@@ -340,7 +340,7 @@ gatingset_to_flowjo(gs, outFile)

 ## Next Steps

 See the [flowWorskspace](http://www.github.com/RGLab/flowWorkspace) and

-\[openCyto\](<http://www.github.com/RGLab/openCyto>) packages to learn

+[openCyto](<http://www.github.com/RGLab/openCyto>) packages to learn

 more about what can be done with `GatingSet` objects.

 ## Code of conduct

@@ -340,7 +340,7 @@ gatingset_to_flowjo(gs, outFile)

 ## Next Steps

 See the [flowWorskspace](http://www.github.com/RGLab/flowWorkspace) and

-\[openCyto\](<http://www.github.com/RGLab/openCyto>\] packages to learn

+\[openCyto\](<http://www.github.com/RGLab/openCyto>) packages to learn

 more about what can be done with `GatingSet` objects.

 ## Code of conduct

@@ -60,8 +60,8 @@ BiocManager::install("openCyto")

 install.packages("devtools") 

 library(devtools) #load it

-install_github("RGLab/flowWorkspace", ref="trunk")

-install_github("RGLab/openCyto", ref="trunk")

+install_github("RGLab/flowWorkspace")

+install_github("RGLab/openCyto")

 ```

 ### Installing from [BioConductor](https://www.bioconductor.org).

@@ -96,7 +96,7 @@ BiocManager::install("CytoML", version = "devel")

 ``` r

 install.packges("devtools")

-devtools::install_github("RGLab/CytoML", ref = "trunk")

+devtools::install_github("RGLab/CytoML")

 ```

   - [Latest GitHub Release](https://github.com/RGLab/CytoML/releases)

@@ -31,6 +31,17 @@ CytoML allows you to:

   - Share computational flow analyses with users on other platforms.

   - Perform comparative analyses between computational and manual gating

     approaches.

+    

+### Reporting Bugs or Issues

+- Use the issue template in github when creating a new issue. 

+- Follow the instructions in the template (do your background reading).

+- Search and verify that the issue hasn't already been addressed.

+- Check the Bioconductor support site. 

+- Make sure your flow packages are up to date.

+- THEN if your issue persists, file a bug report.

+

+Otherwise, we may close your issue without responding.

+

 ## INSTALLATION

@@ -1,3 +1,4 @@

+# <img src="logo_mid.png" align="right" />

 # CytoML: Cross-Platform Cytometry Data Sharing.

@@ -55,7 +55,7 @@ install_github("RGLab/openCyto", ref="trunk")

 ### Installing from [BioConductor](https://www.bioconductor.org).

   - [Current BioConductor

-    Release](https://doi.org/doi:10.18129/B9.bioc.CytoML)

+    Relase](https://doi.org/doi:10.18129/B9.bioc.CytoML)

 <!-- end list -->

@@ -83,7 +83,7 @@ BiocManager::install("CytoML", version = "devel")

 <!-- end list -->

 ``` r

-install.packages("devtools")

+install.packges("devtools")

 devtools::install_github("RGLab/CytoML", ref = "trunk")

 ```

@@ -92,7 +92,7 @@ devtools::install_github("RGLab/CytoML", ref = "trunk")

 <!-- end list -->

 ``` r

-install.packages("devtools")

+install.packges("devtools")

 devtools::install_github("RGLab/CytoML@*release")

 ```

@@ -126,18 +126,20 @@ To import data you need the xml workspace and the raw FCS files.

 ``` r

 library(CytoML)

-xmlfile <- system.file("extdata/cytotrol_tcell_cytobank.xml", package = "CytoML")

-fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "flowWorkspaceData"), full.names = T)

-gs <- cytobank2GatingSet(xmlfile, fcsFiles)

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

+ce <- open_cytobank_experiment(acsfile)

+xmlfile <- ce$gatingML

+fcsFiles <- list.files(ce$fcsdir, full.names = TRUE)

+gs <- cytobank_to_gatingset(xmlfile, fcsFiles)

 ```

 #### Import a [Diva](http://www.bdbiosciences.com/us/instruments/clinical/software/flow-cytometry-acquisition/bd-facsdiva-software/m/333333/overview) workspace.

 ``` r

-ws <- openDiva(system.file('extdata/diva/PE_2.xml', package = "flowWorkspaceData"))

+ws <- open_diva_xml(system.file('extdata/diva/PE_2.xml', package = "flowWorkspaceData"))

 # The path to the FCS files is stored in ws@path.

 # It can also be passed in to parseWorksapce via the `path` argument.

-gs <- parseWorkspace(ws, name = 2, subset = 1)

+gs <- diva_to_gatingset(ws, name = 2, subset = 1, swap_cols = FALSE)

 ```

 #### Interact with the gated data (`GatingSet`)

@@ -167,7 +169,7 @@ We can print all the cell populations defined in the gating tree.

 ``` r

 #show all the cell populations(/nodes)

-getNodes(gh)

+gs_get_pop_paths(gh)

 ```

     ## [1] "root"            "/P1"             "/P1/P2"          "/P1/P2/P3"      

@@ -177,7 +179,7 @@ We can extract the cell population statistics.

 ``` r

 #show the population statistics

-getPopStats(gh)

+gh_pop_compare_stats(gh)

 ```

     ##    openCyto.freq   xml.freq openCyto.count xml.count node

@@ -209,13 +211,13 @@ plotGate(gh)

 Because CytoML and flowWorkspace reproduce the entire analysis in a

 workspace in R, we have access to information about which cells are part

-of which cell populations.

+of which cell popualtions.

 flowWorkspace has convenience methods to extract the cells from specific

 cell populations:

 ``` r

-getData(gh,"P3")

+gh_pop_get_data(gh,"P3")

 ```

     ## flowFrame object '9a1897d7-ebc9-4077-aa34-6d9e1367fa67'

@@ -242,10 +244,11 @@ This returns a `flowFrame` with the cells in gate P3 (70% of the cells

 according to the plot).

 The matrix of expression can be extracted from a `flowFrame` using the

-`exprs()` method:

+`exprs()` method from the `flowCore` package:

 ``` r

-e <- exprs(getData(gh,"P3"))

+library(flowCore)

+e <- exprs(gh_pop_get_data(gh,"P3"))

 class(e)

 ```

@@ -273,9 +276,9 @@ colMeans(e[,8:15])

 ```

     ##            FITC-A              PE-A     PerCP-Cy5-5-A          PE-Cy7-A 

-    ##         0.8305544         1.3162145         0.7746655         0.8017132 

+    ##         0.8305630         1.3162132         0.7743459         0.8017827 

     ##             APC-A         APC-Cy7-A Bd Horizon V450-A  Pacific Orange-A 

-    ##         1.0482656         1.1636819         2.2960560         1.3684352

+    ##         1.0482663         1.1636818         2.2960554         1.3684453

 ### Export gated data to other platforms.

@@ -302,29 +305,30 @@ gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))

 ``` r

 #Cytobank

 outFile <- tempfile(fileext = ".xml")

-GatingSet2cytobank(gs, outFile)

+gatingset_to_cytobank(gs, outFile)

 ```

-    ## Warning in GatingSet2cytobank(gs, outFile): With 'cytobank.default.scale'

-    ## set to 'TRUE', data and gates will be re-transformed with cytobank's

-    ## default scaling settings, which may affect how gates look like.

+    ## Warning in gatingset_to_cytobank(gs, outFile): With

+    ## 'cytobank.default.scale' set to 'TRUE', data and gates will be re-

+    ## transformed with cytobank's default scaling settings, which may affect how

+    ## gates look like.

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpS4mDCu/file154f72419eb4f.xml"

+    ## [1] "/tmp/RtmpV1ZasG/file4b9f7e4e25c1.xml"

 ##### Export to FlowJo

 ``` r

 #flowJo

 outFile <- tempfile(fileext = ".wsp")

-GatingSet2flowJo(gs, outFile)

+gatingset_to_flowjo(gs, outFile)

 ```

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpS4mDCu/file154f774d1c840.wsp"

+    ## [1] "/tmp/RtmpV1ZasG/file4b9f18da0869.wsp"

 ## Next Steps

 See the [flowWorskspace](http://www.github.com/RGLab/flowWorkspace) and

-[openCyto](http://www.github.com/RGLab/openCyto] packages to learn

+\[openCyto\](<http://www.github.com/RGLab/openCyto>\] packages to learn

 more about what can be done with `GatingSet` objects.

