to-many message exchange. The proposed scheme adaptively determines the optimal transmission time of successive Interest messages, thus effectively adjusts the transmission window size to further enhance link utilization while preserving the benefit of reducing the total number of Interest messages. The optimality of this transmission time interval between Interest messages is a critical factor to the downlink throughput especially with a narrowband uplink as in cellular network, because too frequent Interest message transmissions likely cause uplink congestion while too infrequent transmissions may also cause underutilization of the downlink. This paper proposes an adaptive scheme for optimal Interest transmission time that fully utilizes the downlink while minimizing the unnecessary uplink congestion. The performance of proposed scheme is validated in an experimental testbed built with the CCNx [10] open source code.

MPTCP is a promising transport technique to boost throughput of wireless multi-homed device by supporting multiple concurrent transmissions through heterogeneous wireless interfaces. As the number of concurrent subflows increase, MPTCP can achieve a linearly increasing throughput performance, but it is unclear how much improvement in the end-to-end delay performance can be attained from additional subflows. In this paper, we develop an analytical framework to understand the end-to-end delay performance of MPTCP that accounts for TCP dynamics and subflow interactions. Interestingly, it turns out that the delay performance can be even degraded with additional subflows. Considering MPTCP in heterogeneous wireless networks of Wi-Fi and LTE, we formulate a cost minimization problem subject to the end-to-end delay constraint. Based on the insight obtained from our model, we approximate the problem and develop a greedy scheme that splits traffic to minimize the cost while satisfying the delay constraints. Through simulations, we demonstrate that our proposed scheme outperforms the conventional MPTCP, and significantly improves the delay performance while lowering the user cost.

We propose E$PA, an adaptive LTE/WiFi network interface activation algorithm with supporting system design and multi-attribute cost function for smartphones file transfer services (e.g., downloading a movie file). E$PA's cost function incorporates battery life (energy), data usage quota ($), and expected file transfer completion time (performance) simultaneously, and is motivated by the growing sensitivity of todays smartphone users to these attributes. Each time the individual attributes are calculated and updated, E$PA selects one of the three modes that minimizes the overall cost: (i) activation of both LTE and WiFi interfaces for parallel data transfer; (ii) LTE interface activation only; or (iii) WiFi interface activation only. The primary benefit of the E$PA is that it enables the smartphone to always operate in the “best” mode without the need for users manual control; the energy saving mode if the remaining battery energy is becoming nearly depleted; the cost-saving mode if the remaining data quota is almost running out; or, the maximum throughput mode if remaining data quota and battery life are both sufficient. Our multi-attribute cost model also takes into account the overheads (delays and energy consumption) associated with network interface turn-on/off and switching, as they impact the estimations of both performance (transfer time) and energy (battery life) attributes. Simulation results show that E$PA indeed chooses the “best” operational mode by maintaining dynamic balance among transfer time, energy consumption, and service charge.

MOTIVATION: DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings. RESULTS: We present a statistical analysis package, methylSig, to analyse genome-wide methylation differences between samples from different treatments or disease groups. MethylSig takes into account both read coverage and biological variation by utilizing a beta-binomial approach across biological samples for a CpG site or region, and identifies relevant differences in CpG methylation. It can also incorporate local information to improve group methylation level and/or variance estimation for experiments with small sample size. A permutation study based on data from enhanced RRBS samples shows that methylSig maintains a well-calibrated type-I error when the number of samples is three or more per group. Our simulations show that methylSig has higher sensitivity compared with several alternative methods. The use of methylSig is illustrated with a comparison of different subtypes of acute leukemia and normal bone marrow samples. AVAILABILITY: methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.