A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data

TL;DR

This paper introduces a novel framework for identifying and explaining rare cell types in single-cell transcriptomics data, emphasizing interpretability by avoiding PCA and using advanced anomaly detection and explanation methods.

Contribution

It presents a new explainable anomaly detection framework that directly identifies rare cells and provides gene-based explanations without relying on PCA.

Findings

Eliminates the PCA step for better interpretability

Employs state-of-the-art anomaly detection and explanation techniques

Provides visual, gene-based explanations for detected rare cells

Abstract

The detection of rare cell types in single-cell transcriptomics data is crucial for elucidating disease pathogenesis and tissue development dynamics. However, a critical gap that persists in current methods is their inability to provide an explanation based on genes for each cell they have detected as rare. We identify three primary sources of this deficiency. First, the anomaly detectors often function as "black boxes", designed to detect anomalies but unable to explain why a cell is anomalous. Second, the standard analytical framework hinders interpretability by relying on dimensionality reduction techniques, such as Principal Component Analysis (PCA), which transform meaningful gene expression data into abstract, uninterpretable features. Finally, existing explanation algorithms cannot be readily applied to this domain, as single-cell data is characterized by high dimensionality, noise, and substantial sparsity. To overcome these limitations, we introduce a framework for explainable anomaly detection in single-cell transcriptomics data which not only identifies individual anomalies, but also provides a visual explanation based on genes that makes an instance anomalous. This framework has two key ingredients that are not existed in current methods applied in this domain. First, it eliminates the PCA step which is deemed to be an essential component in previous studies. Second, it employs the state-of-art anomaly detector and explainer as the efficient and effective means to find each rare cell and the relevant gene subspace in order to provide explanations for each rare cell as well as the typical normal cell associated with the rare cell's closest normal cells.