![\"\"](\"https:\/\/docs.flowjo.com\/wp-content\/uploads\/2021\/08\/ScriptEd.png\")
\nThe Script Editor is a platform that allows the user to program their own routines into FlowJo using JavaScript without having to worry about compiling a program.<\/p>\n
The script editor allows low-level access to FlowJo, and uses JavaScript to execute some powerful operations. \u00a0 The editor can be found by default in the Cytometry band on the Workspace Tab.<\/p>\n\n
Scripts can be saved, copied, and shared.<\/p>\n
Below is a simple tutorial to guide you through the key features of the scripting interface.<\/p>\n
![\"FlowJo_Script_Editor-5\"](\"https:\/\/docs.flowjo.com\/wp-content\/uploads\/sites\/6\/2013\/08\/FlowJo_Script_Editor-5.jpg\")
<\/a>\nThe Script Editor’s console input provides a quick way to test code snippets, perform calculations, and view doc pages for FlowJo objects.<\/p>\n
To perform simple calculations, simply type them into the console input and press enter.\u00a0 To navigate previously typed commands, use the up and down arrows.\u00a0 Mac OSX provides basic Emacs bindings to text fields, so shortcuts Ctrl-A and Ctrl-E move the cursor to the beginning and end respectively of the input line.<\/p>\n
From here, example javascript commands which can be entered into the console input will be in blue<\/span><\/i><\/p>\n Examples:<\/p>\n Variables are defined just as in standard javascript.<\/p>\n Every script starts with a workspace object defined at the global scope.\u00a0 From this you have access to the workspace’s samples and groups.<\/p>\n To view functions and properties for FlowJo objects within the scripting console, simply access their ‘help’ property<\/p>\n Examples:<\/p>\n The script editor has the ability to read and write keywords within the sample.<\/p>\n Note that FlowJo never modifies the original sample.\u00a0 Any keyword modifications will only be remembered within the context of the current workspace.<\/i><\/p>\n A sample’s parameter info is stored under its parameter property.<\/p>\n The Script Editor’s syntax is optimized to select corresponding populations across many samples simultaneously. \u00a0Previously we have used the workspace.samples property to access samples loaded into the current workspace. \u00a0To the Script Editor, this is a special type of list which supports searching and filtering of the entire gating tree. \u00a0Populations are referenced as paths, much like files on a computer’s hard drive.<\/p>\n Examples:<\/p>\n Wild cards may be used to avoid specifying sections of the search path. \u00a0For example to find all T Cells gates childed to Lymphocytes gates, you could use the deep-wildcard operator “**”<\/p>\n While the “**” (double-star) operator may search any number of levels deep to find populations, the “*” (single-star) operator is used as a wild for a single gating level. \u00a0It is useful to select all direct children of a population:<\/p>\n Note that all the previous search techniques are available for individual samples or sample groups within the workspace.<\/p>\n Statistic-Tethered Gating: Allows you to bind a gate to statistics\u00a0from another population,\u00a0from another sample, or\u00a0from an aggregate across many samples.<\/p>\n500\/499<\/span><\/pre>\n= 1.002004008016032<\/pre>\n
math.sin(math.PI\/3)<\/span><\/pre>\n= 0.8660254037844386<\/pre>\n
Defining Variables<\/b><\/h3>\n
var a = 2 + 2;<\/span><\/pre>\nvar name = \"NK Cells\";<\/span><\/pre>\nvar population = name + a;<\/span><\/pre>\nconsole.log(\"Population name is \" + population);<\/span><\/pre>\nNavigating the workspace<\/b><\/h3>\n
