Author

Satoi Nagasawa

St. Marianna University School of MedicineORCID 0000-0003-3967-7644Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Assay:spatial transcriptomics· 2 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Yutaka SuzukiiD· 3 shared Ayako SuzukiiD· 2 shared Ai Kawana‐TachikawaiD· 1 shared Kaori Nakayama‐Hosoya· 1 shared Katsuya TsuchiharaiD· 1 shared Keiya Kaneko· 1 shared Lucky Ronald RuntuweneiD· 1 shared Mamiko Sakata‐YanagimotoiD· 1 shared

Activity by Year

2022-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersGenomics

Yutaka SuzukiORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 18, 2024

Spatial domain analysis to estimate spatiotemporal pathological mechanisms in microenvironment with single-cell spatial omics data

Shunsuke A. Sakai, Ryosuke Nomura, Satoi Nagasawa…·Riu Yamashita

The paper introduces SpatialKNife, a toolkit for analyzing single-cell spatial omics data to estimate spatiotemporal pathological mechanisms in microenvironments.

DOI 10.1101/2024.06.18.599475

humanspatial transcriptomics

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GenomicsLife Science AlliancePeer-reviewedApr 5, 2022

Intensive Single Cell Analysis Reveals Immune Cell Diversity among Healthy Individuals

Yukie Kashima, Keiya Kaneko, Patrick Reteng…·Yutaka Suzuki

The paper analyzes single-cell immune data from healthy individuals to reveal cell diversity and daily fluctuations, showing how immune responses vary between individuals and over time.

10 citations

humanscRNA-seq

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GenomicsScientific ReportsPeer-reviewedMar 8, 2022

Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

Taku Monjo, Masaru Koido, Satoi Nagasawa…·Yoichiro Kamatani

The paper develops DeepSpaCE, a deep learning model that predicts spatial transcriptomics profiles to improve histological interpretation of breast cancer tissue sections without additional experiments.

103 citationstop 2%

humanspatial transcriptomics

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