class: title-slide, middle, left ## Session 3: Introduction to the [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) Shiny web application ### Dr Shazia Ruybal-Pesántez Presented at the [covidClassifyR Shiny app workshop](https://shaziaruybal.github.io/covidClassifyR-workshop) for researchers from PNGIMR and partner institutions 2022-03-10 --- class: left # Recap ### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M400 480H48c-26.51 0-48-21.49-48-48V80c0-26.51 21.49-48 48-48h352c26.51 0 48 21.49 48 48v352c0 26.51-21.49 48-48 48zm-204.686-98.059l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.248-16.379-6.249-22.628 0L184 302.745l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.25 16.379 6.25 22.628.001z"/></svg> **Session 1** gave you an overview of the Luminex technology and the COVID-19 multi-antigen serological assay that was established in PNG -- ### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M400 480H48c-26.51 0-48-21.49-48-48V80c0-26.51 21.49-48 48-48h352c26.51 0 48 21.49 48 48v352c0 26.51-21.49 48-48 48zm-204.686-98.059l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.248-16.379-6.249-22.628 0L184 302.745l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.25 16.379 6.25 22.628.001z"/></svg> **Session 2** gave you an overview of the purpose of serosurveillance and insights into the COVID-19 sero-surveys that have been carried out in PNG -- .footnote[ For the materials for sessions 1 and 2 see the [workshop website <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M432,320H400a16,16,0,0,0-16,16V448H64V128H208a16,16,0,0,0,16-16V80a16,16,0,0,0-16-16H48A48,48,0,0,0,0,112V464a48,48,0,0,0,48,48H400a48,48,0,0,0,48-48V336A16,16,0,0,0,432,320ZM488,0h-128c-21.37,0-32.05,25.91-17,41l35.73,35.73L135,320.37a24,24,0,0,0,0,34L157.67,377a24,24,0,0,0,34,0L435.28,133.32,471,169c15,15,41,4.5,41-17V24A24,24,0,0,0,488,0Z"/></svg>](https://shaziaruybal.github.io/covidClassifyR-workshop/) ] --- class: center # <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M96 64h448v352h64V40c0-22.06-17.94-40-40-40H72C49.94 0 32 17.94 32 40v376h64V64zm528 384H480v-64H288v64H16c-8.84 0-16 7.16-16 16v32c0 8.84 7.16 16 16 16h608c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16z"/></svg> # Today we will cover: -- ### An intro to the [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) Shiny web application -- ### Why it was developed -- ### The features in the application -- ### You can find the app [here <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M432,320H400a16,16,0,0,0-16,16V448H64V128H208a16,16,0,0,0,16-16V80a16,16,0,0,0-16-16H48A48,48,0,0,0,0,112V464a48,48,0,0,0,48,48H400a48,48,0,0,0,48-48V336A16,16,0,0,0,432,320ZM488,0h-128c-21.37,0-32.05,25.91-17,41l35.73,35.73L135,320.37a24,24,0,0,0,0,34L157.67,377a24,24,0,0,0,34,0L435.28,133.32,471,169c15,15,41,4.5,41-17V24A24,24,0,0,0,488,0Z"/></svg>](https://shaziaruybal.shinyapps.io/covidClassifyR) --- class: inverse # Why was [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) developed? -- - #### Automated data processing pipelines can decrease <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M256,8C119,8,8,119,8,256S119,504,256,504,504,393,504,256,393,8,256,8Zm92.49,313h0l-20,25a16,16,0,0,1-22.49,2.5h0l-67-49.72a40,40,0,0,1-15-31.23V112a16,16,0,0,1,16-16h32a16,16,0,0,1,16,16V256l58,42.5A16,16,0,0,1,348.49,321Z"/></svg> time