### CellChat Basic Setup

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_multiomics_data.html

Basic setup for CellChat analysis, including loading the library and creating a CellChat object. Assumes a Seurat object is available.

```r
library(CellChat)
# Load the Seurat object
# seurat_obj <- readRDS("path/to/your/seurat_object.rds")
# Create CellChat object
# cellchat <- createCellChat(object = seurat_obj, group.by = "cell_type_column")
# Set the database directory
# CellChatDB <- CellChatDB.mouse
# showDatabaseCategory(CellChatDB)
# Use CellChatDB.mouse or CellChatDB.human
# Set the organism
# CellChat <- setVariable(CellChat, organism = "mouse")
# Subset the database by cell-cell communication pathways
# CellChat <- subsetData(CellChat, assay = "RNA", slot = "data")
# options($=TRUE)
# CellChat <- identifyOverExpressedGenes(CellChat)
# CellChat <- identifyOverExpressedInteractions(CellChat)
# project data and compute cell-cell communication probabilities
# CellChat <- computeCommunProb(CellChat, raw.use = TRUE)
# Filter out the cell-cell communication that are inferred as global
# CellChat <- filterCommunication(CellChat, min.cells = 10)
# Infer the signaling pathway
# CellChat <- inferPathway(CellChat)
# Compute communication probability and 통계
# CellChat <- computeCommunProbPathway(CellChat)
# Calculate the aggregated cell-cell communication network
# CellChat <- aggregateNet(CellChat)
# Plot all the interactions ofany signaling pathway
# groupSize <- as.numeric(table(CellChat@idents))
# par(mfrow = c(3,4), xpd = TRUE)
# netVisual_circle(CellChat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions")
# netVisual_circle(CellChat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")
# Plot aggregated cell-cell communication network
# mat <- CellChat@net$weight
# par(mfrow = c(3,4), xpd = TRUE)
# for (i in 1:nrow(mat)) {
#   # ietwork for each ligand-receptor pair
#   netVisual_heatmap(CellChat, signaling = rownames(mat)[i], color.heatmap = "Reds")
# }
# Save CellChat object
# saveRDS(CellChat, file = "cellchat_object.rds")
# Load CellChat object
# cellchat <- readRDS("cellchat_object.rds")
```

--------------------------------

### Create a New CellChat Database (Example)

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/Update-CellChatDB.html

Demonstrates creating a new CellChat database object from scratch. This example defines new interaction data and metadata.

```R
new_data = data.frame(interaction_type = "paracrine", 
                        Ev = "ligand", 
                        De = "receptor", 
                        Evidence = "literature", 
                        PPI = "protein_protein_interaction", 
                        Annotation = "annotation", 
                        Evidence_novel = "novel", 
                        annotated_complex = "complex")
new_meta = data.frame(CellGroup = "cell_type", 
                      CellID = "cell_id")
new_cellchatdb = list(data = new_data, meta = new_meta)
# Create a new CellChat database
new_cellchatdb = list(data = new_data, meta = new_meta)
```

--------------------------------

### Install CellChat

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_multiomics_data.html

Install the CellChat package using BiocManager. Ensure you have R and BiocManager installed.

```r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CellChat")
```

--------------------------------

### Install BiocNeighbors R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the BiocNeighbors package using BiocManager if you encounter installation issues with Spatial CellChat.

```r
BiocManager::install("BiocNeighbors")
```

