# imcExperiment: containerization of IMC data

#### Anthony Colombo

#### 18 August, 2021

# Heatmap plotting script (helper)

- The heatmap function we do not want to include with this package, but here is the helper function.

# example

- The IMC container contains slots for intensity, coordinates, neighborhood features, networking ID, distance measures, morphology, unique cell labels, panel information, and ROIID.
- The input to the imcExperiment object has the cells (columns) and rows are the proteins.
- For spatial features, the rows are the cells, and columns are the spatial features. the 2-dim coordinates (x,y), neighborHood features, network assignment, unique Label, ROIID, and distance matrix. These data are required for the constructure but can be edited later.
- The IMC container inherits many function used for SummarizedExperiment classes (assays, etc).

     library(imcExperiment)
     showClass("imcExperiment")

    ## Class "imcExperiment" [package "imcExperiment"]
    ##
    ## Slots:
    ##
    ## Name:                   coordinates                cellIntensity
    ## Class:                       matrix                       matrix
    ##
    ## Name:                  neighborHood                      network
    ## Class:                       matrix                   data.frame
    ##
    ## Name:                      distance                   morphology
    ## Class:                       matrix                       matrix
    ##
    ## Name:                   uniqueLabel          int_elementMetadata
    ## Class:                    character                    DataFrame
    ##
    ## Name:                   int_colData                 int_metadata
    ## Class:                    DataFrame                         list
    ##
    ## Name:                     rowRanges                      colData
    ## Class: GenomicRanges_OR_GRangesList                    DataFrame
    ##
    ## Name:                        assays                        NAMES
    ## Class:               Assays_OR_NULL            character_OR_NULL
    ##
    ## Name:               elementMetadata                     metadata
    ## Class:                    DataFrame                         list
    ##
    ## Extends:
    ## Class "SingleCellExperiment", directly
    ## Class "RangedSummarizedExperiment", by class "SingleCellExperiment", distance 2
    ## Class "SummarizedExperiment", by class "SingleCellExperiment", distance 3
    ## Class "RectangularData", by class "SingleCellExperiment", distance 4
    ## Class "Vector", by class "SingleCellExperiment", distance 4
    ## Class "Annotated", by class "SingleCellExperiment", distance 5
    ## Class "vector_OR_Vector", by class "SingleCellExperiment", distance 5

      #10 cells with 10 proteins
      # 10 neighbors
     # and square distance matrix
     # note that for SCE objects the columns are the cells, and rows are proteins
      x<-imcExperiment(cellIntensity=matrix(1,nrow=10,ncol=10),
        coordinates=matrix(1,nrow=10,ncol=2),
        neighborHood=matrix(1,nrow=10,ncol=10),
        network=data.frame(matrix(1,nrow=10,ncol=10)),
        distance=matrix(1,nrow=10,ncol=10),
        morphology=matrix(1,nrow=10,ncol=10),
        uniqueLabel=paste0("A",seq_len(10)),
        panel=letters[1:10],
        ROIID=data.frame(ROIID=rep("A",10)))

     #7 cells with 10 proteins
     # but the spatial information is 10 rows and this will fail to build.
     #x<-imcExperiment(cellIntensity=matrix(1,nrow=7,ncol=10),
    #   coordinates=matrix(1,nrow=10,ncol=2),
    #   neighborHood=matrix(1,nrow=10,ncol=20),
    #   network=matrix(1,nrow=10,ncol=3),
    #   distance=matrix(1,nrow=10,ncol=2),
    #   uniqueLabel=rep("A",10),
    #   ROIID=data.frame(ROIID=rep("A",10)))

### Accessors, and setters

- the cellIntensity() function accesses cell Intensity
- getCoordinates() accesses spatial data.
- getNeighborhood() access the histoCAT neighborhood features
- getDistance() accesses distance matrix input.
- getMorphology() accesses morphology features.
- the metadata() function can store extras .

      #get cellintensities
      cellIntensity(x)

    ##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
    ##  [1,]    1    1    1    1    1    1    1    1    1     1
    ##  [2,]    1    1    1    1    1    1    1    1    1     1
    ##  [3,]    1    1    1    1    1    1    1    1    1     1
    ##  [4,]    1    1    1    1    1    1    1    1    1     1
    ##  [5,]    1    1    1    1    1    1    1    1    1     1
    ##  [6,]    1    1    1    1    1    1    1    1    1     1
    ##  [7,]    1    1    1    1    1    1    1    1    1     1
    ##  [8,]    1    1    1    1    1    1    1    1    1     1
    ##  [9,]    1    1    1    1    1    1    1    1    1     1
    ## [10,]    1    1    1    1    1    1    1    1    1     1

      #set intensities
      newIn<-matrix(rnorm(100,2,5),nrow=10,ncol=10)
      rownames(newIn)<-rownames(x)
      colnames(newIn)<-colnames(x)
       cellIntensity(x)<-newIn
      cellIntensity(x)==newIn

    ##     A1   A2   A3   A4   A5   A6   A7   A8   A9  A10
    ## a TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## b TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## c TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## d TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## e TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## f TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## g TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## h TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## i TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
    ## j TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

