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  1. ...differential gene expression for six major cell types intensified at severe ADNC. Integrating peak-to-gene linkages and motif enrichment analyses, we reconstruct transcription factor (TF)–target gene networks across six major brain cell types. By integrating -wide association study (GWAS) loci with cell type...
  2. ...of the observations with latent dimension equal to the number of cell types. By constraining the number of latent factors (rows of Q or columns of P) reflecting underlying cell types, SIID jointly estimates P and Q, enabling imputation of the absent Xenium genes and deconvolution of the Visium spots.View larger...
  3. ...in influencing functional pathways critical for specific brain functions (Supplemental Fig. S1D).View larger version: In this window In a new window Figure 2. Landscape of global 3′ UTR usage across 261 cell types. (A) The circular plot summarizing APA trends across 1231 genes. The outer heat map track...
  4. ...issues with the annotation of rod cells in the reference data, in which some bipolar neurons also expressed the gene rho, which is the marker of the rod cell (Supplemental Fig. S4B,C; Hoang et al. 2020). Rod cells happen to be the most prevalent cell type in the Macosko data set (Fig. 3E). This indicates...
  5. ...NNLS framework to address the consistently observed technological discrepancy at the gene level. The distinct advantage of DeMixSC lies in its superior deconvolution accuracy and broad generalizability. As demonstrated in the benchmarking study, DeMixSC achieves more accurate estimates of cell type proportions...
  6. ...in mitotic mESCs. A second important question raised by our findings is to what extent mitotic binding by CTCF could be functional. Recent work has shown that, whereas mitotic CTCF binding correlates with rapidly reactivated genes after mitosis (Owens et al. 2019; Zhang et al. 2019; Pelham-Webb et al. 2021...
  7. ...highlights STMSC's potential to enhance our understanding of complex tissue structures. In the mouse brain coronal data set, STMSC accurately identified and distinguished fine spatial structures, showcasing advantages in spatial region segmentation accuracy and gene expression pattern analysis. High-precision...
  8. ...undetectable at baseline. To explore this possibility, we examined the overlap of eGenes with GWAS loci assembled from 402 brain-relevant traits in each cell type (Methods; Supplemental Table S7). Across cell types, we identified 571 eGenes implicated in GWAS of brain-related traits. Furthermore, eGenes...
  9. ...-expressing), and an unidentified wb-expressing somatic lineage (Fig. 1C; Supplemental Fig. S1H; Supplemental Table S1; Kuhn et al. 1991; Kelly et al. 2002). For this study, we focus on the GSC-like and CySC-like clusters.The GSC-like and CySC-like clusters followed expected gene expression patterns for known cell-type- and stage...
  10. ...a prominent role. In the adult mouse brain, we uncover cell type–specific PAs and visualize such events using spatial transcriptomic data. Over two dozen neurodevelopmental disorder–associated genes such as Csnk2a1 and Mecp2 show differential PAs during brain development. This study presents Infernape...
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