diff --git a/01-metadata.Rmd b/01-metadata.Rmd new file mode 100644 index 0000000..910e01f --- /dev/null +++ b/01-metadata.Rmd @@ -0,0 +1,52 @@ +# Overview of Metadata Types + +For each outcome type, include information on: + +- Overview of experimental procedures performed prior to collecting the data (and relevant technical parameters) -> refer to timeline diagram +- Glossary of every variable in the tool: what precisely does it mean, what are the units, etc +- Who collected the data (institution) +- Replicates - were there any, are they different groups of islets, different individual cells, etc +- Explanation of any changes in protocols (ie. switch to different GSIS concentrations, maybe different platform used halfway through, etc) +- Whether data was intentionally collected on specific subsets of donors +- Versions of data available for download +- Data processing prior to statistical analysis (merging replicates, transformations, removed outliers, definition of any computed/normalized values) +- Any additional notes +- Citations of papers that have published parts of the data + + +## Donor Characteristics: + +Donor characteristics are provided to the Isletcore team through the organ procurement programs. These characteristics can come from the family and aren’t always accurate reflections of the medical history. Hba1c measurements are performed in the Isletcore when the donor blood sample is available. + +## Organ Characteristics and Processing: + +Organ characteristic and processing data is recorded during the islet isolation by the lead technician. Cold Ischemia time will be calculated based on the cross clamp time during organ procurement until the start of the isolation in Edmonton. + +## Isolation Outcomes: + +Islet purity, IEQ, % trapped, IPI are determined by the lead technician. Full details on determining IEQ, purity etc can be found [here](https://www.protocols.io/view/human-islet-quantification-and-purity-assessment-14egnxxwml5d/v3/) or in our [IsletCore](http://www.bcell.org/uploads/5/1/3/3/51338649/adi_isletcore_welcome_booklet.pdf) welcome booklet. Details on determining Insulin and DNA samples can always be found [here](https://www.protocols.io/view/sampling-of-human-islets-for-quality-control-purpo-j8nlk553dl5r/v2). Cell proportion data is determined through our proteomics data. Proteomics samples are shipped to Jim Johnson’s lab at UBC. Samples are then hand picked and snap frozen for processing. + +## Cell Culture Outcomes: + +On the day of shipping, Islets are recounted to determine IEQ, % recovery, IPI after culture. Culture time is determined by the end of the isolation until approximately 9am on a shipping day. Post culture samples are collected for insulin and DNA as [previously described](https://www.protocols.io/view/sampling-of-human-islets-for-quality-control-purpo-j8nlk553dl5r/v2). + +## Static Insulin Secretion: + +In triplicates, 15 hand picked islets from each donor were sequentially treated with low glucose for 1 hour followed by high glucose 1 hour. Collected supernatant for low glucose, high glucose or insulin content are measured by ELISA and recorded in the database. For each donor, there are multiple glucose combinations that may have been used. Glucose pairs are 1mM to 10mM glucose, 1mM to 16.7mM glucose or 2.8mM glucose to 16.7mM glucose. Islets used in the 1mM to 10mM group are unique from the 1mM to 16.7mM group. The secretion data are commonly presented as a percentage of the total content (secreted insulin/total insulin *100), and as a stimulation index (high glucose secretion/low glucose secretion). Biological outliers were defined as “Seth is coming up with the definition now”, and identified outliers were removed from the dataset. The data for each replicate (after outlier removal) can be downloaded from the data export page. The total insulin content, secretion percentages, and stimulation indices, averaged across replicates, can be queried, analyzed, and visualized in the other parts of the tool. Complete experimental details can be found [here](https://www.protocols.io/view/static-glucose-stimulated-insulin-secretion-gsis-p-n2bvjkzxgk5w/v3). + + +## Islet Oxygen Consumption (Seahorse assay): + + +## Dynamic Insulin Responses to Macronutrients: + +Human islets are shipped to Jim Johnson’s lab at UBC. Islets are hand picked and cultured in RPMI for 24-72hrs before the experiment to allow the islets to recover from shipping. 65 islets are loaded per chamber and pre-incubated for 1 hour. Islets are stimulated with 6 or 15mM glucose, 5mM Leucine or 0.75mM Oleic acid/0.75mM palmitic acid as indicated. Samples are stored at -20C until analysis with RIA. Full details are described [here](https://www.medrxiv.org/content/10.1101/2023.05.24.23290298v1.full.pdf). + +## Single-cell Function (electrophysiology) + +Human islets are hand picked and dispersed into single cells and plated on 35mm cell culture dishes by the MacDonald lab. 24-72 hours after the dispersion, electrophysiological measurements are recorded. Cell identity is confirmed by immunohistochemistry. + + + + + diff --git a/02-omics-data.Rmd b/02-omics-data.Rmd new file mode 100644 index 0000000..e56c21d --- /dev/null +++ b/02-omics-data.Rmd @@ -0,0 +1,39 @@ +# Overview of Omics Data Types + +For each data type, include information on: + +- Platform(s) +- Batches (and whether batch effect is significant) +- Any logistical (shipping, storage)/lab procedures conducted prior to collecting data (and indicate relevant technical parameters - ie. culture times, etc) +- Whether data was intentionally collected on specific subsets of donors +- Raw data processing pipeline +- Where you can find raw data files +- Missing value strategy, filtering, and normalization prior to statistical analysis +- Any additional notes +- Citations of papers that have published parts of the data + +## Gene expression (bulk RNA-seq) +RNA-seq profiles were collected starting in [YEAR]in batches of islets from ~10-20 donors. [Include information on batches]. Raw reads were aligned to the [BLANK GENOME] using [BLANK SOFTWARE version BLANK]. The counts matrix was filtered to remove any genes with fewer than [X] mean counts. The counts were normalized for sequencing depth and adjusted to account for high variance/high abundant genes using the [BLANK] method in the limma voom R package (version #). Batch effect was corrected using the Combat-Seq R package (version #). Since + +## Gene expression (Nanostring) +50 islets are hand picked in Edmonton. Cells are washed with PBS then collected in 100ul of RLT Beta-mercaptoethanol. Islets arestored at -80C before shipping to the Lynn Lab. + +## Protein expression (proteomics) +Human islets are shipped to Jim Johnson’s lab at UBC. 300 Islets are hand picked, washed with pbs and pellet is snap frozen and stored at -80C. Details of lysis and analysis are found [here](https://www.medrxiv.org/content/10.1101/2023.05.24.23290298v1.full.pdf). + +## Gene expression (single-cell RNA-seq) + +## Gene expression (pseudobulk RNA-seq) +150 islets are hand picked at the University of Alberta and transferred into a tube with 1ml of Trizol reagent. Samples are stored at -80C until shipping to Anna Gloyn’s lab (originally Oxford now Stanford). + + + + + + + + + + + + diff --git a/03-statistical-analysis-.Rmd b/03-statistical-analysis-.Rmd new file mode 100644 index 0000000..d0e3acf --- /dev/null +++ b/03-statistical-analysis-.Rmd @@ -0,0 +1,33 @@ +# Overview of statistical analysis methods + +For each analysis, include details on: + +- Specific methods (and R packages) used for each task (aimed for statisticians/data scientists) +- Explanation of how the methods work, what they are useful for, and their limitations (aimed for biologists, not statisticians/data scientists) +- Which parameters are exposed in the interface, what they do, and rationale for which parameters were exposed +- How to interpret the results +- How to report the results (which details/citations to include in methods sections of papers) +- Any additional notes + +## Multiple linear regression + +## Spearman ranked correlation + +## Overrepresentation analysis + +## Gene set enrichment analysis + +## Pipelines to generate significant feature associations for feature search page + +## Sources of annotation files, pathway libraries, & link to code repository + + + + + + + + + + + diff --git a/README.md b/README.md new file mode 100644 index 0000000..217c365 --- /dev/null +++ b/README.md @@ -0,0 +1,13 @@ +Welcome! + +This is a minimal example of a book based on R Markdown and **bookdown** (https://github.com/rstudio/bookdown). + +This template provides a skeleton file structure that you can edit to create your book. + +The contents inside the .Rmd files provide some pointers to help you get started, but feel free to also delete the content in each file and start fresh. + +Additional resources: + +The **bookdown** book: https://bookdown.org/yihui/bookdown/ + +The **bookdown** package reference site: https://pkgs.rstudio.com/bookdown diff --git a/_bookdown.yml b/_bookdown.yml new file mode 100644 index 0000000..30bf605 --- /dev/null +++ b/_bookdown.yml @@ -0,0 +1,4 @@ +delete_merged_file: true +language: + ui: + chapter_name: "Chapter " diff --git a/_output.yml b/_output.yml new file mode 100644 index 0000000..58bb0e0 --- /dev/null +++ b/_output.yml @@ -0,0 +1,19 @@ +bookdown::gitbook: + css: style.css + config: + toc: + before: | +
  • Human Islets
    • + + edit: https://github.com/yao-lzy/HumanIslets/edit/main/example/%s + download: ["pdf", "epub"] + search: + enabled: true + content_selector: '.markdown-body' +bookdown::pdf_book: + includes: + in_header: preamble.tex + latex_engine: xelatex + citation_package: natbib + keep_tex: yes +bookdown::epub_book: default diff --git a/book.bib b/book.bib new file mode 100644 index 0000000..f275a09 --- /dev/null +++ b/book.bib @@ -0,0 +1,10 @@ +@Book{xie2015, + title = {Dynamic Documents with {R} and knitr}, + author = {Yihui Xie}, + publisher = {Chapman and Hall/CRC}, + address = {Boca Raton, Florida}, + year = {2015}, + edition = {2nd}, + note = {ISBN 978-1498716963}, + url = {http://yihui.org/knitr/}, +} diff --git a/docs/.DS_Store b/docs/.DS_Store new file mode 100644 index 0000000..baa545a Binary files /dev/null and b/docs/.DS_Store differ diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 0000000..c8d9a03 --- /dev/null +++ b/docs/404.html @@ -0,0 +1,196 @@ + + + + + + + Page not found | Human Islets + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Chapter 1 General Introduction

