CReSCENT: CanceR Single Cell ExpressioN Toolkit

Mohanraj, Suluxan; Díaz-Mejía, J. Javier; Pham, Martin D.; Elrick, Hillary; Husić, Mia; Rashid, Shaikh; Luo, Ping; Bal, Prabnur; Lu, Kevin; Patel, Samarth; Mahalanabis, Alaina; Naidas, Alaine; Christensen, Erik; Croucher, Danielle; Richards, Laura M.; Shooshtari, Parisa; Brudno, Michael; Pugh, Trevor J.

doi:10.1101/2020.03.27.012740

Cited by 1 publication

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several tools for scRNA-seq analysis are written in R and therefore a SingleCellExperiment object can easily be incorporated into these pipelines and tools. However, many other analysis tools are written in Python or as webapps (18, 40, 41). To facilitate the use of TMExplorer with these tools, we wrote a function saveTME that writes individual TME datasets to disk as CSV or Matrix Market files, depending on whether data was loaded as dense or sparse matrices by queryTME , respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Most existing scRNA-seq databases include a mixture of samples from normal tissues and tissues affected by cancer or other diseases (12–14), while others focus primarily on samples from normal tissues (16, 17). A recently published toolkit called CReSCENT (18) contains only cancer scRNA-seq data, however it mainly acts as a cancer data analysis pipeline rather than a database. A comprehensive database for the collection and sharing of TME scRNA-seq datasets from a range of tumour types does not yet exist, and researchers interested in using publicly available TME data must search through several databases to collect relevant datasets for their study.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TMExplorer: A Tumour Microenvironment Single-cell RNAseq Database and Search Tool

Christensen

Naidas

Husić

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Tumour microenvironments (TME) contain a variety of cells including but not limited to stromal fibroblasts, endothelial cells, immune cells, malignant cells, and cells of the tissues of origin, whose interactions likely influence tumour behaviour and response to cancer treatment. The specific composition of the TME can be elucidated using single-cell RNA sequencing (scRNA-seq) by measuring the expression profiles of individual cells. Several scRNA-seq datasets from multiple cancer types have been published in recent years, yet we still lack a comprehensive database for the collection and presentation of TME data from these studies in an easily accessible format. We have thus built a database of TME scRNA-seq data, containing 21 TME scRNA-seq datasets from 12 different cancer types. We have also created an R package called TMExplorer, which provides an interface to easily search and access all available datasets and their metadata. Data and metadata are kept in a consistent format across all datasets, with multiple expression formats available depending on the use case. Users can view a table of metadata and select individual datasets or filter them by specific characteristics. Users may also select a specific type of cancer and view all published scRNA-seq data for that cancer type available in our database. Users are provided with an option to save the data in multiple formats in order to view or process it outside of R. Thus, the TMExplorer database and search tool allow for thorough examination of the TME using scRNA-seq in a way that is streamlined and allows for easy integration into already existing scRNA-seq analysis pipelines.

show abstract

Section: Methodsmentioning

confidence: 99%