Biomedical Data Science
Welcome
Preface
Introduction for readers
What you will learn from this course/book
What we recommend you do while reading this book
Other reference books
Acknowledgements
I Data Science Foundations
1
Introduction to R
1.1
Intro to R programming (Session 1)
1.1.1
R as a basic calculator
1.1.2
Variables
1.1.3
Data types
1.1.4
Functions
1.1.5
R scripts
1.1.6
Resource links
1.2
Intro to R programming (Session 2)
1.2.1
Data structures
1.2.2
Vector
1.2.3
Matrix
1.2.4
List
1.2.5
Data Frame
1.2.6
Reading/writing data from/to R
1.3
Intro to R programming (Session 4)
1.3.1
Operators
1.3.2
If/else
1.3.3
Loop
2
Introduction to Hypothesis test
2.1
Hypothesis test and
p
-value
2.1.1
Extreme events and random chance
2.1.2
Hypothesis test for statistical decision
2.2
Basic hypothesis test methods
2.2.1
Permutation test
2.2.2
t
-test and regression-based test
2.2.3
Fisher’s exact test
2.3
Evaluation of hypothesis testing
2.3.1
Types of errors
2.3.2
Multiple testing and correction
2.3.3
Power analysis and sample size
2.4
Summary
2.4.1
Exercises
2.4.2
Acknowledgement
3
Introduction to Linear Regression
3.1
Linear Regression Basics
3.1.1
Simulating data
3.1.2
Least squares method
3.1.3
Model efficacy
3.1.4
R-Squared
3.2
Diagnostic metrics
3.2.1
Residual Standard Errors
3.2.2
p-values
3.2.3
F-statistics
3.3
Simple Linear Regression
3.4
Multiple Regression
3.5
Exercise
4
Introduction to Classification
4.1
Logistic regression
4.1.1
Linear decision boundary
4.1.2
Logistic and logit functions
4.1.3
Visualise logistic and logit functions
4.2
Application on Diabetes
4.2.1
Load Pima Indians Diabetes Database
4.2.2
Fit logistic regression
4.2.3
Assess on test data
4.2.4
Model selection and diagnosis
4.3
Cross-validation
4.3.1
how to increase test sets
4.3.2
K-fold CV with caret package
4.4
Metrics and ROC curve
4.4.1
Two types of error
4.4.2
ROC curve
4.5
Exercise
II Biomedical Data Modules
5
Medical Image and Digital Health
6
Cancer genomics
6.1
Case study 1: analysis of cBioportal mutation data
6.1.1
Exploratory analysis
6.1.2
Statistical analysis
6.1.3
Literature search
7
Epidemiology
8
Population Genetics
8.1
Case study 1: Heritability and human traits
8.1.1
Part 1
8.1.2
Part 2
8.1.3
References
8.1.4
Open discussion
III Appendix
Appendix A: Install R & RStudio
A.1 Install R (>=4.3.1)
R on Windows
R on macOS
R on Linux (Ubuntu)
A.2 Install RStudio
A.3 Use R inside RStudio
R studio
Set working directory
Some general knowledge
Install packages
A4. Cloud computing
References
Published with bookdown
Biomedical Data Science - introduction with case studies
Chapter 5
Medical Image and Digital Health
Contents to be added.