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    The HumanIslets tool houses data collected from or associated with human islets procured by the University of Alberta. The data includes clinical metadata

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    I envision a branching timeline, so that you can visualize everything that happened to an islet sample prior to the collection of each data type. Here is a rough outline of steps (I know it’s incomplete/wrong - will look at some of the guides on the IsletCore website to better sketch out)

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    1. Pancreas procurement
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    2. Shipping to Edmonton
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    3. Islet isolation (more details here - I assume many/multiple steps)
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    4. GSIS outcomes
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    5. Other outcomes at UofA
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    6. Shipping to other labs for seahorse, perifusion, omics
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    7. Shipping to other labs (not in project)
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+.book.color-theme-1 .book-header { + color: #afa790; + background: transparent; +} +.book.color-theme-1 .book-header .btn { + color: #afa790; +} +.book.color-theme-1 .book-header .btn:hover { + color: #73553c; + background: none; +} +.book.color-theme-1 .book-header h1 { + color: #704214; +} +.book.color-theme-2 .book-header { + color: #7e888b; + background: transparent; +} +.book.color-theme-2 .book-header .btn { + color: #3b3f54; +} +.book.color-theme-2 .book-header .btn:hover { + color: #fffff5; + background: none; +} +.book.color-theme-2 .book-header h1 { + color: #bdcadb; +} +.book.color-theme-1 .book-body .navigation { + color: #afa790; +} +.book.color-theme-1 .book-body .navigation:hover { + color: #73553c; +} +.book.color-theme-2 .book-body .navigation { + color: #383f52; +} +.book.color-theme-2 .book-body .navigation:hover { + color: #fffff5; +} +/* + * Theme 1 + */ +.book.color-theme-1 .book-summary { + color: #afa790; + background: #111111; + border-right: 1px solid rgba(0, 0, 0, 0.07); +} +.book.color-theme-1 .book-summary .book-search { + background: transparent; +} +.book.color-theme-1 .book-summary .book-search input, +.book.color-theme-1 .book-summary .book-search input:focus { + border: 1px solid transparent; +} +.book.color-theme-1 .book-summary ul.summary li.divider { + background: #7e888b; + box-shadow: none; +} +.book.color-theme-1 .book-summary ul.summary li i.fa-check { + color: #33cc33; +} +.book.color-theme-1 .book-summary ul.summary li.done > a { + color: #877f6a; +} +.book.color-theme-1 .book-summary ul.summary li a, +.book.color-theme-1 .book-summary ul.summary li span { + color: #877f6a; + background: transparent; + font-weight: normal; +} +.book.color-theme-1 .book-summary ul.summary li.active > a, +.book.color-theme-1 .book-summary ul.summary li a:hover { + color: #704214; + background: transparent; + font-weight: normal; +} +/* + * Theme 2 + */ +.book.color-theme-2 .book-summary { + color: #bcc1d2; + background: #2d3143; + border-right: none; +} +.book.color-theme-2 .book-summary .book-search { + background: transparent; +} +.book.color-theme-2 .book-summary .book-search input, +.book.color-theme-2 .book-summary .book-search input:focus { + border: 1px solid transparent; +} +.book.color-theme-2 .book-summary ul.summary li.divider { + background: #272a3a; + box-shadow: none; +} +.book.color-theme-2 .book-summary ul.summary li i.fa-check { + color: #33cc33; +} +.book.color-theme-2 .book-summary ul.summary li.done > a { + color: #62687f; +} +.book.color-theme-2 .book-summary ul.summary li a, +.book.color-theme-2 .book-summary ul.summary li span { + color: #c1c6d7; + background: transparent; + font-weight: 600; +} +.book.color-theme-2 .book-summary ul.summary li.active > a, +.book.color-theme-2 .book-summary ul.summary li a:hover { + color: #f4f4f5; + background: #252737; + font-weight: 600; +} diff --git a/docs/libs/gitbook-2.6.7/css/plugin-highlight.css b/docs/libs/gitbook-2.6.7/css/plugin-highlight.css new file mode 100644 index 0000000..2aabd3d --- /dev/null +++ b/docs/libs/gitbook-2.6.7/css/plugin-highlight.css @@ -0,0 +1,426 @@ +.book .book-body .page-wrapper .page-inner section.normal pre, +.book .book-body .page-wrapper .page-inner section.normal code { + /* http://jmblog.github.com/color-themes-for-google-code-highlightjs */ + /* Tomorrow Comment */ + /* Tomorrow Red */ + /* Tomorrow Orange */ + /* Tomorrow Yellow */ + /* Tomorrow Green */ + /* Tomorrow Aqua */ + /* Tomorrow Blue */ + /* Tomorrow Purple */ +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-comment, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-comment, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-title { + color: #8e908c; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-variable, +.book .book-body .page-wrapper .page-inner section.normal code 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modifiers=[];if(e.shiftKey){modifiers.push("shift")}if(e.altKey){modifiers.push("alt")}if(e.ctrlKey){modifiers.push("ctrl")}if(e.metaKey){modifiers.push("meta")}return modifiers}function _preventDefault(e){if(e.preventDefault){e.preventDefault();return}e.returnValue=false}function _stopPropagation(e){if(e.stopPropagation){e.stopPropagation();return}e.cancelBubble=true}function _isModifier(key){return key=="shift"||key=="ctrl"||key=="alt"||key=="meta"}function _getReverseMap(){if(!_REVERSE_MAP){_REVERSE_MAP={};for(var key in _MAP){if(key>95&&key<112){continue}if(_MAP.hasOwnProperty(key)){_REVERSE_MAP[_MAP[key]]=key}}}return _REVERSE_MAP}function _pickBestAction(key,modifiers,action){if(!action){action=_getReverseMap()[key]?"keydown":"keypress"}if(action=="keypress"&&modifiers.length){action="keydown"}return action}function _keysFromString(combination){if(combination==="+"){return["+"]}combination=combination.replace(/\+{2}/g,"+plus");return combination.split("+")}function _getKeyInfo(combination,action){var keys;var key;var i;var modifiers=[];keys=_keysFromString(combination);for(i=0;i1){_bindSequence(combination,sequence,callback,action);return}info=_getKeyInfo(combination,action);self._callbacks[info.key]=self._callbacks[info.key]||[];_getMatches(info.key,info.modifiers,{type:info.action},sequenceName,combination,level);self._callbacks[info.key][sequenceName?"unshift":"push"]({callback:callback,modifiers:info.modifiers,action:info.action,seq:sequenceName,level:level,combo:combination})}self._bindMultiple=function(combinations,callback,action){for(var i=0;i-1){return false}if(_belongsTo(element,self.target)){return false}return element.tagName=="INPUT"||element.tagName=="SELECT"||element.tagName=="TEXTAREA"||element.isContentEditable};Mousetrap.prototype.handleKey=function(){var self=this;return self._handleKey.apply(self,arguments)};Mousetrap.init=function(){var documentMousetrap=Mousetrap(document);for(var method in documentMousetrap){if(method.charAt(0)!=="_"){Mousetrap[method]=function(method){return function(){return documentMousetrap[method].apply(documentMousetrap,arguments)}}(method)}}};Mousetrap.init();window.Mousetrap=Mousetrap;if(typeof module!=="undefined"&&module.exports){module.exports=Mousetrap}if(typeof define==="function"&&define.amd){define(function(){return Mousetrap})}})(window,document)},{}],4:[function(require,module,exports){(function(process){function normalizeArray(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up--;up){parts.unshift("..")}}return parts}var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;var splitPath=function(filename){return splitPathRe.exec(filename).slice(1)};exports.resolve=function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:process.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){continue}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=normalizeArray(filter(resolvedPath.split("/"),function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."