Circular RNAs (circRNAs) derived from back-spliced exons have been widely identified as being co-expressed with their linear counterparts. A single gene locus can produce multiple circRNAs through alternative back-splice site selection and/or alternative splice site selection; however, a detailed map of alternative back-splicing/splicing in circRNAs is lacking. Here, with the upgraded CIRCexplorer2 pipeline, we systematically annotated different types of alternative back-splicing and alternative splicing events in circRNAs from various cell lines. Compared with their linear cognate RNAs, circRNAs exhibited distinct patterns of alternative back-splicing and alternative splicing. Alternative back-splice site selection was correlated with the competition of putative RNA pairs across introns that bracket alternative back-splice sites. In addition, all four basic types of alternative splicing that have been identified in the (linear) mRNA process were found within circRNAs, and many exons were predominantly spliced in circRNAs. Unexpectedly, thousands of previously unannotated exons were detected in circRNAs from the examined cell lines. Although these novel exons had similar splice site strength, they were much less conserved than known exons in sequences. Finally, both alternative back-splicing and circRNA-predominant alternative splicing were highly diverse among the examined cell lines. All of the identified alternative back-splicing and alternative splicing in circRNAs are available in the CIRCpedia database (http://www.picb.ac.cn/rnomics/ circpedia). Collectively, the annotation of alternative back-splicing and alternative splicing in circRNAs provides a valuable resource for depicting the complexity of circRNA biogenesis and for studying the potential functions of circRNAs in different cells.

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.

The rapid development of novel spatial transcriptomics technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational algorithms and pipelines. Here we present Giotto, a comprehensive, flexible, robust, and open-source pipeline for spatial transcriptomic data analysis and visualization. The data analysis module implements a wide range of algorithms ranging from basic tasks such as data pre-processing to innovative approaches for cell-cell interaction characterization. The data visualization module provides a user-friendly workspace that allows users to interactively visualize, explore and compare multiple layers of information. These two modules can be used iteratively for refined analysis and hypothesis development. We illustrate the functionalities of Giotto by using the recently published seqFISH+ dataset for mouse brain. Our analysis highlights the utility of Giotto for characterizing tissue spatial organization as well as for the interactive exploration of multi-layer information in spatial transcriptomic and imaging data. We find that single-cell resolution spatial information is essential for the investigation of ligandreceptor mediated cell-cell interactions. Giotto is generally applicable and can be easily integrated with external software packages for multi-omic data integration. Giotto facilitates the comprehensive analysis of single-cell spatial transcriptomic dataGiotto Analyzer is written in the popular language R. The core data structure is a simple and flexible S4 object ( Fig. 2A). Raw and processed count matrices are represented as a base matrix in R, while other annotations and metadata is encoded by an igraph network or a data.table. The former is a powerful library to work with networks, and the latter is a simple but intuitive table format with excellent performance for large-scale operations. In total, the Giotto uncovers different layers of spatial expression variabilityA key component of Giotto Analyzer is the implementation of a wide range of computational methods for spatial gene expression pattern identification. On a basic level, Giotto Analyzer can reduce the single-cell resolution data to a spatial grid through averaging (Supplementary Fig. 2A, B). Principal component analysis (PCA) is applied to the gridaverage data and significant principal components, along with their associated genes, are identified and reported. Using the aforementioned seqFISH+ dataset as an example, we found that the first principal component (PC) separates the outer layer extremities from the other layers. This is likely due to differences in cell-type compositions as most layers correlate with Slc17a7 expression, a marker for glutamatergic neurons, while the extremities show higher abundance of astrocytes and oligodendrocytes (Fig. 3A, top, Fig. 2D). In contrast, the second PC separates the outer and inner layers, which have similar cell-type composit...