     # if we want to store both the raw and normalized values in assays we can.
      assays(x,withDimnames = FALSE)$raw<-matrix(1,nrow=10,ncol=10)
      #store the normalized values.
       cellIntensity(x)<-asinh(counts(x)/0.5)
       all(cellIntensity(x)==asinh(assays(x)$counts/0.5))

    ## [1] TRUE

      x

    ## class: imcExperiment
    ## dim: 10 10
    ## metadata(0):
    ## assays(2): counts raw
    ## rownames(10): a b ... i j
    ## rowData names(0):
    ## colnames(10): A1 A2 ... A9 A10
    ## colData names(1): ROIID
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):

     ## access the coordinates
      getCoordinates(x)

    ##       [,1] [,2]
    ##  [1,]    1    1
    ##  [2,]    1    1
    ##  [3,]    1    1
    ##  [4,]    1    1
    ##  [5,]    1    1
    ##  [6,]    1    1
    ##  [7,]    1    1
    ##  [8,]    1    1
    ##  [9,]    1    1
    ## [10,]    1    1

      getCoordinates(x)<-matrix(rnorm(20,0,10),nrow=10,ncol=2)
    head(  getCoordinates(x))

    ##           [,1]        [,2]
    ## [1,]  1.138630   0.4920486
    ## [2,] -7.483197  14.0072048
    ## [3,] -2.995988   5.7263834
    ## [4,] -3.952030 -11.4399828
    ## [5,]  8.534859  -2.8095482
    ## [6,]  9.879779  19.3120798

     ## access the neighborhood profile.  Note each row must equal the number of cells, but the columns can be extended depending on the radius of interactions.
      ## access the coordinates
      getNeighborhood(x)

    ##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
    ##  [1,]    1    1    1    1    1    1    1    1    1     1
    ##  [2,]    1    1    1    1    1    1    1    1    1     1
    ##  [3,]    1    1    1    1    1    1    1    1    1     1
    ##  [4,]    1    1    1    1    1    1    1    1    1     1
    ##  [5,]    1    1    1    1    1    1    1    1    1     1
    ##  [6,]    1    1    1    1    1    1    1    1    1     1
    ##  [7,]    1    1    1    1    1    1    1    1    1     1
    ##  [8,]    1    1    1    1    1    1    1    1    1     1
    ##  [9,]    1    1    1    1    1    1    1    1    1     1
    ## [10,]    1    1    1    1    1    1    1    1    1     1

      getNeighborhood(x)<-matrix(rnorm(100,1,5),nrow=10,ncol=10)
     head( getNeighborhood(x))

    ##             [,1]      [,2]        [,3]      [,4]      [,5]      [,6]       [,7]
    ## [1,] -11.5292016  5.750663  5.32795370 -8.018236  1.701827 -5.041385  4.7460738
    ## [2,]  -1.1284688  5.185412 -0.12147623 -1.805766  4.498192  7.648025  0.6660934
    ## [3,]  -0.2312129  6.175255 -4.87813785 10.450752 -1.764999  1.617683 -0.3658961
    ## [4,]  -6.6399166 -7.081547  5.61946737  7.130634  4.629363  6.306020  4.5311604
    ## [5,]   7.9826941  6.890117  0.01332724  6.049404  1.661246 -2.705478 -7.2621050
    ## [6,]   3.4256881 -1.114269 -7.27957485  4.411063  2.104813 10.298950 -1.4588797
    ##            [,8]       [,9]      [,10]
    ## [1,]  1.4981864  8.2157026 -4.0862024
    ## [2,] -6.2570109  3.2871688 -6.7956895
    ## [3,] -1.6710766  4.0130912 -1.6708544
    ## [4,] -0.3772099 -1.6926917 -0.7368132
    ## [5,] -4.9718817 -4.1329155  4.3349482
    ## [6,]  7.8650851  0.8307328 -0.4697563

     ## get the distance usually a square matrix, or can be just first nearest etc.
        getDistance(x)

    ##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
    ##  [1,]    1    1    1    1    1    1    1    1    1     1
    ##  [2,]    1    1    1    1    1    1    1    1    1     1
    ##  [3,]    1    1    1    1    1    1    1    1    1     1
    ##  [4,]    1    1    1    1    1    1    1    1    1     1
    ##  [5,]    1    1    1    1    1    1    1    1    1     1
    ##  [6,]    1    1    1    1    1    1    1    1    1     1
    ##  [7,]    1    1    1    1    1    1    1    1    1     1
    ##  [8,]    1    1    1    1    1    1    1    1    1     1
    ##  [9,]    1    1    1    1    1    1    1    1    1     1
    ## [10,]    1    1    1    1    1    1    1    1    1     1

      getDistance(x)<-matrix(rnorm(100,1,5),nrow=10,ncol=10)
      head(getDistance(x))

