The Swiss Portal for Immune Cell Analysis (SPICA) is a web resource dedicated to the exploration and analysis of single-cell RNA-seq data of immune cells. In contrast to other single-cell databases, SPICA hosts curated, cell type-specific reference atlases that describe immune cell states at high resolution, and published single-cell datasets analysed in the context of these atlases. Additionally, users can privately analyse their own data in the context of existing atlases and contribute to the SPICA database.The SPICA portal consists of three main components:
- a database of interactive, curated immune cell reference atlases
- a searchable database of pre-analysed datasets, allowing the comparison of immune cell states in different studies and conditions
- an interface for the projection of new data, enabling the analysis of user query datasets in the context of existing reference atlases.
1. Explore SPICA atlasesAccessing the Explore Atlases functionality in SPICA, the user can interrogate one of the existing reference atlases in the database. The left UMAP plot displays an overview of the curated cell subtype annotation of the selected atlas; two additional UMAP plots allow visualizing the expression pattern of two user-selected genes in the same reference space. To facilitate quantitative comparison of gene expression for the two selected genes in different cell subtypes, a series of split violin plots are also provided.
2. Browse pre-analysed datasetsSPICA hosts a collection of public scRNA-seq datasets of mouse and human immune cells from different tissues and diseases, pre-projected onto a relevant reference atlas. A search bar allows filtering the database selection by organism, by atlas type, or by a free text description. Each pre-analysed dataset (a “project”) can be investigated online or downloaded for downstream analysis.
3. Project new data into an atlasTo analyse your own data in the context of these reference atlases, try our web-app for dataset projection based on the ProjecTILs method [Andreatta et al. (2021) Nat Comms]. Given a single-cell expression matrix (see Input formats), the algorithm performs batch correction to align the query dataset with the reference atlas; then it embeds the query into the low-dimensionality representations of the query (PCA, UMAP). This process allows projecting a query dataset without altering the reference atlas, so that different samples (e.g. from multiple experimental conditions) can be directly compared in a fixed system of coordinates. More details about the method are available in the publication and the ProjecTILs GitHub repository.
For more details on SPICA functionalities, have a look at our list of Tutorials
SPICA was developed by M. Andreatta and S.J. Carmona at the Cancer Systems Immunology group (UNIL - SIB) in collaboration with F. David, C. Iseli and N. Guex at the Bioinformatics Competence Center at UNIL/EPFL
See our open-source code repository on GitHub: https://github.com/carmonalab
CitationIf you use SPICA, please consider citing:
Andreatta M, David FPA, Iseli C, Guex N, Carmona SJ. SPICA: Swiss portal for immune cell analysis. Nucleic Acids Res. 2022 Jan 7;50(D1):D1109-D1114. doi: 10.1093/nar/gkab1055. PMID: 34747477. link