Kazumitsu Maehara

spSheaf infers spatial domains from raw molecular data without cell segmentation, outperforming existing methods on benchmark datasets.

The paper introduces ddHodge, a method for reconstructing vector fields from scRNA-seq data to analyze cell-state dynamics, revealing gene expression follows a gradient system during mouse embryogenesis.

The paper investigates the role of H2bc27, an H2B isoform, in mouse brain development through gene expression analysis and knockout studies, finding it regulates gene expression in brain development.

The paper introduces PECAbs for high-specificity multiplexed imaging of cell states and combines them with seq-smFISH to classify cells and identify signal activation states in human tissue.

The paper introduces DFC, a method using adaptive LASSO logistic regression to identify a small subset of genes that distinguish cell populations, addressing issues with P-value inflation in single-cell transcriptomics.

The paper introduces tsChIL, a method to analyze tissue-level epigenomic data from single-thin sections, enabling independent evaluation of cell population changes and gene activation in regenerating skeletal muscle.

This paper introduces PIC, a method for high-resolution spatial transcriptome analysis, and shows it detects gene expression in small regions of mouse embryos.

The paper tracks H3K27me3 and H4K20me1 dynamics during XCI and finds they accumulate concurrently but have distinct genomic distributions, with H4K20me1 possibly contributing to chromatin compaction.

This study identifies four temporal patterns of calcineurin-dependent gene expression during skeletal muscle differentiation and shows Cn promotes gene expression by facilitating chromatin remodeling and transcription factor interactions.

This paper introduces ddhodge, a method using Hodge decomposition to model latent flows in single-cell data, enabling visualization and interpretation of complex biological processes.

SraTailor converts SRA files into BigWig format and allows graphical visualization, making SRAs accessible to biologists without command-line expertise.

The paper identifies five nucleosome positioning patterns around transcription factor binding sites that predict transcriptional regulation function in mouse cells.