Introductory R and Data Visualization for Bioinformatics

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About This Course

Introductory R & Data Visualization For Bioinformatics Allows You To Enhance Your Biological Programming Skills In R

Everyday Bioinformatics analysis involves bridging together different steps of computational processing into a single pipeline, and then applying that pipeline to many other files repeatedly. Biological data visualization is also an important aspect of bioinformatics which involves the graphical representation of unstructured or structured data to display information hidden in the plots/graphs.

In this course you’ll learn various concepts related to how to write your first script in R, various built-in functions and packages provided by R. Along with, how to write built-in functions in R, work with loops and how to control the flow of your program and script. You’ll also be able to write programs to generate publication-ready and high quality graphical plots of biological datasets to visualize, analyze and compare the datasets in a more insightful way.

Joining and learning from the Introductory R & Data Visualization  for Bioinformatics can enhance your biological programming career by learning through various useful & informative pre-recorded lectures on R & various ggplot2 functions to create interactive plots on biological datasets.

Learning Objectives

Introduction
Variables & Functions
Vectors & Data Types
Packages
Biological Data Analysis
Control Flow
Data Visualization: ggplot2

Target Audience

  • The target audience forIntroductory R & Data Visualization for Bioinformatics course are biologists, beginner or intermediate Bioinformaticians or data analysts with no or little experience in Bioinformatics scripting and programming.
  • However, a superficial understanding of logic development for coding is expected from you before you join the course.
  • Bioinformatics is quite easy to get started in, even if you lack a proper understanding of the underlying concepts of Bioinformatics databases, servers, tools and the algorithms working behind them.

Curriculum

53 Lessons5h 48m

R

Introduction to R in Bioinformatics & R Installation9:48
The R Studio Interface Explanation6:23
Comments4:17
Sample & Replacement9:09
Variable Declaration and Objects5:24
Built-in Functions & ARGS4:32
Write Your Own Functions And Arguments5:39
Scripts7:36
Attributes and Names4:46
Characters4:43
Doubles3:31
Logicals2:27
Factors6:41
Atomic Vectors2:43
Integers3:23
Dim & Dimensions5:46
Coercion4:27
Lists6:42
Matrix & Matrices4:43
Arrays3:42
Packages4:00
Class3:13
Getting Help with Help Packages3:43
Install Bioinformatics Packages5:25
Library & Initialization of Packages2:28
Loading Biological Data7:56
Zero Notation for Subsetting Biological Datasets1:09
Saving Biological Data5:27
R Notation & Selecting Values from Biological Dataset4:09
Data Frames6:30
Positive Integers for Subsetting Biological Dataset5:26
Negative Integers for Subsetting Biological Dataset5:28
Dollar Signs for Biological Dataset Subsetting2:58
Blank Spaces For Biological Data Subsetting3:21
Modifying Values in Existing Datasets7:06
NA Values in Biological Dataset5:25
Figuring out NA Values in Biological Dataset2:06
Logical Subsetting in Biological Datasets9:46
If Else Statement4:15
For Loops & Biological Data Binding16:30
While Loops & Reading Multiple Biological Datasets00:16:16
ggplot2: Key components00:08:26
Exercise
ggplot2: Human Mitochondrial Proteome & Aesthetics (Size, Shape, Color)00:26:06
Exercise
ggplot2: Facetting of Human Genome00:22:25
Exercise
ggplot2: Smooth Out the Biological Data00:08:43
Exercise
ggplot2: Boxplots for Human Mitochondrial Proteome00:07:56
Exercise
ggplot2: Histograms for Human Mitochondrial Pattern Finding00:06:02
Exercise
ggplot2: Frequency Plots for Human Mitochondrial Information Frequency Mining00:06:13
Exercise
ggplot2: Bar Charts Human Mitochondrial Knowledge Mining00:10:43
Exercise
ggplot2 – Scaling and Limiting Data Visualization00:03:53
Exercise
ggplot2 – Changing Labels and Finalizing Visualization00:08:42
Exercise
ggtree – Phylogenetic Tree Visualization00:05:41
Exercise
ggsave – Saving the Visualizations in High Resolution00:04:45
Exercise

Evaluation

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$53.99

Level
All Levels
Duration 5.8 hours
Lectures
53 lectures
Language
English

Material Includes

  • Step-by-step pre-recorded lectures
  • Learn any time, any where!
  • Transcription
  • Notes
  • Coding scripts
  • BioPresentations
  • Exercises
  • Automated Evaluations (MCQs)
  • 100% Authentic Certificate

Enrolment validity: 90 days

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