--------------------------------

### Initialize Affix Plugin

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_multiomics_data.html

Initializes the affix plugin for Bootstrap. Use this to make elements stick to the viewport when scrolling.

```javascript
+function(a) {
  "use strict";
  function b(b) {
    return this.each(function() {
      var d = a(this),
        e = d.data("bs.affix"),
        f = "object" == typeof b && b;
      e || d.data("bs.affix", e = new c(this, f)),
        "string" == typeof b && e[b]()
    })
  }
  var c = function(b, d) {
    this.options = a.extend({}, c.DEFAULTS, d),
      this.$target = a(this.options.target).on("scroll.bs.affix.data-api", a.proxy(this.checkPosition, this)).on("click.bs.affix.data-api", a.proxy(this.checkPositionWithEventLoop, this)),
      this.$element = a(b),
      this.affixed = null,
      this.unpin = null,
      this.pinnedOffset = null,
      this.checkPosition()
  };
  c.VERSION = "3.3.5",
  c.RESET = "affix affix-top affix-bottom",
  c.DEFAULTS = {
    offset: 0,
    target: window
  },
  c.prototype.getState = function(a, b, c, d) {
    var e = this.$target.scrollTop(),
      f = this.$element.offset(),
      g = this.$target.height();
    if (null != c && "top" == this.affixed) return c > e ? "top" : !1;
    if ("bottom" == this.affixed) return null != c ? e + this.unpin <= f.top ? !1 : "bottom" : a - d >= e + g ? !1 : "bottom";
    var h = null == this.affixed,
      i = h ? e : f.top,
      j = h ? g : b;
    return null != c && c >= e ? "top" : null != d && i + j >= a - d ? "bottom" : !1
  },
  c.prototype.getPinnedOffset = function() {
    if (this.pinnedOffset) return this.pinnedOffset;
    this.$element.removeClass(c.RESET).addClass("affix");
    var a = this.$target.scrollTop(),
      b = this.$element.offset();
    return this.pinnedOffset = b.top - a
  },
  c.prototype.checkPositionWithEventLoop = function() {
    setTimeout(a.proxy(this.checkPosition, this), 1)
  },
  c.prototype.checkPosition = function() {
    if (this.$element.is(":hidden")) return
  };
  var d = a.fn.affix;
  a.fn.affix = b,
  a.fn.affix.Constructor = c,
  a.fn.affix.noConflict = function() {
    return a.fn.affix = d,
      this
  }
}(jQuery)
```

--------------------------------

### Install CellChat v1/v2 R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Use this command to install the CellChat R package (v1/v2) from GitHub. Ensure devtools is installed.

```r
devtools::install_github("jinworks/CellChat")
```

--------------------------------

### Install Spatial CellChat R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Use this command to install the Spatial CellChat R package from GitHub. Ensure devtools is installed.

```r
devtools::install_github("jinworks/SpatialCellChat")
```

--------------------------------

### Load and Prepare Data for Comparison

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/Comparison_analysis_of_multiple_datasets.html

Loads and prepares data from multiple sources for comparative analysis. Ensure data is in a compatible format before proceeding.

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load datasets
df1 = pd.read_csv('dataset1.csv')
df2 = pd.read_csv('dataset2.csv')
df3 = pd.read_csv('dataset3.csv')

# Basic preprocessing (example: handling missing values)
df1.fillna(method='ffill', inplace=True)
df2.fillna(method='ffill', inplace=True)
df3.fillna(method='ffill', inplace=True)

# Align columns if necessary (example: selecting common columns)
common_cols = list(set(df1.columns) & set(df2.columns) & set(df3.columns))
df1 = df1[common_cols]
df2 = df2[common_cols]
df3 = df3[common_cols]

print("Data loaded and preprocessed successfully.")
```

--------------------------------

### Set Up CellChat with a Reference Database

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_transcriptomics_data.html

Initialize CellChat with a cell-cell communication database. This step is crucial for identifying signaling ligands and receptors. The tutorial uses the human database by default.

```R
# Set the cellchat database (e.g., human or mouse)
# Use 'CellChatDB.human' or 'CellChatDB.mouse'
# Download the database if not already present
# CellChatDB <- CellChatDB.download.from.web(path = "/path/to/save/db")
# cellchat <- addMeta(cellchat, meta = list(type = "spatial"))

# Load the cellchat database
CellChatDB <- CellChatDB.human

# Use a subset of the database for faster computation if needed
# Show available cell chat databases
# showDatabaseCategory(CellChatDB)

# Set the database for CellChat object
cellchat <- setDB(cellchat, DB = CellChatDB)
```

--------------------------------

### Install CellChat

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat-vignette.html

Install the CellChat package from Bioconductor. Ensure you have R version 4.0 or higher.

```R
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Install CellChat from Bioconductor
BiocManager::install("CellChat")
```

--------------------------------

### Install ComplexHeatmap R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the ComplexHeatmap R package from GitHub if needed for CellChat.

```r
devtools::install_github("jokergoo/ComplexHeatmap")
```

--------------------------------

### Initialize and Show Bootstrap Modal

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_transcriptomics_data.html

This snippet demonstrates how to initialize and show a Bootstrap modal dialog. It handles event listeners for closing and focus management.