    ##            [,1]      [,2]       [,3]      [,4]       [,5]      [,6]       [,7]
    ## [1,] -4.4459580 0.3832962  1.2861954  5.191198  8.1144338  1.250347  3.5746833
    ## [2,]  2.2501458 8.5357020  6.0879762 -1.389698 -0.7572171  1.056745  3.4762454
    ## [3,]  1.8648350 7.6683573 -6.1207535  8.613386 -3.3720330 -1.958730  3.3212437
    ## [4,]  3.6676806 0.8794159  5.7258095 -3.174093  2.0034287  7.917606  3.1580990
    ## [5,] -0.1038772 3.1644572  0.1525853 -5.029461  6.0548022  3.773487 -8.7464480
    ## [6,]  3.9208435 8.4196328  2.5519046 -3.191540  8.7255777 -5.454160 -0.1319931
    ##           [,8]      [,9]       [,10]
    ## [1,] -1.414093 6.9717400   0.4301284
    ## [2,]  5.167939 4.3821819   4.1317587
    ## [3,]  8.287247 1.9860126 -10.6450490
    ## [4,] -4.710048 0.4533308   8.1478340
    ## [5,] -5.732384 3.8630923   5.3916885
    ## [6,] -4.847652 2.3099469   6.9436779

     # get morphological features
      getMorphology(x)

    ##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
    ##  [1,]    1    1    1    1    1    1    1    1    1     1
    ##  [2,]    1    1    1    1    1    1    1    1    1     1
    ##  [3,]    1    1    1    1    1    1    1    1    1     1
    ##  [4,]    1    1    1    1    1    1    1    1    1     1
    ##  [5,]    1    1    1    1    1    1    1    1    1     1
    ##  [6,]    1    1    1    1    1    1    1    1    1     1
    ##  [7,]    1    1    1    1    1    1    1    1    1     1
    ##  [8,]    1    1    1    1    1    1    1    1    1     1
    ##  [9,]    1    1    1    1    1    1    1    1    1     1
    ## [10,]    1    1    1    1    1    1    1    1    1     1

      getMorphology(x)<-matrix(rnorm(100,1,5),nrow=10,ncol=60)
      head(getMorphology(x))

