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@MISC{Lee:279462,
      author       = {Lee, Jaehyun},
      othercontributors = {Danzer, Karin},
      title        = {{D}ataset: {V}isium {S}patial data of {B}rain section from
                      {P}arkinson {M}ouse {M}odel based on inducible expression of
                      human a-syn constructs: 3-months},
      publisher    = {Zenodo},
      reportid     = {DZNE-2025-00789},
      year         = {2025},
      abstract     = {Using 3-months old mice of a inducible expression of human
                      a-syn constructs based Parkinson mouse model, we produced a
                      Visium Spatial V1 platform (10x Genomics) data. Sequencing
                      was performed on a NovaSeq 6000 with PE150. Sequences were
                      fiducially aligned to spots using Loupe Browser ver. 8. All
                      aligned sequences were mapped using spaceranger count 3.0.1
                      with a custom refence, which included sequences for the
                      promotor and transgene (Camk2aTTA, V1S/SV2) to the mouse
                      genome mm39. We filtered each sample of the Visium Spatial
                      dataset based on the MAD filtering of number of reads
                      (nUMI), number of genes (nGene), and percentage of
                      mitochondrial genes (percent.mt). A spot was filtered out if
                      it was outside of 3x MAD value in at least two metrics.
                      Filtered samples were merged into one Seurat 5.1.0 object
                      and we obtained normalized counts by the SCTransform
                      function of Seurat. Integration was performed using Harmony
                      1.2.0 on 50 PCA embeddings and clustering was done using
                      Leiden clustering based on 30 harmony embeddings. Integrated
                      clusters were visualized using the UMAP method. Samples that
                      were not successfully integrated (based on similarity
                      measures of the harmony embeddings) and showed high
                      percentage.mt or low nUMI levels compared to other samples,
                      were removed from subsequent analysis. A final integration
                      and clustering were performed after filtering. Regions were
                      first annotated based on a 0.1 resolution clustering to get
                      high level region annotation (Cortex, Hippocampus,
                      Subcortex). Each high-level region was further annotated
                      based on either more granular resolutions or subclustering.
                      Marker genes from mousebrain.org and literature were used in
                      combination with the Allen mouse brain atlas to obtain
                      anatomically relevant annotations.},
      cin          = {AG Danzer},
      cid          = {I:(DE-2719)5000072},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)32},
      doi          = {10.5281/ZENODO.15274014},
      url          = {https://pub.dzne.de/record/279462},
}