Modeling, inference and clustering for equivalence classes of 3-D orientations

Investigating cubic crystalline structures of specimens is an important way to study properties of materials in text analysis. Crystals in metal specimens have internally homogeneous orientations relative to a pre-chosen reference coordinate system. Clusters of crystals in the metal with locally similar orientations constitute so-called grains. The nature of these grains (shape, size, etc.) affects physical properties (e.g., hardness, conductivity, etc.) of the material. Electron backscatter diffraction (EBSD) machines are often use to measure orientations of crystals in metal specimens. However, orientations reported by EBSD machines are in truth equivalence classes of crystallographically symmetric orientations. Motivated by the materials science applications, we formulate parametric probability models for unlabeled orientation data. This amounts to developing models on equivalence classes of 3-D rotations. A Bayesian method is developed for inferencing parameters in the models, which is generally superior to large-sample methods based on likelihood estimation. We also proposed an algorithms for clustering equivalence classes of 3-D orientations. As we continue to work on this area, we found and studied an interesting class of Markov chains with state spaces partitions of a finite set. These Markov chains have some properties that make them attractive in their own right, and they are potentially helpful in Bayesian model-based clustering

Ontogeny of the maize shoot apical meristem

The maize (Zea mays) shoot apical meristem (SAM) arises early in embryogenesis and functions during stem cell maintenance and organogenesis to generate all the aboveground organs of the plant. Despite its integral role in maize shoot development, little is known about the molecular mechanisms of SAM initiation. Laser microdissection of apical domains from developing maize embryos and seedlings was combined with RNA sequencing for transcriptomic analyses of SAMontogeny. Molecular markers of key events during maize embryogenesis are described, and comprehensive transcriptional data from six stages in maize shoot development are generated. Transcriptomic profiling before and after SAM initiation indicates that organogenesis precedes stem cell maintenance in maize; analyses of the first three lateral organs elaborated from maize embryos provides insight into their homology and to the identity of the single maize cotyledon. Compared with the newly initiated SAM, the mature SAM is enriched for transcripts that function in transcriptional regulation, hormonal signaling, and transport. Comparisons of shoot meristems initiating juvenile leaves, adult leaves, and husk leaves illustrate differences in phase-specific (juvenile versus adult) and meristem-specific (SAM versus lateral meristem) transcript accumulation during maize shoot development. This study provides insight into the molecular genetics of SAMinitiation and function in maize

QQS orphan gene and its interactor NF‐YC4 reduce susceptibility to pathogens and pests

Enhancing the nutritional quality and disease resistance of crops without sacrificing productivity is a key issue for developing varieties that are valuable to farmers and for simultaneously improving food security and sustainability. Expression of the Arabidopsis thaliana species‐specific AtQQS (Qua‐Quine Starch) orphan gene or its interactor, NF‐YC4 (Nuclear Factor Y, subunit C4), has been shown to increase levels of leaf/seed protein without affecting the growth and yield of agronomic species. Here, we demonstrate that overexpression of AtQQS and NF‐YC4 in Arabidopsis and soybean enhances resistance/reduces susceptibility to viruses, bacteria, fungi, aphids and soybean cyst nematodes. A series of Arabidopsis mutants in starch metabolism were used to explore the relationships between QQS expression, carbon and nitrogen partitioning, and defense. The enhanced basal defenses mediated by QQS were independent of changes in protein/carbohydrate composition of the plants. We demonstrate that either AtQQS or NF‐YC4 overexpression in Arabidopsis and in soybean reduces susceptibility of these plants to pathogens/pests. Transgenic soybean lines overexpressing NF‐YC4 produce seeds with increased protein while maintaining healthy growth. Pull‐down studies reveal that QQS interacts with human NF‐YC, as well as with Arabidopsis NF‐YC4, and indicate two QQS binding sites near the NF‐YC‐histone‐binding domain. A new model for QQS interaction with NF‐YC is speculated. Our findings illustrate the potential of QQS and NF‐YC4 to increase protein and improve defensive traits in crops, overcoming the normal growth‐defense trade‐offs

QQS orphan gene regulates carbon and nitrogen partitioning across species via NF-YC interactions

