30:T444,html`

Results

We'll keep experiments throughout the day in here, with methods kept in a seperate section below (Obserevable is an assynchronous functional platform)

Reproduce someonelse's work

So let's warm up for the workshop with one of your papers, PMC5683428 Wilson, Grossman et. al (2017)Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API. Cancer Res. 2017 Nov 1; 77(21): e15–e18., where a sample data query is provided: https://api.gdc.cancer.gov/files?filters={"op":"=","content":{"field":"cases.demographic.gender","value":["male"]}}.to which I'm adding "from=0&size=250" to make it prettier with more data. For example, the disribution of types the first ${n} entries would be:

`31:T4ec,demoData = (await fetch("https://nci-crdc-demo.datacommons.io/api/v0/submission/graphql/",{ method:'POST', headers:{ 'authorization': 'bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZCI6ImZlbmNlX2tleV8yMDE4LTA2LTI3VDE4OjQ1OjQxWiJ9.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.q9CRz6Njr_Bb9SDSkmyIkMUR9o-lZ188DnHflb4LeWZtwWmcrr3-Aytnl5G198FGLf6ACWL-twO1LkRQ1ZK790C0vgJ7IehML76XQZCQUIqmeZqCORzTOWGYNCDNPqgry-cp28ep_anMfco8i9wl84EEbLH0V3_6Ji-MNKx_kw4kKozcNNtfJCGfvZtYx8vZ9o5FnujJ_HYObOewFLDoVkXHmuzvGnL1yLWrL5WCJ8jpZ1ST6zHayDd1SmwM_1oUetKniKHnbHCAbklvIl1xfLn2_cN-IVvTco7f7cKmFCAE9ucN_qz_YwQpmPGeBzwK-Zvi9k0eVIzifs96ckwSrw' }, mode:'cors', body:JSON.stringify({"query":'{sample(first:1000){id, subjects { demographics { gender } } }}'}) })).json() 32:[] 2e:{"title":"Gen3","cells":[{"id":0,"value":"md`# Gen3`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"html`\n

Hi Bob, this is a place holder for \"Web Jupyter\" conversation, which I'm arguing may be a better representation of the App use case then regular Jupyter. The argument goes that in large cohort consortia, the distributed data governance is the norm. The investment design/policy implications were discussed at AACR Modernizing Population Sciences in the Digital Age, February 19 - 22, 2019.

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":76,"value":"n=250","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"dataMale = (await fetch(`https://api.gdc.cancer.gov/files?from=0&size=${n}&filters={\"op\":\"=\",\"content\":{\"field\":\"cases.demographic.gender\",\"value\":[\"male\"]}}`)).json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":74,"value":"dataFemale=(await fetch(`https://api.gdc.cancer.gov/files?from=0&size=${n}&filters={\"op\":\"=\",\"content\":{\"field\":\"cases.demographic.gender\",\"value\":[\"female\"]}}`)).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":81,"value":"{\n let trace = (dt,field)=>{\n let attrs={}\n dt.data.hits.forEach(d=>{\n if(!attrs[d[field]]){attrs[d[field]]=0}\n attrs[d[field]]+=1\n })\n let tr = {\n x:Object.keys(attrs),\n y:Object.keys(attrs).map(a=>attrs[a]),\n type:'bar'\n }\n //debugger\n return tr \n }\n let traceMale=trace(dataMale,'type')\n traceMale.name='male'\n let traceFemale=trace(dataFemale,'type')\n traceFemale.name='female'\n \n return plotIt([traceMale,traceFemale],{\n title:'type'\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":116,"value":"html`\n

Now targetting the workshop demo API

What DC should we use? Let's start with ${demoUrl}

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":119,"value":"demoUrl='https://nci-crdc-demo.datacommons.io'","pinned":false,"hidden":false,"mode":"ojs"},{"id":150,"value":"md`no worries, bearer token here is outdated, as the message will reflect:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":130,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":62,"value":"html`\n

Methods

A simplified plotly based plotting method, plotIt, will be used. Note it is defined as a pure function (dependencies satisfied within scope) such that it can be imported directly in other notebooks as import {plotIt} from '@episphere/gen3'.

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"plotly= await require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":60,"value":"plotIt=async function(trace,layout){\n if(typeof(plotly)==\"undefined\"){\n let plotly= await require(\"https://cdn.plot.ly/plotly-latest.min.js\")\n }\n let div = DOM.element('div')\n trace=trace||false\n return plotly.plot(div,trace,layout)\n}","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}