README.md

In okadalabipr/ASURAT: Functional annotation-driven unsupervised clustering for single-cell data

ASURAT

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.

Vignettes

Below is a vignette reviewed by Bioconductor reviewers:

• https://keita-iida.github.io/ASURAT/

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):

• https://keita-iida.github.io/ASURATDB/

Change Log

v1.0.0

ASURAT was released on Bioconductor 3.1.5. * https://bioconductor.org/packages/devel/bioc/html/ASURAT.html

Installation

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")

News

5 August, 2022: Our research article (v4) was published in Bioinformatics. Link

10 June, 2021: Our research article (v1) was appeared from bioRxiv. Link

License

GPL-3

Citation

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

okadalabipr/ASURAT documentation built on Nov. 25, 2022, 5:40 p.m.