PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data

Abstract

Single-cell RNA sequencing is an increasingly used method to measure gene expression at the single cell level and build cell-type atlases of tissues. Hundreds of single-cell sequencing datasets have already been published. However, studies are frequently deposited as raw data, a format difficult to access for biological researchers due to the need for data processing using complex computational pipelines. We have implemented an online database, PanglaoDB, accessible through a user-friendly interface that can be used to explore published mouse and human single cell RNA sequencing studies. PanglaoDB contains pre-processed and pre-computed analyses from more than 1054 single-cell experiments covering most major single cell platforms and protocols, based on more than 4 million cells from a wide range of tissues and organs. The online interface allows users to query and explore cell types, genetic pathways and regulatory networks. In addition, we have established a community-curated cell-type marker compendium, containing more than 6000 gene-cell-type associations, as a resource for automatic annotation of cell types.

A curated database reveals trends in single-cell transcriptomics

“…The conquer database provides uniformly processed single-cell expression data to facilitate benchmarking of computational tools (Soneson and Robinson 2018). The PanglaoDB database provides single-cell RNA-seq count matrices from public sequencing data in the National Center for Biotechnology Information Sequence Read Archive (Franzén, Gan, and Björkegren 2019). The EMBL-EBI Single Cell Expression Atlas provides uniformly processed data from submissions to ArrayExpress.…”

Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

“…In case of CellMarker database, 13 605 cell markers for 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers for 389 cell types in 81 mouse tissues/sub-tissues were manually collected and curated from more than 100 000 published papers 6 . In the PanglaoDB, 6631 gene markers mapping to 155 cell types have been identified by differential expression analysis in particular cell types using single cell data and a community-based crowdsourcing approach for curation of gene expression markers 7 . Therefore, we firstly converted the non-uniform gene IDs to approved gene symbols within and between the databases.…”

“…In the next step, the most differentially expressed genes between a selected cluster and all the other detected clusters are identified as marker genes. These marker genes are then manually inspected using available information in the literature or cell marker databases 6,7 to assign cell-type labels to each detected cluster. However, the manual selection of cluster-specific marker genes is a time-consuming and error-prone task, since (i) differentially-expressed genes are often expressed in multiple clusters, and (ii) the identified genes may be known markers for multiple cell-types.…”

Fast searches of large collections of single cell data using scfind

“…However, none of the computational tools available today for collections of scRNA-seq datasets, e.g. Panglao (Franzén et al, 2019) , the UCSC Cell Browser (Haeussler et al, 2019) , scRNASeqDB (Cao et al, 2017) , SCPortalen (Abugessaisa et al, 2018) , and the EBI Single Cell Expression Atlas (Athar et al, 2019) , provide the required functionality and versatility.Here, we present scfind, a search engine that makes single cell data accessible to a wide range of users by enabling sophisticated queries for large datasets through an interface which is both very fast and familiar to users from any background. The central operation carried out by scfind is to identify the set of cells that express a set of genes or peaks (i.e.…”

“…However, none of the computational tools available today for collections of scRNA-seq datasets, e.g. Panglao (Franzén et al, 2019) , the UCSC Cell Browser (Haeussler et al, 2019) , scRNASeqDB (Cao et al, 2017) , SCPortalen (Abugessaisa et al, 2018) , and the EBI Single Cell Expression Atlas (Athar et al, 2019) , provide the required functionality and versatility.…”