 ## Code of conduct

@@ -55,7 +55,7 @@ install_github("RGLab/openCyto", ref="trunk")

 ### Installing from [BioConductor](https://www.bioconductor.org).

   - [Current BioConductor

-    Relase](https://doi.org/doi:10.18129/B9.bioc.CytoML)

+    Release](https://doi.org/doi:10.18129/B9.bioc.CytoML)

 <!-- end list -->

@@ -83,7 +83,7 @@ BiocManager::install("CytoML", version = "devel")

 <!-- end list -->

 ``` r

-install.packges("devtools")

+install.packages("devtools")

 devtools::install_github("RGLab/CytoML", ref = "trunk")

 ```

@@ -92,7 +92,7 @@ devtools::install_github("RGLab/CytoML", ref = "trunk")

 <!-- end list -->

 ``` r

-install.packges("devtools")

+install.packages("devtools")

 devtools::install_github("RGLab/CytoML@*release")

 ```

@@ -209,7 +209,7 @@ plotGate(gh)

 Because CytoML and flowWorkspace reproduce the entire analysis in a

 workspace in R, we have access to information about which cells are part

-of which cell popualtions.

+of which cell populations.

 flowWorkspace has convenience methods to extract the cells from specific

 cell populations:

@@ -324,7 +324,7 @@ GatingSet2flowJo(gs, outFile)

 ## Next Steps

 See the [flowWorskspace](http://www.github.com/RGLab/flowWorkspace) and

-\[openCyto\](<http://www.github.com/RGLab/openCyto>\] packages to learn

+[openCyto](http://www.github.com/RGLab/openCyto] packages to learn

 more about what can be done with `GatingSet` objects.

 ## Code of conduct

@@ -99,13 +99,14 @@ devtools::install_github("RGLab/CytoML@*release")

 ## Reproducible examples from the CytoML paper

   - A reproducible workflow can be found at the [RGLab

-    site](http://www.rglab.org/CytoML), and was prepared with the

-    release version of CytoML and its dependencies that can be installed

+    site](http://www.rglab.org/CytoML), and was prepared with version

+    1.7.10 of CytoML, R v3.5.0, and dependencies that can be installed

     by:

 <!-- end list -->

 ``` r

+# We recomend using R version 3.5.0

 devtools::install_github("RGLab/RProtoBufLib@v1.3.7")

 devtools::install_github("RGLab/cytolib@v1.3.2")

 devtools::install_github("RGLab/flowCore@v1.47.7")

@@ -308,7 +309,7 @@ GatingSet2cytobank(gs, outFile)

     ## set to 'TRUE', data and gates will be re-transformed with cytobank's

     ## default scaling settings, which may affect how gates look like.

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//Rtmposae0G/file54fa5ff95e51.xml"

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpS4mDCu/file154f72419eb4f.xml"

 ##### Export to FlowJo

@@ -318,7 +319,7 @@ outFile <- tempfile(fileext = ".wsp")

 GatingSet2flowJo(gs, outFile)

 ```

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//Rtmposae0G/file54fa7a2b1d84.wsp"

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpS4mDCu/file154f774d1c840.wsp"

 ## Next Steps

@@ -111,7 +111,7 @@ devtools::install_github("RGLab/cytolib@v1.3.2")

 devtools::install_github("RGLab/flowCore@v1.47.7")

 devtools::install_github("RGLab/flowWorkspace@v3.29.7")

 devtools::install_github("RGLab/openCyto@v1.19.2")

-devtools::install_github("RGLab/CytoML@1.7.10")

+devtools::install_github("RGLab/CytoML@v1.7.10")

 devtools::install_github("RGLab/ggcyto@v1.9.12")

 ```

@@ -308,7 +308,7 @@ GatingSet2cytobank(gs, outFile)

     ## set to 'TRUE', data and gates will be re-transformed with cytobank's

     ## default scaling settings, which may affect how gates look like.

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpM2PAHX/file161a6407f9e0c.xml"

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//Rtmposae0G/file54fa5ff95e51.xml"

 ##### Export to FlowJo

@@ -318,7 +318,7 @@ outFile <- tempfile(fileext = ".wsp")

 GatingSet2flowJo(gs, outFile)

