DeLuca, D. S. | Search bioRxiv

bioRxiv2024

Extending the capabilities of deconvolution to provide cell type specific pathway analysis of bulk RNA-seq data for idiopathic pulmonary fibrosis

MotivationTranscriptome data are confounded by differences in cell type proportions. Differentiating between regulated changes in gene expression and changes due to differing cell type proportions remains a challenge. Therefore, we apply a deconvolution method to correct for changes in cell type proportions and provide a novel cell-type specific pathway analysis. ResultsWe demonstrate the technique in the context of idiopathic pulmonary fibrosis. Inferred cell type proportions indicated a significant increase in fibroblasts, myofibroblasts, and a decrease in vascular endothelial capillary cells. Pathway analysis after adjustment for proportions indicated IPF-related changes in extracellular matrix organization and TGF-{beta} regulation. Cell-type specific pathway analysis suggested the role of interferon signaling in ATII cells. These results demonstrate that deconvolution is not only useful for assessing cell type proportions, but also can provide cell type-specific pathway analysis, allowing for a much more nuanced interpretation of bulk RNA-seq data.

bioinformatics↗

bioRxiv2023

A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE which utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After training five different autoencoder architectures on 22310 transcriptomes, we benchmarked their performance on organ and disease classification tasks on separate selection of 5577 test samples. Every tested architecture succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability. Author summaryThe ability to measure the human transcriptome has been a critical tool to studying health and disease. However, transcriptomes data sets are too large and complex for direct human interpretation. Deep learning techniques such as autoencoders are capable of distilling high-level features from complex data. However, even if deep learning models find patterns, these patterns are not necessarily represented in a way that humans can easily understand. By bringing in the prior knowledge of biological pathways, we have trained the model to "speak the language" of the biologist, and represent complex transcrtomes, in simpler concepts that are already familiar to biologists. We can then apply the tool to compare for example samples from lung cancer cells to healthy cells, and show which biological processes are perturbed.

bioinformatics↗