ESFS: A Noise-Resilient Framework for Feature Selection and Marker Gene Discovery in Single-Cell Transcriptomics
Radley, A.; Boezio, G.; Shand, C.; Perez-Carrasco, R.; Briscoe, J.
Show abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity, but extracting meaningful signals remains challenging due to technical noise, batch effects, and the limitations of current feature selection methods. We present Entropy Sorting Feature Selection (ESFS), a modular, user-friendly framework that captures multivariate gene expression relationships without imputation or denoising via latent spaces. Across diverse datasets, ESFS improves interpretability and reveals biology missed by standard workflows: identifying coherent developmental programs in eight independent human embryo datasets without batch integration; resolving spatial gene expression in mouse colon obscured by conventional analyses; distinguishing shared and tumour-specific microenvironments in glioblastoma; and disambiguating spatial, temporal, and neurogenic programs in the developing mouse neural tube. By operating in gene expression space, ESFS produces interpretable, biologically meaningful outputs while reducing artefacts introduced by feature extraction. These results position ESFS as a powerful means to uncover relevant molecular signatures in noisy, high-dimensional transcriptomics data.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis 97%
- STHD: probabilistic cell typing of single Spots in whole Transcriptome spatial data with High Definition 97%
- High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper 97%
Similar papers in this journal
- NEST: Spatially-mapped cell-cell communication patterns using a deep learning-based attention mechanism 97%
- Scalable sequence-informed embedding of single-cell ATAC-seq data with CellSpace 97%
- Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions 97%
Similar papers in this journal
- Integrative, high-resolution analysis of single cell gene expression across experimental conditions with PARAFAC2-RISE 97%
- Conserved epigenetic regulatory logic infers genes governing cell identity 96%
- Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.