HIDE: hierarchical cell-type deconvolution

TL;DR

HIDE is a new method for analyzing bulk RNA data to better understand cell type composition, using a hierarchical approach that improves accuracy and clarity.

Contribution

HIDE introduces a hierarchical framework for cell-type deconvolution that leverages cell differentiation structures to enhance performance and interpretability.

Findings

HIDE outperforms existing methods in simulation studies by producing more reliable and consistent results.

The method successfully identifies major cell populations and their subpopulations in bulk transcriptomics data.

HIDE was applied to breast cancer specimens from TCGA, demonstrating its practical utility.

Abstract

Cell-type deconvolution is a computational approach to infer cellular distributions from bulk transcriptomics data. Several methods have been proposed, each with its own advantages and disadvantages. Reference based approaches make use of archetypic transcriptomic profiles representing individual cell types. Those reference profiles are ideally chosen such that the observed bulks can be reconstructed as a linear combination thereof. This strategy, however, ignores the fact that cellular populations arise through the process of cellular differentiation, which entails the gradual emergence of cell groups with diverse morphological and functional characteristics.

Here, we propose Hierarchical cell-type Deconvolution (HIDE), a cell-type deconvolution approach which incorporates a cell hierarchy for improved performance and interpretability. This is achieved by a hierarchical procedure that preserves estimates of major cell populations while inferring their respective subpopulations. We show in simulation studies that this procedure produces more reliable and more consistent results than other state-of-the-art approaches. Finally, we provide an example application of HIDE to explore breast cancer specimens from TCGA.

A python implementation of HIDE is available at zenodo (doi: 10.5281/zenodo.14724906).

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