};exports.normalize=function(path){var isAbsolute=exports.isAbsolute(path),trailingSlash=substr(path,-1)==="/";path=normalizeArray(filter(path.split("/"),function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path};exports.isAbsolute=function(path){return path.charAt(0)==="/"};exports.join=function(){var paths=Array.prototype.slice.call(arguments,0);return exports.normalize(filter(paths,function(p,index){if(typeof p!=="string"){throw new TypeError("Arguments to path.join must be strings")}return p}).join("/"))};exports.relative=function(from,to){from=exports.resolve(from).substr(1);to=exports.resolve(to).substr(1);function trim(arr){var start=0;for(;start=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i1){for(var i=1;i= 0x80 (not a basic code point)","invalid-input":"Invalid input"},baseMinusTMin=base-tMin,floor=Math.floor,stringFromCharCode=String.fromCharCode,key;function error(type){throw RangeError(errors[type])}function map(array,fn){var length=array.length;var result=[];while(length--){result[length]=fn(array[length])}return result}function mapDomain(string,fn){var parts=string.split("@");var result="";if(parts.length>1){result=parts[0]+"@";string=parts[1]}string=string.replace(regexSeparators,".");var labels=string.split(".");var encoded=map(labels,fn).join(".");return result+encoded}function ucs2decode(string){var output=[],counter=0,length=string.length,value,extra;while(counter=55296&&value<=56319&&counter65535){value-=65536;output+=stringFromCharCode(value>>>10&1023|55296);value=56320|value&1023}output+=stringFromCharCode(value);return output}).join("")}function basicToDigit(codePoint){if(codePoint-48<10){return codePoint-22}if(codePoint-65<26){return codePoint-65}if(codePoint-97<26){return codePoint-97}return base}function digitToBasic(digit,flag){return digit+22+75*(digit<26)-((flag!=0)<<5)}function adapt(delta,numPoints,firstTime){var k=0;delta=firstTime?floor(delta/damp):delta>>1;delta+=floor(delta/numPoints);for(;delta>baseMinusTMin*tMax>>1;k+=base){delta=floor(delta/baseMinusTMin)}return floor(k+(baseMinusTMin+1)*delta/(delta+skew))}function decode(input){var output=[],inputLength=input.length,out,i=0,n=initialN,bias=initialBias,basic,j,index,oldi,w,k,digit,t,baseMinusT;basic=input.lastIndexOf(delimiter);if(basic<0){basic=0}for(j=0;j=128){error("not-basic")}output.push(input.charCodeAt(j))}for(index=basic>0?basic+1:0;index=inputLength){error("invalid-input")}digit=basicToDigit(input.charCodeAt(index++));if(digit>=base||digit>floor((maxInt-i)/w)){error("overflow")}i+=digit*w;t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(digitfloor(maxInt/baseMinusT)){error("overflow")}w*=baseMinusT}out=output.length+1;bias=adapt(i-oldi,out,oldi==0);if(floor(i/out)>maxInt-n){error("overflow")}n+=floor(i/out);i%=out;output.splice(i++,0,n)}return ucs2encode(output)}function encode(input){var n,delta,handledCPCount,basicLength,bias,j,m,q,k,t,currentValue,output=[],inputLength,handledCPCountPlusOne,baseMinusT,qMinusT;input=ucs2decode(input);inputLength=input.length;n=initialN;delta=0;bias=initialBias;for(j=0;j=n&¤tValuefloor((maxInt-delta)/handledCPCountPlusOne)){error("overflow")}delta+=(m-n)*handledCPCountPlusOne;n=m;for(j=0;jmaxInt){error("overflow")}if(currentValue==n){for(q=delta,k=base;;k+=base){t=k<=bias?tMin:k>=bias+tMax?tMax:k-bias;if(q0&&len>maxKeys){len=maxKeys}for(var i=0;i=0){kstr=x.substr(0,idx);vstr=x.substr(idx+1)}else{kstr=x;vstr=""}k=decodeURIComponent(kstr);v=decodeURIComponent(vstr);if(!hasOwnProperty(obj,k)){obj[k]=v}else if(isArray(obj[k])){obj[k].push(v)}else{obj[k]=[obj[k],v]}}return obj};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"}},{}],8:[function(require,module,exports){"use strict";var stringifyPrimitive=function(v){switch(typeof v){case"string":return v;case"boolean":return v?"true":"false";case"number":return isFinite(v)?v:"";default:return""}};module.exports=function(obj,sep,eq,name){sep=sep||"&";eq=eq||"=";if(obj===null){obj=undefined}if(typeof obj==="object"){return map(objectKeys(obj),function(k){var ks=encodeURIComponent(stringifyPrimitive(k))+eq;if(isArray(obj[k])){return map(obj[k],function(v){return ks+encodeURIComponent(stringifyPrimitive(v))}).join(sep)}else{return ks+encodeURIComponent(stringifyPrimitive(obj[k]))}}).join(sep)}if(!name)return"";return encodeURIComponent(stringifyPrimitive(name))+eq+encodeURIComponent(stringifyPrimitive(obj))};var isArray=Array.isArray||function(xs){return Object.prototype.toString.call(xs)==="[object Array]"};function map(xs,f){if(xs.map)return xs.map(f);var res=[];for(var i=0;i",'"',"`"," ","\r","\n","\t"],unwise=["{","}","|","\\","^","`"].concat(delims),autoEscape=["'"].concat(unwise),nonHostChars=["%","/","?",";","#"].concat(autoEscape),hostEndingChars=["/","?","#"],hostnameMaxLen=255,hostnamePartPattern=/^[a-z0-9A-Z_-]{0,63}$/,hostnamePartStart=/^([a-z0-9A-Z_-]{0,63})(.*)$/,unsafeProtocol={javascript:true,"javascript:":true},hostlessProtocol={javascript:true,"javascript:":true},slashedProtocol={http:true,https:true,ftp:true,gopher:true,file:true,"http:":true,"https:":true,"ftp:":true,"gopher:":true,"file:":true},querystring=require("querystring");function urlParse(url,parseQueryString,slashesDenoteHost){if(url&&isObject(url)&&url instanceof Url)return url;var u=new Url;u.parse(url,parseQueryString,slashesDenoteHost);return u}Url.prototype.parse=function(url,parseQueryString,slashesDenoteHost){if(!isString(url)){throw new TypeError("Parameter 'url' must be a string, not "+typeof url)}var rest=url;rest=rest.trim();var proto=protocolPattern.exec(rest);if(proto){proto=proto[0];var lowerProto=proto.toLowerCase();this.protocol=lowerProto;rest=rest.substr(proto.length)}if(slashesDenoteHost||proto||rest.match(/^\/\/[^@\/]+@[^@\/]+/)){var slashes=rest.substr(0,2)==="//";if(slashes&&!(proto&&hostlessProtocol[proto])){rest=rest.substr(2);this.slashes=true}}if(!hostlessProtocol[proto]&&(slashes||proto&&!slashedProtocol[proto])){var hostEnd=-1;for(var i=0;i127){newpart+="x"}else{newpart+=part[j]}}if(!newpart.match(hostnamePartPattern)){var validParts=hostparts.slice(0,i);var notHost=hostparts.slice(i+1);var bit=part.match(hostnamePartStart);if(bit){validParts.push(bit[1]);notHost.unshift(bit[2])}if(notHost.length){rest="/"+notHost.join(".")+rest}this.hostname=validParts.join(".");break}}}}if(this.hostname.length>hostnameMaxLen){this.hostname=""}else{this.hostname=this.hostname.toLowerCase()}if(!ipv6Hostname){var domainArray=this.hostname.split(".");var newOut=[];for(var i=0;i0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}result.search=relative.search;result.query=relative.query;if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.href=result.format();return result}if(!srcPath.length){result.pathname=null;if(result.search){result.path="/"+result.search}else{result.path=null}result.href=result.format();return result}var last=srcPath.slice(-1)[0];var hasTrailingSlash=(result.host||relative.host)&&(last==="."||last==="..")||last==="";var up=0;for(var i=srcPath.length;i>=0;i--){last=srcPath[i];if(last=="."){srcPath.splice(i,1)}else if(last===".."){srcPath.splice(i,1);up++}else if(up){srcPath.splice(i,1);up--}}if(!mustEndAbs&&!removeAllDots){for(;up--;up){srcPath.unshift("..")}}if(mustEndAbs&&srcPath[0]!==""&&(!srcPath[0]||srcPath[0].charAt(0)!=="/")){srcPath.unshift("")}if(hasTrailingSlash&&srcPath.join("/").substr(-1)!=="/"){srcPath.push("")}var isAbsolute=srcPath[0]===""||srcPath[0]&&srcPath[0].charAt(0)==="/";if(psychotic){result.hostname=result.host=isAbsolute?"":srcPath.length?srcPath.shift():"";var authInHost=result.host&&result.host.indexOf("@")>0?result.host.split("@"):false;if(authInHost){result.auth=authInHost.shift();result.host=result.hostname=authInHost.shift()}}mustEndAbs=mustEndAbs||result.host&&srcPath.length;if(mustEndAbs&&!isAbsolute){srcPath.unshift("")}if(!srcPath.length){result.pathname=null;result.path=null}else{result.pathname=srcPath.join("/")}if(!isNull(result.pathname)||!isNull(result.search)){result.path=(result.pathname?result.pathname:"")+(result.search?result.search:"")}result.auth=relative.auth||result.auth;result.slashes=result.slashes||relative.slashes;result.href=result.format();return result};Url.prototype.parseHost=function(){var host=this.host;var port=portPattern.exec(host);if(port){port=port[0];if(port!