    ##            [,1]      [,2]       [,3]      [,4]       [,5]      [,6]       [,7]
    ## [1,] -0.2602892 -2.873465 -1.1884496 -8.110879  7.1433864 -3.124403  0.6445156
    ## [2,]  6.6712063  4.358327  0.8852602 -1.839347  4.7459084  2.049456  3.0144223
    ## [3,]  0.4508546  7.471209 -3.1749765 -4.185419  9.9250273  2.718686  4.2543621
    ## [4,]  5.3070444 -1.126719 -2.4471718 -6.437787  2.1392430  3.716662  9.4709743
    ## [5,]  0.2663402  1.076424 -2.7057904 -1.810752 -0.6675042  2.135795 -6.5746575
    ## [6,] -0.9224336 11.654316  2.2870520  4.787939  0.4979738 -4.675196 -6.1942677
    ##            [,8]       [,9]     [,10]      [,11]     [,12]      [,13]     [,14]
    ## [1,] -0.1020808  0.5043882 -2.792838 -0.2602892 -2.873465 -1.1884496 -8.110879
    ## [2,] -3.7028341  2.9473449 -1.527435  6.6712063  4.358327  0.8852602 -1.839347
    ## [3,]  2.5449114  1.8542034  2.661986  0.4508546  7.471209 -3.1749765 -4.185419
    ## [4,]  8.8134069  7.8361904  3.072036  5.3070444 -1.126719 -2.4471718 -6.437787
    ## [5,] 16.7918222 -1.0880037  3.122839  0.2663402  1.076424 -2.7057904 -1.810752
    ## [6,] -2.0700062 -1.1436743  3.858231 -0.9224336 11.654316  2.2870520  4.787939
    ##           [,15]     [,16]      [,17]      [,18]      [,19]     [,20]      [,21]
    ## [1,]  7.1433864 -3.124403  0.6445156 -0.1020808  0.5043882 -2.792838 -0.2602892
    ## [2,]  4.7459084  2.049456  3.0144223 -3.7028341  2.9473449 -1.527435  6.6712063
    ## [3,]  9.9250273  2.718686  4.2543621  2.5449114  1.8542034  2.661986  0.4508546
    ## [4,]  2.1392430  3.716662  9.4709743  8.8134069  7.8361904  3.072036  5.3070444
    ## [5,] -0.6675042  2.135795 -6.5746575 16.7918222 -1.0880037  3.122839  0.2663402
    ## [6,]  0.4979738 -4.675196 -6.1942677 -2.0700062 -1.1436743  3.858231 -0.9224336
    ##          [,22]      [,23]     [,24]      [,25]     [,26]      [,27]      [,28]
    ## [1,] -2.873465 -1.1884496 -8.110879  7.1433864 -3.124403  0.6445156 -0.1020808
    ## [2,]  4.358327  0.8852602 -1.839347  4.7459084  2.049456  3.0144223 -3.7028341
    ## [3,]  7.471209 -3.1749765 -4.185419  9.9250273  2.718686  4.2543621  2.5449114
    ## [4,] -1.126719 -2.4471718 -6.437787  2.1392430  3.716662  9.4709743  8.8134069
    ## [5,]  1.076424 -2.7057904 -1.810752 -0.6675042  2.135795 -6.5746575 16.7918222
    ## [6,] 11.654316  2.2870520  4.787939  0.4979738 -4.675196 -6.1942677 -2.0700062
    ##           [,29]     [,30]      [,31]     [,32]      [,33]     [,34]      [,35]
    ## [1,]  0.5043882 -2.792838 -0.2602892 -2.873465 -1.1884496 -8.110879  7.1433864
    ## [2,]  2.9473449 -1.527435  6.6712063  4.358327  0.8852602 -1.839347  4.7459084
    ## [3,]  1.8542034  2.661986  0.4508546  7.471209 -3.1749765 -4.185419  9.9250273
    ## [4,]  7.8361904  3.072036  5.3070444 -1.126719 -2.4471718 -6.437787  2.1392430
    ## [5,] -1.0880037  3.122839  0.2663402  1.076424 -2.7057904 -1.810752 -0.6675042
    ## [6,] -1.1436743  3.858231 -0.9224336 11.654316  2.2870520  4.787939  0.4979738
    ##          [,36]      [,37]      [,38]      [,39]     [,40]      [,41]     [,42]
    ## [1,] -3.124403  0.6445156 -0.1020808  0.5043882 -2.792838 -0.2602892 -2.873465
    ## [2,]  2.049456  3.0144223 -3.7028341  2.9473449 -1.527435  6.6712063  4.358327
    ## [3,]  2.718686  4.2543621  2.5449114  1.8542034  2.661986  0.4508546  7.471209
    ## [4,]  3.716662  9.4709743  8.8134069  7.8361904  3.072036  5.3070444 -1.126719
    ## [5,]  2.135795 -6.5746575 16.7918222 -1.0880037  3.122839  0.2663402  1.076424
    ## [6,] -4.675196 -6.1942677 -2.0700062 -1.1436743  3.858231 -0.9224336 11.654316
    ##           [,43]     [,44]      [,45]     [,46]      [,47]      [,48]      [,49]
    ## [1,] -1.1884496 -8.110879  7.1433864 -3.124403  0.6445156 -0.1020808  0.5043882
    ## [2,]  0.8852602 -1.839347  4.7459084  2.049456  3.0144223 -3.7028341  2.9473449
    ## [3,] -3.1749765 -4.185419  9.9250273  2.718686  4.2543621  2.5449114  1.8542034
    ## [4,] -2.4471718 -6.437787  2.1392430  3.716662  9.4709743  8.8134069  7.8361904
    ## [5,] -2.7057904 -1.810752 -0.6675042  2.135795 -6.5746575 16.7918222 -1.0880037
    ## [6,]  2.2870520  4.787939  0.4979738 -4.675196 -6.1942677 -2.0700062 -1.1436743
    ##          [,50]      [,51]     [,52]      [,53]     [,54]      [,55]     [,56]
    ## [1,] -2.792838 -0.2602892 -2.873465 -1.1884496 -8.110879  7.1433864 -3.124403
    ## [2,] -1.527435  6.6712063  4.358327  0.8852602 -1.839347  4.7459084  2.049456
    ## [3,]  2.661986  0.4508546  7.471209 -3.1749765 -4.185419  9.9250273  2.718686
    ## [4,]  3.072036  5.3070444 -1.126719 -2.4471718 -6.437787  2.1392430  3.716662
    ## [5,]  3.122839  0.2663402  1.076424 -2.7057904 -1.810752 -0.6675042  2.135795
    ## [6,]  3.858231 -0.9224336 11.654316  2.2870520  4.787939  0.4979738 -4.675196
    ##           [,57]      [,58]      [,59]     [,60]
    ## [1,]  0.6445156 -0.1020808  0.5043882 -2.792838
    ## [2,]  3.0144223 -3.7028341  2.9473449 -1.527435
    ## [3,]  4.2543621  2.5449114  1.8542034  2.661986
    ## [4,]  9.4709743  8.8134069  7.8361904  3.072036
    ## [5,] -6.5746575 16.7918222 -1.0880037  3.122839
    ## [6,] -6.1942677 -2.0700062 -1.1436743  3.858231

     ## for each cell we can obtain the ROI that it belongs to
      rowData(x)

    ## DataFrame with 10 rows and 0 columns

     ## if we want to add patient features to each ROI we can
      metas<-data.frame(ROIID=factor(rep("A",10)),treatment=factor(rep('none',10)))
      colData(x)<-DataFrame(metas)

     ##other slots for covariates
       metadata(x)$experiment<-'test'
       metadata(x)

    ## $experiment
    ## [1] "test"

### From histoCAT to R

- We use the raw histoCAT output and containerize the data.