 ```

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpM2PAHX/file161a615ad4c42.wsp"

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//Rtmposae0G/file54fa7a2b1d84.wsp"

 ## Next Steps

@@ -99,7 +99,21 @@ devtools::install_github("RGLab/CytoML@*release")

 ## Reproducible examples from the CytoML paper

   - A reproducible workflow can be found at the [RGLab

-    site](http://www.rglab.org/CytoML).

+    site](http://www.rglab.org/CytoML), and was prepared with the

+    release version of CytoML and its dependencies that can be installed

+    by:

+

+<!-- end list -->

+

+``` r

+devtools::install_github("RGLab/RProtoBufLib@v1.3.7")

+devtools::install_github("RGLab/cytolib@v1.3.2")

+devtools::install_github("RGLab/flowCore@v1.47.7")

+devtools::install_github("RGLab/flowWorkspace@v3.29.7")

+devtools::install_github("RGLab/openCyto@v1.19.2")

+devtools::install_github("RGLab/CytoML@1.7.10")

+devtools::install_github("RGLab/ggcyto@v1.9.12")

+```

 ## Examples

@@ -294,7 +308,7 @@ GatingSet2cytobank(gs, outFile)

     ## set to 'TRUE', data and gates will be re-transformed with cytobank's

     ## default scaling settings, which may affect how gates look like.

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmprlbWvn/filed187336c542a.xml"

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpM2PAHX/file161a6407f9e0c.xml"

 ##### Export to FlowJo

@@ -304,7 +318,7 @@ outFile <- tempfile(fileext = ".wsp")

 GatingSet2flowJo(gs, outFile)

 ```

-    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmprlbWvn/filed1877040990a.wsp"

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpM2PAHX/file161a615ad4c42.wsp"

 ## Next Steps

@@ -1,65 +1,319 @@

-# CytoML: A tool designed to work with openCyto to exchange gated cytometry data with third-party platforms

+# CytoML: Cross-Platform Cytometry Data Sharing.

-This package is designed to import/export the hierarchical gated flow data to/from the `openCyto` framework using the standard cytometry format: `gatingML2.0` and `FCS3.0`. This package makes use of our `GatingSet` R object and data model such that imported data can easily be manipulated and visualized in R using tools like `OpenCyto` and `ggcyto`.

+This package is designed to import/export the hierarchical gated

+cytometry data to and from R (specifically the

+[openCyto](https://github.com/RGLab/openCyto) framework) using the

+[`gatingML2.0`](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874733/)

+and [`FCS3.0`](http://isac-net.org/Resources/Standards/FCS3-1.aspx)

+cytometry data standards. This package makes use of the `GatingSet` R

+object and data model so that imported data can easily be manipulated

+and visualized in R using tools like

+[openCyto](https://github.com/RGLab/openCyto) and

+[ggCyto](https://github.com/RGLab/ggcyto).

+## What problems does CytoML solve?

-### INSTALLATION

+CytoML allows you to:

-```r

-# First, install it from bionconductor so that it will pull all the dependent packages automatically

-library(BiocInstalller)

-biocLite(openCyto) # may be older

-# Then, install the latest version from github using devtools package 

+  - Import manually gated data into R from

+    [Diva](http://www.bdbiosciences.com/us/instruments/clinical/software/flow-cytometry-acquisition/bd-facsdiva-software/m/333333/overview),

+    [FlowJo](https://www.flowjo.com/) and

+    [Cytobank](https://cytobank.org/).

+  - Combine manual gating strategies with automated gating strategies in

+    R.

+  - Export data gated manually, auto-gated, or gated using a combination

+    of manual and automated strategies from R to

+    [Diva](http://www.bdbiosciences.com/us/instruments/clinical/software/flow-cytometry-acquisition/bd-facsdiva-software/m/333333/overview),

+    [FlowJo](https://www.flowjo.com/) and

+    [Cytobank](https://cytobank.org/).

+  - Share computational flow analyses with users on other platforms.

+  - Perform comparative analyses between computational and manual gating

+    approaches.

+

+## INSTALLATION

+

+CytoML can be installed in several ways:

+

+### For all versions:

+

+For all versions, you must have dependencies installed

+

+``` r

+library(BiocManager)

+# This should pull all dependencies.

+BiocManager::install("openCyto") 

+

+# Then install latest dependencies from github, using devtools.

 install.packages("devtools") 

 library(devtools) #load it

+

 install_github("RGLab/flowWorkspace", ref="trunk")

 install_github("RGLab/openCyto", ref="trunk")

