ABI OMICSS Guide 2021
  • Welcome
  • Week 1
    • Molecular Biology
      • Introduction to Biology
      • Macromolecules
      • DNA & RNA
      • Cell Division
    • Command-Line
      • Connecting to the Server
      • The Linux Command-Line For Beginners
      • The Bash Terminal
    • R Programming
      • Getting Started
      • The Basics
      • Data Types
    • Week 1 Exam
  • Week 2
    • Molecular Biology
      • DNA Proofreading And Repair
      • Telomeres and Telomerase
      • Genes
      • The One Gene, One Enzyme Hypothesis
      • Transcription
      • Translation
    • R Programming
      • Data Types (continuation)
      • Reading Data
      • Subsetting
      • Control Structures
      • Functions
      • Scoping Rules
    • Week 2 Exam
  • Week 3
    • Molecular Biology
      • tRNA and Ribosomes
      • Stages of Translation & Protein Targeting
      • Heredity
      • Probabilities In Genetics
    • R Programming
      • Loop Functions
      • Base Graphics
    • Statistics and R
      • The Basics
    • Week 3 Exam
  • Week 4
    • Molecular Biology
      • Interesting Cases of Genes
      • The Chromosomal Basis of Inheritance
      • Variation in Species
      • Phenotype plasticity
    • R Programming
      • Practice 2
      • Practice 3
    • Statistics and R
      • Random Variables and Probability Distributions
      • Central Limit Theorem
    • Week 4 Exam
  • Week 5
    • Statistics and R
      • Confidence Interval
      • Introduction to Inference
      • t-distribution and Comparing Means
      • Linear Models
    • Experimental Techniques
      • Polymerase Chain Reaction (PCR)
      • Gel Electrophoresis
      • Sanger Sequencing
      • NGS Sequencing
    • Week 5 Exam
  • Week 6
    • Statistics and R
      • Power
      • ANOVA
      • Covariance and Correlation
    • NGS
      • Basic Unix Commands
      • Sequences and Genomic Features
      • FastQC
      • Practice Exercises
    • Week 6 Exam
  • Week 7
    • Statistics and R
      • Monte Carlo Simulation
      • Exploratory Data Analysis
      • Linear Regression
    • NGS
      • BEDtools
      • Alignment and Sequence Variation
      • Integrated Genomics Viewer
    • Week 7 Exam
  • Week 8
    • NGS
      • Variant Calling With GATK
      • Practice 1
    • Week 8 Exam
  • Module Syllabi
  • Additional Resources
  • Conclusion
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  • Introduction to Exploratory Data Analysis
  • Histograms
  • QQ-Plot
  • Boxplots
  • Congratulations!

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  1. Week 3
  2. Statistics and R

The Basics

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Lastly, we review the basics of statistics and R using .

In order to access course material, make sure to create an Edx account (if you do not have one already) and enroll in the course. It's completely free.

Introduction to Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a key part of what we do when we analyze data. To learn more about it, read from the course, and return here afterward.

You can use the buttons on the course page to navigate through the various topics. Alternatively, you can always refer to the links here. Whichever way you feel more comfortable with.

Histograms

A histogram is a graphical representation that organizes a group of data points into user-specified ranges.

To learn about plotting histograms in R:

  1. Watch from the course

  2. Complete .

Return here after you finish.

QQ-Plot

A Q-Q plot is a scatterplot created by plotting two sets of quantiles against one another.

  1. Watch from the course about plotting Q-Q plots in R

  2. Complete

Return here after you finish.

Boxplots

Finally, we cover boxplots - a standardized way of displaying the dataset based on a five-number summary: the minimum, the maximum, the sample median, and the first and third quartiles.

Return here after you finish.

Congratulations!

If you made it here, then congratulations! You have successfully completed this section. Move to the next portion of the guide with the arrow buttons below.

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