      ### load the data from package.
      library(imcExperiment)
     ##load the data 1000 cells from IMC experiment.
      data(data)
      dim(data)

    ## [1] 1000   62

      ##output from histoCAT to R
      expr<-data[,3:36]
      normExp<-percentilenormalize(data=expr,percentile=0.99)
      normExp<-as.matrix(normExp)

      ##spatial component
      spatial<-(data[,c("X_position","Y_position")])
      spatial<-as.matrix(spatial)
     ##uniqueLabel
      uniqueLabel<-paste0(data[,"ImageId"],"_",data[,"CellId"])

     phenotypes<-data[,grepl("Phenograph",colnames(data))]
     morph<-as.matrix(data[,c("Area","Eccentricity",
        "Solidity",
        "Extent",
        "Perimeter")])

      x<-imcExperiment(cellIntensity=t(normExp),
        coordinates=spatial,
        neighborHood=as.matrix(data[,grepl("neighbour_",colnames(data))]),
        network=phenotypes,
        distance=matrix(1,nrow=nrow(data),ncol=10),
        morphology=morph,
        panel=colnames(normExp),
        uniqueLabel=paste0(data$ImageId,"_",data$CellId),
        ROIID=data.frame(ROIID=data$ImageId))

     ## explore the container.
      dim(assay(x))

    ## [1]   34 1000

       colData(x)$treatment<-DataFrame(treatment=rep('none',1000))
       head(colData(x))

    ## DataFrame with 6 rows and 2 columns
    ##              ROIID   treatment
    ##          <integer> <DataFrame>
    ## 274864_1    274864        none
    ## 274864_2    274864        none
    ## 274864_3    274864        none
    ## 274864_4    274864        none
    ## 274864_5    274864        none
    ## 274864_6    274864        none

     head( colnames(x))

    ## [1] "274864_1" "274864_2" "274864_3" "274864_4" "274864_5" "274864_6"

      rownames(x)

    ##  [1] "marker1"  "marker2"  "marker3"  "marker4"  "marker5"  "marker6"
    ##  [7] "marker7"  "marker8"  "marker9"  "marker10" "marker11" "marker12"
    ## [13] "marker13" "marker14" "marker15" "marker16" "marker17" "marker18"
    ## [19] "marker19" "marker20" "marker21" "marker22" "marker23" "marker24"
    ## [25] "marker25" "marker26" "marker27" "marker28" "marker29" "marker30"
    ## [31] "marker31" "marker32" "marker33" "marker34"

      ## Intensity
      all(t(cellIntensity(x))==normExp)

    ## [1] TRUE

      head(t(cellIntensity(x)))

    ##     marker1   marker2   marker3   marker4   marker5    marker6   marker7
    ## 1 0.4656571 0.2736824 0.7237669 0.5204605 0.6330375 0.45935533 0.2857143
    ## 2 0.3487101 0.2897530 0.3958101 0.3548617 0.5588399 0.11483883 0.5000000
    ## 3 0.8883986 0.7622286 0.8866145 0.4873408 0.2400462 0.18374213 0.0000000
    ## 4 0.3963552 0.1451175 0.2714126 1.0000000 0.2100260 0.19686657 0.0000000
    ## 5 0.5407341 0.2522549 0.4221974 0.1064634 0.8991254 0.07655922 0.3333333
    ## 6 1.0000000 0.7622286 0.0000000 0.7854187 0.2018387 0.82683960 0.8000000
    ##     marker8   marker9  marker10   marker11  marker12   marker13  marker14
    ## 1 0.4237137 0.1981346 0.5895631 0.38423632 0.5596034 1.00000000 0.5421737
    ## 2 0.2118568 0.1812504 0.3699262 0.04531335 0.1284526 0.04016094 0.1667561
    ## 3 0.4237137 0.6934055 0.2150245 0.69305332 1.0000000 0.11646341 0.4103197
    ## 4 0.7263663 0.3632249 0.1866203 1.00000000 0.9999276 0.02467097 0.9450608
    ## 5 0.3530947 0.1155895 0.8496593 0.55169689 0.1819642 0.06291782 0.1453909
    ## 6 0.2542282 0.2731756 0.4462211 0.21726828 0.6036358 0.05220870 0.1539370
    ##     marker15  marker16  marker17  marker18   marker19  marker20  marker21
    ## 1 0.46990599 0.0952381 0.4987356 0.5269829 0.04774145 0.5735263 0.7197223
    ## 2 0.15883470 0.1666667 0.4851215 0.4321421 0.34528973 0.3484509 0.4443348
    ## 3 0.45147213 0.2666667 0.6154904 0.6206074 0.30741995 0.5060037 0.8448015
    ## 4 0.07050581 0.5714286 0.4715074 0.4803021 0.04774145 0.4609886 0.1938211
    ## 5 0.60815990 0.3333333 0.3834694 0.3128651 0.12438267 0.2171569 0.4567717
    ## 6 0.58665373 0.4000000 1.0000000 1.0000000 0.08020126 0.6635565 0.4070243
    ##    marker22  marker23   marker24  marker25   marker26  marker27  marker28
    ## 1 0.4735184 0.9999835 0.12229278 0.2651672 0.19823739 1.0000000 0.2379700
    ## 2 0.3429377 0.4963629 0.08592210 0.3093618 0.06938309 0.1036510 0.2082237
    ## 3 0.4343442 0.6072488 0.40161962 0.3093618 0.11101294 1.0000000 0.6663160
    ## 4 0.4735184 0.8192498 0.04747309 0.0000000 0.23788486 0.9639418 0.0000000
    ## 5 0.4191098 0.3712448 0.22898012 0.3609221 0.00000000 0.4915438 0.2776317
    ## 6 0.7085638 0.1612178 0.33178791 0.0000000 0.49955821 1.0000000 0.0000000
    ##    marker29  marker30  marker31   marker32   marker33  marker34
    ## 1 0.5826594 0.4963881 0.4285714 0.57132469 0.32432082 0.7448313
    ## 2 0.1140364 0.3285300 0.1250000 0.53626613 0.44897580 0.2375474
    ## 3 0.9637037 0.6810320 0.5000000 0.37811307 0.02758118 0.5500343
    ## 4 1.0000000 0.8740688 0.8571429 0.13504038 0.07298397 0.9535425
    ## 5 0.4187694 0.6712403 0.3333333 0.07271405 0.44188161 0.2307836
    ## 6 0.3614079 0.9747837 0.3000000 1.00000000 0.54971323 0.5500343