 ```

-### Import `XML` and `FCS` data from other platforms into `openCyto`

+### Installing from [BioConductor](https://www.bioconductor.org).

+

+  - [Current BioConductor

+    Relase](https://doi.org/doi:10.18129/B9.bioc.CytoML)

+

+<!-- end list -->

+

+``` r

+library(BiocManager)

+#this should pull all dependencies.

+BiocManager::install("CytoML", version = "devel") 

+```

+

+  - [Current BioConductor Development

+    Version](http://bioconductor.org/packages/devel/bioc/html/CytoML.html)

+

+<!-- end list -->

+

+``` r

+library(BiocManager)

+#this should pull all dependencies.

+BiocManager::install("CytoML", version = "devel") 

+```

+

+### Installing from GitHub

+

+  - [Latest GitHub Version](https://github.com/RGLab/CytoML)

+

+<!-- end list -->

+

+``` r

+install.packges("devtools")

+devtools::install_github("RGLab/CytoML", ref = "trunk")

+```

+

+  - [Latest GitHub Release](https://github.com/RGLab/CytoML/releases)

+

+<!-- end list -->

+

+``` r

+install.packges("devtools")

+devtools::install_github("RGLab/CytoML@*release")

+```

+

+## Reproducible examples from the CytoML paper

+

+  - A reproducible workflow can be found at the [RGLab

+    site](http://www.rglab.org/CytoML).

+

+## Examples

+

+### Import data

-#### Parse `gatingML` generated from `Cytobank` 

-```r

+To import data you need the xml workspace and the raw FCS files.

+

+#### Import `gatingML` generated from [Cytobank](https://cytobank.org/).

+

+``` r

 library(CytoML)

 xmlfile <- system.file("extdata/cytotrol_tcell_cytobank.xml", package = "CytoML")

-fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "flowWorkspaceData"), full = T)

+fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "flowWorkspaceData"), full.names = T)

 gs <- cytobank2GatingSet(xmlfile, fcsFiles)

 ```

-#### Parse `diva` workspace 

-```r

+#### Import a [Diva](http://www.bdbiosciences.com/us/instruments/clinical/software/flow-cytometry-acquisition/bd-facsdiva-software/m/333333/overview) workspace.

+

+``` r

 ws <- openDiva(system.file('extdata/diva/PE_2.xml', package = "flowWorkspaceData"))

+# The path to the FCS files is stored in ws@path.

+# It can also be passed in to parseWorksapce via the `path` argument.

 gs <- parseWorkspace(ws, name = 2, subset = 1)

 ```

+#### Interact with the gated data (`GatingSet`)

+

+We need `flowWorkspace` to interact with the imported data.

+

+``` r

+library(flowWorkspace)

+```

-### Then you can interact with the gated data (`GatingSet`)

+We can visualize the gating tree as follows:

-```r

+``` r

 #get the first sample

 gh <- gs[[1]]

+

 #plot the hierarchy tree

 plot(gh)

+```

+

+![](README_files/figure-gfm/unnamed-chunk-4-1.png)<!-- -->

+

+For more information see the

+[flowWorkspace](http://www.github.com/RGLab/flowWorkspace) package.

+

+We can print all the cell populations defined in the gating tree.

+

+``` r

 #show all the cell populations(/nodes)

 getNodes(gh)

+```

+

+    ## [1] "root"            "/P1"             "/P1/P2"          "/P1/P2/P3"      

+    ## [5] "/P1/P2/P3/P4"    "/P1/P2/P3/P4/P5"

+

+We can extract the cell population statistics.

