Biology subjects

Kiryu, H.

Publications and source records attributed to Kiryu, H..

6 recordsLinked to original sources

bioRxiv2026

Multi-scale microenvironment covariance for unsupervised spatial domain identification

Motivation: Many spatial domain identification methods describe the neighbourhood of a cell by mean gene expression. However, the mean cannot separate neighbourhoods that differ only in gene-gene covariance and tissue is organised at several spatial scales. Results: We propose CoSMIM, which represents the niche of each cell by a weighted fusion of gene-gene covariance matrices of its spatial neighbourhood at multiple scales. Principal component analysis and Leiden clustering of the matrix square root of the fused matrix identify spatial domains without model training. On six benchmark datasets from five platforms, CoSMIM attains the best overall rank over eight evaluation metrics among 16 methods. It also ranks first on a Xenium mouse brain dataset, and on all seven datasets its median NMI exceeds that of single-scale COVET. The domain covariance matrices also show gene pairs whose correlation differs between domains.

bioinformatics↗

bioRxiv2023

Inference of gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations

Single-cell RNA-seq analysis coupled with CRISPR-based perturbation (scCRISPR) has enabled the inference of gene regulatory networks (GRNs) with causal relationships. However, a snapshot of scCRISPR data may not lead to an accurate inference, since a gene knockout can influence multi-layered downstream over time. Here, we developed RENGE, a new computational method that infers GRNs using a time-series scCRISPR dataset. RENGE models the propagation process of the effects elicited by a gene knockout on its GRN. It can distinguish between direct and indirect regulations, which allows for the inference of regulations by genes that are not knocked out. RENGE therefore outperforms current methods in the accuracy of inferring GRNs. When used on a dataset we derived from human-induced pluripotent stem cells, RENGE yielded a GRN consistent with multiple databases and literature. Accurate inference of GRNs by RENGE would enable the identification of key factors for various biological systems.

bioinformatics↗

bioRxiv2021

Ergodic patterns of cell state transitions underlie the reproducibility of embryonic development

The reproducibility of embryonic development is a remarkable feat of biological organization, but the underlying mechanisms are poorly understood. Clearly, gene regulatory networks are central to the orderly progression of development, but noisy molecular and cellular processes should reduce reproducibility. Here, we identify ergodicity, a type of dynamical stability, as underlying the reproducibility of development. In ergodic systems, a single timepoint measurement equals a time average. Focusing on the zebrafish tailbud, we define gene expression and cell motion states using a parallel statistical analyses of single cell RNA sequencing data and in vivo timelapse cell tracking data and a change point detection algorithm. Strikingly, the cell motion state transitions in each embryo exhibit the same patterns for both a single timepoint and a 2-3 hour time average. Both the cell motion and gene expression cell states exhibit balanced influx and outflux rates reflecting a spatiotemporal stability. Stated simply, these data indicate the pattern of changes in the tailbud doesnt change. This ergodic pattern of cell state transitions may represent an emergent meta-state that links gene networks to the reproducible progression of embryogenesis.

developmental biology↗

bioRxiv2018

reactIDR: Evaluation of the statistical reproducibility of high-throughput structural analyses for a robust RNA reactivity classification

MotivationRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures called high-throughput RNA structural (HTS) analy- sis, and dozens of different protocols were used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on experimental data generation methodology, which results in many difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.\n\nResultsHere, we introduced a statistical framework, reactIDR, which is applicable to the experimental data obtained using multiple HTS methodologies, and it classifies the nucleotides into three structural categories, stem, loop, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model (HMM) to discriminate accurately between the true and spurious signals obtained in the replicated HTS experiments. In reactIDR, IDR and HMM parameters are efficiently optimized by using an expectation-maximization algorithm. Furthermore, if known reference structures are given, a supervised learning can be applicable in a semi-supervised manner. The results of our analyses for real HTS data showed that reactIDR achieved the highest accuracy in the classification problem of stem/loop structures of rRNA using both individual and integrated HTS datasets as well as the best correspondence with the three-dimensional structure. Because reactIDR is the first method to compare HTS datasets obtained from multiple sources in a single unified model, it has a great potential to increase the accuracy of RNA secondary structure prediction at transcriptome-wide level with further experiments performed.\n\nAvailabilityreactIDR is implemented in Python. Source code is publicly available at https://github.com/carushi/reactIDRhttps://github.com/carushi/reactIDR.\n\nContactkawaguchi-rs@aist.go.jp\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗

bioRxiv2017

An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis

The spatial patterns of gene expression in early Drosophila melanogaster embryogenesis have been studied experimentally and theoretically to reveal the molecular basis of morphogenesis. In particular, the gene regulatory network (GRN) of gap genes has been investigated through mathematical modeling and simulation. Although these simulation-based approaches are useful for describing complex dynamics and have revealed several important regulations in spatial patterning, they are computationally intensive because they optimize GRN with iterative simulation. Recently, the advance of experimental technologies is enabling the acquisition of comprehensive spatial expression data, and an efficient algorithm will be necessary to analyze such large-scale data. In this research, we developed an efficient algorithm to infer the GRN based on a linear reaction-diffusion model. First, we qualitatively analyzed the GRNs of gap genes and pair-rule genes based on our algorithm and showed that two mutual repressions are fundamental regulations. Then, we inferred the GRN from gap gene data, and identified asymmetric regulations in addition to the two mutual repressions. We analyzed the effect of these asymmetric regulations on spatial patterns, and showed that they have the potential to adjust peak position. Our algorithm runs in sub-second time, which is significantly smaller than the runtime of simulation-based approaches (between 8 and 160 h, for exmaple). Neverthe-less, our inferred GRN was highly correlated with the simulation-based GRNs. We also analyzed the gap gene network of Clogmia albipunctata and showed that different mutual repression regulations might be important in comparison with those of Drosophila melanogaster. As our algorithm can infer GRNs efficiently and can be applied to several different network analysis, it will be a valuable approach for analyzing large-scale data.

bioinformatics↗

bioRxiv2016

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.\n\nIn this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.\n\nThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

bioinformatics↗