      ##coordinate
      all(getCoordinates(x)==spatial)

    ## [1] TRUE

      head(getCoordinates(x))

    ##   X_position Y_position
    ## 1   508.0000   3.000000
    ## 2   672.1667   3.000000
    ## 3   154.2000   4.200000
    ## 4   538.5714   4.857143
    ## 5   913.2778   3.444444
    ## 6  1200.0000   4.200000

      #neighbor attraction data form histoCAT
     all(getNeighborhood(x)==as.matrix(data[,grepl("neighbour_",colnames(data))]))

    ## [1] TRUE

     head(getNeighborhood((x)))

    ##   neighbour_4_CellId1 neighbour_4_CellId2 neighbour_4_CellId3
    ## 1                  36                 168                   0
    ## 2                 144                   0                   0
    ## 3                   0                   0                   0
    ## 4                  67                  92                   0
    ## 5                  13                  97                 249
    ## 6                 135                   0                   0
    ##   neighbour_4_CellId4 neighbour_4_CellId5 neighbour_4_CellId6
    ## 1                   0                   0                   0
    ## 2                   0                   0                   0
    ## 3                   0                   0                   0
    ## 4                   0                   0                   0
    ## 5                   0                   0                   0
    ## 6                   0                   0                   0
    ##   neighbour_4_CellId7 neighbour_4_CellId8 neighbour_4_CellId9
    ## 1                   0                   0                   0
    ## 2                   0                   0                   0
    ## 3                   0                   0                   0
    ## 4                   0                   0                   0
    ## 5                   0                   0                   0
    ## 6                   0                   0                   0
    ##   neighbour_4_CellId10
    ## 1                    0
    ## 2                    0
    ## 3                    0
    ## 4                    0
    ## 5                    0
    ## 6                    0

     ##phenotype cluster ID
      head(getNetwork(x))

    ##   (network)
    ## 1         1
    ## 2         1
    ## 3         4
    ## 4         4
    ## 5         1
    ## 6         1

     all(getNetwork(x)==phenotypes)

    ## [1] TRUE

     ###distance calculations
      head(getDistance(x))

    ##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
    ## [1,]    1    1    1    1    1    1    1    1    1     1
    ## [2,]    1    1    1    1    1    1    1    1    1     1
    ## [3,]    1    1    1    1    1    1    1    1    1     1
    ## [4,]    1    1    1    1    1    1    1    1    1     1
    ## [5,]    1    1    1    1    1    1    1    1    1     1
    ## [6,]    1    1    1    1    1    1    1    1    1     1

      ##morphology
       all(getMorphology(x)==morph)

    ## [1] TRUE

      head(getMorphology(x))

    ##   Area Eccentricity  Solidity    Extent Perimeter
    ## 1   21    0.6956573 0.9545455 0.8400000    13.844
    ## 2   24    0.8034622 0.9230769 0.6666667    16.565
    ## 3   15    0.9014174 1.0000000 0.7142857    12.918
    ## 4    7    0.8717717 0.8750000 0.5833333     7.683
    ## 5   18    0.9593719 0.7500000 0.3214286    19.766
    ## 6    5    0.7437865 1.0000000 0.5555556     5.814

      ##uniqueLabel
       head(getLabel(x))

    ## [1] "274864_1" "274864_2" "274864_3" "274864_4" "274864_5" "274864_6"

       all(getLabel(x)==paste0(data$ImageId,"_",data$CellId) )

    ## [1] TRUE

### PCA demo analysis

- PCA data and tSNE coordinates can be added to the container.

     ### inherited accessor.
      pca_data <- prcomp(t(counts(x)), rank=50)
    tsDat<-data.frame(tsne.x=data$tSNE4148542692_1,tsne.y=data$tSNE4148542692_2,row.names=colnames(x))

    reducedDims(x)<-list(PCA=pca_data$x,TSNE=tsDat)
    x

    ## class: imcExperiment
    ## dim: 34 1000
    ## metadata(0):
    ## assays(1): counts
    ## rownames(34): marker1 marker2 ... marker33 marker34
    ## rowData names(0):
    ## colnames(1000): 274864_1 274864_2 ... 274864_999 274864_1000
    ## colData names(2): ROIID treatment
    ## reducedDimNames(2): PCA TSNE
    ## mainExpName: NULL
    ## altExpNames(0):

    reducedDimNames(x)

    ## [1] "PCA"  "TSNE"

         dim(reducedDims(x)$PCA)

    ## [1] 1000   34

         dim(reducedDims(x)$TSNE)

    ## [1] 1000    2

     imc<-x
     x<-NULL

### IMC container and Phenograph cluster neighborhood

- Phenograph an IMC contrainer and heatmap result.

