Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.

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Background: This article offers an alternative look at the experiential character of the built environment by combining objective analysis and subjective perception. The aim is to measure and elaborate on quantitative descriptions of 'hidden' urban characteristics, attempting to build correlations between different unseen but detectable qualities of cities. Methods: The study introduces an applied research method to quantify objective features of the built environment and the related subjective experience, prototyping a mobile phone application that both actively and passively measures urban parameters and human perceptions. To test the validity of the research process, a few experiments were performed in Cambridge, MA mapping out a series of different places. Results: The implementation of the application data in conjunction with the more passive, objective dataset extracted from complementary sensors, resulted in an alternative understanding of everyday spatial interactions and in a taxonomy of urban conditions-revealing the 'mood' of urban environments. Conclusions: The combination of objective and subjective datasets can help reveal more comprehensive insights and characters of spaces and places within the city, mediating between technology and the built environment and leveraging emotive perceptions of the urban actors in order to influence and inform design decisions.

The increasing popularity of spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue sample's spatial context. Various methods have been developed for detecting SV (spatially variable) genes, with distinct spatial expression patterns. However, the accuracy of using such SV genes in clustering cell types has not been thoroughly studied. On the other hand, in single cell resolution sequencing data, clustering analysis is usually done on highly variable (HV) genes. Here we investigate if integrating SV genes and HV genes from spatial transcriptomics data can improve clustering performance beyond using SV genes alone. We evaluated six methods that integrate different features measured from the same samples including MOFA+, scVI, Seurat v4 , CIMLR, SNF, and the straightforward concatenation approach. We applied these methods on 19 real datasets from three different spatial transcriptomics technologies (merFISH, SeqFISH+, and Visium) as well as 20 simulated datasets of varying spatial expression conditions. Our evaluations show that the performances of these integration methods are largely dependent on spatial transcriptomics platforms. Despite the variations among the results, in general MOFA+ and simple concatenation have good performances across different types of spatial transcriptomics platforms. This work shows that integrating quantitative and spatial marker genes in the spatial transcriptomics data can improve clustering. It also provides practical guides on the choices of computational methods to accomplish this goal.

We present GranatumX, the next-generation software environment for single-cell data analysis. It enables biologists access to the latest single-cell bioinformatics methods in a graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named "Gboxes", that wrap around bioinformatics tools written in various programming languages. GranatumX can be run in the cloud or private servers, and generate reproducible results. It is expected to become a community-engaging, flexible, and evolving software ecosystem for scRNA-Seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at: MainSingle-cell RNA sequencing (scRNA-Seq) technologies have advanced our understanding of cell-level biology significantly 1 . Many exciting scientific discoveries are attributed to new experimental technologies and sophisticated computational methods 2,3 . Despite the progress on both sides, it has become obvious that an increasingly larger gap exists between the wet-lab biology and the bioinformatics community. Although some analytical packages such as SINCERA 4 , Seurat 5 , and Scanpy 6 provide complete scRNA-Seq pipelines, they require users to be familiar with their corresponding programming language (typically R or Python) and/or command line interface, hindering a wide adoption experimental biologists. A few platforms, such as ASAP 7 and our own tool Granatum 8 , provide an intuitive graphical user interface. However, these platforms are not modularized and lack the flexibility to incorporate a continuously growing list of new computational tools. Furthermore, these tools have limited scalability and cannot handle extremely large datasets. Here we present GranatumX, the new generation of scRNA-Seq analysis platform that aims to solve these issues systematically. Its architecture facilitates the rapid incorporation of cutting-edge tools and enables the handling of large datasets very efficiently.The objective of GranatumX is to provide scRNA-Seq biologists better access to bioinformatics tools and ability to conduct single cell data analysis independently ( Figure 1). Currently other single-cell RNA-Seq platforms usually only provide a fixed set of methods implemented by the authors themselves. Adding new methods developed by the community is difficult, due to programming language lock-in as well as monolithic code architectures. As a solution, GranatumX uses the plugin framework that provides an easy and unified approach to add new methods. The plugin system is developer code/scripting language agnostic. It also eliminates inter-module incompatibilities, by isolating the dependencies of each module ( Figure 2A). As a data portal, GranatumX provides a graphical user interface (GUI) that requires no programming experience. Its web-based GUI can be accessed on various devices including desktop, tablets, and smartphones ( Figure 2A). In addition ...