Back to search

Article

HiDDEN: A machine learning label refinement method for detection of disease-relevant populations in case-control single-cell transcriptomics

2023-01-07

Abstract excerpt

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, but only a small fraction of them may actually be perturbed. Here, using simulations, we demonstrate that the standard approach to single cell analysis fails to isolate the subset of affected case cells and their markers when either the affected subset is small, or when the s...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
3fd2e562-e1b6-5e77-8cab-07c719d08516
DOI
10.1101/2023.01.06.523013
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
HiDDEN: A machine learning label refinement method for detection of disease-relevant populations in case-control single-cell transcriptomicsDOI 10.1101/2023.01.06.523013
Select a neighboring publication to make it the new centre.