Data Visualization for Genome Biology from Coursera

What's inside

Syllabus

Week 1

In this module we'll cover 3 straightforward approaches for generating simple plots. As we'll see in the lab, often visualizing datasets can help us see the overall shape of the data that might not be captured in descriptive statistics like mean and standard deviation. Plotting datasets is also a useful way to identify outliers. In the mini-lectures we go over some common biological data visualization paradigms and more generally what the common chart types are, and we also talk about the context and grammar of data visualization.

Week 2

In this week's module we explore ways of displaying biological variation and a little bit of background about track viewers. We also cover visual perception, Gestalt principles, and issues related to colour perception, important for accessibility-related reasons. In the lab we'll use an online app, PlotsOfDifferences, to generate some charts that display variation nicely, and we'll also use R to generate some box plots, histograms, and violin plots. Last but not least, we'll try adjusting some of the settings in JBrowse to help assess gene expression levels in a more intuitive manner. Thanks to Dr. Joachim Goedhart, University of Amsterdam, Netherlands for permission to use PlotsOfDifferences in the lab.

Week 3

In this week's module we explore ways of visualizing gene expression data after briefly covering how we can measure gene expression levels with RNA-seq and identify significantly differentially expressed genes using statistical tests. We also cover design thinking. In the lab we'll use an online platform, Galaxy, to generate a volcano plot for visualizing significantly differentially expressed genes, and we'll also use R to generate some heatmaps of gene expression. Last but not least, we'll create our own "electronic fluorescent pictographs" for a gene expression data set.

Week 4

In this week's module we cover how the Gene Ontology can be used to make sense of often overwhelmingly long lists of genes from transcriptomic and other kind of 'omic experiments, especially through Gene Ontology enrichment analyses. We'll also look at Agile Development and User Testing and how these can help improve data visualization tools. In the lab, we'll try our hand at 3 online Gene Ontology analysis apps, and create some nice overview charts for GO enrichment results in R. Thanks to Dr. Roy Navon, Technion University, Israel, for permission to use GOrilla in the lab. Thanks to Dr. Juri Reimand of the University of Toronto for permission to use g:Profiler. And thanks to Dr. Zhen Su of the China Agricultural University for permission to use AgriGO.

Week 5

In this week's module, we explore tools for displaying and analyzing graph networks, notably those created when we generate protein-protein interactions, especially in a high-throughput manner. These PPIs are deposited in online databases like BioGRID, and can be retrieved on-the-fly via web services for display in powerful network visualization apps like Cytoscape. We'll talk about other web services/APIs that are available for biology in one of the mini-lectures, and in the lab we'll use Cytoscape to explore interactors of BRCA2. We'll also use a plug-in called BiNGO to do Gene Ontology enrichment analyses of its interactors, continuing our exploration of GO that we started last week. Last, we'll try using D3 to display an interaction network in a web page.

Week 6

In this module we cover methods for generating and making sense of ever bigger biological data sets. The growth in sequencing capacity has enabled projects that we unimaginable even a few years ago, such as the Earth Biogenomes Project, which aims to sequence the genome of a representative of every eukaryotic species on the planet. In order to make sense of these large data sets, it is often useful to use dimentionality reduction methods, like t-SNE, PCA, and UMAP, to help visualize how similar samples are. Logic diagrams (Venn-Euler or Upset plots) are also useful for displaying how sets of genes are similar one to another. Thanks to Dr. Tim Hulsen (Philips Research, the Netherlands) for permission to use the DeepVenn app in the lab.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Assumes a base level of comfort with command-line software and programming

Offers a great overview of data visualization

Places particular emphasis on the analysis of biological datasets and omics data

Uses Bioconductor R packages

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Visualization for Genome Biology with these activities:

Data Visualization Practice Drills

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Reinforce your understanding of data visualization techniques through hands-on practice exercises.

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Access online visualization tools like PlotsOfDifferences or Gapminder and explore different data sets.
Generate and interpret basic plots (e.g., histograms, scatterplots, line graphs) to identify patterns and trends.
Experiment with color schemes, chart types, and annotations to enhance the clarity of your visualizations.

Create different types of data visualizations

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Creating different types of data visualizations will provide hands-on practice, improving proficiency in selecting the most appropriate visual encodings and chart types for different types of data.

Browse courses on Chart Creation

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Choose a dataset and create a variety of visualizations, including bar charts, line charts, and scatter plots.
Experiment with different data visualization tools, such as Tableau, Power BI, or Google Data Studio.
Share your visualizations with others and get feedback.

Advanced Data Visualization Tutorials

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Enhance your understanding and skills in advanced data visualization techniques by following guided tutorials.

