1/6/2024 0 Comments Instaling SpectreCytoNorm, FlowSOM, tSNE, UMAP, etc) that are critical elements of your analysis work. Please also consider citing the authors of the individual packages or tools (e.g. To continue providing open-source tools such as Spectre, it helps us if we can demonstrate that our efforts are contributing to analysis efforts in the community. If you use Spectre in your work, please consider citing Ashhurst TM, Marsh-Wakefield F, Putri GH et al. Spectre was developed by Thomas Ashhurst, Felix Marsh-Wakefield, and Givanna Putri. For more information, please see our paper: Ashhurst TM, Marsh-Wakefield F, Putri GH et al. Recently we have extended the functionality of Spectre to support the analysis of Imaging Mass Cytometry (IMC) and scRNAseq data. Critically, the design of Spectre allows for a simple, clear, and modular design of analysis workflows, that can be utilised by data and laboratory scientists. To manage large cytometry datasets, Spectre was built on the data.table framework – this simple table-like structure allows for fast and easy processing of large datasets in R. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualisation and population labelling, as well as quantitative and statistical analysis. Spectre is an R package that enables comprehensive end-to-end integration and analysis of high-dimensional cytometry data from different batches or experiments. A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |