Introductory R for Biologists Training Course

I. Introduction and Preliminaries

1. Overview

• Enhancing user-friendliness of R, including available graphical user interfaces (GUIs)
• RStudio as a development environment for government
• Related software and documentation resources for government
• The role of R in statistical analysis for government
• Interactive use of R for government operations
• A basic session guide for government users
• Accessing help with functions and features for government
• R commands, case sensitivity, and other syntax considerations
• Recalling and correcting previous commands for efficient workflow in government
• Executing commands from or diverting output to a file for documentation and record-keeping in government
• Data permanency and object management for government
• Best practices in programming: self-contained scripts, readability, structured scripts, documentation, and markdown for government
• Installing packages from CRAN and Bioconductor for government applications

2. Reading Data

• TXT files (read.delim)
• CSV files

3. Simple Manipulations; Numbers and Vectors + Arrays

• Vectors and assignment operations for government data
• Vector arithmetic in government applications
• Generating regular sequences for government datasets
• Logical vectors for conditional operations in government
• Handling missing values in government data
• Character vectors for text manipulation in government reports
• Index vectors; selecting and modifying subsets of a dataset
  • Arrays for multidimensional data management in government
• Array indexing: working with subsections of an array for government analysis
• Index matrices for advanced data manipulation in government
• The array() function and simple operations on arrays, such as multiplication and transposition, for government use
• Other types of objects for comprehensive data handling in government

4. Lists and Data Frames

• Lists for structured data storage in government
• Constructing and modifying lists
  • Concatenating lists for integrated data management in government
• Data frames for tabular data representation in government
  • Making data frames from various sources for government datasets
  • Working with data frames for efficient data analysis in government
  • Attaching arbitrary lists to expand data scope in government
  • Managing the search path for seamless data access in government

5. Data Manipulation

• Selecting and subsetting observations and variables for targeted analysis in government
• Filtering and grouping data for focused insights in government
• Recoding and transformations for accurate data representation in government
• Aggregation and combining datasets for comprehensive reporting in government
• Forming partitioned matrices using cbind() and rbind() for structured data presentation in government
• The concatenation function, c(), with arrays for flexible data manipulation in government
• Character manipulation using the stringr package for text-based data in government
• A short introduction to grep and regexpr for pattern matching in government

6. More on Reading Data

• XLS, XLSX files for government spreadsheets
• The readr and readxl packages for efficient data import in government
• SPSS, SAS, Stata, and other formats for interoperability in government
• Exporting data to TXT, CSV, and other formats for sharing and archiving in government

6. Grouping, Loops, and Conditional Execution

• Grouped expressions for organized data processing in government
• Control statements for structured programming in government
• Conditional execution: if statements for decision-making in government
• Repetitive execution: for loops, repeat, and while for iterative tasks in government
• An introduction to apply, lapply, sapply, tapply functions for batch processing in government

7. Functions

• Creating functions for reusable code in government
• Optional arguments and default values for flexible function design in government
• Variable number of arguments for dynamic function behavior in government
• Scope and its consequences for data integrity in government

8. Simple Graphics in R

• Creating a graph for visual data representation in government
• Density plots for distribution visualization in government
• Bar plots for categorical data comparison in government
• Line charts for trend analysis in government
• Pie charts for proportional data representation in government
• Boxplots for statistical summary visualization in government
• Scatter plots for relationship exploration in government
• Combining plots for comprehensive visual reports in government

II. Statistical Analysis in R

1. Probability Distributions

• R as a set of statistical tables for government research
• Examining the distribution of a dataset for informed decision-making in government

2. Testing of Hypotheses

• Tests about a population mean for government studies
• Likelihood Ratio Test for model comparison in government
• One- and two-sample tests for hypothesis verification in government
• Chi-Square Goodness-of-Fit Test for distribution validation in government
• Kolmogorov-Smirnov One-Sample Statistic for distribution testing in government
• Wilcoxon Signed-Rank Test for non-parametric analysis in government
• Two-Sample Test for comparative studies in government
• Wilcoxon Rank Sum Test for independent samples in government
• Mann-Whitney Test for non-parametric comparison in government
• Kolmogorov-Smirnov Test for distribution similarity in government

3. Multiple Testing of Hypotheses

• Type I Error and False Discovery Rate (FDR) for robust statistical inference in government
• ROC curves and AUC for performance evaluation in government
• Multiple Testing Procedures (BH, Bonferroni, etc.) for comprehensive hypothesis testing in government

4. Linear Regression Models

• Generic functions for extracting model information in government
• Updating fitted models for iterative improvement in government
• Generalized linear models
  • Families of distributions for diverse data types in government
  • The glm() function for generalized linear modeling in government
• Classification techniques
  • Logistic Regression for binary outcomes in government
  • Linear Discriminant Analysis for multiclass classification in government
• Unsupervised learning methods
  • Principal Components Analysis (PCA) for dimensionality reduction in government
  • Clustering Methods (k-means, hierarchical clustering, k-medoids) for data segmentation in government

5. Survival Analysis (survival package)

• Survival objects in R for time-to-event analysis in government
• Kaplan-Meier estimate, log-rank test, and parametric regression for survival data in government
• Confidence bands for uncertainty quantification in government
• Censored (interval censored) data analysis for handling incomplete information in government
• Cox Proportional Hazards (PH) models with constant covariates for time-dependent risk assessment in government
• Cox PH models with time-dependent covariates for dynamic risk factors in government
• Simulation: Model comparison for evaluating different survival models in government

6. Analysis of Variance (ANOVA)

• One-Way ANOVA for single-factor analysis in government
• Two-Way Classification of ANOVA for multi-factor analysis in government
• Multivariate Analysis of Variance (MANOVA) for multiple response variables in government

III. Worked Problems in Bioinformatics

• A short introduction to the limma package for gene expression analysis in government
• Microarray data analysis workflow for comprehensive biological studies in government
• Data download from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397 for reproducible research in government
• Data processing (QC, normalization, differential expression) for accurate results in government
• Volcano plot for visualizing significant changes in gene expression in government
• Clustering examples and heatmaps for pattern recognition in biological data for government