searchCluster.batch {MetaCyto} | R Documentation |
A function that searches for clusters using pre-defined labels in cytometry data from different studies in batch.
searchCluster.batch(preprocessOutputFolder, outpath = "search_output", clusterLabel, ifPlot = TRUE)
preprocessOutputFolder |
Directory where the pre-processed results are stored. |
outpath |
A string indicating the directory the results should be written to. |
clusterLabel |
A vector containing labels, such as c("CD3+|CD4+|CD8-") |
ifPlot |
True or False. Used to specify if a the density plot for each cluster should be plotted |
The function writes out the summary statistics for each cluster. A separate directory will be created for each study.
Results will be written to the outpath folder
#get meta-data fn=system.file("extdata","fcs_info.csv",package="MetaCyto") fcs_info=read.csv(fn,stringsAsFactors=FALSE,check.names=FALSE) fcs_info$fcs_files=system.file("extdata",fcs_info$fcs_files, package="MetaCyto") # Make sure the transformation parameter "b" and the "assay" argument # are correct of FCM and CyTOF files b=assay=rep(NA,nrow(fcs_info)) b[grepl("CyTOF",fcs_info$study_id)]=1/8 b[grepl("FCM",fcs_info$study_id)]=1/150 assay[grepl("CyTOF",fcs_info$study_id)]="CyTOF" assay[grepl("FCM",fcs_info$study_id)]="FCM" # preprocessing preprocessing.batch(inputMeta=fcs_info, assay=assay, b=b, outpath="Example_Result/preprocess_output", excludeTransformParameters=c("FSC-A","FSC-W","FSC-H", "Time","Cell_length")) # Make sure marker names are consistant in different studies files=list.files("Example_Result",pattern="processed_sample",recursive=TRUE, full.names=TRUE) nameUpdator("CD8B","CD8",files) # find the clusters cluster_label=c("CD3-|CD19+","CD3+|CD4-|CD8+") searchCluster.batch(preprocessOutputFolder="Example_Result/preprocess_output", outpath="Example_Result/search_output", clusterLabel=cluster_label)