```javascript
var d,e,f,g,h,i,j;

+function(a){function b(b){return this.each(function(){var d=a(this),e=d.data("bs.modal"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b);
e||d.data("bs.modal",e=new c(this,f)),"string"==typeof b?e[b]():f.show&&e.show()})}

var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=!1,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded")},this)),a(document).on("keydown.bs.modal",a.proxy(function(a){27==a.which&&this.isShown&&this.hide()},this)),"undefined"==typeof this.options.allowFocusInput&& (this.options.allowFocusInput=!0)}
c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={"backdrop":!0,"keyboard":!0,"show":!0,"remote":!1},
c.prototype.toggle=function(a){return this.isShown?this.hide():b.call(a,a)},c.prototype.show=function(){var c=this,d=a.Event("show.bs.modal",{relatedTarget:this});this.$element.trigger(d);if(d.isDefaultPrevented())return;this.$body.addClass("modal-open"),this.options.override=
!0,this.checkScrollbar(),this.setScrollbar(),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.backdrop(a.proxy(function(){c.$element.trigger("shown.bs.modal"),c.enforceFocus()},this))},c.prototype.hide=function(b){b&&b.preventDefault();var c=this;this.isShown=!!a(this.$element).trigger(b=a.Event("hide.bs.modal")).isDefaultPrevented();if(!this.isShown)return;a(document,'.modal-open').off('focusin.bs.modal'),this.$element.off('keydown.dismiss.bs.modal').off('click.dismiss.bs.modal').removeClass('in').one('transitionend',function(){c.$element.hide().trigger('hidden.bs.modal')}).emulateTransitionEnd(c.TRANSITION_DURATION)},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(a.proxy(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")},this))},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};f?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||
 e&&e.replace(/.*(?=#\[^\\s]+\s*$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||
 f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={"animation":!0,"placement":"top","selector":!1,"template":"<div class=\"tooltip\" role=\"tooltip\"><div class=\"tooltip-arrow\"></div><div class=\"tooltip-inner\"></div></div>","trigger":"hover focus","title":"","delay":0,"html":!1,"container":!1,"viewport":{"selector":"body","padding":0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={"click":!1,"hover":!1,"focus":!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this+" tooltip")}
```

--------------------------------

### Install RcppML R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the RcppML R package from GitHub if needed for Spatial CellChat.

```r
devtools::install_github("zdebruine/RcppML")
```

--------------------------------

### Initialize Tabsets

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat-vignette.html

Builds tabsets for navigation. This is a common utility for organizing content within web pages.

```javascript
$(document).ready(function () {
    window.buildTabsets("TOC");
});
```

--------------------------------

### Affix Plugin Initialization

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/Update-CellChatDB.html

Initializes the affix plugin, which pins elements to the viewport when they reach a certain scroll position. Handles 'affix.bs.affix' and 'unaffix.bs.affix' events.

```javascript
a(window).on("load.bs.affix.data-api", function(b) {
  a('[data-spy="affix"]').each(function() {
    var c = a(this)
    var d = c.data('offset') ? c.data('offset') : {}
    if (d.target) {
      d['target'] = '> ' + d.target
    }
    c.affix(d)
  })
})
```

--------------------------------

### Install ALRA R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the ALRA R package from GitHub if needed for Spatial CellChat.

```r
devtools::install_github("KlugerLab/ALRA")
```

--------------------------------

### Install MERINGUE R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the MERINGUE R package from GitHub if needed for Spatial CellChat.

```r
devtools::install_github("JEFworks-Lab/MERINGUE")
```

--------------------------------

### AJAX Request Setup and Execution with jQuery

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_multiomics_data.html

Configures and initiates AJAX requests. Handles URL construction, data processing, cross-domain requests, and response handling. Includes mechanisms for global AJAX event handling.

```javascript
var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){
var t,n;
if(!e||"string"!=typeof e)return null;
try{
t=(new C.DOMParser).parseFromString(e,"text/xml")
}catch(e){}
return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):
e)),t
};
var Ct=/\\\[\\\]/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;
function At(n,e,r,i){
var t;
if(Array.isArray(e))S.each(e,function(e,t){
r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)
});
else if(r||"object"!==w(e))i(n,e);
else for(t in e)At(n+"["+t+"]",e[t],r,i)
}
S.param=function(e,t){
var n,r=[],i=function(e,t){
var n=m(t)?t():t;
r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)
};
if(null==e)return"";
if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){
i(this.name,this.value)
});
else for(n in e)At(n,e[n],t,i);
return r.join("&")
},
S.fn.extend({
serialize:function(){
return S.param(this.serializeArray())
},
serializeArray:function(){
return this.map(function(){
var e=S.prop(this,"elements");
return e?S.makeArray(e):this
}).filter(function(){
var e=this.type;
return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))
}).map(function(e,t){
var n=S(this).val();
return null==n?null:Array.isArray(n)?S.map(n,function(e){
return{name:t.name,value:e.replace(Et,"\r\n")}
}):{name:t.name,value:n.replace(Et,"\r\n")}
}).get()
}});
var Nt=/%20/g,jt=/#[^.]*$/,Dt=/(\[?&])_=[^&]*$/,qt=/^(.*?:)[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*", "*"),Mt=E.createElement("a");
function Wt(t,i,o,a){
var s={},u=t===Pt;
function l(e){
var r;
return s[e]=!0,S.each(t[e]||[],function(e,t){
var n=t(i,o,a);
return"string"!=typeof n||u||s[n]?u?! (r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)
}),r
}
return l(i.dataTypes[0])||!s["*"]&&l("*")
}
function Ft(e,t){
var n,r,i=S.ajaxSettings.flatOptions||{};
for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);
return r&&S.extend(!0,e,r),e
}
Mt.href=bt.href,S.extend({
active:0,
lastModified:{},
etag:{},
ajaxSettings:{
url:bt.href,
type:"GET",
isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),
global:!0,
processData:!0,
async:!0,
contentType:"application/x-www-form-urlencoded; charset=UTF-8",
accepts:{
"*":Rt,
text:"text/plain",
html:"text/html",
xml:"application/xml, text/xml",
json:"application/json, text/javascript"
},
contents:{
xml:/\bxml\b/,
html:/\bhtml/,
json:/\bjson\b/
},
responseFields:{
xml:"responseXML",
text:"responseText",
json:"responseJSON"
},
converters:{
"* text":String,
"text html":!0,
"text json":JSON.parse,
"text xml":S.parseXML
},
flatOptions:{
url:!0,
context:!0
}
},
ajaxSetup:function(e,t){
return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)
},
ajaxPrefilter:It(Ot),
ajaxTransport:It(Pt),
ajax:function(e,t){
"object"==typeof e&&(t=e,e=void 0),
t=t||{};
var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),
y=v.context||v,
m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,
x=S.Deferred(),
b=S.Callbacks("once memory"),
w=v.statusCode||{},
a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){
var t;
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if(!n){
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while(t=qt.exec(p))
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}
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},
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return h?p:null
},
setRequestHeader:function(e,t){
return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this
},
overrideMimeType:function(e){
return null==h&&(v.mimeType=e),this
},
statusCode:function(e){
var t;
if(e)if(h)T.always(e[T.status]);
else for(t in e)w[t]=[w[t],e[t]]
return this
},
abort:function(e){
var t=e||u;
return c&&c.abort(t),l(0,t),this
}};
if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,
v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;
for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.conte
```

--------------------------------

### Initialize and Run CellChat

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_multiomics_data.html

This snippet demonstrates the basic initialization and running of CellChat for analyzing cell-cell communication. Ensure you have the necessary libraries installed.

```python
import cellchat
import scanpy as sc

# Load the data
cellchat = cellchat.CellChat(object, group.by = "celltype")

# Set the database
cellchat.data.ann.var['highly_variable'] = True
cellchat.useData(data.use = 'raw', symbolic.name = "raw")
cellchat.selectCommunicatingCells(source.cells = "celltype", target.cells = "celltype")

# Compute the communication probability and infer the communication network
cellchat.computeCommunProb(population.size = 30, raw.use = TRUE, population.threshold = 0.001)
cellchat.filterCommunication(min.cells = 10, do.filter.by.assumption = TRUE)

# Infer the cell-cell communication network
cellchat.inference.RNA(object.list = cellchat, max.size = 10, nIter = 400, iter.max = 300, num.threads = 4)
cellchat.computeNet(object.list = cellchat, net.weight = TRUE, optional.args = list(comparison = c("Ctrl", "Expt"), …))
```

--------------------------------

### CellChat Data Input Example

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_multiomics_data.html

Example of input data format for CellChat, typically a count matrix and cell metadata.

```R
data.input <- read.csv(file = "count.csv", row.names = 1)
```

```R
meta <- read.csv(file = "meta.csv", row.names = 1)
```

--------------------------------

### Install CellChat Package

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_transcriptomics_data.html

Installs the CellChat R package and its dependencies. Ensure you have a compatible R version and necessary system libraries.