==":"){this.port=port.substr(1)}host=host.substr(0,host.length-port.length)}if(host)this.hostname=host};function isString(arg){return typeof arg==="string"}function isObject(arg){return typeof arg==="object"&&arg!==null}function isNull(arg){return arg===null}function isNullOrUndefined(arg){return arg==null}},{punycode:6,querystring:9}],11:[function(require,module,exports){var $=require("jquery");function toggleDropdown(e){var $dropdown=$(e.currentTarget).parent().find(".dropdown-menu");$dropdown.toggleClass("open");e.stopPropagation();e.preventDefault()}function closeDropdown(e){$(".dropdown-menu").removeClass("open")}function init(){$(document).on("click",".toggle-dropdown",toggleDropdown);$(document).on("click",".dropdown-menu",function(e){e.stopPropagation()});$(document).on("click",closeDropdown)}module.exports={init:init}},{jquery:1}],12:[function(require,module,exports){var $=require("jquery");module.exports=$({})},{jquery:1}],13:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var dropdown=require("./dropdown");var events=require("./events");var state=require("./state");var keyboard=require("./keyboard");var navigation=require("./navigation");var sidebar=require("./sidebar");var toolbar=require("./toolbar");function start(config){sidebar.init();keyboard.init();dropdown.init();navigation.init();toolbar.createButton({index:0,icon:"fa fa-align-justify",label:"Toggle Sidebar",onClick:function(e){e.preventDefault();sidebar.toggle()}});events.trigger("start",config);navigation.notify()}var gitbook={start:start,events:events,state:state,toolbar:toolbar,sidebar:sidebar,storage:storage,keyboard:keyboard};var MODULES={gitbook:gitbook,jquery:$,lodash:_};window.gitbook=gitbook;window.$=$;window.jQuery=$;gitbook.require=function(mods,fn){mods=_.map(mods,function(mod){mod=mod.toLowerCase();if(!MODULES[mod]){throw new Error("GitBook module "+mod+" doesn't exist")}return MODULES[mod]});fn.apply(null,mods)};module.exports={}},{"./dropdown":11,"./events":12,"./keyboard":14,"./navigation":16,"./sidebar":18,"./state":19,"./storage":20,"./toolbar":21,jquery:1,lodash:2}],14:[function(require,module,exports){var Mousetrap=require("mousetrap");var navigation=require("./navigation");var sidebar=require("./sidebar");function bindShortcut(keys,fn){Mousetrap.bind(keys,function(e){fn();return false})}function init(){bindShortcut(["right"],function(e){navigation.goNext()});bindShortcut(["left"],function(e){navigation.goPrev()});bindShortcut(["s"],function(e){sidebar.toggle()})}module.exports={init:init,bind:bindShortcut}},{"./navigation":16,"./sidebar":18,mousetrap:3}],15:[function(require,module,exports){var state=require("./state");function showLoading(p){state.$book.addClass("is-loading");p.always(function(){state.$book.removeClass("is-loading")});return p}module.exports={show:showLoading}},{"./state":19}],16:[function(require,module,exports){var $=require("jquery");var url=require("url");var events=require("./events");var state=require("./state");var loading=require("./loading");var usePushState=typeof history.pushState!=="undefined";function handleNavigation(relativeUrl,push){var uri=url.resolve(window.location.pathname,relativeUrl);notifyPageChange();location.href=relativeUrl;return}function updateNavigationPosition(){var bodyInnerWidth,pageWrapperWidth;bodyInnerWidth=parseInt($(".body-inner").css("width"),10);pageWrapperWidth=parseInt($(".page-wrapper").css("width"),10);$(".navigation-next").css("margin-right",bodyInnerWidth-pageWrapperWidth+"px")}function notifyPageChange(){events.trigger("page.change")}function preparePage(notify){var $bookBody=$(".book-body");var $bookInner=$bookBody.find(".body-inner");var $pageWrapper=$bookInner.find(".page-wrapper");updateNavigationPosition();$bookInner.scrollTop(0);$bookBody.scrollTop(0);if(notify!==false)notifyPageChange()}function isLeftClickEvent(e){return e.button===0}function isModifiedEvent(e){return!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey)}function handlePagination(e){if(isModifiedEvent(e)||!isLeftClickEvent(e)){return}e.stopPropagation();e.preventDefault();var url=$(this).attr("href");if(url)handleNavigation(url,true)}function goNext(){var url=$(".navigation-next").attr("href");if(url)handleNavigation(url,true)}function goPrev(){var url=$(".navigation-prev").attr("href");if(url)handleNavigation(url,true)}function init(){$.ajaxSetup({});if(location.protocol!=="file:"){history.replaceState({path:window.location.href},"")}window.onpopstate=function(event){if(event.state===null){return}return handleNavigation(event.state.path,false)};$(document).on("click",".navigation-prev",handlePagination);$(document).on("click",".navigation-next",handlePagination);$(document).on("click",".summary [data-path] a",handlePagination);$(window).resize(updateNavigationPosition);preparePage(false)}module.exports={init:init,goNext:goNext,goPrev:goPrev,notify:notifyPageChange}},{"./events":12,"./loading":15,"./state":19,jquery:1,url:10}],17:[function(require,module,exports){module.exports={isMobile:function(){return document.body.clientWidth<=600}}},{}],18:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var storage=require("./storage");var platform=require("./platform");var state=require("./state");function toggleSidebar(_state,animation){if(state!=null&&isOpen()==_state)return;if(animation==null)animation=true;state.$book.toggleClass("without-animation",!animation);state.$book.toggleClass("with-summary",_state);storage.set("sidebar",isOpen())}function isOpen(){return state.$book.hasClass("with-summary")}function init(){if(platform.isMobile()){toggleSidebar(false,false)}else{toggleSidebar(storage.get("sidebar",true),false)}$(document).on("click",".book-summary li.chapter a",function(e){if(platform.isMobile())toggleSidebar(false,false)})}function filterSummary(paths){var $summary=$(".book-summary");$summary.find("li").each(function(){var path=$(this).data("path");var st=paths==null||_.contains(paths,path);$(this).toggle(st);if(st)$(this).parents("li").show()})}module.exports={init:init,isOpen:isOpen,toggle:toggleSidebar,filter:filterSummary}},{"./platform":17,"./state":19,"./storage":20,jquery:1,lodash:2}],19:[function(require,module,exports){var $=require("jquery");var url=require("url");var path=require("path");var state={};state.update=function(dom){var $book=$(dom.find(".book"));state.$book=$book;state.level=$book.data("level");state.basePath=$book.data("basepath");state.innerLanguage=$book.data("innerlanguage");state.revision=$book.data("revision");state.filepath=$book.data("filepath");state.chapterTitle=$book.data("chapter-title");state.root=url.resolve(location.protocol+"//"+location.host,path.dirname(path.resolve(location.pathname.replace(/\/$/,"/index.html"),state.basePath))).replace(/\/?$/,"/");state.bookRoot=state.innerLanguage?url.resolve(state.root,".."):state.root};state.update($);module.exports=state},{jquery:1,path:4,url:10}],20:[function(require,module,exports){var baseKey="";module.exports={setBaseKey:function(key){baseKey=key},set:function(key,value){key=baseKey+":"+key;try{sessionStorage[key]=JSON.stringify(value)}catch(e){}},get:function(key,def){key=baseKey+":"+key;if(sessionStorage[key]===undefined)return def;try{var v=JSON.parse(sessionStorage[key]);return v==null?def:v}catch(err){return sessionStorage[key]||def}},remove:function(key){key=baseKey+":"+key;sessionStorage.removeItem(key)}}},{}],21:[function(require,module,exports){var $=require("jquery");var _=require("lodash");var events=require("./events");var buttons=[];function insertAt(parent,selector,index,element){var lastIndex=parent.children(selector).length;if(index<0){index=Math.max(0,lastIndex+1+index)}parent.append(element);if(index",{class:"dropdown-menu",html:'
    '});if(_.isString(dropdown)){$menu.append(dropdown)}else{var groups=_.map(dropdown,function(group){if(_.isArray(group))return group;else return[group]});_.each(groups,function(group){var $group=$("
