Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data

Carlos Torroja, Fátima Sánchez‐Cabo

Frontiers in Genetics 2019 article cited by 51

Source Open-access PDF More in Biology & Life Sciences

importance 5/10 ai assisted Single-cell and spatial transcriptomics genomics stage: research

Problem

Bulk RNA-Seq cannot distinguish whether observed expression changes come from shifting cell-type proportions or aberrant behavior of a specific cell type, while single-cell experiments remain complex and expensive.

Why it matters

Misinterpreting bulk expression changes can mislead disease research ranging from cancer to atherosclerosis.

What the AI did

Digitaldlsorter applies deep learning to scRNA-Seq data to deconvolute bulk gene expression measurements into cell-type contributions.

Results

not stated in source

Impact

Proposes a deep-learning tool for cell-type deconvolution of bulk data; performance not quantified in abstract.

Record

Venue	Frontiers in Genetics
Published	2019
Work type	article
Authors	Carlos Torroja, Fátima Sánchez‐Cabo
Citations	51 (OpenAlex, at import time)
References	50
Model or method	deep learning (specific architecture not stated)
Organization	not stated in source
Primary source	https://doi.org/10.3389/fgene.2019.00978
Open access	gold
Keywords	scRNA-Seq, bulk RNA-Seq, deconvolution, deep learning, Digitaldlsorter

Importance rating

5 / 10

real but narrower advance

Deconvolution tool aids disease research from cancer to atherosclerosis; a real but narrower methodological advance, performance not quantified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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