Platelet and myeloid lineage biases of transplanted single perinatal mouse hematopoietic stem cells

Belander Strålin, Karin; Carrelha, Joana; Winroth, Axel; Ziegenhain, Christoph; Hagemann-Jensen, Michael; Kettyle, Laura M.; Hillen, Amy; Högstrand, Kari; Markljung, Ellen; Grasso, Francesca; Seki, Masafumi; Mazzi, Stefania; Meng, Yiran; Wu, Bishan; Chari, Edwin; Lehander, Madeleine; Sandberg, Rickard; Woll, Petter S.; Jacobsen, Sten Eirik W.

doi:10.1038/s41422-023-00866-4

Cell Res

2023

DOI: 10.1038/s41422-023-00866-4

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Platelet and myeloid lineage biases of transplanted single perinatal mouse hematopoietic stem cells

Karin Belander Strålin,

Joana Carrelha,

Axel Winroth

et al.

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2024

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Cited by 2 publications

References 16 publications

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Epigenetic regulation of hematopoietic stem cell fate

Meng,

Nerlov

2024

Trends in Cell Biology

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Epigenetic regulation of hematopoietic stem cell fate

Meng,

Nerlov

2024

Trends in Cell Biology

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scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

Kim,

Chang,

Chae

et al. 2024

Nat Commun

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High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

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Platelet and myeloid lineage biases of transplanted single perinatal mouse hematopoietic stem cells

Cited by 2 publications

References 16 publications

Epigenetic regulation of hematopoietic stem cell fate

Epigenetic regulation of hematopoietic stem cell fate

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

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