SingleCellMultiModal: Integrating multi-modal single cell experiment datasets
Year: 2020
Type: other
Abstract: Developments in sequencing and computational technology have created a surge of single cell multimodal experimental methods which assay complementary molecular data on the same individual cells. However, analysis of these data is hindered by complex and non-standard data representation, manipulation, and analysis. We present a new Bioconductor package, SingleCellMultiModal, that provides ready-to-use datasets from mouse and human samples for benchmarking and methods development from single-cell multimodal technologies including CITE-seq, scNMT, and seqFISH. SingleCellMultiModal provides pre-processed datasets using the integrative MultiAssayExperiment Bioconductor class to coordinate management of multiple experimental assays performed on an overlapping set of cells and samples. The package is intended to facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of tracking molecular layers to phenotypic outputs including cell differentiation, activity, and disease.
Institution: City University of New York
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W3134798642