Lens epithelial explants are comprised of lens epithelial cells cultured in vitro on their native basement membrane, the lens capsule. Biologists have used lens epithelial explants to study many different cellular processes including lens fiber cell differentiation. In these studies, fiber differentiation is typically measured by cellular elongation and the expression of a few proteins characteristically expressed by lens fiber cells in situ. Chromatin and RNA was collected from lens epithelial explants cultured in either un-supplemented media or media containing 50% bovine vitreous humor for one or five days. Chromatin for ATAC-sequencing and RNA for RNA-sequencing was prepared from explants to assess regions of accessible chromatin and to quantitatively measure gene expression, respectively. Vitreous humor increased chromatin accessibility in promoter regions of genes associated with fiber differentiation and, surprisingly, an immune response, and this was associated with increased transcript levels for these genes. In contrast, vitreous had little effect on the accessibility of the genes highly expressed in the lens epithelium despite dramatic reductions in their mRNA transcripts. An unbiased analysis of differentially accessible regions revealed an enrichment of cis-regulatory motifs for RUNX, SOX and TEAD transcription factors that may drive differential gene expression in response to vitreous.

Motivation: In single-cell RNA sequencing analysis, addressing batch effects—technical artifacts stemming from factors such as varying sequencing technologies, equipment, and capture times—is crucial. These factors can cause unwanted variation and obfuscate the underlying biological signal of interest. The joint and individual variation explained (JIVE) method can be used to extract shared biological patterns from multi-source sequencing data while adjusting for individual non-biological variations (i.e. batch effect). However, its current implementation is originally designed for bulk sequencing data, making it computationally infeasible for large-scale single-cell sequencing datasets. Results: In this study, we enhance JIVE for large-scale single-cell data by boosting its computational efficiency. Additionally, we introduce a novel application of JIVE for batch-effect correction on multiple single-cell sequencing datasets. Our enhanced method aims to decompose single-cell sequencing datasets into a joint structure capturing the true biological variability and individual structures, which capture technical variability within each batch. This joint structure is then suitable for use in downstream analyses. We benchmarked the results against four popular tools, Seurat v5, Harmony, LIGER, and Combat-seq, which were developed for this purpose. JIVE performed best in terms of preserving cell-type effects and in scenarios in which the batch sizes are balanced. Availability and implementation: The JIVE implementation used for this analysis can be found at https://github.com/oconnell-statistics-lab/scJIVE.

In cancer research, different levels of high-dimensional data are often collected for the same subjects. Effective integration of these data by considering the shared and specific information from each data source can help us better understand different types of cancer. In this study we propose a novel autoencoder (AE) structure with explicitly defined orthogonal loss between the shared and specific embeddings to integrate different data sources. We compare our model with previously proposed AE structures based on simulated data and real cancer data from The Cancer Genome Atlas. Using simulations with different proportions of differentially expressed genes, we compare the performance of AE methods for subsequent classification tasks. We also compare the model performance with a commonly used dimension reduction method, joint and individual variance explained (JIVE). In terms of reconstruction loss, our proposed AE models with orthogonal constraints have a slightly better reconstruction loss. All AE models achieve higher classification accuracy than the original features, demonstrating the usefulness of the embeddings extracted by the model. We show that the proposed models have consistently high classification accuracy on both training and testing sets. In comparison, the recently proposed MOCSS model that imposes an orthogonality penalty in the post-processing step has lower classification accuracy that is on par with JIVE.