ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster

Delete or merge regressors for linear model selection

We consider a problem of linear model selection in the presence of both continuous and categorical predictors. Feasible models consist of subsets of numerical variables and partitions of levels of factors. A new algorithm called delete or merge regressors (DMR) is presented which is a stepwise backward procedure involving ranking the predictors according to squared t-statistics and choosing the final model minimizing BIC. In the article we prove consistency of DMR when the number of predictors tends to infinity with the sample size and describe a simulation study using a pertaining R package. The results indicate significant advantage in time complexity and selection accuracy of our algorithm over Lasso-based methods described in the literature. Moreover, a version of DMR for generalized linear models is proposed

Steganographer Identification

Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the Steganographer Identification Problem (SIP). One might use the conventional steganalysis algorithms to separate stego objects from cover objects and then identify the guilty actors. However, the guilty actors may be lost due to a number of false alarms. To deal with the SIP, most of the state-of-the-arts use unsupervised learning based approaches. In their solutions, each actor holds multiple digital objects, from which a set of feature vectors can be extracted. The well-defined distances between these feature sets are determined to measure the similarity between the corresponding actors. By applying clustering or outlier detection, the most suspicious actor(s) will be judged as the steganographer(s). Though the SIP needs further study, the existing works have good ability to identify the steganographer(s) when non-adaptive steganographic embedding was applied. In this chapter, we will present foundational concepts and review advanced methodologies in SIP. This chapter is self-contained and intended as a tutorial introducing the SIP in the context of media steganography.Comment: A tutorial with 30 page

On morphological hierarchical representations for image processing and spatial data clustering

Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing

Conserved defense responses between maize and sorghum to Exserohilum turcicum.

BACKGROUND:Exserohilum turcicum is an important pathogen of both sorghum and maize, causing sorghum leaf blight and northern corn leaf blight. Because the same pathogen can infect and cause major losses for two of the most important grain crops, it is an ideal pathosystem to study plant-pathogen evolution and investigate shared resistance mechanisms between the two plant species. To identify sorghum genes involved in the E. turcicum response, we conducted a genome-wide association study (GWAS). RESULTS:Using the sorghum conversion panel evaluated across three environments, we identified a total of 216 significant markers. Based on physical linkage with the significant markers, we detected a total of 113 unique candidate genes, some with known roles in plant defense. Also, we compared maize genes known to play a role in resistance to E. turcicum with the association mapping results and found evidence of genes conferring resistance in both crops, providing evidence of shared resistance between maize and sorghum. CONCLUSIONS:Using a genetics approach, we identified shared genetic regions conferring resistance to E. turcicum in both maize and sorghum. We identified several promising candidate genes for resistance to leaf blight in sorghum, including genes related to R-gene mediated resistance. We present significant advancements in the understanding of host resistance to E. turcicum, which is crucial to reduce losses due to this important pathogen