Motoki Shiga

A spectral clustering approach to optimally combining numericalvectors with a modular network

We address the issue of clustering numerical vectors with a network. The problem setting is basically equivalent to constrained clustering by Wagstaff and Cardie and semi-supervised clustering by Basu et al., but our focus is more on the optimal combination of two heterogeneous data sources. An application of this setting is web pages which can be numerically vectorized by their contents, e.g. term frequencies, and which are hyperlinked to each other, showing a network. Another typical application is genes whose behavior can be numerically measured and a gene network can be given from another data source.We first define a new graph clustering measure which we call normalized network modularity, by balancing the cluster size of the original modularity. We then propose a new clustering method which integrates the cost of clustering numerical vectors with the cost of maximizing the normalized network modularity into a spectral relaxation problem. Our learning algorithm is based on spectral clustering which makes our issue an eigenvalue problem and uses k-means for final cluster assignments. A significant advantage of our method is that we can optimize the weight parameter for balancing the two costs from the given data by choosing the minimum total cost. We evaluated the performance of our proposed method using a variety of datasets including synthetic data as well as real-world data from molecular biology. Experimental results showed that our method is effective enough to have good results for clustering by numerical vectors and a network.

Annotating gene function by combining expression data with a modular gene network

MOTIVATION A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks. RESULTS We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.

Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization.

Advances in scanning transmission electron microscopy (STEM) techniques have enabled us to automatically obtain electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectral datasets from a specified region of interest (ROI) at an arbitrary step width, called spectral imaging (SI). Instead of manually identifying the potential constituent chemical components from the ROI and determining the chemical state of each spectral component from the SI data stored in a huge three-dimensional matrix, it is more effective and efficient to use a statistical approach for the automatic resolution and extraction of the underlying chemical components. Among many different statistical approaches, we adopt a non-negative matrix factorization (NMF) technique, mainly because of the natural assumption of non-negative values in the spectra and cardinalities of chemical components, which are always positive in actual data. This paper proposes a new NMF model with two penalty terms: (i) an automatic relevance determination (ARD) prior, which optimizes the number of components, and (ii) a soft orthogonal constraint, which clearly resolves each spectrum component. For the factorization, we further propose a fast optimization algorithm based on hierarchical alternating least-squares. Numerical experiments using both phantom and real STEM-EDX/EELS SI datasets demonstrate that the ARD prior successfully identifies the correct number of physically meaningful components. The soft orthogonal constraint is also shown to be effective, particularly for STEM-EELS SI data, where neither the spatial nor spectral entries in the matrices are sparse.

Genome-Wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-Regulated by Histone Modification Patterns

N-terminal tails of H2A, H2B, H3 and H4 histone families are subjected to posttranslational modifications that take part in transcriptional regulation mechanisms, such as transcription factor binding and gene expression. Regulation mechanisms under control of histone modification are important but remain largely unclear, despite of emerging datasets for comprehensive analysis of histone modification. In this paper, we focus on what we call genetic harmonious units (GHUs), which are co-occurring patterns among transcription factor binding, gene expression and histone modification. We present the first genome-wide approach that captures GHUs by combining ChIP-chip with microarray datasets from Saccharomyces cerevisiae. Our approach employs noise-robust soft clustering to select patterns which share the same preferences in transcription factor-binding, histone modification and gene expression, which are all currently implied to be closely correlated. The detected patterns are a well-studied acetylation of lysine 16 of H4 in glucose depletion as well as co-acetylation of five lysine residues of H3 with H4 Lys12 and H2A Lys7 responsible for ribosome biogenesis. Furthermore, our method further suggested the recognition of acetylated H4 Lys16 being crucial to histone acetyltransferase ESA1, whose essential role is still under controversy, from a microarray dataset on ESA1 and its bypass suppressor mutants. These results demonstrate that our approach allows us to provide clearer principles behind gene regulation mechanisms under histone modifications and detect GHUs further by applying to other microarray and ChIP-chip datasets. The source code of our method, which was implemented in MATLAB (http://www.mathworks.com/), is available from the supporting page for this paper: http://www.bic.kyoto-u.ac.jp/pathway/natsume/hm_detector.htm.

Efficiently finding genome-wide three-way gene interactions from transcript-and genotype-data

Motivation: We address the issue of finding a three-way gene interaction, i.e. two interacting genes in expression under the genotypes of another gene, given a dataset in which expressions and genotypes are measured at once for each individual. This issue can be a general, switching mechanism in expression of two genes, being controlled by categories of another gene, and finding this type of interaction can be a key to elucidating complex biological systems. The most suitable method for this issue is likelihood ratio test using logistic regressions, which we call interaction test, but a serious problem of this test is computational intractability at a genome-wide level. Results: We developed a fast method for this issue which improves the speed of interaction test by around 10 times for any size of datasets, keeping highly interacting genes with an accuracy of ∼85%. We applied our method to ∼3 × 108 three-way combinations generated from a dataset on human brain samples and detected three-way gene interactions with small P-values. To check the reliability of our results, we first conducted permutations by which we can show that the obtained P-values are significantly smaller than those obtained from permuted null examples. We then used GEO (Gene Expression Omnibus) to generate gene expression datasets with binary classes to confirm the detected three-way interactions by using these datasets and interaction tests. The result showed us some datasets with significantly small P-values, strongly supporting the reliability of the detected three-way interactions. Availability: Software is available from http://www.bic.kyoto-u.ac.jp/pathway/kayano/bioinfo_three-way.html Contact: kayano@kuicr.kyoto-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Efficient semi-supervised learning on locally informative multiple graphs

We address an issue of semi-supervised learning on multiple graphs, over which informative subgraphs are distributed. One application under this setting can be found in molecular biology, where different types of gene networks are generated depending upon experiments. Here an important problem is to annotate unknown genes by using functionally known genes, which connect to unknown genes in gene networks, in which informative parts vary over networks. We present a powerful, time-efficient approach for this problem by combining soft spectral clustering with label propagation for multiple graphs. We demonstrate the effectiveness and efficiency of our approach using both synthetic and real biological datasets.

Temperature-dependent Eu 3 d − 4 f x-ray absorption and resonant photoemission study of the valence transition in Eu Ni 2 ( Si 0.2 Ge 0.8 ) 2

We study the mixed valence transition (

Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides

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Annotating gene functions with integrative spectral clustering on microarray expressions and sequences.

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