standR-package: Tools for analyzing NanoString's GeoMX spatial...
In DavisLaboratory/standR: Spatial transcriptome analyses of Nanostring's DSP data in R
standR-package R Documentation
Tools for analyzing NanoString's GeoMX spatial transcriptomics data
Description
standR implements a series of functions to facilitate inspection,
analysis and visualization of the NanoString's GeoMX DSP datasets. standR
takes the either the csv files from the Nanostring or DGEList object as
input, allowing for multiple methods to be analyzed together.
Details
standR represents the GeoMX DSP data as SpatialExperiment objects, which can
easily be integrated with a wide variety of Bioconductor packages.standR
generates various plots, such as QC distribution plots, dimension reduction
plots and RLE plots, for quality control of genes and region of interest
(ROI) samples. Multiple normalization and batch correction methods are also
provided in the package as well, with the ability to compute statistics for
assessing the normalization/batch correction results.
Author(s)
Ning Liu liu.n@wehi.edu.au
DavisLaboratory/standR documentation built on March 28, 2024, 4:23 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| standR-package | R Documentation |
Tools for analyzing NanoString's GeoMX spatial transcriptomics data
Description
standR implements a series of functions to facilitate inspection,
analysis and visualization of the NanoString's GeoMX DSP datasets. standR
takes the either the csv files from the Nanostring or DGEList object as
input, allowing for multiple methods to be analyzed together.
Details
standR represents the GeoMX DSP data as SpatialExperiment objects, which can
easily be integrated with a wide variety of Bioconductor packages.standR
generates various plots, such as QC distribution plots, dimension reduction
plots and RLE plots, for quality control of genes and region of interest
(ROI) samples. Multiple normalization and batch correction methods are also
provided in the package as well, with the ability to compute statistics for
assessing the normalization/batch correction results.
Author(s)
Ning Liu liu.n@wehi.edu.au
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.