# Feature selection revisited in the single-cell era

arXiv: https://arxiv.org/abs/2110.14329 · HTML: https://mlsanity.com/paper/2110.14329/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 128 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 6 of 6)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [A scalable saliency-based Feature selection method with instance level information](https://mlsanity.com/paper/1904.13127/) (2019)
- [Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data](https://mlsanity.com/paper/2106.08317/) (2021)

## Built on by (most cited first, 7 of 7)

- [Interpretable Deep Clustering for Tabular Data](https://mlsanity.com/paper/2306.04785/) (2023)
- [MarkerMap: nonlinear marker selection for single-cell studies](https://mlsanity.com/paper/2207.14106/) (2022)
- [Interpretable deep learning in single-cell omics](https://mlsanity.com/paper/2401.06823/) (2024)
- [LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems](https://mlsanity.com/paper/2404.16870/) (2024)
- [Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data](https://mlsanity.com/paper/2210.02197/) (2022)
- [BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data](https://mlsanity.com/paper/2511.15330/) (2025)
- [MAFS: Multi-head Attention Feature Selection for High-Dimensional Data via Deep Fusion of Filter Methods](https://mlsanity.com/paper/2601.02668/) (2026)

## Abstract (arXiv)

Feature selection techniques are essential for high-dimensional data analysis. In the last two decades, their popularity has been fuelled by the increasing availability of high-throughput biomolecular data where high-dimensionality is a common data property. Recent advances in biotechnologies enable global profiling of various molecular and cellular features at single-cell resolution, resulting in large-scale datasets with increased complexity. These technological developments have led to a resurgence in feature selection research and application in the single-cell field. Here, we revisit feature selection techniques and summarise recent developments. We review their versatile application to a range of single-cell data types including those generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions on which feature selection could have a significant impact. Finally, we consider the scalability and make general recommendations on the utility of each type of feature selection method. We hope this review serves as a reference point to stimulate future research and application of feature selection in the single-cell era.