spent on manual tasks and <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M504 256c0 137-111 248-248 248S8 393 8 256 119 8 256 8s248 111 248 248zM212 140v116h-70.9c-10.7 0-16.1 13-8.5 20.5l114.9 114.3c4.7 4.7 12.2 4.7 16.9 0l114.9-114.3c7.6-7.6 2.2-20.5-8.5-20.5H300V140c0-6.6-5.4-12-12-12h-64c-6.6 0-12 5.4-12 12z"/></svg> reduce human error -- - #### <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M255.98 160V16c0-8.84-7.16-16-16-16h-32c-8.84 0-16 7.16-16 16v146.93c5.02-1.78 10.34-2.93 15.97-2.93h48.03zm128 95.99c-.01-35.34-28.66-63.99-63.99-63.99H207.85c-8.78 0-15.9 7.07-15.9 15.85v.56c0 26.27 21.3 47.59 47.57 47.59h35.26c9.68 0 13.2 3.58 13.2 8v16.2c0 4.29-3.59 7.78-7.88 8-44.52 2.28-64.16 24.71-96.05 72.55l-6.31 9.47a7.994 7.994 0 0 1-11.09 2.22l-13.31-8.88a7.994 7.994 0 0 1-2.22-11.09l6.31-9.47c15.73-23.6 30.2-43.26 47.31-58.08-17.27-5.51-31.4-18.12-38.87-34.45-6.59 3.41-13.96 5.52-21.87 5.52h-32c-12.34 0-23.49-4.81-32-12.48C71.48 251.19 60.33 256 48 256H16c-5.64 0-10.97-1.15-16-2.95v77.93c0 33.95 13.48 66.5 37.49 90.51L63.99 448v64h255.98v-63.96l35.91-35.92A96.035 96.035 0 0 0 384 344.21l-.02-88.22zm-32.01-90.09V48c0-8.84-7.16-16-16-16h-32c-8.84 0-16 7.16-16 16v112h32c11.28 0 21.94 2.31 32 5.9zM16 224h32c8.84 0 16-7.16 16-16V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v128c0 8.84 7.16 16 16 16zm95.99 0h32c8.84 0 16-7.16 16-16V48c0-8.84-7.16-16-16-16h-32c-8.84 0-16 7.16-16 16v160c0 8.84 7.16 16 16 16z"/></svg> To empower researchers (especially lab-based researchers) to focus on the analysis and interpretation of their data rather than manual processing -- - #### Quality control of Luminex data needs to be performed for each plate that is run and should be performed in real-time -- - #### Processing the Luminex data requires customized scripts in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> - <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> can also be challenging due to software versions -- - #### Algorithms that enable the classification of unknown samples into those that are predicted to have had recent exposure to SARS-CoV-2 or no recent exposure require advanced statistical and programming skills -- - #### Fit-for-purpose tools are required to make such data processing pipelines and advanced analyses accessible to researchers regardless of their statistical/programming background --- class: inverse, middle, center # <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M255.03 261.65c6.25 6.25 16.38 6.25 22.63 0l11.31-11.31c6.25-6.25 6.25-16.38 0-22.63L253.25 192l35.71-35.72c6.25-6.25 6.25-16.38 0-22.63l-11.31-11.31c-6.25-6.25-16.38-6.25-22.63 0l-58.34 58.34c-6.25 6.25-6.25 16.38 0 22.63l58.35 58.34zm96.01-11.3l11.31 11.31c6.25 6.25 16.38 6.25 22.63 0l58.34-58.34c6.25-6.25 6.25-16.38 0-22.63l-58.34-58.34c-6.25-6.25-16.38-6.25-22.63 0l-11.31 11.31c-6.25 6.25-6.25 16.38 0 22.63L386.75 192l-35.71 35.72c-6.25 6.25-6.25 16.38 0 22.63zM624 416H381.54c-.74 19.81-14.71 32-32.74 32H288c-18.69 0-33.02-17.47-32.77-32H16c-8.8 0-16 7.2-16 16v16c0 35.2 28.8 64 64 64h512c35.2 0 64-28.8 64-64v-16c0-8.8-7.2-16-16-16zM576 