The importance of study design for detecting differentially abundant features in high-throughput experiments.

High-throughput assays, such as RNA-seq, to detect differential abundance are widely used. Variable performance across statistical tests, normalizations, and conditions leads to resource wastage and reduced sensitivity. EDDA represents a first, general design tool for RNA-seq, Nanostring, and metagenomic analysis, that rationally selects tests, predicts performance, and plans experiments to minimize resource wastage.

Transcriptomic analysis of a transgenic zebrafish hepatocellular carcinoma model reveals a prominent role of immune responses in tumour progression and regression.

Using our previously established xmrk transgenic zebrafish, hepatocellular carcinoma (HCC) was generated by induced expression of xmrk, which encoded a hyperactive epidermal growth factor receptor (EGFR) homolog, and regressed by suppression of xmrk expression. To investigate molecular changes in liver tumour progression and regression, RNA-Seq was performed for induced HCC and early and late stages of liver tissues during tumour regression. We found that Xmrk-induced zebrafish HCC shared strong molecular characteristics with a human HCC subtype (S2), which shows activated Myc signalling, upregulated phosphor-S6 and epithelial cell adhesion molecule.

Transcriptomic analyses of sexual dimorphism of the zebrafish liver and the effect of sex hormones.

The liver is one of the most sex-dimorphic organs in both oviparous and viviparous animals. In order to understand the molecular basis of the difference between male and female livers, high-throughput RNA-SAGE (serial analysis of gene expression) sequencing was performed for zebrafish livers of both sexes and their transcriptomes were compared. Both sexes had abundantly expressed genes involved in translation, coagulation and lipid metabolism, consistent with the general function of the liver.

Genome-wide estimation of firing efficiencies of origins of DNA replication from time-course copy number variation data.

Background: DNA replication is a fundamental biological process during S phase of cell division. It is initiated from several hundreds of origins along whole chromosome with different firing efficiencies (or frequency of usage). Direct measurement of origin firing efficiency by techniques such as DNA combing are time-consuming and lack the ability to measure all origins.

Analysis of FMRI data with drift: modified general linear model and Bayesian estimator.

The slowly varying drift poses a major problem in the analysis of functional magnetic resonance imaging (fMRI) data. In this paper, based on the observation that noise in fMRI is long memory fractional noise and the slowly varying drift resides in a subspace spanned only by large scale wavelets, we examine a modified general linear model (GLM) in wavelet domain under Bayesian framework. This modified model estimates the activation parameters at each scale of wavelet decomposition.

fMRI data analysis with nonstationary noise models: a Bayesian approach.

The assumption of noise stationarity in the functional magnetic resonance imaging (fMRI) data analysis may lead to the loss of crucial dynamic features of the data and thus result in inaccurate activation detection. In this paper, a Bayesian approach is proposed to analyze the fMRI data with two nonstationary noise models (the time-varying variance noise model and the fractional noise model). The covariance matrices of the time-varying variance noise and the fractional noise after wavelet transform are diagonal matrices.

Estimation of the hemodynamic response of fMRI Data using RBF neural network.

Functional magnetic resonance imaging (fMRI) is an important technique for neuroimaging. The conventional system identification methods used in fMRI data analysis assume a linear time-invariant system with the impulse response described by the hemodynamic responses (HDR). However, the measured blood oxygenation level-dependent (BOLD) signals to a particular processing task (for example, rapid event-related fMRI design) show nonlinear properties and vary with different brain regions and subjects.

Bayesian radial basis function network for modeling fMRI data.

Noisy and nonlinear nature make fMRI signal processing a challenging problem. We proposed and analyzed the Bayesian trained radial basis function (RBF) neural network in fMRI data processing. The method, which determines the regularization parameter in RBF network automatically by Bayesian learning, is especially suitable for fMRI data processing.

Spatio-temporal modeling and analysis of fMRI data using NARX neural network.

This paper presents spatio-temporal modeling and analysis methods to fMRI data. Based on the nonlinear autoregressive with exogenous inputs (NARX) model realized by the Bayesian radial basis function (RBF) neural networks, two methods (NARX-1 and NARX-2) are proposed to capture the unknown complex dynamics of the brain activities. Simulation results on both synthetic and real fMRI data clearly show that the proposed schemes outperform the conventional t-test method in detecting the activated regions of the brain.