+

+``` r

 #show the population statistics

 getPopStats(gh)

+```

+

+    ##    openCyto.freq   xml.freq openCyto.count xml.count node

+    ## 1:    1.00000000 1.00000000          19090     19090 root

+    ## 2:    0.93609219 0.93776847          17870     17902   P1

+    ## 3:    0.97991046 0.97994637          17511     17543   P2

+    ## 4:    0.70327223 0.70307245          12315     12334   P3

+    ## 5:    0.09378806 0.09404897           1155      1160   P4

+    ## 6:    0.95151515 0.94827586           1099      1100   P5

+

+The `openCyto.count` column shows the cell counts computed via the

+import. The `xml.count` column shows the cell counts computed by FlowJo

+(note not all platforms report cell counts in the workspace). It is

+normal for these to differ by a few cells due to numerical differences

+in the implementation of data transformations. CytoML and openCyto are

+*reproducing* the data analysis from the raw data based on the

+information in the workspace.

+

+We can plot all the gates defined in the workspace.

+

+``` r

 #plot the gates

 plotGate(gh) 

 ```

-### Export the existing `GatingSet` from `openCyto` to `Cytobank` or `flowJo`

+![](README_files/figure-gfm/unnamed-chunk-7-1.png)<!-- -->

+

+#### Access information about cells in a specific population.

+

+Because CytoML and flowWorkspace reproduce the entire analysis in a

+workspace in R, we have access to information about which cells are part

+of which cell popualtions.

+

+flowWorkspace has convenience methods to extract the cells from specific

+cell populations:

+

+``` r

+getData(gh,"P3")

+```

+

+    ## flowFrame object '9a1897d7-ebc9-4077-aa34-6d9e1367fa67'

+    ## with 12315 cells and 15 observables:

+    ##                   name desc  range  minRange maxRange

+    ## $P1               Time <NA> 262144 0.0000000 262144.0

+    ## $P2              FSC-A <NA> 262144 0.0000000 262144.0

+    ## $P3              FSC-H <NA> 262144 0.0000000 262144.0

+    ## $P4              FSC-W <NA> 262144 0.0000000 262144.0

+    ## $P5              SSC-A <NA> 262144 0.0000000 262144.0

+    ## $P6              SSC-H <NA> 262144 0.0000000 262144.0

+    ## $P7              SSC-W <NA> 262144 0.0000000 262144.0

+    ## $P8             FITC-A <NA> 262144 0.1516347      4.5

+    ## $P9               PE-A  CD3 262144 0.2953046      4.5

+    ## $P10     PerCP-Cy5-5-A <NA> 262144 0.4697134      4.5

+    ## $P11          PE-Cy7-A <NA> 262144 0.5638024      4.5

+    ## $P12             APC-A  bob 262144 0.7838544      4.5

+    ## $P13         APC-Cy7-A Viab 262144 0.6886181      4.5

+    ## $P14 Bd Horizon V450-A CD44 262144 0.6413334      4.5

+    ## $P15  Pacific Orange-A  CD8 262144 0.3376040      4.5

+    ## 231 keywords are stored in the 'description' slot

+

+This returns a `flowFrame` with the cells in gate P3 (70% of the cells

+according to the plot).

+

+The matrix of expression can be extracted from a `flowFrame` using the

+`exprs()` method:

+

+``` r

+e <- exprs(getData(gh,"P3"))

+class(e)

+```

+

+    ## [1] "matrix"

+

+``` r

+dim(e)

+```

+

+    ## [1] 12315    15

+

+``` r

+colnames(e)

+```

+

+    ##  [1] "Time"              "FSC-A"             "FSC-H"            

+    ##  [4] "FSC-W"             "SSC-A"             "SSC-H"            

+    ##  [7] "SSC-W"             "FITC-A"            "PE-A"             

+    ## [10] "PerCP-Cy5-5-A"     "PE-Cy7-A"          "APC-A"            

+    ## [13] "APC-Cy7-A"         "Bd Horizon V450-A" "Pacific Orange-A"

-```r

+``` r

+#compute the MFI of the fluorescence channels.

+colMeans(e[,8:15])

+```

+

+    ##            FITC-A              PE-A     PerCP-Cy5-5-A          PE-Cy7-A 

+    ##         0.8305544         1.3162145         0.7746655         0.8017132 

+    ##             APC-A         APC-Cy7-A Bd Horizon V450-A  Pacific Orange-A 

+    ##         1.0482656         1.1636819         2.2960560         1.3684352

+

+### Export gated data to other platforms.

+

+In order to export gated data, it must be in `GatingSet`

+format.

+

+#### Export a `GatingSet` from R to [Cytobank](https://cytobank.org/) or [FlowJo](https://www.flowjo.com/)

+

+Load something to export.

+

+``` r

 dataDir <- system.file("extdata",package="flowWorkspaceData")

 gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))

+```

+

+    ## loading R object...

+

+    ## loading tree object...

+

+    ## Done

+##### Export to Cytobank

+

+``` r

 #Cytobank

 outFile <- tempfile(fileext = ".xml")

 GatingSet2cytobank(gs, outFile)

+```

+

+    ## Warning in GatingSet2cytobank(gs, outFile): With 'cytobank.default.scale'

+    ## set to 'TRUE', data and gates will be re-transformed with cytobank's

+    ## default scaling settings, which may affect how gates look like.

+

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmprlbWvn/filed187336c542a.xml"

+

+##### Export to FlowJo

+``` r

 #flowJo

 outFile <- tempfile(fileext = ".wsp")

 GatingSet2flowJo(gs, outFile)

