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Koki Tsuyuzaki
National Presto Industries
CellGeneticsRNAComputational biologyBiology
14Publications
4H-index
81Citations
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Publications 13
Newest
#1Hirotaka MatsumotoH-Index: 8
#2Tetsutaro HayashiH-Index: 16
Last. Itoshi Nikaido (University of Tsukuba)H-Index: 2
view all 9 authors...
2 CitationsSource
#1Koki Tsuyuzaki (National Presto Industries)H-Index: 4
#2Hiroyuki Sato (Kyoto University)H-Index: 9
Last. Itoshi Nikaido (University of Tsukuba)H-Index: 20
view all 4 authors...
Background Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation time is long and consumes large amounts of memory.
6 CitationsSource
#1Koki TsuyuzakiH-Index: 4
#2Hiroyuki SatoH-Index: 9
Last. Itoshi NikaidoH-Index: 2
view all 4 authors...
Source
#1Koki Tsuyuzaki (National Presto Industries)H-Index: 4
#2Hiroyuki Sato (Kyoto University)H-Index: 9
Last. Itoshi Nikaido (University of Tsukuba)H-Index: 20
view all 4 authors...
Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) dataset but for large-scale scRNA-seq datasets, the computation consumes a long time and large memory space. In this work, we review the existing fast and memory-efficient PCA algorithms and implementations and evaluate their practical application to large-scale scRNA-seq dataset. Our benchmark showed that some PCA algorithms based on Krylov subspace and randomized singular value decomposition...
1 CitationsSource
#1Koki TsuyuzakiH-Index: 4
#2Manabu IshiiH-Index: 2
Last. Itoshi NikaidoH-Index: 20
view all 3 authors...
Complex biological systems can be described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing technologies have enabled the detection of CCIs and related ligand-receptor (L-R) gene expression simultaneously. However, previous data analysis methods have focused on only one-to-one CCIs between two cell types. To also detect many-to-many CCIs, we propose scTensor, a novel method for extracting representative triadic relationships (hypergraphs), which include (i) lig...
4 CitationsSource
#1Kenta Sato (UTokyo: University of Tokyo)H-Index: 3
#2Koki TsuyuzakiH-Index: 4
Last. Itoshi Nikaido (University of Tsukuba)H-Index: 20
view all 4 authors...
Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method de...
8 CitationsSource
#1Hirotaka MatsumotoH-Index: 3
#1Hirotaka MatsumotoH-Index: 8
Last. Itoshi Nikaido (University of Tsukuba)H-Index: 20
view all 9 authors...
Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types, and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have dev...
Source
In the real world, most objects and data have multiple types of attributes and inter-connections. Such data structures are named "Heterogeneous Information Networks" (HIN) and have been widely researched. Biological systems are also considered to be highly complicated HIN. In this work, we review various applications of HIN methods to biological and chemical data, discuss some advanced topics, and describe some future research directions.
3 Citations
#1Koki TsuyuzakiH-Index: 4
#2Gota MorotaH-Index: 14
Last. Itoshi NikaidoH-Index: 2
view all 6 authors...
#1Gota Morota (NU: University of Nebraska–Lincoln)H-Index: 14
#2Francisco Peñagaricano (UF: University of Florida)H-Index: 18
Last. Itoshi NikaidoH-Index: 2
view all 6 authors...
Summary An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers ...
24 CitationsSource
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