Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

vizAPA: visualizing dynamics of alternative polyadenylation from bulk and single-cell data

MOTIVATION

Alternative polyadenylation (APA) is a widespread post-transcriptional regulatory mechanism across all eukaryotes. With the accumulation of genome-wide APA sites, especially those with single-cell resolution, it is imperative to develop easy-to-use visualization tools to guide APA analysis.

RESULTS

We developed an R package called vizAPA for visualizing APA dynamics from bulk and single-cell data. vizAPA implements unified data structures for APA data and genome annotations. vizAPA also enables identification of genes with differential APA usage across biological samples and/or cell types. vizAPA provides four unique modules for extensively visualizing APA dynamics across biological samples and at the single-cell level. vizAPA could serve as a plugin in many routine APA analysis pipelines to augment studies for APA dynamics.

AVAILABILITY AND IMPLEMENTATION

https://github.com/BMILAB/vizAPA.

SingleScan: a comprehensive resource for single-cell sequencing data processing and mining

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .

RNA-Scoop: interactive visualization of transcripts in single-cell transcriptomes

Recent advances in single-cell RNA sequencing technologies have made detection of transcripts in single cells possible. The level of resolution provided by these technologies can be used to study changes in transcript usage across cell populations and help investigate new biology. Here, we introduce RNA-Scoop, an interactive cell cluster and transcriptome visualization tool to analyze transcript usage across cell categories and clusters. The tool allows users to examine differential transcript expression across clusters and investigate how usage of specific transcript expression mechanisms varies across cell groups.

Single-cell differential splicing analysis reveals high heterogeneity of liver tumor-infiltrating T cells

Recent advances in single-cell RNA sequencing (scRNA-seq) have improved our understanding of the association between tumor-infiltrating lymphocyte (TILs) heterogeneity and cancer initiation and progression. However, studies investigating alternative splicing (AS) as an important regulatory factor of heterogeneity remain limited. Here, we developed a new computational tool, DESJ-detection, which accurately detects differentially expressed splicing junctions (DESJs) between cell groups at the single-cell level. We analyzed 5063 T cells of hepatocellular carcinoma (HCC) and identified 1176 DESJs across 11 T cell subtypes. Interestingly, DESJs were enriched in UTRs, and have putative effects on heterogeneity. Cell subtypes with a similar function closely clustered together at the AS level. Meanwhile, we identified a novel cell state, pre-activation with the isoform markers ARHGAP15-205. In summary, we present a comprehensive investigation of alternative splicing differences, which provided novel insights into T cell heterogeneity and can be applied to other full-length scRNA-seq datasets.

An NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data

Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. Our algorithm decomposes mapped count data matrix for a gene region using non-negative matrix factorization, quantifies the differential expression level based on the decomposed matrix, and compares the differential expression level based on annotation-based approach to discover previously unannotated DE transcripts. We performed single-cell RNA sequencing for human neural stem cells and applied our algorithm to the dataset. We also applied our algorithm to two public single-cell RNA sequencing datasets correspond to mouse ES and primitive endoderm cells, and human preimplantation embryos. As a result, we discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation.