Hierarchical Clustering using Euclidean Distance

ofrecido por
Coursera Project Network
En este proyecto guiado, tú:

Understand the importance and usage of the hierarchical clustering using skew profiles.

Locate and process the viral cDNA genome files to calculate the skew profiles.

Understand the theory for using the Pythagorean equation to calculate the Euclidean distance. And apply that using python to build a linkage matrix.

Understand how errors occur, how to avoid them, and resolve their sources.

ClockAbout 75 minutes required for the project and 45 for the other materials (reading and assignment).
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

By the end of this project, you will create a Python program using a jupyter interface that analyzes a group of viruses and plot a dendrogram based on similarities among them. The dendrogram that you will create will depend on the cumulative skew profile, which in turn depends on the nucleotide composition. You will use complete genome sequences for many viruses including, Corona, SARS, HIV, Zika, Dengue, enterovirus, and West Nile viruses.

Habilidades que desarrollarás

  • Python Programming
  • Genomics
  • plotting

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Task 1: Getting Started with Hierarchical Clustering

  2. Task 2: Locate and Process The Data Files

  3. Task 3: Understand The Result Dataset

  4. Task 4: Hierarchical Clustering - Metric

  5. Task 5: Hierarchical Clustering - Ordering & Methods

  6. Task 6: Dendrogram Plotting

  7. Task 7: Dendrogram - Analysis

  8. Task 8: Errors to Avoid

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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