var samples = workspace.samples;<\/span><\/pre>\nsamples.length<\/span><\/pre>\n= number of samples in the workspace<\/pre>\n
samples.names<\/span><\/pre>\n= names of all samples in workspace<\/pre>\n
workspace.groups.names<\/span><\/pre>\n= names of all sample groups in workspace<\/pre>\n
var exp1Group = workspace.groups['Exp. 1']<\/span><\/pre>\nexp1Group.samples.names<\/span><\/pre>\n= names of all samples in the group Exp. 1<\/pre>\n
var myGroup = workspace.groups.create(\"My Group\");<\/span><\/pre>\nmyGroup.add(workspace.samples.slice(0, 10, 2));<\/span><\/pre>\nvar sample1 = exp1Group.samples[0];<\/span><\/pre>\n= first sample in the Exp. 1 group<\/pre>\n
var sampleA1 = exp1Group.samples['A1.fcs'];<\/span><\/pre>\n= the sample named A1.fcs in the Exp. 1 group<\/pre>\n
Inline Help & Object Documentation<\/b><\/h3>\n
workspace.help<\/span><\/pre>\nvar samples = workspace.samples;<\/span><\/pre>\nsamples.help<\/span><\/pre>\nvar firstSample = samples[0];<\/span><\/pre>\nfirstSample.help<\/span><\/pre>\nmath.help<\/span><\/pre>\nstats.help<\/span><\/pre>\nSample Keywords<\/b><\/h3>\n
var fil = sampleA1.keywords['$FIL'];<\/span><\/pre>\nvar withoutExt = fil.substring(0, fil.lastIndexOf('.'));<\/span><\/pre>\nsampleA1.keywords['Custom Keyword'] = withoutExt;<\/span><\/pre>\nsample.keywords[\u2018PATIENT_ID\u2019] = filename.substring(4, 8);<\/span><\/pre>\nSample Parameters<\/h3>\n
var sample = workspace.samples[0];<\/span><\/pre>\n\u00a0= select first sample in workspace<\/span><\/pre>\nsample.parameters.names<\/span><\/pre>\n= names of all parameter within the sample<\/span><\/pre>\nvar fscParam = sample.parameters[1];<\/span><\/pre>\n\u00a0= the sample's first parameter (here we are presuming it to be FSC). \u00a0Note\nthat parameter indices are one-based, in order to correspond with the FCS parameter keywords.<\/span><\/span><\/pre>\nfscParam.name<\/span><\/pre>\n= \"FSC\"<\/span><\/pre>\nfscParam.help<\/span><\/pre>\n\u00a0= available properties for the parameter<\/span><\/pre>\nfscParam.stain<\/span><\/pre>\n\u00a0= The parameter's secondary or 'stain' name. \u00a0For FSC on most cytometers, this will be empty.<\/span><\/pre>\nTraversing The Gating Tree<\/h3>\n
var samples = workspace.samples;<\/span><\/pre>\nvar live = samples.populations(\"Live\");<\/span><\/pre>\n\u00a0= all first level Live cell gates in the workspace<\/pre>\n
var tcells = samples.populations(\"Live\/Singlets\/Lymphocytes\/T Cells\");<\/span><\/pre>\n\u00a0= all T Cells gates in the workspace, assuming a gating tree like that in the FlowJo Advanced Tutorial<\/pre>\n
var tcells = samples.populations(\"**\/Lymphocytes\/T Cells\");<\/span><\/pre>\nvar tcellsSubgates = tcells.populations(\"*\");<\/span><\/pre>\n\u00a0= All direct child gates of the previously selected T Cells populations.<\/pre>\n
var sample1 = workspace.samples[0];<\/span><\/pre>\nvar tcells = sample1.populations(\"**\/Lymphocytes\/T Cells\");<\/span><\/pre>\nvar exp1Samples = workspace.groups['Exp. 1'].samples;<\/span><\/pre>\n\u00a0= All samples within the Exp. 1 group<\/pre>\n
var exp1Lymphs = exp1Samples.populations(\"**\/Lymphocytes\");<\/span><\/pre>\nAnnotate based on statistical characteristics:<\/b><\/h4>\n
var important = median_CD4 > threshold && cd4_pos.count > 200;<\/span><\/pre>\nsample.keywords[\u2018IMPORTANT\u2019] = important;<\/span><\/pre>\n<\/h3>\n
Creating Gates<\/b><\/h3>\n
var rectangle_gate = population.gating.rectangle(\u201cTethered\u201d, \u201cPE-A\u201d, \u201cFITC-A\u201d);<\/span><\/pre>\nrectangle_gate.minX = mean_median_PE * 1.5;<\/span><\/pre>\nrectangle_gate.maxX = percentile_80_PE + 100;<\/span><\/pre>\n