PostProcessing: Post-processing for scone analysks.

In Sconify: A toolkit for performing KNN-based statistics for flow and mass cytometry data

Description

Performs final processing and transformations on the scone data

Usage

PostProcessing(scone.output, cell.data, input, tsne = TRUE,
  log.transform.qvalue = TRUE)

Arguments

scone.output	tibble of the output of the given scone analysis
cell.data	the tibble used as input for the scone.values function
input	the input markers used for the knn calculation (to be used for tsne here)
tsne	boolean value to indicate whether tSNE is to be done
log.transform.qvalue	boolean to indicate whether log transformation of all q values is to be done

Value

result: the concatenated original input data with the scone derived data, with the option of the q values being inverse log10 transformed, and two additional tSNE columns being added to the data (from the Rtsne package)

Examples

PostProcessing(wand.scone, wand.combined, input.markers, tsne = FALSE)

Sconify documentation built on Nov. 8, 2020, 6:45 p.m.