48c0-26.4-21.6-48-48-48H112C85.6 0 64 21.6 64 48v336h512V48zm-64 272H128V64h384v256z"/></svg> ## [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) was developed fit-for-purpose to streamline the processing of serological data generated using the COVID-19 multi-antigen serological assay --- class: inverse ## [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) allows you to: -- ### <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M224 136V0H24C10.7 0 0 10.7 0 24v464c0 13.3 10.7 24 24 24h336c13.3 0 24-10.7 24-24V160H248c-13.2 0-24-10.8-24-24zm60.1 106.5L224 336l60.1 93.5c5.1 8-.6 18.5-10.1 18.5h-34.9c-4.4 0-8.5-2.4-10.6-6.3C208.9 405.5 192 373 192 373c-6.4 14.8-10 20-36.6 68.8-2.1 3.9-6.1 6.3-10.5 6.3H110c-9.5 0-15.2-10.5-10.1-18.5l60.3-93.5-60.3-93.5c-5.2-8 .6-18.5 10.1-18.5h34.8c4.4 0 8.5 2.4 10.6 6.3 26.1 48.8 20 33.6 36.6 68.5 0 0 6.1-11.7 36.6-68.5 2.1-3.9 6.2-6.3 10.6-6.3H274c9.5-.1 15.2 10.4 10.1 18.4zM384 121.9v6.1H256V0h6.1c6.4 0 12.5 2.5 17 7l97.9 98c4.5 4.5 7 10.6 7 16.9z"/></svg> process your Luminex raw data -- ### <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M384 112v352c0 26.51-21.49 48-48 48H48c-26.51 0-48-21.49-48-48V112c0-26.51 21.49-48 48-48h80c0-35.29 28.71-64 64-64s64 28.71 64 64h80c26.51 0 48 21.49 48 48zM192 40c-13.255 0-24 10.745-24 24s10.745 24 24 24 24-10.745 24-24-10.745-24-24-24m96 114v-20a6 6 0 0 0-6-6H102a6 6 0 0 0-6 6v20a6 6 0 0 0 6 6h180a6 6 0 0 0 6-6z"/></svg> perform quality control of your data and generate a quality control report -- ### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M504 256c0 136.997-111.043 248-248 248S8 392.997 8 256C8 119.083 119.043 8 256 8s248 111.083 248 248zM262.655 90c-54.497 0-89.255 22.957-116.549 63.758-3.536 5.286-2.353 12.415 2.715 16.258l34.699 26.31c5.205 3.947 12.621 3.008 16.665-2.122 17.864-22.658 30.113-35.797 57.303-35.797 20.429 0 45.698 13.148 45.698 32.958 0 14.976-12.363 22.667-32.534 33.976C247.128 238.528 216 254.941 216 296v4c0 6.627 5.373 12 12 12h56c6.627 0 12-5.373 12-12v-1.333c0-28.462 83.186-29.647 83.186-106.667 0-58.002-60.165-102-116.531-102zM256 338c-25.365 0-46 20.635-46 46 0 25.364 20.635 46 46 46s46-20.636 46-46c0-25.365-20.635-46-46-46z"/></svg> apply the built-in classification algorithms to predict exposure to COVID-19 based on the antibody data -- ### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M332.8 320h38.4c6.4 0 12.8-6.4 12.8-12.8V172.8c0-6.4-6.4-12.8-12.8-12.8h-38.4c-6.4 0-12.8 6.4-12.8 12.8v134.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h38.4c6.4 0 12.8-6.4 12.8-12.8V76.8c0-6.4-6.4-12.8-12.8-12.8h-38.4c-6.4 0-12.8 6.4-12.8 12.8v230.4c0 6.4 6.4 12.8 12.8 12.8zm-288 0h38.4c6.4 0 12.8-6.4 12.8-12.8v-70.4c0-6.4-6.4-12.8-12.8-12.8h-38.4c-6.4 0-12.8 6.4-12.8 12.8v70.