 ```

+

+    ## [1] "/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmprlbWvn/filed1877040990a.wsp"

+

+## Next Steps

+

+See the [flowWorskspace](http://www.github.com/RGLab/flowWorkspace) and

+\[openCyto\](<http://www.github.com/RGLab/openCyto>\] packages to learn

+more about what can be done with `GatingSet` objects.

+

+## Code of conduct

+

+Please note that this project is released with a [Contributor Code of

+Conduct](CODE_OF_CONDUCT.md). By participating in this project you agree

+to abide by its terms.

@@ -17,8 +17,9 @@ install_github("RGLab/flowWorkspace", ref="trunk")

 install_github("RGLab/openCyto", ref="trunk")

 ```

-### Import `gatingML` and `FCS` data from other platforms into `openCyto`

+### Import `XML` and `FCS` data from other platforms into `openCyto`

+#### Parse `gatingML` generated from `Cytobank` 

 ```r

 library(CytoML)

 xmlfile <- system.file("extdata/cytotrol_tcell_cytobank.xml", package = "CytoML")

@@ -26,6 +27,13 @@ fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "f

 gs <- cytobank2GatingSet(xmlfile, fcsFiles)

 ```

+#### Parse `diva` workspace 

+```r

+ws <- openDiva(system.file('extdata/diva/PE_2.xml', package = "flowWorkspaceData"))

+gs <- parseWorkspace(ws, name = 2, subset = 1)

+```

+

+

 ### Then you can interact with the gated data (`GatingSet`)

 ```r

@@ -9,7 +9,7 @@ This package is designed to import/export the hierarchical gated flow data to/fr

 ```r

 # First, install it from bionconductor so that it will pull all the dependent packages automatically

 library(BiocInstalller)

-bicLite(openCyto) # may be older

+biocLite(openCyto) # may be older

 # Then, install the latest version from github using devtools package 

 install.packages("devtools") 

 library(devtools) #load it

@@ -53,5 +53,5 @@ GatingSet2cytobank(gs, outFile)

 #flowJo

 outFile <- tempfile(fileext = ".wsp")

-GatingSet2cytobank(gs, outFile)

+GatingSet2flowJo(gs, outFile)

 ```

@@ -23,7 +23,7 @@ install_github("RGLab/openCyto", ref="trunk")

 library(CytoML)

 xmlfile <- system.file("extdata/cytotrol_tcell_cytobank.xml", package = "CytoML")

 fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "flowWorkspaceData"), full = T)

-gs <- parse.gatingML(xmlfile, fcsFiles)