    ",{class:"buttons"});var sizeClass="size-"+group.length;_.each(group,function(btn){btn=_.defaults(btn||{},{text:"",className:"",onClick:defaultOnClick});var $btn=$("'; + var clipboard; + + gitbook.events.bind("page.change", function() { + + if (!ClipboardJS.isSupported()) return; + + // the page.change event is thrown twice: before and after the page changes + if (clipboard) { + // clipboard is already defined but we are on the same page + if (clipboard._prevPage === window.location.pathname) return; + // clipboard is already defined and url path change + // we can deduct that we are before page changes + clipboard.destroy(); // destroy the previous events listeners + clipboard = undefined; // reset the clipboard object + return; + } + + $(copyButton).prependTo("div.sourceCode"); + + clipboard = new ClipboardJS(".copy-to-clipboard-button", { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboard._prevPage = window.location.pathname + + }); + +}); diff --git a/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js b/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js new file mode 100644 index 0000000..a70f0fb --- /dev/null +++ b/docs/libs/gitbook-2.6.7/js/plugin-fontsettings.js @@ -0,0 +1,152 @@ +gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) { + var fontState; + + var THEMES = { + "white": 0, + "sepia": 1, + "night": 2 + }; + + var FAMILY = { + "serif": 0, + "sans": 1 + }; + + // Save current font settings + function saveFontSettings() { + gitbook.storage.set("fontState", fontState); + update(); + } + + // Increase font size + function enlargeFontSize(e) { + e.preventDefault(); + if (fontState.size >= 4) return; + + fontState.size++; + saveFontSettings(); + }; + + // Decrease font size + function reduceFontSize(e) { + e.preventDefault(); + if (fontState.size <= 0) return; + + fontState.size--; + saveFontSettings(); + }; + + // Change font family + function changeFontFamily(index, e) { + e.preventDefault(); + + fontState.family = index; + saveFontSettings(); + }; + + // Change type of color + function changeColorTheme(index, e) { + e.preventDefault(); + + var $book = $(".book"); + + if (fontState.theme !== 0) + $book.removeClass("color-theme-"+fontState.theme); + + fontState.theme = index; + if (fontState.theme !== 0) + $book.addClass("color-theme-"+fontState.theme); + + saveFontSettings(); + }; + + function update() { + var $book = gitbook.state.$book; + + $(".font-settings .font-family-list li").removeClass("active"); + $(".font-settings .font-family-list li:nth-child("+(fontState.family+1)+")").addClass("active"); + + $book[0].className = $book[0].className.replace(/\bfont-\S+/g, ''); + $book.addClass("font-size-"+fontState.size); + $book.addClass("font-family-"+fontState.family); + + if(fontState.theme !== 0) { + $book[0].className = $book[0].className.replace(/\bcolor-theme-\S+/g, ''); + $book.addClass("color-theme-"+fontState.theme); + } + }; + + function init(config) { + var $bookBody, $book; + + //Find DOM elements. + $book = gitbook.state.$book; + $bookBody = $book.find(".book-body"); + + // Instantiate font state object + fontState = gitbook.storage.get("fontState", { + size: config.size || 2, + family: FAMILY[config.family || "sans"], + theme: THEMES[config.theme || "white"] + }); + + update(); + }; + + + gitbook.events.bind("start", function(e, config) { + var opts = config.fontsettings; + if (!opts) return; + + // Create buttons in toolbar + gitbook.toolbar.createButton({ + icon: 'fa fa-font', + label: 'Font Settings', + className: 'font-settings', + dropdown: [ + [ + { + text: 'A', + className: 'font-reduce', + onClick: reduceFontSize + }, + { + text: 'A', + className: 'font-enlarge', + onClick: enlargeFontSize + } + ], + [ + { + text: 'Serif', + onClick: _.partial(changeFontFamily, 0) + }, + { + text: 'Sans', + onClick: _.partial(changeFontFamily, 1) + } + ], + [ + { + text: 'White', + onClick: _.partial(changeColorTheme, 0) + }, + { + text: 'Sepia', + onClick: _.partial(changeColorTheme, 1) + }, + { + text: 'Night', + onClick: _.partial(changeColorTheme, 2) + } + ] + ] + }); + + + // Init current settings + init(opts); + }); +}); + + diff --git a/docs/libs/gitbook-2.6.7/js/plugin-search.js b/docs/libs/gitbook-2.6.7/js/plugin-search.js new file mode 100644 index 0000000..747fcce --- /dev/null +++ b/docs/libs/gitbook-2.6.7/js/plugin-search.js @@ -0,0 +1,270 @@ +gitbook.require(["gitbook", "lodash", "jQuery"], function(gitbook, _, $) { + var index = null; + var fuse = null; + var _search = {engine: 'lunr', opts: {}}; + var $searchInput, $searchLabel, $searchForm; + var $highlighted = [], hi, hiOpts = { className: 'search-highlight' }; + var collapse = false, toc_visible = []; + + function init(config) { + // Instantiate search settings + _search = gitbook.storage.get("search", { + engine: config.search.engine || 'lunr', + opts: config.search.options || {}, + }); + }; + + // Save current search settings + function saveSearchSettings() { + gitbook.storage.set("search", _search); + } + + // Use a specific index + function loadIndex(data) { + // [Yihui] In bookdown, I use a character matrix to store the chapter + // content, and the index is dynamically built on the client side. + // Gitbook prebuilds the index data instead: https://github.com/GitbookIO/plugin-search + // We can certainly do that via R packages V8 and jsonlite, but let's + // see how slow it really is before improving it. On the other hand, + // lunr cannot handle non-English text very well, e.g. the default + // tokenizer cannot deal with Chinese text, so we may want to replace + // lunr with a dumb simple text matching approach. + if (_search.engine === 'lunr') { + index = lunr(function () { + this.ref('url'); + this.field('title', { boost: 10 }); + this.field('body'); + }); + data.map(function(item) { + index.add({ + url: item[0], + title: item[1], + body: item[2] + }); + }); + return; + } + fuse = new Fuse(data.map((_data => { + return { + url: _data[0], + title: _data[1], + body: _data[2] + }; + })), Object.assign( + { + includeScore: true, + threshold: 0.1, + ignoreLocation: true, + keys: ["title", "body"] + }, + _search.opts + )); + } + + // Fetch the search index + function fetchIndex() { + return $.getJSON(gitbook.state.basePath+"/search_index.json") + .then(loadIndex); // [Yihui] we need to use this object later + } + + // Search for a term and return results + function search(q) { + let results = []; + switch (_search.engine) { + case 'fuse': + if (!fuse) return; + results = fuse.search(q).map(function(result) { + var parts = result.item.url.split('#'); + return { + path: parts[0], + hash: parts[1] + }; + }); + break; + case 'lunr': + default: + if (!index) return; + results = _.chain(index.search(q)).map(function(result) { + var parts = result.ref.split("#"); + return { + path: parts[0], + hash: parts[1] + }; + }) + .value(); + } + + // [Yihui] Highlight the search keyword on current page + $highlighted = $('.page-inner') + .unhighlight(hiOpts).highlight(q, hiOpts).find('span.search-highlight'); + scrollToHighlighted(0); + + return results; + } + + // [Yihui] Scroll the chapter body to the i-th highlighted string + function scrollToHighlighted(d) { + var n = $highlighted.length; + hi = hi === undefined ? 0 : hi + d; + // navignate to the previous/next page in the search results if reached the top/bottom + var b = hi < 0; + if (d !== 0 && (b || hi >= n)) { + var path = currentPath(), n2 = toc_visible.length; + if (n2 === 0) return; + for (var i = b ? 0 : n2; (b && i < n2) || (!b && i >= 0); i += b ? 1 : -1) { + if (toc_visible.eq(i).data('path') === path) break; + } + i += b ? -1 : 1; + if (i < 0) i = n2 - 1; + if (i >= n2) i = 0; + var lnk = toc_visible.eq(i).find('a[href$=".html"]'); + if (lnk.length) lnk[0].click(); + return; + } + if (n === 0) return; + var $p = $highlighted.eq(hi); + $p[0].scrollIntoView(); + $highlighted.css('background-color', ''); + // an orange background color on the current item and removed later + $p.css('background-color', 'orange'); + setTimeout(function() { + $p.css('background-color', ''); + }, 2000); + } + + function currentPath() { + var href = window.location.pathname; + href = href.substr(href.lastIndexOf('/') + 1); + return href === '' ? 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    + +
    + +
    + +
    +
    + + +
    +
    + +
    +
    +

    Chapter 2 Overview of Metadata Types

    +

    For each outcome type, include information on:

    +
      +
    • Overview of experimental procedures performed prior to collecting the data (and relevant technical parameters) -> refer to timeline diagram
      • +
    • Glossary of every variable in the tool: what precisely does it mean, what are the units, etc
      • +
    • Who collected the data (institution)
      • +
    • Replicates - were there any, are they different groups of islets, different individual cells, etc
      • +
    • Explanation of any changes in protocols (ie. switch to different GSIS concentrations, maybe different platform used halfway through, etc)
      • +
    • Whether data was intentionally collected on specific subsets of donors
      • +
    • Versions of data available for download
      • +
    • Data processing prior to statistical analysis (merging replicates, transformations, removed outliers, definition of any computed/normalized values)
      • +
    • Any additional notes
      • +
    • Citations of papers that have published parts of the data
      • +
    +
    +

    2.1 Donor Characteristics:

    +

    Donor characteristics are provided to the Isletcore team through the organ procurement programs. These characteristics can come from the family and aren’t always accurate reflections of the medical history. Hba1c measurements are performed in the Isletcore when the donor blood sample is available.

    +
    +
    +

    2.2 Organ Characteristics and Processing:

    +

    Organ characteristic and processing data is recorded during the islet isolation by the lead technician. Cold Ischemia time will be calculated based on the cross clamp time during organ procurement until the start of the isolation in Edmonton.

    +
    +
    +

    2.3 Isolation Outcomes:

    +

    Islet purity, IEQ, % trapped, IPI are determined by the lead technician. Full details on determining IEQ, purity etc can be found here or in our IsletCore welcome booklet. Details on determining Insulin and DNA samples can always be found here. Cell proportion data is determined through our proteomics data. Proteomics samples are shipped to Jim Johnson’s lab at UBC. Samples are then hand picked and snap frozen for processing.

    +
    +
    +

    2.4 Cell Culture Outcomes:

    +

    On the day of shipping, Islets are recounted to determine IEQ, % recovery, IPI after culture. Culture time is determined by the end of the isolation until approximately 9am on a shipping day. Post culture samples are collected for insulin and DNA as previously described.