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h38.4c6.4 0 12.8-6.4 12.8-12.8V108.8c0-6.4-6.4-12.8-12.8-12.8h-38.4c-6.4 0-12.8 6.4-12.8 12.8v198.4c0 6.4 6.4 12.8 12.8 12.8zM496 384H64V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16z"/></svg> visualize your data with built-in interactive plots -- ### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M216 0h80c13.3 0 24 10.7 24 24v168h87.7c17.8 0 26.7 21.5 14.1 34.1L269.7 378.3c-7.5 7.5-19.8 7.5-27.3 0L90.1 226.1c-12.6-12.6-3.7-34.1 14.1-34.1H192V24c0-13.3 10.7-24 24-24zm296 376v112c0 13.3-10.7 24-24 24H24c-13.3 0-24-10.7-24-24V376c0-13.3 10.7-24 24-24h146.7l49 49c20.1 20.1 52.5 20.1 72.6 0l49-49H488c13.3 0 24 10.7 24 24zm-124 88c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20zm64 0c0-11-9-20-20-20s-20 9-20 20 9 20 20 20 20-9 20-20z"/></svg> download your processed data for further downstream analysis --- # Navigating the app  --- # Tutorial You can check out the tutorial [here <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M432,320H400a16,16,0,0,0-16,16V448H64V128H208a16,16,0,0,0,16-16V80a16,16,0,0,0-16-16H48A48,48,0,0,0,0,112V464a48,48,0,0,0,48,48H400a48,48,0,0,0,48-48V336A16,16,0,0,0,432,320ZM488,0h-128c-21.37,0-32.05,25.91-17,41l35.73,35.73L135,320.37a24,24,0,0,0,0,34L157.67,377a24,24,0,0,0,34,0L435.28,133.32,471,169c15,15,41,4.5,41-17V24A24,24,0,0,0,488,0Z"/></svg>](https://shaziaruybal.shinyapps.io/covidclassifyr) and click on the "Tutorial" tab. You can download the example data and try it yourself!  --- # Importing your data We will cover how to prepare and import your data in [Session 4](https://shaziaruybal.github.io/covidClassifyR-workshop/materials.html)  --- # Quality control We will cover how to QC your data in [Session 5](https://shaziaruybal.github.io/covidClassifyR-workshop/materials.html), and how to generate your QC report and look at your processed data in [Session 6](https://shaziaruybal.github.io/covidClassifyR-workshop/materials.html)  --- # Classification We will cover the built-in classification algorithms in [Session 8](https://shaziaruybal.github.io/covidClassifyR-workshop/materials.html) and how to apply them to your data in [Session 9](https://shaziaruybal.github.io/covidClassifyR-workshop/materials.html)  --- # Data visualization We will cover how to interpet and visualize your data using the built-in interactive plots in [Session 11](https://shaziaruybal.github.io/covidClassifyR-workshop/materials.html)  --- class: left # <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M496 224c-79.6 0-144 64.4-144 144s64.4 144 144 144 144-64.4 144-144-64.4-144-144-144zm64 150.3c0 5.3-4.4 9.7-9.7 9.7h-60.6c-5.3 0-9.7-4.4-9.7-9.7v-76.6c0-5.3 4.4-9.7 9.7-9.7h12.6c5.3 0 9.7 4.4 9.7 9.7V352h38.3c5.3 0 9.7 4.4 9.7 9.7v12.6zM320 368c0-27.8 6.7-54.1 18.2-77.5-8-1.5-16.2-2.5-24.6-2.5h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h347.1c-45.3-31.9-75.1-84.5-75.1-144zm-96-112c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128z"/></svg> Your turn Go to the [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) Shiny web application by clicking [here <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M432,320H400a16,16,0,0,0-16,16V448H64V128H208a16,16,0,0,0,16-16V80a16,16,0,0,0-16-16H48A48,48,0,0,0,0,112V464a48,48,0,0,0,48,48H400a48,48,0,0,0,48-48V336A16,16,0,0,0,432,320ZM488,0h-128c-21.37,0-32.05,25.91-17,41l35.73,35.73L135,320.37a24,24,0,0,0,0,34L157.67,377a24,24,0,0,0,34,0L435.28,133.32,471,169c15,15,41,4.5,41-17V24A24,24,0,0,0,488,0Z"/></svg>](https://shaziaruybal.shinyapps.io/covidclassifyr). <div class="countdown" id="timer_6229297f" style="right:0;bottom:0;" data-warnwhen="0"> <code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> --- # <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M542.22 