Improve your knowledge in R Programming Language in Genomic Oncology through this program, where you will find the best teaching material with real clinical cases. Learn here about the latest advances in the specialty to be able to perform a quality medical practice"

A fundamental objective of the program is to bring students closer to and disseminate informatics knowledge is already applied in other areas of knowledge, but has a minimal implementation in the medical world, despite the fact that for genomic medicine to become a reality, it is necessary to accurately interpret the huge volume of clinical information currently available and associate it with the biological data generated after a bioinformatics analysis. While this is a difficult challenge, it will allow the effects of genetic variation and potential therapies to be explored quickly, inexpensively and with greater precision than is currently possible.

Humans are not naturally equipped to perceive and interpret genomic sequences, to understand all the mechanisms, pathways and interactions that take place within a living cell, nor to make medical decisions with tens or hundreds of variables. To move forward, a system with superhuman analytical capabilities is required to simplify the work environment and show the relationships and proximities between variables. In genomics and biology, it is now recognized that it is better to spend resources on new computational techniques than on pure data collection, something that is possibly the same in medicine and, of course, oncology. 

We have millions of data or publications, but when they are analyzed by physicians or biologists, the conclusions are totally subjective and relative to the available publications or data, which are prioritized arbitrarily. This generates partial knowledge, which is increasingly distanced from the genetic and biological knowledge available and supported by computation, so a giant step in the implementation of precision medicine is to reduce this distance through the massive analysis of available medical and pharmacological information.

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Module 1. Data Analysis Processing in Big Data Projects:  R Language Programming

1.1. Introduction to R programming language

1.1.1. What is R?
1.1.2. R Installation and the Graphic Interface of R
1.1.3. Packages

1.1.3.1. Standard Packages
1.1.3.2. Contributed Packages and CRAN

1.2. Basic Features of R

1.2.1. The Environment of R
1.2.2. Software and Related Documentation
1.2.3. R and Statistics
1.2.4. R and the Window System
1.2.5. Using R Interactively
1.2.6. An Introductory Session
1.2.7. Obtaining Help with Functions and Features
1.2.8. R Commands, Cap Sensitivity, Etc
1.2.9. Recovery and Correction of Previous Commands
1.2.10. Execute Commands or Diverting the Output to a File
1.2.11. Data Storage and Object Deletion

1.3. Types of Objects in R

1.3.1. Simple Manipulations; Numbers and Vectors

1.3.1.1. Vectors and Their Assignment
1.3.1.2. Vector Arithmetic
1.3.1.3. Generating Regular Sequences
1.3.1.4. Logical Vectors
1.3.1.5. Lost Values
1.3.1.6. Character Vectors
1.3.1.7. Index Vectors

1.3.1.7.1. Selecting and Modifying Subsets of a Dataset

1.3.1.8. Other Types of Objects

1.3.2. Objects, Their Modes and Attributes

1.3.2.1. Intrinsic Attributes: Mode and Length
1.3.2.2. Changing the Length of an Object
1.3.2.3. Obtaining and Configuring Attributes
1.3.2.4. The Class of an Object

1.3.3. Sorted and Unsorted Factors

1.3.3.1. A Specific Example
1.3.3.2. The Tapply () Function and Unequal Matrices
1.3.3.3. Sorted Factors

1.3.4. Matrices

1.3.4.1. Matrices
1.3.4.2. Matrix Indexation. The Subsections of a Matrix
1.3.4.3. Index Matrices
1.3.4.4. The Array () Function
1.3.4.5. Mixed Arithmetic of Vectors and Matrices. The Recycling Rule
1.3.4.6. The Outer Product of Two Matrices
1.3.4.7. The General Transposition of a Matrix
1.3.4.8. Matrix Multiplication
1.3.4.9. Eigenvalues and Eigenvectors
1.3.4.10. Decomposition of Singular Values and Determinants
1.3.4.11. Forming Partitioned Matrices, Cbind () and Rbind ()
1.3.4.12. The Concatenation Function, c (), With Matrices

1.3.5. Factor Frequency Tables
1.3.6. Lists

1.3.6.1. Creating and Modifying Lists
1.3.6.2. Concatenation Lists

1.3.7. Dataframes

1.3.7.1. How to Create Dataframes?
1.3.7.2. Attach () and Separate ()
1.3.7.3. Working With Dataframes

1.4. Reading and Writing Data

1.4.1. The Read.Table () Function
1.4.2. The Scan () Function
1.4.3. Access to the Sets of Incorporated Data
1.4.4. Loading Data from Other R Packages
1.4.5. Editing Data

1.5. Grouping, Loops and Conditional Execution

1.5.1. Grouped Expressions
1.5.2. Control Statements

1.5.2.1. Conditional Execution: If Sentences
1.5.2.2. Repetitive Execution: For Loops, Repetition and Time

1.6. Writing Your Own Functions

1.6.1. Simple Examples
1.6.2. Defining New Binary Operators
1.6.3. Arguments With Name and Default Value
1.6.4. Argument “...”
1.6.5. Assignments Within Functions

Module 2. Statistical Analysis in R

2.1. Discrete Probability Distributions
2.2. Continuous Probability Distributions
2.3. Introduction to Inference and Sampling (Point Estimate)
2.4. Confidence Intervals
2.5. Hypothesis Testing
2.6. ANOVA of a Factor
2.7. Adjustment Bondat (Chi-Square Test)
2.8. Fitdist Package
2.9. Introduction to Multivariant Statistics

Module 3. Graphical Environment in R

3.1. Graphical Procedures

3.1.1. High-Level Plotting Commands

3.1.1.1. The Plot () Function
3.1.1.2. Multivariate Data Visualization
3.1.1.3. Screen Graphics
3.1.1.4. High-Level Plotting Arguments

3.1.2. Low-Level Plotting Commands

3.1.2.1. Mathematical Annotation
3.1.2.2. Hershey Vectorial Sources

3.1.3. Interacting With Graphics
3.1.4. The Use of Graphic Parameters

3.1.4.1. Permanent Changes: The Par () Function
3.1.4.2. Temporal Changes: Arguments to Graphical Functions

3.1.5. List of Graphic Parameters

3.1.5.1. Graphical Elements
3.1.5.2. Axles and Markings
3.1.5.3. Figure Margins
3.1.5.4. Multi-Figure Environment

3.1.6. Descriptive Statistics: Graphical Representations

A unique, key, and decisive training experience to boost your professional development”