CytoML
Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms.
README
CytoML: Cross-Platform Cytometry Data Sharing. This package is designed to import/export the hierarchical gated cytometry data to and from R (specifically the openCyto framework) using the gatingML2.0 and FCS3.0 cytometry data standards. This package makes use of the GatingSet R object and data model so that imported data can easily be manipulated and visualized in R using tools like openCyto and ggCyto. What problems does CytoML solve? CytoML allows you to: Import manually gated data into R…
- Repository
- github.com/rglab/cytoml
Source attribution
- GitHub — github.com/rglab/cytoml
- Bioconductor — CytoML
Related resources
This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data.
This package provides the core data structure and API to represent and interact with the gated cytometry data.
This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy.
This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis.
A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed).
Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment.