Introductory R for Biologists Kurzusok

Kurzuskód

rintrob

Időtartalma

28 hours (usually 4 days including breaks)

Összefoglaló

R egy nyílt forráskódú programozási nyelv a statisztikai számításokhoz, az adatok elemzéséhez és a grafikákhoz. Az R-t egyre nagyobb számú vezető és adatelemző használja a vállalatokon és az egyetemeken. R a statisztikusok, a mérnökök és a tudósok között is találnak követőket számítógépes programozási készségek nélkül, akik könnyen használhatók. A népszerűsége annak köszönhető, hogy az adatbányászat egyre növekvő mértékben használja a különböző célokat, például a hirdetési árakat, az új gyógyszerek gyorsabb felkutatását vagy a pénzügyi modellek finomhangolását. R sokféle csomagot tartalmaz az adatbányászathoz.

Machine Translated

Kurzusleírás

I. Introduction and preliminaries

1. Overview

  • Making R more friendly, R and available GUIs
  • Rstudio
  • Related software and documentation
  • R and statistics
  • Using R interactively
  • An introductory session
  • Getting help with functions and features
  • R commands, case sensitivity, etc.
  • Recall and correction of previous commands
  • Executing commands from or diverting output to a file
  • Data permanency and removing objects
  • Good programming practice:  Self-contained scripts, good    readability e.g. structured scripts, documentation, markdown
  • installing packages; CRAN and Bioconductor

2. Reading data

  • Txt files  (read.delim)
  • CSV files

3. Simple manipulations; numbers and vectors  + arrays

  • Vectors and assignment
  • Vector arithmetic
  • Generating regular sequences
  • Logical vectors
  • Missing values
  • Character vectors
  • Index vectors; selecting and modifying subsets of a data set
    • Arrays
  • Array indexing. Subsections of an array
  • Index matrices
  • The array() function + simple operations on arrays e.g. multiplication, transposition  
  • Other types of objects

4. Lists and data frames

  • Lists
  • Constructing and modifying lists
    • Concatenating lists
  • Data frames
    • Making data frames
    • Working with data frames
    • Attaching arbitrary lists
    • Managing the search path

5. Data manipulation

  • Selecting, subsetting observations and variables         
  • Filtering, grouping
  • Recoding, transformations
  • Aggregation, combining data sets
  • Forming partitioned matrices, cbind() and rbind()
  • The concatenation function, (), with arrays
  • Character manipulation, stringr package
  • short intro into grep and regexpr

6. More on Reading data                                            

  • XLS, XLSX files
  • readr  and readxl packages
  • SPSS, SAS, Stata,… and other formats data
  • Exporting data to txt, csv and other formats

6. Grouping, loops and conditional execution

  • Grouped expressions
  • Control statements
  • Conditional execution: if statements
  • Repetitive execution: for loops, repeat and while
  • intro into apply, lapply, sapply, tapply

7. Functions

  • Creating functions
  • Optional arguments and default values
  • Variable number of arguments
  • Scope and its consequences

8. Simple graphics in R

  • Creating a Graph
  • Density Plots
  • Dot Plots
  • Bar Plots
  • Line Charts
  • Pie Charts
  • Boxplots
  • Scatter Plots
  • Combining Plots

II. Statistical analysis in R 

1.    Probability distributions

  • R as a set of statistical tables
  • Examining the distribution of a set of data

2.   Testing of Hypotheses

  • Tests about a Population Mean
  • Likelihood Ratio Test
  • One- and two-sample tests
  • Chi-Square Goodness-of-Fit Test
  • Kolmogorov-Smirnov One-Sample Statistic 
  • Wilcoxon Signed-Rank Test
  • Two-Sample Test
  • Wilcoxon Rank Sum Test
  • Mann-Whitney Test
  • Kolmogorov-Smirnov Test

3. Multiple Testing of Hypotheses

  • Type I Error and FDR
  • ROC curves and AUC
  • Multiple Testing Procedures (BH, Bonferroni etc.)

4. Linear regression models

  • Generic functions for extracting model information
  • Updating fitted models
  • Generalized linear models
    • Families
    • The glm() function
  • Classification
    • Logistic Regression
    • Linear Discriminant Analysis
  • Unsupervised learning
    • Principal Components Analysis
    • Clustering Methods(k-means, hierarchical clustering, k-medoids)

5.  Survival analysis (survival package)

  • Survival objects in r
  • Kaplan-Meier estimate, log-rank test, parametric regression
  • Confidence bands
  • Censored (interval censored) data analysis
  • Cox PH models, constant covariates
  • Cox PH models, time-dependent covariates
  • Simulation: Model comparison (Comparing regression models)

 6.   Analysis of Variance

  • One-Way ANOVA
  • Two-Way Classification of ANOVA
  • MANOVA

III. Worked problems in bioinformatics           

  • Short introduction to limma package
  • Microarray data analysis workflow
  • Data download from GEO: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
  • Data processing (QC, normalisation, differential expression)
  • Volcano plot             
  • Custering examples + heatmaps

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