Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data

Abstract

Background: Single cell transcriptome sequencing has become an increasingly valuable technology for dissecting complex biology at a resolution impossible with bulk sequencing. However, the gap between the technical expertise required to effectively work with the resultant high dimensional data and the biological expertise required to interpret the results in their biological context remains incompletely addressed by the currently available tools.

Results: Single Cell Explorer is a Python-based web server application we developed to enable computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform. These annotations can be modified and shared by multiple users to allow easy collaboration between computational scientists and experimental biologists. Data processing and analytic workflows can be integrated into the system using Jupyter notebooks. The application enables powerful yet accessible features such as the identification of differential gene expression patterns for user-defined cell populations and convenient annotation of cell types using marker genes or differential gene expression patterns. Users are able to produce plots without needing Python or R coding skills. As such, by making single cell RNA-seq data sharing and querying more user-friendly, the software promotes deeper understanding and innovation by research teams applying single cell transcriptomic approaches.

Conclusions: Single cell explorer is a freely-available single cell transcriptomic analysis tool that enables computational and experimental biologists to collaboratively explore, annotate, and share results in a flexible software environment and a centralized database server that supports data portal functionality.

Keywords: D3; Django; Pipeline; Python; RNA-seq; Single cell; Transcriptomics; Visualization.

Fig. 1

Single Cell Explorer workflow architecture process and component view. a Overview of the data process workflow steps for Single Cell Explorer. Step #1: Run pipeline to process FASTQ files using Python wrapper through Jupyter Notebook. Step #2: Quality control of data, generation of 2d representation, and database upload. Step #3: Interactive data analyses and annotation of cell types. Step #4: Recording of annotated results in MongoDB for sharing with all users. Step #5: All results from MongoDB can be accessed directly or via API. b A screenshot for Single Cell Explorer data navigator page and a t-SNE map for one dataset

Fig. 2

Interactive FeaturePlot. a A t-SNE and UMAP representation from first-trimester placentas with matched maternal blood and decidual cells. Individual pre-labeled cell types are painted in different colors. The function of painting two genes (CD8A and CD3D) highlights the location of CD8 T cell clusters. A 2D plot of circles indicates the proportion of the single positive and double positive cells. b To query a list of genes, a heatmap can be generated after freehand selection of cells of interest. ILC3 cells can be identified using markers including KIT and DLL1

Fig. 3

Understanding single cell clustering results. a UMAP of human normal PBMC with various clustering results using different resolution parameters by the leiden algorithm (scanpy.api.tl.leiden function). b Feature plot of cells which are positive for each individual marker gene. c A heatmap of marker gene expression within each cluster defined by leiden algorithm

Fig. 4

Cell type and feature discovery. Step #1: Load 2D embedding map. Step #2 Use a freehand tool to select the cells of interest. Step #3: Compare the differentially expressed genes of selected cells with all unselected cells. Step #4: Interactively visualize gene expression levels using the resulting table. Step #5: Record cell types and marker genes for future reference. Step #6: Position the newly-labelled cells on the map and compare with other specific cell types