  Single-cell RNA-sequencing (scRNA-seq) stands as a powerful tool for
deciphering cellular heterogeneity and exploring gene expression profiles at
high resolution. However, its high cost renders it impractical for extensive
sample cohorts within routine clinical care, hindering its broader
applicability. Hence, many methodologies have recently arised to estimate cell
type proportions from bulk RNA-seq samples (known as deconvolution methods).
However, they have several limitations: Many depend on selecting a robust
scRNA-seq reference dataset, which is often challenging. Secondly, building
reliable pseudobulk samples requires determining the optimal number of genes or
cells involved in the simulated data generation process, which has not been
studied in depth. Moreover, pseudobulk and bulk RNA-seq samples often exhibit
distribution shifts. Finally, most modern deconvolution approaches behave as a
black box, and the underlying mechanisms of the deconvolution task are still
unknown, which can compromise the reliability of the results. In this work, we
present Sweetwater, an adaptive and interpretable autoencoder able to
efficiently deconvolve bulk RNA-seq and microarray samples leveraging multiple
classes of reference data, such as scRNA-seq and single-nuclei RNA-seq.
Moreover, it can be trained on a mixture of FACS-sorted FASTQ files, which we
newly propose to use as this reduces platform-specific biases and may
potentially outperform single-cell-based references. Also, we demonstrate that
Sweetwater effectively uncovers biologically meaningful patterns during the
training process, increasing the reliability of the results. Sweetwater is
available at https://github.com/ubioinformat/Sweetwater, and we anticipate will
facilitate and expedite the accurate examination of high-throughput clinical
data across diverse applications.