     ### create  phenotypes via Rphenograph
      ##run phenograph
      library(Rphenograph)
    library(igraph)
      phenos<-Rphenograph(t(cellIntensity(imc)),k=35)
       pheno.labels<-as.numeric(igraph::membership(phenos[[2]]))
       getNetwork(imc)<-data.frame(cell_clustering=pheno.labels)
       head( getNetwork(imc))
      ##plot phenograph
      #plot_clustering_heatmap_wrapper(myExperiment=imc)

### histoCAT analysis

- raw input from histoCAT and creating an IMC container.
- containerizes the intensities, neighborhood results, morphology, and computes nearest distances and stores it.
- Can use a hyperframe to identify distances quickly.

    ### reading in a directory of histoCAT csv files.

     ##need to reconstruct the fold revised IMC data.
      ##merges a folder full of histoCAT csv files.
     # use morphology and the 1-10 neighbors.
      dataSet<-NULL
     for(i in c("case8.csv","case5.csv")){
      message(i)
        fpath <- system.file("extdata", i, package="imcExperiment")
        case1<-read.csv(fpath)
       proteins<-colnames(case1)[grepl("Cell_",colnames(case1))]
       neig<-colnames(case1)[grepl("neighbour_",colnames(case1))][1:10]
        case1<-case1[,c("ImageId","CellId","X_position","Y_position",proteins,
                        "Area",
                        "Eccentricity",
        "Solidity",
        "Extent",
        "Perimeter",
         neig)]
        dataSet<-rbind(dataSet,case1)
      }

    ## case8.csv

    ## case5.csv

     expr<-dataSet[,proteins]
      normExp<-percentilenormalize(data=expr,percentile=0.99)
      normExp<-as.matrix(normExp)
      boxplot(normExp)

      ##spatial component
      spatial<-(dataSet[,c("X_position","Y_position")])
      spatial<-as.matrix(spatial)
     ##uniqueLabel
      uniqueLabel<-paste0(dataSet[,"ImageId"],"_",dataSet[,"CellId"])

      ##not yet assigned
      phenotypes<-matrix(0,nrow(dataSet),1)
      ROIID=data.frame(ROIID=dataSet[,"ImageId"])
      morph<-dataSet[,c("Area",
                        "Eccentricity",
        "Solidity",
        "Extent",
        "Perimeter")]

      x<-imcExperiment(cellIntensity=t(normExp),
        coordinates=spatial,
        neighborHood=as.matrix(dataSet[,grepl("neighbour_",colnames(dataSet))]),
        network=phenotypes,
        distance=matrix(1,nrow=nrow(dataSet),ncol=10),
        morphology=morph,
        panel=colnames(normExp),
        uniqueLabel=uniqueLabel,
        ROIID=ROIID)
       x

    ## class: imcExperiment
    ## dim: 34 2000
    ## metadata(0):
    ## assays(1): counts
    ## rownames(34): Cell_CCR4Sm149Di_Sm149 Cell_CD15Dy164Di_Dy164 ...
    ##   Cell_VimentinNd143Di_Nd143 Cell_pStat3Gd158Di_Gd158
    ## rowData names(0):
    ## colnames(2000): 372149_1 372149_2 ... 274864_999 274864_1000
    ## colData names(1): ROIID
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):

       #require(Rphenograph); library(igraph)
      #phenos<-Rphenograph(t(cellIntensity(x)),k=35)
      # pheno.labels<-as.numeric(membership(phenos[[2]]))
      # getNetwork(x)<-data.frame(cell_clustering=pheno.labels)
      #  head(getNetwork(x))
     ##plot phenograph
      #plot_clustering_heatmap_wrapper(myExperiment=x)

### Access unique cell ID

- getLabel(), we can access unique cell ID
- subsetCase(x,ROIID) can subset to a single ROIID
- selectCases(x,c(“ROI1”,“ROI2”,“ROI3”)) selects a subset of multiple ROIs.

     ##store the ROIID in the metadata columns.
      ##access the unique cell labels.

      tail(getLabel(x))

    ## [1] "274864_995"  "274864_996"  "274864_997"  "274864_998"  "274864_999"
    ## [6] "274864_1000"

      roi<-subsetCase(x,372149 )
      roi

    ## class: imcExperiment
    ## dim: 34 1000
    ## metadata(0):
    ## assays(1): counts
    ## rownames(34): Cell_CCR4Sm149Di_Sm149 Cell_CD15Dy164Di_Dy164 ...
    ##   Cell_VimentinNd143Di_Nd143 Cell_pStat3Gd158Di_Gd158
    ## rowData names(0):
    ## colnames(1000): 372149_1 372149_2 ... 372149_999 372149_1000
    ## colData names(1): ROIID
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):

      table(sapply(strsplit(getLabel(roi),"_"),function(x) x[1]))

    ##
    ## 372149
    ##   1000

## Distance computations speedily

- The function ’imcExperimentToHyperFrame(), creates a point pattern useful for distance measures.
- Given a hyperframe pairwise computations can be done.

