Single-cell modeling
3 papers with code • 0 benchmarks • 0 datasets
Single Cell RNA sequencing (scRNAseq) revolutionized our understanding of the fundamental of life sciences. The technology enables an unprecedented resolution to study heterogeneity in cell populations and their functionalities.
Benchmarks
These leaderboards are used to track progress in Single-cell modeling
Most implemented papers
The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data
Center-for-Health-Data-Science/scDGD
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Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis.
SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data
trungnt13/sisua
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ICML Workshop on Computational Biology 2019 2019
In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model.
Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations
SabbaghCodes/ImbalancedLearningForSingleCellFoundationModels
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bioRxiv 2023
We benchmark three foundation models, scGPT, scBERT, and Geneformer, using skewed single-cell cell-type distribution for cell-type annotation.
