toprnk — toprnk • rCASC

This function execute toprnk analysis which search for correspondence between clusters of two different experiments using clusters-pseudobulks, zscored on rows, and the cluster specific genes from comet analysis. Thus, the function clustersBulk and cometsc have to be run in the two datasets before their comparison.

toprnk(
  group = c("sudo", "docker"),
  scratch.folder,
  fileX,
  fileY,
  separatorX,
  separatorY,
  xCometFolder,
  yCometFolder,
  top.ranked = 320,
  outputFolder
)

Arguments

group,: a character string. Two options: sudo or docker, depending to which group the user belongs
scratch.folder,: a character string indicating the path of the scratch folder
fileX,: a character string indicating the path of the pseudobulkRow file, with file name and extension included.
fileY,: a character string indicating the path of the pseudobulkRow file, with file name and extension included.
separatorX,: separator used in count file, e.g. '\t', ','
separatorY,: separator used in count file, e.g. '\t', ','
xCometFolder,: path of Comet results from X experiment
yCometFolder,: path of Comet results from Y experiment
top.ranked,: MAX number of top comet genes to be used for each cluster, default 320
outputFolder,: where results are placed

Value

A folder called XYpb with all the results generated. The function produces an integrated output combining 7 thresholds from top.ranked (top.ranked, top.ranked/2, top.ranked/4, top.ranked/8, top.ranked/16/ top.ranked/32, top.ranked/64) over Pearsons in steps of 01 from 1 to 0.5. XYpb_topZZ.csv can be used to generate HCL. XYpb_cor_0.K_toprnk_ZZ.csv contain the associated clusters given a Pearson correlation threshold of 0.K. The files XYpb_cor_0.K_toprnk_ZZ_CSS_0.H.csv, are the subset of clusters that have a specific cell stability score, we suggest to associate only clusters with at least 0.5 CSS. If validation is TRUE it will be estimated for the chosen XYpb_cor_0.K_toprnk_ZZ.csv a p-value, which is calculated performing correlation between clusters, using randomly selected ZZ genes * cls, and repeating this procedure 1000 times.

Author

Luca Alessandri, alessandri [dot] luca1991 [at] gmail [dot] com, University of Torino

Examples

if (FALSE) {
 library(rCASC)
 toprnk(group="docker", 
        scratch.folder="/scratch", 
        fileX="/data/reanalysis_on_AIsc/comparing_CRC0327/NT_CTX/CRC0327_NT_2_clx/VandE/VandE_bulkRow.csv",
        fileY="/data/reanalysis_on_AIsc/comparing_CRC0327/NT_CTX/CRC0327_cetux_2_clx/VandE/VandE_bulkRow.csv", 
        separatorX=",",
        separatorY=",",
        xCometFolder="/data/reanalysis_on_AIsc/comparing_CRC0327/NT_CTX/CRC0327_NT_2_clx/VandE/Results/VandE/8/outputdata",
        yCometFolder="/data/reanalysis_on_AIsc/comparing_CRC0327/NT_CTX/CRC0327_cetux_2_clx/VandE/Results/VandE/8/outputdata",
        top.ranked=320,
        outputFolder="/data/reanalysis_on_AIsc/comparing_CRC0327/NT_CTX"
 )
}