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imcExperiment data container

Containerizing IMC data into the SummarizedExperiment class, this container inherits packages from FlowSOM and diffcyt to compute clusters and test for differential abundance or state heterogeneity. Creating a flowSet is cumbersome, so we can stream-line into a summarized experiment into a quick and fast way to detect changes in cell populations.

 library(CATALYST)
 library(diffcyt)
 library(imcExperiment)
 data(imcdata)
 head(rownames(imcdata))
 imcData<-imcdata
    # for plot scatter to work need to set the rowData feature in a specific way.
    channel<-sapply(strsplit(rownames(imcData),"_"),function(x) x[3])
    channel[34:35]<-c("Ir1911","Ir1931")
    marker<-sapply(strsplit(rownames(imcData),"_"),function(x) x[2])
    rowData(imcData)<-DataFrame(channel_name=channel,marker_name=marker)
    rownames(imcData)<-marker
    plotScatter(imcData,rownames(imcData)[17:18],assay='counts')

  # convert to flowSet
  ## the warning has to do with duplicated Iridium channels.
   (fsimc <- sce2fcs(imcData, split_by = "ROIID"))
    ## now we have a flowSet.
   pData(fsimc)
   fsApply(fsimc,nrow)
   dim(exprs(fsimc[[1]]))
   exprs(fsimc[[1]])[1:5,1:5]
  ## set up the metadata files.
  head(marker_info)
   
   exper_info<-data.frame(group_id=colData(imcData)$Treatment[match(pData(fsimc)$name,
   colData(imcData)$ROIID)],
                           patient_id=colData(imcData)$Patient.Number[match(pData(fsimc)$name,
                           colData(imcData)$ROIID)],
                           sample_id=pData(fsimc)$name)
   
   ## create design
   design<-createDesignMatrix(
   exper_info,cols_design=c("group_id","patient_id"))
   
   ##set up contrast 
   contrast<-createContrast(c(0,1,0))
   nrow(contrast)==ncol(design)
   data.frame(parameters=colnames(design),contrast)
   
    ## flowSet to DiffCyt
    out_DA<-diffcyt(
      d_input=fsimc,
      experiment_info=exper_info,
      marker_info=marker_info,
      design=design,
      contrast=contrast,
      analysis_type = "DA",
      seed_clustering = 123
    )
   topTable(out_DA,format_vals = TRUE)
   
   out_DS<-diffcyt(
     d_input=fsimc,
     experiment_info=exper_info,
     marker_info=marker_info,
     design=design,
     contrast=contrast,
     analysis_type='DS',
     seed_clustering = 123,
     plot=FALSE)
   
      topTable(out_DS,format_vals = TRUE)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.