Web-based Tools for Integrative Analysis of Pancreatic Cancer Data
•Download as PPTX, PDF•
0 likes•180 views
This document summarizes web-based tools for integrative analysis of pancreatic cancer data. It discusses moving from monolithic to modular cancer analysis apps and portals. Specific apps developed at the Wolfson Wohl Cancer Research Centre are described, including pathway analysis, gene variants, and survival apps. The pathway analysis app allows searching pathways, viewing heatmaps of patient vs. genes, and overlaying pathway diagrams. The gene variants app allows filtering and visualizing variants. The survival app generates Kaplan-Meier curves. Future directions include incorporating more data and making the apps available on a shared network site.
More Related Content
What's hot
What's hot (8)
Similar to Web-based Tools for Integrative Analysis of Pancreatic Cancer Data
Similar to Web-based Tools for Integrative Analysis of Pancreatic Cancer Data (20)
Recently uploaded
Recently uploaded (20)
Web-based Tools for Integrative Analysis of Pancreatic Cancer Data
- 1. Web-based Tools for Integrative Analysis of Pancreatic Cancer Data Derek Wright Wolfson Wohl Cancer Research Centre Visualisation in Science Conference 5th April 2017
- 3. Cancer Analysis Apps • Monolithic vs Modular • Architecture moving to connected apps/microservices • Portals • ICGC Data Portal • cBioPortal • generic, many capabilities, many classes of user • Apps - our approach • bespoke, use case driven, needs of user group
- 4. • Rapid development of interactive web applications in R • Incorporate existing R analysis scripts • No need to create separate web server/front-end layers • Extend using custom JavaScript/CSS/HTML • Powerful data visualisation: ggplot2, ggvis • Database access with dplyr • Hosting and deployment locally, on own servers, on cloud • Free, open source (RStudio's shinyapps.io service is paid)
- 5. Genomics Apps (WWCRC) Pathway Analysis Visualise gene regulation Gene Variants Browse mutations Survival Kaplan-Meier analysis
- 6. PDCL Pathway Analysis • Search • KEGG GENES database • KEGG PATHWAY database • Pathways involving genes • Interactive heatmap • Patient vs Genes in pathway • GSVA ranking • Scaling (-3 to 3) • KEGG Diagrams • Download pathway diagram • Overlay values in colour
- 8. Key Pathways Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52 (2016).
- 10. Gene Variants Select cohort • Primary tumour • Cell lines Filter ❖SNV, structural variants, CNV ❖Sample ID ❖Gene, variant type, chromosome ❖Key mutations: pathway/gene ❖Tumour subtype ❖CNV loss/gain Visualise • Circos
- 11. Genome Viewer Visualise patient-centric variants
- 14. Survival • Generate gene-centric Kaplan-Meier curves. • Adjust probability value • Select time/censor • Boxplots of pancreatic cancer subtype
- 16. Summary • Pathway Analysis • Gene Variants • Survival • Future directions: • PRECISION-Panc data • PanCancer Analysis of Whole Genomes (PCAWG) • shiny.tcrc.gla.ac.uk (UOG network) • Project website precisionpanc.org
- 17. 3rd BiVi Annual Meeting (2017) http://bivi.co 20th-21st April Edinburgh Napier University Craiglockhart Campus
Editor's Notes
- I’m a bioinformatician in Andrew Biankin’s group at the Wolfson Wohl Cancer Research Centre. I’m here to talk to you today about interactive web applications we have been developing for cancer bioinformatics.
- University of Glasgow is at the forefront of research into precision medicine. Our project forms the pancreatic cancer stream, known as Precision-Panc. This is a project involving academia, NHS and the private sector. Pancreatic cancer has seen little improvement in mortality rates over the years and we hope to make inroads into this. Our project was in the news recently as we received funding from Cancer Research UK. Patients will be recruited on to the project and tumour sequencing data and clinical data will be collected and analysed. We intend to produce a molecular profile, detailing the mutational landscape individual patient, guiding clinical trial options for the consultant to offer to the patient.
- The traditional approach for developing cancer web applications has been to build large and complicated portals with multiple functions, such as cBioPortal and the ICGC Data Portal. In software development generally, there is a move towards smaller applications or services for more specific use cases.
- I’m aware that today’s audience is multidisciplinary, with researchers, medics and people from the creative industries, so I would like to talk a bit about our approach to app development, using the Shiny framework, which many of you may find useful in the data visualisation arena. R is a statistical programming language that is popular in bioinformatics. It is free and open source, with many packages (or libraries) available for statistics, big data and bioinformatics. There are excellent packages available for data visualisation - in particular ggplot2 for static visualisations and the newer ggvis which produces interactive plots. There are also packages for geographical mapping which are useful for public health studies. A bioinformatician typically has a toolbox of analysis scripts that they run each time a wet lab scientist wants data analysed. Shiny is a server environment that allows R scripts to be turned into interactive web applications, promoting code reuse and empowering users to perform their own analyses. Shiny enables the entire application to be written in R. Typically a web application will have layers of HTML and JavaScript on the front end, a software framework written in PHP, Java, Ruby etc. and a database access layer to translate between software objects and SQL. I have worked with these kinds of systems in my previous life as a software engineer and R/Shiny is an absolute breeze in comparison. HTML is generated from R code, which creates page layouts using the popular Bootstrap framework. Pages are responsive and will work readily on mobile devices. However, you don’t have to go with the Shiny look and feel and it is possible to customise the presentation layer with your own front-end HTML code, style sheets and JavaScript visualisation frameworks such as D3. Plots are generated dynamically according to the inputs that the user selects. The basic data structure in R is a tabular structure and the excellent dplyr package allows you to treat R data tables like a database, performing selects, joins, group bys and so on,. It is also possible to visualise data stored in a relational database. dplyr can be linked to a tables in a database such Postgres, MySQL or SQLite. You manipulate R’s tabular data structures, and dplyr constructs SQL automatically and executes the query, bringing the results back into R. Database queries may thus be performed transparently without the need to write SQL.
- We have developed 3 initial apps as part of this workflow to perform genomic analysis. I will talk about each each one in more detail.
- Our Pathway app is based on RNA-Seq data from tumour cell lines. You can visualise the activity of your gene of interest, in the context of other genes it interacts with in a pathway. We overlay gene expression activity onto pathway diagrams that have been retrieved from the KEGG biological pathway database using a web service. We also draw heat maps, showing up- or down-regulated gene expression in a pathway using the gene set variation analysis ranking algorithm. Cell lines with similar patterns of activity cluster together.
- Our group have identified key pathways and genes involved in pancreatic cancer, detailed by my colleague Peter Bailey in his Nature paper and shown in this figure from the paper.
- 4 molecular subtypes of pancreatic cancer, derived from clustering analysis of RNA-seq, were identified in the paper and are shown in these figures from the paper. These key pathways and molecular subtypes have been incorporated into our apps.
- The Gene Variants app allows browsing and visualisation of single nucleotide, structural and copy number variants in the APGI patients, from tumour or cell line datasets. I have added filtering options such as key genes and pathways as identified in Peter’s Nature and a copy number slider. Underlying data is stored in a SQLite database.
- The Gene Variants app provides an interactive Circos plot showing an overview variants for an individual patient. Circos is a popular visualisation technique where a network is displayed with a circular layout. It is often used to show structural variation between chromosomes in genomics with translocations and so on shown as arcs. Additional features may be added as concentric layers. A patient’s variants may be visualised in an interactive Circos plot. The outer track shows copy number variants as a line plot. The chromosome may be clicked to expand, providing detail of individual copy number gain or loss variants shown as green or red ticks. Single nucleotide variants are shown in the scatter plot. Structural variants are shown as arcs in the centre. The user can click on a chromosome to expand and reveal detail.
- Our Survival app draws gene-centric Kaplan-Meier survival curves. The app lets you look at the association of expression of an individual gene with patient survival. Patients are subdivided into the 4 cancer subtypes, identified in our studies, and differential gene expression between these groups is visualised as a boxplot.
- These 3 apps from the project have been made available for users on the University of Glasgow network. We are currently not permitted to host the data on a public web server due to data sharing agreements. We hope to make these more widely available in future, possibly publishing a more generic toolkit. Future directions – we will incorporate new data generated by PRECISION-Panc. I am also currently analysing the PCAWG dataset. This is whole genome sequencing data from about 3000 cancer patients from various cancer projects around the world and I would like to incorporate some of this data into the apps. Our project website precisionpanc.org is where you can learn more about the project and pancreatic cancer in general and we have associated social media accounts you can follow.
- Very briefly before I finish. I would like to draw your attention for another data visualisation conference taking place later this month in Edinburgh. It’s a great meeting with some excellent speakers and tutorials. Registration fees are very reasonable at £50 and registration closes on 10th April.