CytofRUV: Removing unwanted variation to integrate multiple CyTOF datasets. Please cite the appropriate article when you use results from the software in a publication. Such citations are the main means by which the authors receive professional credit for their work.
The CytofRUV software package itself can be cited as:
Trussart M, Teh CE, Tan T, Leong L, Gray DH, Speed TP. Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets. Elife. 2020 Sep 7;9:e59630. doi: 10.7554/eLife.59630. PMID: 32894218; PMCID: PMC7500954.
After installing the dependent libraries, CytofRUV can be installed by running the following lines
library(devtools)
install_github('mtrussart/CytofRUV')
CytofRUV is a computational algorithm which permits the integration of data across CyTOF batches. We provided a vignette Introduction_to_CytofRUV.Rmd that explains step by step how to load and normalise datasets and also how to visualise the diagnostic plots using the R-Shiny application before and after CytofRUV normalisation.
Users are required to provide: the path to the fcs files from all the samples in the study, a metadata file containing the details of each sample and their respective .fcs files, and a panel file containing the details of all proteins used in the study. We also provided in this vignette an example of a set of data that included all .fcs files, a metadata file and a panel file. The metadata file is an excel file with the following column names: “file_name”, “sample_id”, “condition”, “patient_id”, “batch”. The panel file is an excel file with the following column names: “fcs_colname”, “antigen”, “marker_class”. Please follow the instructions and refer to the following vignette to visualise and normalise your dataset:
Introduction_to_CytofRUV.Rmd
R-Shiny interface for the identification of batch effects before and after normalisation To examine the batch effects found when comparing CyTOF data from samples replicated across batches, we built an R-Shiny application that exhibits any batch effects present in samples replicated across batches using four different diagnostics plots on the data that has been arcsinh transformed with a cofactor of 5: Median Protein Expression, Protein Expression Distributions, Clustering Results and Cluster Proportions. Please follow the instructions in the vignette “Introduction_to_CytofRUV.Rmd” that explains step by step how to load the data before launching the command:
launch_Shiny()
Normalisation procedure The normalize_data function allow the user to adjust for batch effects with parameter settings for the CytofRUV algorithm, such as the replicated samples to use, the clusters to be nornmalised and the value of k. Please follow the instructions in the vignette “Introduction_to_CytofRUV.Rmd”” that explains step by step how to load the data before launching the command:
normalise_data()