      ##you can append more clinical features to the columns of the sampleDat data.frame.
      H<-imcExperimentToHyperFrame(imcExperiment=x,phenotypeToUse = 1)
      helper<-function(pp=NULL,i=NULL,j=NULL){
      ps<-split(pp)
      nnd<-nncross(ps[[i]],ps[[j]])
      }
     ## 1-NN analysis.
      ## compare cluster 10 to cluster 9
       #eachNND<-with(H,helper(pp=point,i="10",j="8"))
     ## first choose 2 clusters to compare.
     # sapply(eachNND,function(x) mean(x[,"dist"]))

### Changing class from IMC to SummarizedExperiment

- IMC container can change to SummarizedExperiment and the FlowSOM algorithm can be used for cell classification.

      library(CATALYST)
    library(cowplot)
    library(flowCore)
    library(ggplot2)
    library(SingleCellExperiment)
    library(diffcyt)

     ## from IMCexperiment to SCE
     sce<- SingleCellExperiment(x)
     class(sce)

     ## FIX ME: add the SOM algorithms for over-clustering and meta-clustering by using class inheritance.

     #### for the cytof, the columns are sample info and the rows are cells. it uses the SummarizedExperiment format.

     ## change into a flowFrame?

     ## change into a daFrame (CATALYST)

     #data("data")

     rd<-DataFrame(colData(imcdata))

      marker_info<-data.frame(channel_name=sapply(strsplit(rownames(imcdata),"_"),function(x) x[3]),
                  marker_name=rownames(imcdata),
                  marker_class=c(rep("type",13),
                                 rep("state",18),
                                 rep("none",13)))

      ## Switching into Summarized Experiment class.
        dse<-SummarizedExperiment(assays=SimpleList(exprs=t(cellIntensity(imcdata))),
                               rowData=rd,
                               colData=DataFrame(marker_info)
                               )
       stopifnot(all(colnames(dse)==marker_info$marker_name))
       dse

     # Transform data recommended
     dse <- transformData(dse)
    ## maybe look a the normalization.
    # Generate clusters
    dse <- (generateClusters(dse,meta_clustering=TRUE,meta_k=30,seed_clustering=828))

     ## examine each cluster.
    cluster<-rowData(dse)
     data<-data.frame(t(logcounts(imcdata)),cluster)
     #plot_matrix_heatmap_wrapper(expr=data[,rownames(imcdata)],
      #                               cell_clustering=data$cluster_id)
     #

## IMC and flowSet conversions

- utilize CATALYST package for IMC we can see scatter plots after converting to FlowSets.
- the rownames for IMC are set to the panel names, and we can split these to create the marker and metal.
- The ROIID is useful for quickly creating a FlowSet object as a class inheritance method.
- The rownames of IMC container are usually in “Cell_Marker_ChannelMetal”

       ## the assay returns matrix class! required for CATALYST.
      is(assay(imcdata,'counts'),'matrix')
      rownames(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.
       table(colData(imcdata)$ROIID)
           (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)

           ### from flowSet to SE.
     d_se<-prepareData(fsimc,exper_info,marker_info)

### Diffcyt package tutorial (extra)

- Given a flowSet object we can utilize the diffCyt package for measuring phenotype differences.
- This code section is an example of diffcyt from their manual.
- Creates a random summarized experiment.

     library(diffcyt)
    # Function to create random data (one sample)
    d_random <- function(n = 20000, mean = 0, sd = 1, ncol = 20, cofactor = 5) {
      d <- sinh(matrix(rnorm(n, mean, sd), ncol = ncol)) * cofactor
      colnames(d) <- paste0("marker", sprintf("%02d", 1:ncol))
      d
    }
    # Create random data (without differential signal)
    set.seed(123)
    d_input <- list(
      sample1 = d_random(),
      sample2 = d_random(),
      sample3 = d_random(),
      sample4 = d_random()
    )
    experiment_info <- data.frame(
      sample_id = factor(paste0("sample", 1:4)),
      group_id = factor(c("group1", "group1", "group2", "group2")),
      stringsAsFactors = FALSE
    )
    marker_info <- data.frame(
      channel_name = paste0("channel", sprintf("%03d", 1:20)),
      marker_name = paste0("marker", sprintf("%02d", 1:20)),
      marker_class = factor(c(rep("type", 10), rep("state", 10)),
                            levels = c("type", "state", "none")),
      stringsAsFactors = FALSE
    )
    # Prepare data
    d_se <- prepareData(d_input, experiment_info, marker_info)
    # Transform data
    d_se <- transformData(d_se)
    # Generate clusters
    d_se <- generateClusters(d_se)