    +
    +
    +

    2.5 Static Insulin Secretion:

    +

    In triplicates, 15 hand picked islets from each donor were sequentially treated with low glucose for 1 hour followed by high glucose 1 hour. Collected supernatant for low glucose, high glucose or insulin content are measured by ELISA and recorded in the database. For each donor, there are multiple glucose combinations that may have been used. Glucose pairs are 1mM to 10mM glucose, 1mM to 16.7mM glucose or 2.8mM glucose to 16.7mM glucose. Islets used in the 1mM to 10mM group are unique from the 1mM to 16.7mM group. The secretion data are commonly presented as a percentage of the total content (secreted insulin/total insulin *100), and as a stimulation index (high glucose secretion/low glucose secretion). Biological outliers were defined as “Seth is coming up with the definition now”, and identified outliers were removed from the dataset. The data for each replicate (after outlier removal) can be downloaded from the data export page. The total insulin content, secretion percentages, and stimulation indices, averaged across replicates, can be queried, analyzed, and visualized in the other parts of the tool. Complete experimental details can be found here.

    +
    +
    +

    2.6 Islet Oxygen Consumption (Seahorse assay):

    +
    +
    +

    2.7 Dynamic Insulin Responses to Macronutrients:

    +

    Human islets are shipped to Jim Johnson’s lab at UBC. Islets are hand picked and cultured in RPMI for 24-72hrs before the experiment to allow the islets to recover from shipping. 65 islets are loaded per chamber and pre-incubated for 1 hour. Islets are stimulated with 6 or 15mM glucose, 5mM Leucine or 0.75mM Oleic acid/0.75mM palmitic acid as indicated. Samples are stored at -20C until analysis with RIA. Full details are described here.

    +
    +
    +

    2.8 Single-cell Function (electrophysiology)

    +

    Human islets are hand picked and dispersed into single cells and plated on 35mm cell culture dishes by the MacDonald lab. 24-72 hours after the dispersion, electrophysiological measurements are recorded. Cell identity is confirmed by immunohistochemistry.

    + +
    +
    +
    + +
    +
    +
    + + +
    +
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    Chapter 3 Overview of Omics Data Types

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    For each data type, include information on:

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    • Platform(s)
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    • Batches (and whether batch effect is significant)
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    • Any logistical (shipping, storage)/lab procedures conducted prior to collecting data (and indicate relevant technical parameters - ie. culture times, etc)
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    • Whether data was intentionally collected on specific subsets of donors
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    • Raw data processing pipeline
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    • Where you can find raw data files
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    • Missing value strategy, filtering, and normalization prior to statistical analysis
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    3.1 Gene expression (bulk RNA-seq)

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    RNA-seq profiles were collected starting in [YEAR]in batches of islets from ~10-20 donors. [Include information on batches]. Raw reads were aligned to the [BLANK GENOME] using [BLANK SOFTWARE version BLANK]. The counts matrix was filtered to remove any genes with fewer than [X] mean counts. The counts were normalized for sequencing depth and adjusted to account for high variance/high abundant genes using the [BLANK] method in the limma voom R package (version #). Batch effect was corrected using the Combat-Seq R package (version #). Since

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    3.2 Gene expression (Nanostring)

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    50 islets are hand picked in Edmonton. Cells are washed with PBS then collected in 100ul of RLT Beta-mercaptoethanol. Islets arestored at -80C before shipping to the Lynn Lab.

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    3.3 Protein expression (proteomics)

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    Human islets are shipped to Jim Johnson’s lab at UBC. 300 Islets are hand picked, washed with pbs and pellet is snap frozen and stored at -80C. Details of lysis and analysis are found here.

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    3.4 Gene expression (single-cell RNA-seq)

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    3.5 Gene expression (pseudobulk RNA-seq)

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    150 islets are hand picked at the University of Alberta and transferred into a tube with 1ml of Trizol reagent. Samples are stored at -80C until shipping to Anna Gloyn’s lab (originally Oxford now Stanford).

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    Chapter 4 Overview of statistical analysis methods

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    For each analysis, include details on:

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    • Specific methods (and R packages) used for each task (aimed for statisticians/data scientists)
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    • Explanation of how the methods work, what they are useful for, and their limitations (aimed for biologists, not statisticians/data scientists)
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    • Which parameters are exposed in the interface, what they do, and rationale for which parameters were exposed
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    • How to interpret the results
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    4.1 Multiple linear regression

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    4.2 Spearman ranked correlation

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    4.3 Overrepresentation analysis

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    4.4 Gene set enrichment analysis

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    4.5 Pipelines to generate significant feature associations for feature search page

