Resources - Github

Github Resources

Statistical Analysis and Experimental Design

• Statistical Comparisons Using R
  Covers hypothesis testing, correlation analysis, ANOVA, and visualisation in R.

• Experimental Design
  Guide to basic experimental design principles.

• Statistical Experimental Design
  Advanced guide to statistical design for experiments.

• Statistical Inference for Biology
  Statistical modeling for biological data analysis.

• Introductory Statistics with R
  Basics of statistical analysis in R, including data exploration and hypothesis testing.

• Inference for High-dimensional Data
  Techniques for analysing high-dimensional datasets.

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RNA-Seq Analysis

• RNA-Seq QC and Alignment
  Detailed guide on quality control and alignment for RNA-Seq data.

• Analysing RNA-Seq Data with DESeq2
  Comprehensive tutorial on differential expression analysis.

• Beginner Guide to DESeq2
  Beginner-friendly guide for RNA-Seq analysis with DESeq2.

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Single-Cell and Spatial Transcriptomics

• Single Cell RNA-Seq Training
  Introductory course for single-cell RNA sequencing.

• Spatial Transcriptomics
  Comprehensive guide on spatial transcriptomics methodologies.

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QTL Mapping

• QTL Mapping in Mice
  QTL mapping methodology specifically for mouse genetics.

• Human QTL Mapping:

  • PubMed Resource
    
  • Nature Protocols Resource
    Includes scripts, data, and links to related software.

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General Bioinformatics Training

• Intro to Bash
  Beginner-friendly Bash tutorial for bioinformatics workflows.

• Intro to NGS
  Covers fundamentals of Next-Generation Sequencing (NGS).

• Intro to R for Genomics
  Introduction to R for genomic data analysis.

• Genomics Applications
  Practical guides for genomic data applications.

• Transcriptomics Applications
  Tutorials for transcriptomics data analysis.

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Specialised Training

• Intro R Adelaide
  Introductory R workshop content.

• Intro to Genomics (December 2016)
  Workshop materials for genomic data analysis in R.

• Roseworthy R Workshop (May 2017)
  Covers R applications in bioinformatics.

• Spring Into Bioinformatics
  Comprehensive bioinformatics workshop resources.

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Pipeline Best Practices

• Best Practices
  Guidelines and standards for bioinformatics workflows.

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