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

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

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

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Bibliographic Details
Main Author: Nuray Sogunmez Erdogan (21533872) (author)
Other Authors: Deniz Eroglu (21533875) (author)
Published: 2025
Subjects:
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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