Identify, quantify and characterize cellular communication from single cell RNA sequencing data with scSeqComm
- 1. Department of Information Engineering, University of Padova
- 2. Department of Information Engineering, University of Padova; Department of Comparative Biomedicine and Food Science, University of Padova; CRIBI Innovative Biotechnology Center, University of Padova
Description
This page contains the software and test data used in "Identify, quantify and characterize cellular communication from single cell RNA sequencing data with scSeqComm" [Giacomo Baruzzo, Giulia Cesaro, Barbara Di Camillo; Bioinformatics, 2022]
Software
Folder Software contains the code of R package scSeqComm (file scseqcomm.zip). The R package contains also 21 ligand-receptor pairs databases (including the ones used in the manuscripts) and 9 transcriptional regulatory network databases (including the ones used in the manuscript). scSeqComm R package is freely available at https://gitlab.com/sysbiobig/scseqcomm and https://sysbiobig.dei.unipd.it/software/#scSeqComm.
Test data
Folder Test data contains the datasets used in the manuscript.
- Tirosh.rda: scRNA-seq dataset from Tirosh et al. 2016 (DOI: 10.1126/science.aad0501). Data were retrieved by GEO database (GSE72056). The rda file (R data file) contains the normalized count matrix (after filtering out genes that do not achieve a TPM > 1 in at least 25% of cells in any of the seven cells clusters) and cell cluster annotation.
- Eng.rda: SeqFISH+ spatial transcriptomics datasets from Eng et al. 2019 (DOI: 10.1038/s41586-019-1049-y). Data were retrieved by authors’ repository at https://github.com/CaiGroup/seqFISH-PLUS. The rda file (R data file) contains the gene expression matrices and cells spatial information.
Additional references:
- Eng, C.H.L. et al. (2019) Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nat., 568, 235–239. https://doi.org/10.1038/s41586-019-1049-y
- Tirosh, I. et al. (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science (80-. )., 352, 189–196. https://doi.org/10.1126/science.aad0501
Files
scSeqComm_software_and_data.zip
Files
(134.5 MB)
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Additional details
Related works
- Cites
- Journal article: 10.1126/science.aad0501 (DOI)
- Journal article: 10.1038/s41586-019-1049-y (DOI)
References
- Eng, C.H.L. et al. (2019) Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nat. 2019, 568, 235–239.
- Tirosh, I. et al. (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science (80-. )., 352, 189–196.