Beginners R For Bioinformatics

The major part of bioinformatics is connecting together different processing steps into a single pipeline, and then applying that pipeline to many other files repeatedly, which often involves massive and tedious data processing. R is one of the most widely-utilized and powerful programming languages in bioinformatics. R stands out in the areas of research where a variety of statistical tools are required, e.g. RNA-Seq, microarray analysis, statistical testing, biological data visualization, population genomics and in the production of publication-quality graphs and figures.

BioCode is offering a Beginners R for Bioinformatics course in which you’ll learn various concepts related to writing customized scripts in R language, you’ll learn about various built-in functions and packages provided by R. You will be able to write built-in functions in R, work with loops and know how to control the flow of your program and script. R language is considered as the most suitable prohgramming language for biological data analysis, statistics, and graph plotting. 

If you’re a beginner in bioinformatics, an experimental biologist, a bioinformatics researcher, or someone with no knowledge of scripting or bioinformatics, our course will help you greatly. Everything will be taught in this course from basics of scripting in R language to advanced level. This course will also provide you with a great foundation and understanding of biological scripting in R.

This course will include the following sections:

Section 1: Introduction to the R Language

Description: This section will focus on making sure that the students gain an understanding about R language and how it is installed.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Discuss R Language.
2. Install R Language.
3. Describe the R Studio Interface.
4. Explain the Concept of Comments.

Section 2: Variable and Function in R Language

Description: This section will focus on making sure that the students learn about the various variables and functions provided by the R language and their use.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Explain Samples and Replacement.
2. Declare Variables and Objects.
3. Use Built-in Functions and ARGS.
4. Write their Own Functions and Arguments.
5. Create Customized Scripts.

Section 3: Vectors and Data Types in R Language

Description: This section will focus on making sure that the students learn about the vectors and data types that are used in R language.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Explain Attributes and Names.
2. Explain Characters, Integers, Doubles, Logicals, and Factors in R.
3. Explain Atomic Vectors in R.
4. Discuss Dim and Dimensions in R.
5. Describe Coercion.
6. Describe Matrix and Matrices.
7. Explain Arrays and Lists.
8. Describe Class in R.

Section 4: Packages in R Language

Description: This section will focus on making sure that the students learn about the packages that are needed to install in R language.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Discuss Packages in R.
2. Install Bioinformatics Packages in R.

Section 5: Biological Data Analysis in R

Description: This section will focus on making sure that the students learn how is biological data analysis performed in R language.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Describe Zero Notation for Subsetting Biological Datasets.
2. Load Biological Data.
3. Save Biological Data.
4. Perform R Notation and Select Values from Biological Datasets.
5. Explain Data Frames.
6. Discuss Positive Integers for Subsetting Biological Datasets.
7. Discuss Negative Integers for Subsetting Biological Datasets.
8. Explain Dollar Signs for Subsetting Biological Datasets.
9. Explain Blank Spaces for Subsetting Biological Datasets.
10. Modify Values in Existing Datasets.
11. Explain NA (Not Available) Values in Biological Datasets.
12. Figure out NA Values in Biological Datasets.
13. Perform Logical Subsetting in Biological Datasets.

Section 6: Control Flow

Description: This section will focus on making sure that the students learn how the flow of the program is controlled while scripting.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Use If Else Statement in Code.
2. Use For Loops and Perform Biological Data Binding.
3. Use While Loops and Read Multiple Biological Datasets.