The allocation of carbon and nitrogen resources to the synthesis of plant proteins, carbohydrates, and lipids is complex and under the control of many genes; much remains to be understood about this process. QQS (Qua-Quine Starch; At3g30720), an orphan gene unique to Arabidopsis thaliana, regulates metabolic processes affecting carbon and nitrogen partitioning among proteins and carbohydrates, modulating leaf and seed composition in Arabidopsis and soybean. Here the universality of QQS function in modulating carbon and nitrogen allocation is exemplified by a series of transgenic experiments. We show that ectopic expression of QQS increases soybean protein independent of the genetic background and original protein content of the cultivar. Furthermore, transgenic QQS expression increases the protein content of maize, a C4 species (a species that uses 4-carbon photosynthesis), and rice, a protein-poor agronomic crop, both highly divergent from Arabidopsis. We determine that QQS protein binds to the transcriptional regulator AtNF-YC4 (Arabidopsis nuclear factor Y, subunit C4). Overexpression of AtNF-YC4 in Arabidopsis mimics the QQS-overexpression phenotype, increasing protein and decreasing starch levels. NF-YC, a component of the NF-Y complex, is conserved across eukaryotes. The NF-YC4 homologs of soybean, rice, and maize also bind to QQS, which provides an explanation of how QQS can act in species where it does not occur endogenously. These findings are, to our knowledge, the first insight into the mechanism of action of QQS in modulating carbon and nitrogen allocation across species. They have major implications for the emergence and function of orphan genes, and identify a nontransgenic strategy for modulating protein levels in crop species, a trait of great agronomic significance

Modeling, inference and clustering for equivalence classes of 3-D orientations

Investigating cubic crystalline structures of specimens is an important way to study properties of materials in text analysis. Crystals in metal specimens have internally homogeneous orientations relative to a pre-chosen reference coordinate system. Clusters of crystals in the metal with locally similar orientations constitute so-called "grains." The nature of these grains (shape, size, etc.) affects physical properties (e.g., hardness, conductivity, etc.) of the material. Electron backscatter diffraction (EBSD) machines are often use to measure orientations of crystals in metal specimens. However, orientations reported by EBSD machines are in truth equivalence classes of crystallographically symmetric orientations. Motivated by the materials science applications, we formulate parametric probability models for "unlabeled orientation data." This amounts to developing models on equivalence classes of 3-D rotations. A Bayesian method is developed for inferencing parameters in the models, which is generally superior to large-sample methods based on likelihood estimation. We also proposed an algorithms for clustering equivalence classes of 3-D orientations. As we continue to work on this area, we found and studied an interesting class of Markov chains with state spaces partitions of a finite set. These Markov chains have some properties that make them attractive in their own right, and they are potentially helpful in Bayesian model-based clustering.</p

A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets

Background: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding. Results: We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions. Conclusions: The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO.This article is published as Liang, Kun, Chuanlong Du, Hankun You, and Dan Nettleton. "A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets." BMC bioinformatics 19 (2018): 107. doi: 10.1186/s12859-018-2106-5.</p

A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets

Abstract Background Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding. Results We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions. Conclusions The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO

Bayesian Inference for a New Class of Distributions on Equivalence Classes of 3-D Orientations With Applications to Materials Science

<div><p></p><p>Experiments in materials science investigating cubic crystalline structures often collect data which are in truth <i>equivalence classes</i> of crystallographically symmetric orientations. These intend to represent how lattice structures of particles are orientated relative to a reference coordinate system. Motivated by a materials science application, we formulate parametric probability models for “unlabeled orientation data.” This amounts to developing models on equivalence classes of 3-D rotations. We use a flexible existing model class for random rotations (called uniform-axis-random-spin models) to induce probability distributions on the equivalence classes of rotations. We develop one-sample Bayesian inference for the parameters in these models, and compare this methodology to some likelihood-based approaches. We also contrast the new parametric analysis of unlabeled orientation data with other analyses that proceed as if the data have been preprocessed into honest orientation data. On-line supplementary materials are also available, providing additional computational materials.</p></div

Additional file 1 of A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets

Supplementary Material. Details of deterministic annealing and additional simulation result. (PDF 152 kb