Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. These are all among the core problems in the RNA field. With the rapid growth of sequencing technology, we have accumulated a massive amount of unannotated RNA sequences. On the other hand, expensive experimental observatory results in only limited numbers of annotated data and 3D structures. Hence, it is still challenging to design computational methods for predicting their structures and functions. The lack of annotated data and systematic study causes inferior performance. To resolve the issue, we propose a novel RNA foundation model (RNA-FM) to take advantage of all the 23 million non-coding RNA sequences through self-supervised learning. Within this approach, we discover that the pre-trained RNA-FM could infer sequential and evolutionary information of non-coding RNAs without using any labels. Furthermore, we demonstrate RNA-FM's effectiveness by applying it to the downstream secondary/3D structure prediction, SARS-CoV-2 genome structure and evolution prediction, protein-RNA binding preference modeling, and gene expression regulation modeling. The comprehensive experiments show that the proposed method improves the RNA structural and functional modelling results significantly and consistently. Despite only being trained with unlabelled data, RNA-FM can serve as the foundational model for the field.

Motivation: Spatially resolved transcriptomics (SRT) shows its impressive power in yielding biological insights into neuroscience, disease study, and even plant biology. However, current methods do not sufficiently explore the expressiveness of the multi-modal SRT data, leaving a large room for improvement of performance. Moreover, the current deep learning based methods lack interpretability due to the "black box" nature, impeding its further applications in the areas that require explanation. Results: We propose conST, a powerful and flexible SRT data analysis framework utilizing contrastive learning techniques. conST can learn low-dimensional embeddings by effectively integrating multi-modal SRT data, i.e. gene expression, spatial information, and morphology (if applicable). The learned embeddings can be then used for various downstream tasks, including clustering, trajectory and pseudotime inference, cell-to-cell interaction, etc. Extensive experiments in various datasets have been conducted to demonstrate the effectiveness and robustness of the proposed conST, achieving up to 10% improvement in clustering ARI in the commonly used benchmark dataset. We also show that the learned embedding can be used in complicated scenarios, such as predicting cancer progression by analyzing the tumour microenvironment and cell-to-cell interaction (CCI) of breast cancer. Our framework is interpretable in that it is able to find the correlated spots that support the clustering, which matches the CCI interaction pairs as well, providing more confidence to clinicians when making clinical decisions.

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We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream analysis. Compared with current methods, CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events simultaneously. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43 695 single cells from peripheral blood mononuclear cells.

Muilti-modality data are ubiquitous in biology, especially that we have entered the multi-omics era, when we can measure the same biological object (cell) from different aspects (omics) to provide a more comprehensive insight into the cellular system. When dealing with such multi-omics data, the first step is to determine the correspondence among different modalities. In other words, we should match data from different spaces corresponding to the same object. This problem is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Secondly, matched single-cell multi-omics data are rare and hard to collect. Furthermore, due to the limitations of the experimental environment, the data are usually highly noisy. To promote the single-cell multi-omics research, we overcome the above challenges, proposing a novel framework to align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward. Compared with the other state-of-the-art methods, our method performs better in both simulated and real single-cell data. The proposed method is helpful for the single-cell multi-omics research. The improvement for integration on the simulated data is significant.