Browse courses on Data Visualization Best Practices

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Identify online tutorials or courses on specific data visualization tools or techniques (e.g., R's ggplot2, Cytoscape).
Work through the tutorials step-by-step, experimenting with the featured techniques and applying them to your own data.

Two other activities

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Interactive Data Visualization Project

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Consolidate your learning by creating an interactive data visualization project that showcases your skills.

Browse courses on Interactive Data Visualization

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Identify a biological data set of interest and explore it thoroughly.
Design and develop an interactive data visualization using a tool like D3 or Plotly.
Present your visualization, explaining the insights you gained and the techniques you used.

Develop a data visualization dashboard

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Developing a data visualization dashboard will provide experience in designing and implementing interactive visualizations, allowing students to apply their skills in a practical context.

Browse courses on Interactive Data Visualization

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Identify a dataset and determine the key insights that you want to communicate.
Design the layout and structure of your dashboard.
Select and implement appropriate visualizations.
Make your dashboard interactive by adding filters, tooltips, and other features.
Deploy your dashboard and share it with others.

Career center

Learners who complete Data Visualization for Genome Biology will develop knowledge and skills that may be useful to these careers:

Bioinformatician

Bioinformaticians develop and use computational tools to analyze biological data, such as gene sequences and protein structures. This course, Data Visualization for Genome Biology, can be particularly helpful for bioinformaticians because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help bioinformaticians to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help bioinformaticians to communicate their findings to other scientists and to the public.

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Computational Biologist

Computational biologists use computer science and mathematics to study biological systems. This course, Data Visualization for Genome Biology, can be particularly helpful for computational biologists because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help computational biologists to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help computational biologists to communicate their findings to other scientists and to the public.

See salaries and explore the career path for Computational Biologist

Data Scientist

Data scientists use data to solve problems and make predictions. This course, Data Visualization for Genome Biology, can be particularly helpful for data scientists who work in the field of biology or medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help data scientists to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help data scientists to communicate their findings to other scientists and to the public.

See salaries and explore the career path for Data Scientist

Biostatistician

Biostatisticians use statistics to analyze biological data. This course, Data Visualization for Genome Biology, can be particularly helpful for biostatisticians because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help biostatisticians to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help biostatisticians to communicate their findings to other scientists and to the public.

See salaries and explore the career path for Biostatistician

Geneticist

Geneticists study genes and their role in health and disease. This course, Data Visualization for Genome Biology, can be particularly helpful for geneticists because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help geneticists to identify patterns and trends in data, and to make inferences about gene function. Additionally, this course can help geneticists to communicate their findings to other scientists and to the public.

See salaries and explore the career path for Geneticist

Medical Doctor

Medical doctors diagnose and treat diseases. This course, Data Visualization for Genome Biology, can be particularly helpful for medical doctors who are interested in genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help medical doctors to make more informed decisions about patient care.

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Epidemiologist

Epidemiologists study the causes and spread of diseases. This course, Data Visualization for Genome Biology, can be particularly helpful for epidemiologists who are interested in genomic epidemiology. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help epidemiologists to identify patterns and trends in data, and to make inferences about the causes and spread of diseases.

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Health Scientist

Health scientists conduct research to improve human health. This course, Data Visualization for Genome Biology, can be particularly helpful for health scientists who are interested in genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help health scientists to make more informed decisions about research.

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Science Teacher

Science teachers teach science to students. This course, Data Visualization for Genome Biology, can be particularly helpful for science teachers who teach biology or genetics. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science teachers to make their lessons more engaging and effective.

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Science Communicator

Science communicators translate scientific information into a form that is accessible to the public. This course, Data Visualization for Genome Biology, can be particularly helpful for science communicators who want to communicate about genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science communicators to create more informative and engaging content.

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Science Writer

Science writers write about science for the public. This course, Data Visualization for Genome Biology, can be particularly helpful for science writers who want to write about genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science writers to create more informative and engaging articles.

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Policy Advisor

Policy advisors advise policymakers on the development and implementation of policies. This course, Data Visualization for Genome Biology, can be particularly helpful for policy advisors who work on health or science policy. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help policy advisors to make more informed recommendations to policymakers.

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Science Editor

Science editors edit scientific writing. This course, Data Visualization for Genome Biology, can be particularly helpful for science editors who work on scientific journals or books. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science editors to make more informed decisions about what to publish.

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Intellectual Property Attorney

Intellectual property attorneys help their clients protect their intellectual property rights. This course, Data Visualization for Genome Biology, can be particularly helpful for intellectual property attorneys who work on patents related to genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help intellectual property attorneys to more effectively represent their clients.

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Patent Examiner

Patent examiners examine patent applications to determine if they meet the criteria for a patent. This course, Data Visualization for Genome Biology, can be particularly helpful for patent examiners who work on patents related to genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help patent examiners to make more informed decisions about whether to grant a patent.

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