Overcoming limitations in sparse cell-type predictions for a biologically reliable task.

A, Illustration of mouse hippocampus on mouse brain coronal section. B, 2D UMAP representation of the 8 HPF cell types. C, Overlapping CA1 and CA3 due to cell type similarity, with cluster radii calculated using centroid distances and...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Nuray Sogunmez Erdogan (21533872) (author)
مؤلفون آخرون:	Deniz Eroglu (21533875) (author)
منشور في:	2025
الموضوعات:	Genetics Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unlike conventional approaches understanding cellular responses resolution cellular maps preserving biological coherence predicted cellular landscapes machine learning algorithm e ., spatially decode tissue organization induced sparse regression functionally consistent cell distinct cell types leveraging sparse cell integrating reference single cell type medleys spatial transcriptomics spot spatial transcriptomics sparse deconvolution integrates spot cell data spatial datasets type variations type localization type distributions type arrangements wispr ), unmatched datasets specific hyperparameters signaling however practical utility permissive algorithms neglecting biology neglect biology methods rely introduce weight inaccurate predictions grounded constraints genome coverage driven modeling cancerous tissues
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Overcoming limitations in sparse cell-type predictions for a biologically reliable task.

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