# How to import Cytobank into a GatingSet

#### Mike Jiang

This vignette demonstrates how the gatingML files exported from Cytobank can be imported into R as a GatingSet object.

    library(flowWorkspace)
    library(CytoML)
    acs <- system.file("extdata/cytobank_experiment.acs", package = "CytoML")

Create `cytobank_experiment` object from the ACS bundle exported from Cytobank

    ce <- open_cytobank_experiment(acs)
    ce

    ## cytobank Experiment:  tcell
    ## gatingML File:  C:\Users\biocbuild\bbs-3.12-bioc\tmpdir\Rtmp8C8JIn\file23dc5493e08/experiments/3637/cytobank_gate_ml2_v41.xml
    ##     panel samples
    ## 1 Panel 1       1

cytobank_experiment is a wrapper around the `ACS` file, which can be inspected by various accessors.

    sampleNames(ce)

    ## [1] "CytoTrol_CytoTrol_1.fcs"

    colnames(ce)

    ##  [1] "FSC-A"  "FSC-H"  "FSC-W"  "SSC-A"  "B710-A" "R660-A" "R780-A" "V450-A"
    ##  [9] "V545-A" "G560-A" "G780-A" "Time"

    markernames(ce)

    ## $CytoTrol_CytoTrol_1.fcs
    ##  [1] "FSC-A"        "FSC-H"        "FSC-W"        "SSC-A"        "CD4"
    ##  [6] "CD38 APC"     "CD8 APCH7"    "CD3"          "HLA-DR V500"  "CCR7 PE"
    ## [11] "CD45RA PECy7" "Time"

    pData(ce)

    ##                                            name Conditions Individuals
    ## CytoTrol_CytoTrol_1.fcs CytoTrol_CytoTrol_1.fcs condition1       ptid1

Then import `cytobank_experiment` into GatingSet

    gs <- cytobank_to_gatingset(ce)

Alternatively, the import can be done by `gatingML` and `fcs` files that are downloaded separately form Cytobank without `ACS`.

    xmlfile <- ce$gatingML
    fcsFiles <- list.files(ce$fcsdir, full.names = TRUE)
    gs <- cytobank_to_gatingset(xmlfile, fcsFiles)

However, it doesn’t have the information from `yaml` file (part of `ACS`). E.g. sample tags (i.e. `pData`) and customized markernames. So it is recommended to import `ACS`.

Inspect the results

    library(ggcyto)
    ## Plot the gates
    autoplot(gs[[1]])

    # Extract the population statistics
    gs_pop_get_count_fast(gs, statType = "count")

    ##                        name                                     Population
    ##  1: CytoTrol_CytoTrol_1.fcs                                    /not debris
    ##  2: CytoTrol_CytoTrol_1.fcs                           /not debris/singlets
    ##  3: CytoTrol_CytoTrol_1.fcs                       /not debris/singlets/CD3
    ##  4: CytoTrol_CytoTrol_1.fcs                   /not debris/singlets/CD3/CD8
    ##  5: CytoTrol_CytoTrol_1.fcs            /not debris/singlets/CD3/CD8/CD8_Q2
    ##  6: CytoTrol_CytoTrol_1.fcs                   /not debris/singlets/CD3/CD4
    ##  7: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q1
    ##  8: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q2
    ##  9: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q4
    ## 10: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q3
    ## 11: CytoTrol_CytoTrol_1.fcs /not debris/singlets/CD3/CD8/CD8_Q2/CD38 range
    ## 12: CytoTrol_CytoTrol_1.fcs  /not debris/singlets/CD3/CD8/CD8_Q2/HLA range
    ##                                  Parent Count ParentCount
    ##  1:                                root 87876      119531
    ##  2:                         /not debris 79845       87876
    ##  3:                /not debris/singlets 53135       79845
    ##  4:            /not debris/singlets/CD3 12862       53135
    ##  5:        /not debris/singlets/CD3/CD8  2331       12862
    ##  6:            /not debris/singlets/CD3 33653       53135
    ##  7:        /not debris/singlets/CD3/CD4   419       33653
    ##  8:        /not debris/singlets/CD3/CD4 11429       33653
    ##  9:        /not debris/singlets/CD3/CD4  4119       33653
    ## 10:        /not debris/singlets/CD3/CD4 17686       33653
    ## 11: /not debris/singlets/CD3/CD8/CD8_Q2  2331        2331
    ## 12: /not debris/singlets/CD3/CD8/CD8_Q2  2315        2331