+gs <- cytobank2GatingSet(xmlfile, fcsFiles)

 ```

 ### Then you can interact with the gated data (`GatingSet`)

@@ -41,11 +41,17 @@ getPopStats(gh)

 plotGate(gh) 

 ```

-### Export the existing `GatingSet` from `openCyto` to `gatingML` 

+### Export the existing `GatingSet` from `openCyto` to `Cytobank` or `flowJo`

 ```r

 dataDir <- system.file("extdata",package="flowWorkspaceData")

 gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))

+

+#Cytobank

 outFile <- tempfile(fileext = ".xml")

-GatingSet2GatingML(gs, outFile)

+GatingSet2cytobank(gs, outFile)

+

+#flowJo

+outFile <- tempfile(fileext = ".wsp")

+GatingSet2cytobank(gs, outFile)

 ```

@@ -1,7 +1,7 @@

-# CytoML: A tool for openCyto to exchange the hierarchical gated cytometry data with third-party platform

+# CytoML: A tool designed to work with openCyto to exchange gated cytometry data with third-party platforms

-This package is designed import/export the hierarchical gated flow data to/from `openCyto` framework using the standard cytometry format: `gatingML2.0` and `FCS3.0`.

+This package is designed to import/export the hierarchical gated flow data to/from the `openCyto` framework using the standard cytometry format: `gatingML2.0` and `FCS3.0`. This package makes use of our `GatingSet` R object and data model such that imported data can easily be manipulated and visualized in R using tools like `OpenCyto` and `ggcyto`.

 ### INSTALLATION

@@ -15,10 +15,9 @@ install.packages("devtools")

 library(devtools) #load it

 install_github("RGLab/flowWorkspace", ref="trunk")

 install_github("RGLab/openCyto", ref="trunk")

-

 ```

-### Import `gatingML` and `FCS` data from other platform into `openCyto`

+### Import `gatingML` and `FCS` data from other platforms into `openCyto`

 ```r

 library(CytoML)

@@ -28,6 +27,7 @@ gs <- parse.gatingML(xmlfile, fcsFiles)

 ```

 ### Then you can interact with the gated data (`GatingSet`)

+

 ```r

 #get the first sample

 gh <- gs[[1]]

@@ -39,13 +39,13 @@ getNodes(gh)

 getPopStats(gh)

 #plot the gates

 plotGate(gh) 

-

 ```

 ### Export the existing `GatingSet` from `openCyto` to `gatingML` 

+

 ```r

 dataDir <- system.file("extdata",package="flowWorkspaceData")

 gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))

 outFile <- tempfile(fileext = ".xml")

 GatingSet2GatingML(gs, outFile)

-```

\ No newline at end of file

+```

@@ -1,5 +1,5 @@

-# CytoML: A tool for openCyto exchange the hierarchical gated cytometry data with third-party platform

+# CytoML: A tool for openCyto to exchange the hierarchical gated cytometry data with third-party platform

 This package is designed import/export the hierarchical gated flow data to/from `openCyto` framework using the standard cytometry format: `gatingML2.0` and `FCS3.0`.

new file mode 100644

@@ -0,0 +1,51 @@

+

+# CytoML: A tool for openCyto exchange the hierarchical gated cytometry data with third-party platform

+

+This package is designed import/export the hierarchical gated flow data to/from `openCyto` framework using the standard cytometry format: `gatingML2.0` and `FCS3.0`.

+

+

+### INSTALLATION

+

+```r

+# First, install it from bionconductor so that it will pull all the dependent packages automatically

+library(BiocInstalller)

+bicLite(openCyto) # may be older

+# Then, install the latest version from github using devtools package 

+install.packages("devtools") 

+library(devtools) #load it

+install_github("RGLab/flowWorkspace", ref="trunk")

+install_github("RGLab/openCyto", ref="trunk")

+

+```

+

+### Import `gatingML` and `FCS` data from other platform into `openCyto`

+

+```r

+library(CytoML)

+xmlfile <- system.file("extdata/cytotrol_tcell_cytobank.xml", package = "CytoML")

+fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "flowWorkspaceData"), full = T)

+gs <- parse.gatingML(xmlfile, fcsFiles)

+```

+

+### Then you can interact with the gated data (`GatingSet`)