GitHub repositories

CellphoneDB

CellPhoneDB is a publicly available repository of curated receptors, ligands and their interactions. Subunit architecture is included for both ligands and receptors, representing heteromeric complexes accurately. This is crucial, as cell-cell communication relies on multi-subunit protein complexes that go beyond the binary representation used in most databases and studies. CellPhoneDB integrates existing datasets that pertain to cellular communication and new manually reviewed information. CellPhoneDB utilises information from the following data bases: UniProt, Ensembl, PDB, the IMEx consortium, IUPHAR. CellPhoneDB can be used to search for a particular ligand/receptor or interrogate your own single-cell transcriptomics data.

CellPhoneDB: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes Efremova M, Vento-Tormo M, Teichmann S, Vento-Tormo R. Nat Protoc. 2020 Apr;15(4):1484-1506

Single-cell reconstruction of the early maternal-fetal interface in humans. Vento-Tormo R, Efremova M, et al., Nature. 2018 Nov;563(7731):347-353

3DComplex

Hierarchical classification of Protein Complexes

3D Complex is a hierarchical classification of protein complexes that describes similarities in structure, sequence, as well as topology of contacts of the constituent proteins. This is the first automatic method for generating non-redundant sets of complexes, which can be used to derive unbiased statistics on their structure and evolution.

Levy ED, Pereira-Leal JB, Chothia C, Teichmann SA, 3D complex: a structural classification of protein complexes. PLoS Comput Biol. 2006 Nov 17;2(11):e155. Epub 2006 Oct 5

Th-Express

T-Helper Cell Expression Atlas

ThExpress is an integrated expression atlas of single cell RNA-seq T-helper cell types in the mouse immune system.

Stubbington MJT et al.. An Atlas of Mouse CD4+ T cell Transcriptomes. Biology Direct (2015) 10:14

ESpresso

Embryonic stem cell gene expression database

ESpresso is a database for querying gene expression in mouse embryonic stem cells.

Single-Cell RNA-sequencing of Pluripotent States Unlocks Modular Transcriptional Variation Kolodziejczyk A.A.1,2*, Kim, J.K.2, Tsang, J2, Illicic, T. 1,2, Henriksson, J.1, Natarajan K. N.1,2, Tuck A.C.1,2, Gao X2, Bühler M3, Liu, P.2, Marioni, J.C.1,2+ and Teichmann, S.A.1,2+ Cell Stem Cell 2015

Further, the database compares our data with data from:

 Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells (2014)Deng Q, Ramsköld D, Reinius B, Sandberg R, Science

TraCeR

Reconstruction of TCR sequences from single-cell RNA-seq data

TraCeR processes single-cell RNA-seq data from T lymphocytes and reconstructs full-length, paired T cell receptor sequences. It can then generate network graphs indicating sharing of sequences between cells.

Michael JT Stubbington, Tapio Lönnberg, Valentina Proserpio, Simon Clare, Anneliese O Speak, Gordon Dougan, Sarah A Teichmann. (2016) T cell fate and clonality inference from single-cell transcriptomes. Nature Methods, 13 329-332

Celloline and Cellity

Single Cell RNA Quality Control and Pipeline

Celloline is a pipeline that handles parallel mapping and quantification of single cell RNA-seq, using standard mapping tools and quantifiers.

The cellity package contains functions to help to identify low quality cells in scRNA-seq data. It extracts biological and technical features from gene expression data that help to detect low quality cells.

Ilicic, T., Kim, J.K., Kolodziejczyk, A.A., Bagger, F.O., McCarthy, D.J., Marioni, J.C., Teichmann, S.A. (2016) Classification of low quality cells from single-cell RNA-seq data. Genome Biology, 17 (1)