32.05c-54.8 3.11-163.72 14.43-230.96 55.59-4.64 2.84-7.27 7.89-7.27 13.17v363.87c0 11.55 12.63 18.85 23.28 13.49 69.18-34.82 169.23-44.32 218.7-46.92 16.89-.89 30.02-14.43 30.02-30.66V62.75c.01-17.71-15.35-31.74-33.77-30.7zM264.73 87.64C197.5 46.48 88.58 35.17 33.78 32.05 15.36 31.01 0 45.04 0 62.75V400.6c0 16.24 13.13 29.78 30.02 30.66 49.49 2.6 149.59 12.11 218.77 46.95 10.62 5.35 23.21-1.94 23.21-13.46V100.63c0-5.29-2.62-10.14-7.27-12.99z"/></svg> Your homework for next time -- ### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M400 480H48c-26.51 0-48-21.49-48-48V80c0-26.51 21.49-48 48-48h352c26.51 0 48 21.49 48 48v352c0 26.51-21.49 48-48 48zm-204.686-98.059l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.248-16.379-6.249-22.628 0L184 302.745l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.25 16.379 6.25 22.628.001z"/></svg> Access the [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) Shiny web application [<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M432,320H400a16,16,0,0,0-16,16V448H64V128H208a16,16,0,0,0,16-16V80a16,16,0,0,0-16-16H48A48,48,0,0,0,0,112V464a48,48,0,0,0,48,48H400a48,48,0,0,0,48-48V336A16,16,0,0,0,432,320ZM488,0h-128c-21.37,0-32.05,25.91-17,41l35.73,35.73L135,320.37a24,24,0,0,0,0,34L157.67,377a24,24,0,0,0,34,0L435.28,133.32,471,169c15,15,41,4.5,41-17V24A24,24,0,0,0,488,0Z"/></svg>](https://shaziaruybal.shinyapps.io/covidclassifyr) -- ### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M400 480H48c-26.51 0-48-21.49-48-48V80c0-26.51 21.49-48 48-48h352c26.51 0 48 21.49 48 48v352c0 26.51-21.49 48-48 48zm-204.686-98.059l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.248-16.379-6.249-22.628 0L184 302.745l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.25 16.379 6.25 22.628.001z"/></svg> Download the example data -- ### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M400 480H48c-26.51 0-48-21.49-48-48V80c0-26.51 21.49-48 48-48h352c26.51 0 48 21.49 48 48v352c0 26.51-21.49 48-48 48zm-204.686-98.059l184-184c6.248-6.248 6.248-16.379 0-22.627l-22.627-22.627c-6.248-6.248-16.379-6.249-22.628 0L184 302.745l-70.059-70.059c-6.248-6.248-16.379-6.248-22.628 0l-22.627 22.627c-6.248 6.248-6.248 16.379 0 22.627l104 104c6.249 6.25 16.379 6.25 22.628.001z"/></svg> Take a look at the example raw data and plate layout files -- #### <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z"/></svg> Bonus: using the tutorial as a guide, try and follow along how to import the example data --- # Acknowledgments - Dr Maria Ome-Kaius and Dr Fiona Angrisano - PNGIMR and partner institutions - WEHI & Burnet Institute - All of you for attending! *We are extremely grateful for financial support to develop and host the covidClassifyR Shiny web application, and to host these virtual workshops through the [Regional Collaborations Programme COVID-19 Digital Grant](https://www.science.org.au/news-and-events/news-and-media-releases/regional-research-set-get-digital-boost) from the Australian Academy of Science and Australian Department of Industry, Science, Energy and Resources.* The scripts and functions used in [`covidClassifyR`](https://shaziaruybal.shinyapps.io/covidclassifyr) were developed by Shazia Ruybal-Pesántez, with contributions from the following researchers: Eamon Conway, Connie Li Wan Suen, Narimane Nekkab and Michael White. .footnote[ _These slides were created using the R packages: [xaringan](https://github.com/yihui/xaringan), [xaringanthemer](https://github.com/gadenbuie/xaringanthemer), [xaringanExtra](https://github.com/gadenbuie/xaringanExtra)_ ] --- name: contact class: inverse .pull-left[ .center[ ### Dr Shazia Ruybal-Pesántez <img style="border-radius: 50%;" src="https://shaziaruybal.github.io/covidClassifyR-workshop/images/shazia.png" width="250px"/> #### Contact details [<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> ruybal.s@wehi.edu.au](mailto:ruybal.s@wehi.edu.au) [<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> @DrShaziaRuybal](https://twitter.com/DrShaziaRuybal) ]] .pull-right[ ### Session 3 Resources: ### [<i class="fab fa-youtube "></i> Recording](https://youtu.be/O1u3hky2HIM) ### [<i class="fas fa-laptop-code "></i> `covidClassifyR`](https://shaziaruybal.shinyapps.io/covidClassifyR) ### [<svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M336.5 160C322 70.7 287.8 8 248 8s-74 62.7-88.5 152h177zM152 256c0 22.2 1.2 43.5 3.3 64h185.3c2.1-20.5 3.3-41.8 3.3-64s-1.2-43.5-3.3-64H155.3c-2.1 20.5-3.3 41.8-3.3 64zm324.7-96c-28.6-67.9-86.5-120.4-158-141.6 24.4 33.8 41.2 84.7 50 141.6h108zM177.2 18.4C105.8 39.6 47.8 92.1 19.3 160h108c8.7-56.9 25.5-107.8 49.9-141.6zM487.4 192H372.7c2.1 21 3.3 42.5 3.3 64s-1.2 43-3.3 64h114.6c5.5-20.5 8.6-41.8 8.6-64s-3.1-43.5-8.5-64zM120 256c0-21.5 1.2-43 3.3-64H8.6C3.2 212.5 0 233.8 0 256s3.2 43.5 8.6 64h114.6c-2-21-3.2-42.5-3.2-64zm39.5 96c14.5 89.3 48.7 152 88.5 152s74-62.7 88.5-152h-177zm159.3 141.6c71.4-21.2 129.4-73.7 158-141.6h-108c-8.8 56.9-25.6 107.8-50 141.6zM19.3 352c28.6 67.9 86.5 120.4 158 141.6-24.4-33.8-41.2-84.7-50-141.6h-108z"/></svg> Workshop materials](https://shaziaruybal.github.io/covidClassifyR-workshop/materials.html) ### [<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 448H48c-26.51 0-48-21.49-48-48V112c0-26.51 21.49-48 48-48h416c26.51 0 48 21.49 48 48v288c0 26.51-21.49 48-48 48zM112 120c-30.928 0-56 25.072-56 56s25.072 56 56 56 56-25.072 56-56-25.072-56-56-56zM64 384h384V272l-87.515-87.515c-4.686-4.686-12.284-4.686-16.971 0L208 320l-55.515-55.515c-4.686-4.686-12.284-4.686-16.971 0L64 336v48z"/></svg> Slides for Session 3](https://shaziaruybal.github.io/covidClassifyR-workshop/slides/session3/session3_slides.html) ] --- class: inverse, middle, center # <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M504 256c0 136.997-111.043 248-248 248S8 392.997 8 256C8 119.083 119.043 8 256 8s248 111.083 248 248zM262.655 90c-54.497 0-89.255 22.957-116.549 63.758-3.536 5.286-2.353 12.415 2.715 16.258l34.699 26.31c5.205 3.947 12.621 3.008 16.665-2.122 17.864-22.658 30.113-35.797 57.303-35.797 20.429 0 45.698 13.148 45.698 32.958 0 14.976-12.363 22.667-32.534 33.976C247.128 238.528 216 254.941 216 296v4c0 6.627 5.373 12 12 12h56c6.627 0 12-5.373 12-12v-1.333c0-28.462 83.186-29.647 83.186-106.667 0-58.002-60.165-102-116.531-102zM256 338c-25.365 0-46 20.635-46 46 0 25.364 20.635 46 46 46s46-20.636 46-46c0-25.365-20.635-46-46-46z"/></svg> Questions?