Basic statistics

Non-parametric tests

Type 1 and 2 errors and power


Linear regression

How to select an appropriate statistical test

Regression vs ANOVA vs t-test

Two-way ANOVA

Post-hoc tests

Tests based on the false discovery rate (FDR)

Generalized linear models (GLMs)

Logistic regression

Poisson regression

Linear mixed-effect models

Bayesian statistics

Introduction to multivariate statistics

To understand the methods that are used in multivariate statistics, you need to understand some basic linear algebra. For example, you need to understand things like matrix operations, eigenvectors and eigenvalues. You also need to understand the meaning of covariance and different distances in space.

Linear algebra

Covariance and distances

Once you know the basics in the above videos, you can start to learn about multivariate statistical methods. I recommend that you start to learn about PCA.



Multivariate statistical methods

Metrics used for binary classification and validation

Classification methods

Logistic regression

Linear discriminant analysis

k-nearest neighbors and Mahalanobis distance

Decision trees and random forest

Support vector machines

Gaussian naive Bayes

Clustering methods

Partial least squares regression

Canonical correlation analysis

Artificial Neural Networks (ANN)

Survival analysis

Gene set analysis

In gene set analysis, one usually uses Fisher’s exact test to identify an overrepresented set of genes. To understand Fisher’s exact test, we first need to understand a few things about permutations and combinations.

Model selection

Statistics in epidemiology

Additional videos

Mathematical and computational biology/ systems biology

Some basic math

Optimization – find the minimum value of a function

Nonlinear regression

Cellular automata – spatial modeling