SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.
Cell Genom
; 4(6): 100581, 2024 Jun 12.
Article
in En
| MEDLINE
| ID: mdl-38823397
ABSTRACT
Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sequence Analysis, RNA
/
Single-Cell Analysis
/
Deep Learning
Limits:
Animals
/
Humans
Language:
En
Journal:
Cell Genom
Year:
2024
Document type:
Article
Affiliation country: