The past decade has seen a vast increase in the amount of data available to biologists, driven by the dramatic decrease in cost and concomitant rise in throughput of various next-generation sequencing technologies, such that a project unimaginable 10 years ago was recently proposed, the Earth BioGenomes Project, which aims to sequence the genomes of all eukaryotic species on the planet within the next 10 years. So while data are no longer limiting, accessing and interpreting those data has become a bottleneck. One important aspect of interpreting data is data visualization. This course introduces theoretical topics in data visualization through mini-lectures, and applied aspects in the form of hands-on labs. The labs use both web-based tools and R, so students at all computer skill levels can benefit. Syllabus may be viewed at https://tinyurl.com/DataViz4GenomeBio.
Data Visualization for Genome Biology
Instructeur : Nicholas James Provart
2 438 déjà inscrits
Inclus avec
(20 avis)
Expérience recommandée
Détails à connaître
Ajouter à votre profil LinkedIn
12 devoirs
Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées
Obtenez un certificat professionnel
Ajoutez cette qualification à votre profil LinkedIn ou à votre CV
Partagez-le sur les réseaux sociaux et dans votre évaluation de performance
Il y a 6 modules dans ce cours
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.
Inclus
5 vidéos5 lectures2 devoirs1 laboratoire non noté
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.
Inclus
4 vidéos4 lectures2 devoirs1 laboratoire non noté
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.
Inclus
3 vidéos3 lectures2 devoirs1 laboratoire non noté
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.
Inclus
3 vidéos3 lectures2 devoirs1 laboratoire non noté
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.
Inclus
3 vidéos3 lectures2 devoirs
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.
Inclus
3 vidéos3 lectures2 devoirs1 laboratoire non noté
Instructeur
Offert par
Recommandé si vous êtes intéressé(e) par Data Analysis
University of Colorado Boulder
University of Michigan
Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?
Avis des étudiants
20 avis
- 5 stars
75 %
- 4 stars
25 %
- 3 stars
0 %
- 2 stars
0 %
- 1 star
0 %
Affichage de 3 sur 20
Révisé le 10 sept. 2024
Found it very informative. Glad to be aware now that there could be color-blind people present in the room.
Révisé le 25 mai 2024
Great course, especially appreciated the UX design approach to data visualisation
Ouvrez de nouvelles portes avec Coursera Plus
Accès illimité à 10,000+ cours de niveau international, projets pratiques et programmes de certification prêts à l'emploi - tous inclus dans votre abonnement.
Faites progresser votre carrière avec un diplôme en ligne
Obtenez un diplôme auprès d’universités de renommée mondiale - 100 % en ligne
Rejoignez plus de 3 400 entreprises mondiales qui ont choisi Coursera pour les affaires
Améliorez les compétences de vos employés pour exceller dans l’économie numérique
Foire Aux Questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.