The Basics:

• Histograms, Clearly Explained
• What is a statistical distribution?
  • StatTest!
• The Normal Distribution
  • StatTest!
• The Binomial Distribution
• What is a statistical model?
  • StatTest!
• What does it mean to “sample from a distribution”?
  • StatTest!
• The Central Limit Theorem (or “How I Learned to Stop Worrying and Love the t-test”).
• The Difference between Technical and Biological Replicates
  • StatTest!
• The sample size and the effective sample size
  • StatTest!
• Standard Deviation vs Standard Error
  • StatTest!
• The Standard Error
• Bar Charts Are Better Than Pie Charts
• Boxplots, Clearly Explained
• Logs (logarithms), clearly explained
• Confidence Intervals
• R-squared explained
  • StatTest!
• Linear Models Part 0: Fitting a line to data, aka Least Squares, aka Linear Regression
• Fitting a curve to data, aka Lowess, aka Loess
  • Sample Code
• Linear Models Part 1: Linear Regression
• Linear Models: Linear Regression in R
  • Sample Code
• Linear Models Part 1.5: Multiple Regression
• Linear Models: Multiple Regression in R
  • Sample Code
• Linear Models Part 2: t-tests and ANOVA
• Linear Models Part 3: Design Matrices
• Linear Models: Design Matrix Examples in R
  • Sample Code
• Quantiles and Percentiles
  • StatTest!
• Quantile-Quantile Plots (QQ Plots)
• Quantile Normalization
  • StatTest!
• Probability vs Likelihood
• Maximum Likelihood
• Maximum Likelihood: A worked out example for the exponential distribution
• Maximum Likelihood: A worked out example for the binomial distribution
• Maximum Likelihood: A worked out example for the normal distribution
• Odds and Log(Odds)
• Odds Ratios and Log(Odds Ratios)

Statistical Tests:

• Enrichment Analysis using Fisher’s Exact Test and the Hypergeometric Distribution
• Which t-test to use
• p-values, clearly explained
• One or Two Tailed p-values
• Thresholds for Significance
• FDR and the Benjamini-Hochberg Method clearly explained
• p-hacking and power calculations

Machine Learning and Dealing with large datasets that have lots and lots of measurements per sample:

(NOTE: All of the linear model and curve fitting stuff in the “Basics” section is also considered to be Machine Learning, so make sure you check out those videos too).

• Machine Learning Fundamentals: Bias and Variance
• Regularization Part 1: Ridge Regression
• Regularization Part 2: Lasso Regression
• Regularization Part 3: Elastic-Net Regression
• Linear Discriminant Analysis (LDA) clearly explained
• Principal Component Analysis (PCA) Step-by-Step
• Principal Component Analysis (PCA) explained in less than 5 minutes
• PCA – Practical Tips
• DEPRECATED: Principal Component Analysis (PCA) clearly explained (more details)
• PCA in R
  • Sample Code
• PCA in Python
  • Sample Code
• Multi-Dimensional Scaling (MDS) and Principal Coordinate Analysis (PCoA) clearly explained
• MDS and PCoA in R
  • Sample Code
• t-SNE, clearly explained
• Heatmaps – considerations for drawing and interpreting them
• Hierarchical Clustering
• K-Means Clustering
  • Sample Code
• K-Nearest Neighbors
• Decision Trees Part 1: Building and Using
• Decision Trees Part 2: Feature Selection and Missing Data
• Random Forests Part 1: Building, using and evaluating
• Random Forests Part 2: Missing data and clustering
• Random Forests in R
  • Sample Code
• Logistic Regression
• Logistic Regression, Details Part 1: Coefficients
• Logistic Regression, Details Part 2: Maximum Likelihood
• Logistic Regression, Details Part 3: R-squared and its p-value
• Saturated Models and Deviance Statistics
• Deviance Residuals
• Logistic Regression in R
  • Sample Code

• Cross Validation

High-throughput Sequencing Analysis:

• A gentle introduction to RNA-seq
• edgeR, part1: Library Normalization
• DESeq2, part1: Library Normalization
• edgeR and DESeq2, part2: Independent Filtering (removing genes with low read counts)
• RNA-seq – the problem with technical replicates
• RPKM, FPKM, and TPM