```R
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("sqjin/CellChat", ref = "main", build_vignettes = TRUE)
```

--------------------------------

### CellChat Database Setup

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_transcriptomics_data.html

Sets the reference database for CellChat analysis. This step is essential for identifying ligand-receptor interactions.

```r
# Set the reference database
cellchat <- setReference(cellchat, "path/to/c2c_database.v2.csv")
```

--------------------------------

### AJAX Request Setup

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_transcriptomics_data.html

Configures the default settings for all AJAX requests. This function can be used to set global AJAX options.

```javascript
S.ajaxSetup=function(e,t){
return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)
}
```

--------------------------------

### Install circlize R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the circlize R package from GitHub if needed for CellChat. Version 0.4.12 or higher is recommended.

```r
devtools::install_github("jokergoo/circlize")
```

--------------------------------

### Install UMAP Python Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the UMAP Python package for dimension reduction using pip. Refer to the GitHub repository for troubleshooting.

```python
pip install umap-learn
```

--------------------------------

### Load and Preprocess Data for Comparison

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/Comparison_analysis_of_multiple_datasets.html

Loads and preprocesses data from multiple sources. Ensure data is in a compatible format before proceeding.

```python
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
df1 = pd.read_csv('dataset1.csv')
df2 = pd.read_csv('dataset2.csv')
df3 = pd.read_csv('dataset3.csv')

# Combine dataframes
df = pd.concat([df1, df2, df3], ignore_index=True)

# Preprocessing
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df.select_dtypes(include=np.number))

# Normalize data
df_normalized = normalize(df_scaled)

# Handle missing values (example: fill with mean)
df.fillna(df.mean(), inplace=True)

```

--------------------------------

### Set Up CellChat for Analysis

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/CellChat_analysis_of_spatial_multiomics_data.html

Set up the CellChat object with necessary information, including cell type labels and the pathway database. This step is crucial before proceeding with inference.

```R
cellchat <- addMeta(cellchat, meta = seu.object@meta.data$cell_type, meta.name = "cell_type")
cellchat <- setVariableIdentity(cellchat, identity.name = "cell_type")

# Set the ligand-receptor interaction database for CellChat analysis
# Use 'pathway.mouse' or 'pathway.human' depending on your data
# For example, using the human database:
cellchat@DB <- CellChatDB.human

```

--------------------------------

### Initialize Highlight.js for Code Highlighting (JavaScript)

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/Comparison_analysis_of_multiple_datasets.html

This script configures and initializes the Highlight.js library for syntax highlighting on the page. It ensures that code blocks are correctly identified and styled, with a fallback for when the page is fully loaded.

```javascript
if (window.hljs) {
 hljs.configure({languages: []});
 hljs.initHighlightingOnLoad();
 if (document.readyState && document.readyState === "complete") {
 window.setTimeout(function() {
 hljs.initHighlighting();
 }, 0);
 }
}
```

--------------------------------

### Install NMF R Package

Source: https://github.com/jinworks/cellchat/blob/main/README.md

Install the NMF R package using install.packages. Version 0.23.0 or higher is recommended. Check provided links for troubleshooting.

```r
install.packages('NMF')
```

--------------------------------

### Load and preprocess a second single-cell dataset

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/Comparison_analysis_of_multiple_datasets_with_different_cellular_compositions.html

Loads and preprocesses a second single-cell dataset, similar to the first. Ensure the file path is correct.

```python
adata2 = sc.read_h5ad("data/Human_kidney_single_cell_data.h5ad")
adata2.var_names_make_unique()
sc.pp.filter_cells(adata2, min_genes=200)
sc.pp.filter_genes(adata2, min_cells=3)
sc.pp.normalize_total(adata2, target_sum=1e4)
sc.pp.log1p(adata2)
sc.pp.highly_variable_genes(adata2, n_top_genes=2000, subset=True)
sc.tl.pca(adata2, svd_solver='arpack')
```

--------------------------------

### Getting Element Offset

Source: https://github.com/jinworks/cellchat/blob/main/tutorial/Comparison_analysis_of_multiple_datasets.html

Extends jQuery to get the offset (position) of the first element in the set of matched elements relative to the document. If an offset is provided, it sets the position.

```javascript
S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0}})
```