scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing.

BACKGROUND: Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS: Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS: We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework.

We present RNASequest, a customizable RNA sequencing (RNAseq) analysis, app management, and result publishing framework. Its three-in-one RNAseq data analysis ecosystem consists of (1) a reproducible, configurable expression analysis (EA) module, (2) multi-faceted result presentation in R Shiny, a Bookdown document and an online slide deck, and (3) a centralized data management system. In principle, following up our well-received omics data visualization tool Quickomics, RNASequest automates the differential gene expression analysis step, eases statistical model design by built-in covariates testing module, and further provides a web-based tool, ShinyOne, to manage apps powered by Quickomics and reports generated by running the pipeline on multiple projects in one place. Researchers can experience the functionalities by exploring demo data sets hosted at http://shinyone.bxgenomics.com or following the tutorial, https://interactivereport.github.io/RNASequest/tutorial/docs/introduction.html to set up the framework locally to process private RNAseq datasets. The source code released under MIT open-source license is provided at https://github.com/interactivereport/RNASequest.

OmicsView: Omics data analysis through interactive visual analytics.

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000 s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohn's disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

CellDepot: A Unified Repository for scRNA-seq Data and Visual Exploration.

CellDepot containing over 270 datasets from 8 species and many tissues serves as an integrated web application to empower scientists in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical capabilities. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the graphical multi-logic, multi-condition query builder and convenient filtering tool backed by MySQL database system, allows users to quickly find the datasets of interest and compare the expression of gene(s) across these. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types by leveraging more than 20 frequently applied plotting functions and high-level analysis methods in single cell research. In summary, the web portal available at http://celldepot.bxgenomics.com, prompts large scale single cell data sharing, facilitates meta-analysis and visualization, and encourages scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software under MIT license to motivate crowd contribution, broad adoption, and local deployment for private datasets.

Quickomics: exploring omics data in an intuitive, interactive and informative manner.

SUMMARY: We developed Quickomics, a feature-rich R Shiny-powered tool to enable biologists to fully explore complex omics statistical analysis results and perform advanced analysis in an easy-to-use interactive interface. It covers a broad range of secondary and tertiary analytical tasks after primary analysis of omics data is completed. Each functional module is equipped with customizable options and generates both interactive and publication-ready plots to uncover biological insights from data. The modular design makes the tool extensible with ease. AVAILABILITY AND IMPLEMENTATION: Researchers can experience the functionalities with their own data or demo RNA-Seq and proteomics datasets by using the app hosted at http://quickomics.bxgenomics.com and following the tutorial, https://bit.ly/3rXIyhL. The source code under GPLv3 license is provided at https://github.com/interactivereport/Quickomics for local installation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An extended regulatory landscape drives Tbx18 activity in a variety of prostate-associated cell lineages.

The evolutionarily conserved transcription factor, Tbx18, is expressed in a dynamic pattern throughout embryonic and early postnatal life and plays crucial roles in the development of multiple organ systems. Previous studies have indicated that this dynamic function is controlled by an expansive regulatory structure, extending far upstream and downstream of the gene. With the goal of identifying elements that interact with the Tbx18 promoter in developing prostate, we coupled chromatin conformation capture (4C) and ATAC-seq from embryonic day 18.5 (E18.5) mouse urogenital sinus (UGS), where Tbx18 is highly expressed. The data revealed dozens of active chromatin elements distributed throughout a 1.5 million base pair topologically associating domain (TAD). To identify cell types contributing to this chromatin signal, we used lineage tracing methods with a Tbx18 Cre "knock-in" allele; these data show clearly that Tbx18-expressing precursors differentiate into wide array of cell types in multiple tissue compartments, most of which have not been previously reported. We also used a 209 kb Cre-expressing Tbx18 transgene, to partition enhancers for specific precursor types into two rough spatial domains. Within this central 209 kb compartment, we identified ECR1, previously described to regulate Tbx18 expression in ureter, as an active regulator of UGS expression. Together these data define the diverse fates of Tbx18+ precursors in prostate-associated tissues for the first time, and identify a highly active TAD controlling the gene's essential function in this tissue.

Tbx18 Regulates the Differentiation of Periductal Smooth Muscle Stroma and the Maintenance of Epithelial Integrity in the Prostate.

The T-box transcription factor TBX18 is essential to mesenchymal cell differentiation in several tissues and Tbx18 loss-of-function results in dramatic organ malformations and perinatal lethality. Here we demonstrate for the first time that Tbx18 is required for the normal development of periductal smooth muscle stromal cells in prostate, particularly in the anterior lobe, with a clear impact on prostate health in adult mice. Prostate abnormalities are only subtly apparent in Tbx18 mutants at birth; to examine postnatal prostate development we utilized a relatively long-lived hypomorphic mutant and a novel conditional Tbx18 allele. Similar to the ureter, cells that fail to express Tbx18 do not condense normally into smooth muscle cells of the periductal prostatic stroma. However, in contrast to ureter, the periductal stromal cells in mutant prostate assume a hypertrophic, myofibroblastic state and the adjacent epithelium becomes grossly disorganized. To identify molecular events preceding the onset of this pathology, we compared gene expression in the urogenital sinus (UGS), from which the prostate develops, in Tbx18-null and wild type littermates at two embryonic stages. Genes that regulate cell proliferation, smooth muscle differentiation, prostate epithelium development, and inflammatory response were significantly dysregulated in the mutant urogenital sinus around the time that Tbx18 is first expressed in the wild type UGS, suggesting a direct role in regulating those genes. Together, these results argue that Tbx18 is essential to the differentiation and maintenance of the prostate periurethral mesenchyme and that it indirectly regulates epithelial differentiation through control of stromal-epithelial signaling.