DenoIST
DenoIST: Denoising Image-based Spatial Transcriptomics data
Bioconductor version: 3.24 · Package version: 1.1.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
DenoIST identifies and removes contamination in Image-based Spatial Transcriptomics data, using a transposed poisson mixture model with local neighbourhood offsets to infer genes that are likely to be due to neighbourhood contamination rather than endogenous expression.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DenoIST")Details
| Maintainer | Aaron Kwok <akwok@svi.edu.au> |
| Author | Aaron Kwok [aut, cre] (ORCID: <https://orcid.org/0000-0001-7831-4198>), Heejung Shim [aut], Davis McCarthy [aut] |
| License | MIT + file LICENSE |
| URL | https://github.com/aaronkwc/DenoIST |
| Bug Reports | https://github.com/aaronkwc/DenoIST/issues |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | GeneExpression, Preprocessing, SingleCell, Software, Spatial, Transcriptomics |
| Package Short Url | https://bioconductor.org/packages/DenoIST/ |
Citation
From within R, enter citation("DenoIST"):
Aaron Kwok, Heejung Shim, Davis McCarthy. DenoIST: DenoIST: Denoising Image-based Spatial Transcriptomics data. doi:10.18129/B9.bioc.DenoIST, R package version 1.1.0, https://bioconductor.org/packages/DenoIST.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | DenoIST_1.1.0.tar.gz |
| Windows binary (x86_64) | DenoIST_1.1.0.zip |
| macOS binary (arm64) | DenoIST_1.1.0.tgz |
| macOS binary (x86_64) | DenoIST_1.1.0.tgz |
Dependencies
Depends: R (>= 3.5.0)
Imports: flexmix, hexbin, pbapply, sparseMatrixStats, SpatialExperiment, stats, SummarizedExperiment, parallel, Matrix, dbscan, methods
Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, patchwork