Links and useful resources

If you are interested in learning data science for the life sciences I recommend the following:

• Data Analysis for the Life Sciences by Rafael A Irizarry and Michael I Love - A very nice introduction to the basic statistical concepts and programming skills for everyone who wants to start analysing his/her own data. Accompanied by an online course.
• Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber - A great resource providing a hands-on approach to learn the important statical concepts in the analysis of modern biological data

If you want to learn more on Machine Learning or Causal Inference or dive deeper into high-dimensional statistics or Bayesian inference I can recommend

• Pattern Recognition and Machine Learning by Christopher Bishop
• Gaussian Processes for Machine Learning by C. E. Rasmussen & C. K. I. Williams
• Elements of Causal Inference by Jonas Peters, Dominik Janzing and Bernhard Schölkopf
• Statistics for High-Dimensional Data: Methods, Theory and Applications by Peter Bühlmann and Sara van de Geer
• Bayesian Methods for Data Analysis by Bradley P. Carlin and Thomas A. Louis

For an introduction to computational analysis of single cell data I find the following reviews and tutorials a good starting point:

• Current best practices in single‐cell RNA‐seq analysis: a tutorial by Malte Luecken and Fabian Theis
• Scanpy and Seurat tutorials
• Analysis of single cell RNA-seq data
• Orchestrating Single-Cell Analysis with Bioconductor
• https://github.com/seandavi/awesome-single-cell

A nice collection of links on multi-modal/multi-omics data is available here: https://github.com/mikelove/awesome-multi-omics