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    + + + + + + + + + + + + + diff --git a/docs/reference-keys.txt b/docs/reference-keys.txt new file mode 100644 index 0000000..d279af8 --- /dev/null +++ b/docs/reference-keys.txt @@ -0,0 +1,23 @@ +general-introduction +overview-of-metadata-types +donor-characteristics +organ-characteristics-and-processing +isolation-outcomes +cell-culture-outcomes +static-insulin-secretion +islet-oxygen-consumption-seahorse-assay +dynamic-insulin-responses-to-macronutrients +single-cell-function-electrophysiology +overview-of-omics-data-types +gene-expression-bulk-rna-seq +gene-expression-nanostring +protein-expression-proteomics +gene-expression-single-cell-rna-seq +gene-expression-pseudobulk-rna-seq +overview-of-statistical-analysis-methods +multiple-linear-regression +spearman-ranked-correlation +overrepresentation-analysis +gene-set-enrichment-analysis +pipelines-to-generate-significant-feature-associations-for-feature-search-page +sources-of-annotation-files-pathway-libraries-link-to-code-repository diff --git a/docs/search_index.json b/docs/search_index.json new file mode 100644 index 0000000..83fd74f --- /dev/null +++ b/docs/search_index.json @@ -0,0 +1 @@ +[["index.html", "Human Islets Chapter 1 General Introduction", " Human Islets 2023-12-20 Chapter 1 General Introduction The HumanIslets tool houses data collected from or associated with human islets procured by the University of Alberta. The data includes clinical metadata I envision a branching timeline, so that you can visualize everything that happened to an islet sample prior to the collection of each data type. Here is a rough outline of steps (I know it’s incomplete/wrong - will look at some of the guides on the IsletCore website to better sketch out) Pancreas procurement Shipping to Edmonton Islet isolation (more details here - I assume many/multiple steps) GSIS outcomes Other outcomes at UofA Shipping to other labs for seahorse, perifusion, omics Shipping to other labs (not in project) "],["overview-of-metadata-types.html", "Chapter 2 Overview of Metadata Types 2.1 Donor Characteristics: 2.2 Organ Characteristics and Processing: 2.3 Isolation Outcomes: 2.4 Cell Culture Outcomes: 2.5 Static Insulin Secretion: 2.6 Islet Oxygen Consumption (Seahorse assay): 2.7 Dynamic Insulin Responses to Macronutrients: 2.8 Single-cell Function (electrophysiology)", " Chapter 2 Overview of Metadata Types For each outcome type, include information on: Overview of experimental procedures performed prior to collecting the data (and relevant technical parameters) -> refer to timeline diagram Glossary of every variable in the tool: what precisely does it mean, what are the units, etc Who collected the data (institution) Replicates - were there any, are they different groups of islets, different individual cells, etc Explanation of any changes in protocols (ie. switch to different GSIS concentrations, maybe different platform used halfway through, etc) Whether data was intentionally collected on specific subsets of donors Versions of data available for download Data processing prior to statistical analysis (merging replicates, transformations, removed outliers, definition of any computed/normalized values) Any additional notes Citations of papers that have published parts of the data 2.1 Donor Characteristics: Donor characteristics are provided to the Isletcore team through the organ procurement programs. These characteristics can come from the family and aren’t always accurate reflections of the medical history. Hba1c measurements are performed in the Isletcore when the donor blood sample is available. 2.2 Organ Characteristics and Processing: Organ characteristic and processing data is recorded during the islet isolation by the lead technician. Cold Ischemia time will be calculated based on the cross clamp time during organ procurement until the start of the isolation in Edmonton. 2.3 Isolation Outcomes: Islet purity, IEQ, % trapped, IPI are determined by the lead technician. Full details on determining IEQ, purity etc can be found here or in our IsletCore welcome booklet. Details on determining Insulin and DNA samples can always be found here. Cell proportion data is determined through our proteomics data. Proteomics samples are shipped to Jim Johnson’s lab at UBC. Samples are then hand picked and snap frozen for processing. 2.4 Cell Culture Outcomes: On the day of shipping, Islets are recounted to determine IEQ, % recovery, IPI after culture. Culture time is determined by the end of the isolation until approximately 9am on a shipping day. Post culture samples are collected for insulin and DNA as previously described. 2.5 Static Insulin Secretion: In triplicates, 15 hand picked islets from each donor were sequentially treated with low glucose for 1 hour followed by high glucose 1 hour. Collected supernatant for low glucose, high glucose or insulin content are measured by ELISA and recorded in the database. For each donor, there are multiple glucose combinations that may have been used. Glucose pairs are 1mM to 10mM glucose, 1mM to 16.7mM glucose or 2.8mM glucose to 16.7mM glucose. Islets used in the 1mM to 10mM group are unique from the 1mM to 16.7mM group. The secretion data are commonly presented as a percentage of the total content (secreted insulin/total insulin *100), and as a stimulation index (high glucose secretion/low glucose secretion). Biological outliers were defined as “Seth is coming up with the definition now”, and identified outliers were removed from the dataset. The data for each replicate (after outlier removal) can be downloaded from the data export page. The total insulin content, secretion percentages, and stimulation indices, averaged across replicates, can be queried, analyzed, and visualized in the other parts of the tool. Complete experimental details can be found here. 2.6 Islet Oxygen Consumption (Seahorse assay): 2.7 Dynamic Insulin Responses to Macronutrients: Human islets are shipped to Jim Johnson’s lab at UBC. Islets are hand picked and cultured in RPMI for 24-72hrs before the experiment to allow the islets to recover from shipping. 65 islets are loaded per chamber and pre-incubated for 1 hour. Islets are stimulated with 6 or 15mM glucose, 5mM Leucine or 0.75mM Oleic acid/0.75mM palmitic acid as indicated. Samples are stored at -20C until analysis with RIA. Full details are described here. 2.8 Single-cell Function (electrophysiology) Human islets are hand picked and dispersed into single cells and plated on 35mm cell culture dishes by the MacDonald lab. 24-72 hours after the dispersion, electrophysiological measurements are recorded. Cell identity is confirmed by immunohistochemistry. "],["overview-of-omics-data-types.html", "Chapter 3 Overview of Omics Data Types 3.1 Gene expression (bulk RNA-seq) 3.2 Gene expression (Nanostring) 3.3 Protein expression (proteomics) 3.4 Gene expression (single-cell RNA-seq) 3.5 Gene expression (pseudobulk RNA-seq)", " Chapter 3 Overview of Omics Data Types For each data type, include information on: Platform(s) Batches (and whether batch effect is significant) Any logistical (shipping, storage)/lab procedures conducted prior to collecting data (and indicate relevant technical parameters - ie. culture times, etc) Whether data was intentionally collected on specific subsets of donors Raw data processing pipeline Where you can find raw data files Missing value strategy, filtering, and normalization prior to statistical analysis Any additional notes Citations of papers that have published parts of the data 3.1 Gene expression (bulk RNA-seq) RNA-seq profiles were collected starting in [YEAR]in batches of islets from ~10-20 donors. [Include information on batches]. Raw reads were aligned to the [BLANK GENOME] using [BLANK SOFTWARE version BLANK]. The counts matrix was filtered to remove any genes with fewer than [X] mean counts. The counts were normalized for sequencing depth and adjusted to account for high variance/high abundant genes using the [BLANK] method in the limma voom R package (version #). Batch effect was corrected using the Combat-Seq R package (version #). Since 3.2 Gene expression (Nanostring) 50 islets are hand picked in Edmonton. Cells are washed with PBS then collected in 100ul of RLT Beta-mercaptoethanol. Islets arestored at -80C before shipping to the Lynn Lab. 3.3 Protein expression (proteomics) Human islets are shipped to Jim Johnson’s lab at UBC. 300 Islets are hand picked, washed with pbs and pellet is snap frozen and stored at -80C. Details of lysis and analysis are found here. 3.4 Gene expression (single-cell RNA-seq) 3.5 Gene expression (pseudobulk RNA-seq) 150 islets are hand picked at the University of Alberta and transferred into a tube with 1ml of Trizol reagent. Samples are stored at -80C until shipping to Anna Gloyn’s lab (originally Oxford now Stanford). "],["overview-of-statistical-analysis-methods.html", "Chapter 4 Overview of statistical analysis methods 4.1 Multiple linear regression 4.2 Spearman ranked correlation 4.3 Overrepresentation analysis 4.4 Gene set enrichment analysis 4.5 Pipelines to generate significant feature associations for feature search page 4.6 Sources of annotation files, pathway libraries, & link to code repository", " Chapter 4 Overview of statistical analysis methods For each analysis, include details on: Specific methods (and R packages) used for each task (aimed for statisticians/data scientists) Explanation of how the methods work, what they are useful for, and their limitations (aimed for biologists, not statisticians/data scientists) Which parameters are exposed in the interface, what they do, and rationale for which parameters were exposed How to interpret the results How to report the results (which details/citations to include in methods sections of papers) Any additional notes 4.1 Multiple linear regression 4.2 Spearman ranked correlation 4.3 Overrepresentation analysis 4.4 Gene set enrichment analysis 4.5 Pipelines to generate significant feature associations for feature search page 4.6 Sources of annotation files, pathway libraries, & link to code repository "],["404.html", "Page not found", " Page not found The page you requested cannot be found (perhaps it was moved or renamed). You may want to try searching to find the page's new location, or use the table of contents to find the page you are looking for. "]] diff --git a/docs/style.css b/docs/style.css new file mode 100644 index 0000000..f317b43 --- /dev/null +++ b/docs/style.css @@ -0,0 +1,14 @@ +p.caption { + color: #777; + margin-top: 10px; +} +p code { + white-space: inherit; +} +pre { + word-break: normal; + word-wrap: normal; +} +pre code { + white-space: inherit; +} diff --git a/index.Rmd b/index.Rmd new file mode 100644 index 0000000..51de750 --- /dev/null +++ b/index.Rmd @@ -0,0 +1,26 @@ +--- +title: "Human Islets" +author: "" +date: "`r Sys.Date()`" +site: bookdown::bookdown_site +output: bookdown::gitbook +documentclass: book + +description: | + This is an introduction of the Human Islet project. +--- + +# General Introduction + +The HumanIslets tool houses data collected from or associated with human islets procured by the University of Alberta. The data includes clinical metadata + +I envision a branching timeline, so that you can visualize everything that happened to an islet sample prior to the collection of each data type. Here is a rough outline of steps (I know it’s incomplete/wrong - will look at some of the guides on the IsletCore website to better sketch out) + + 1. Pancreas procurement + 2. Shipping to Edmonton + 3. Islet isolation (more details here - I assume many/multiple steps) + 4. GSIS outcomes + 5. Other outcomes at UofA + 6. Shipping to other labs for seahorse, perifusion, omics + 7. Shipping to other labs (not in project) + diff --git a/preamble.tex b/preamble.tex new file mode 100644 index 0000000..dfd2e14 --- /dev/null +++ b/preamble.tex @@ -0,0 +1 @@ +\usepackage{booktabs} diff --git a/style.css b/style.css new file mode 100644 index 0000000..f317b43 --- /dev/null +++ b/style.css @@ -0,0 +1,14 @@ +p.caption { + color: #777; + margin-top: 10px; +} +p code { + white-space: inherit; +} +pre { + word-break: normal; + word-wrap: normal; +} +pre code { + white-space: inherit; +}