ASURAT (functional annotation-driven unsupervised clustering of single-cell transcriptomes) is a computational tool, implemented in R programming language, for single-cell transcriptomics. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity.
Below is a vignette reviewed by Bioconductor reviewers:
Below are documents for analyzing several single-cell and spatial transcriptome datasets (see here for the details):
- PBMC 4k from healthy donors (10x Genomics)
- PBMC 6k from healthy donors (10x Genomics)
- PBMCs from control and sepsis donors (Reyes et al., 2020)
- Small cell lung cancer (Stewart et al., 2020)
- Pancreatid ductal adenocarcinoma (Moncada et al., 2020)
Below are supporting information:
- Computations for PBMCs datasets (parameter search, benchmark tests, etc.)
- Miscellaneous (separation index, correlation analysis, etc.)
Below is a document for collecting databases for functional annotation of genes (see here for the details):
ASURAT was released on Bioconductor 3.1.5.
One can install ASURAT (Bioconductor version) by the following code:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ASURAT")To install a developmental version, execute the following code:
devtools::install_github("keita-iida/ASURAT", upgrade = "never")5 August, 2022: Our research article (v4) was published in Bioinformatics. Link
10 June, 2021: Our research article (v1) was appeared from bioRxiv. Link
K. Iida, J. Kondo, J. N. Wibisana, M. Inoue, M. Okada, ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes, Bioinformatics 38(18), 4330-4336 (2022). Link