Statistics for Life Scientists

Applications for this course are now closed

Overview

Statistics are an integral aspect of scientific research, particularly for the life sciences which rely heavily on quantitative methodologies. This course is designed to provide researchers in the life sciences with experience in the application of basic statistical analysis techniques to a variety of biological problems.

Attendees will work through short tutorials on the topics discussed in the class. These practical exercises will be implemented in the widely used "R" language and environment for statistical computing and graphics.

Topics covered during the course include: exploratory data analysis, hypothesis tests, linear models, correlation and regression, tests and models for NGS expression data, and cluster analysis.

Audience

This course is intended for life scientists from all levels and disciplines who are not experts in statistics. Although participants are not required to have a strong background in statistics, they should be comfortable with the R environment and be able to read, understand and write R commands before attending this course.

Learning objectives

At the end of the course, participants should be comfortable exploring their data towards choosing the most appropriate model(s) for their analysis, implementing the model(s) and exploring the parameter space, and meaningfully interpreting the results.

Prerequisites

Knowledge / competencies:

Participants should come from a biological background and ideally have basic knowledge of genomics such as next generation sequencing. Additionally, participants should have already taken a "First Steps with R" course or be similarly comfortable with using R/RStudio.

Technical:

Participants must bring a a Wi-Fi enabled laptop with R and RStudio installed. More information about the packages needed will be provided before the course.

Application

Applications for this course are now closed

The registration fees for academics are 200 CHF. This includes course content material, coffee breaks, and a social dinner. Participants from non-academic institutions should contact us before application.

Deadline for registration and free-of-charge cancellation is set to October 10. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to this and other general conditions, available here.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Schedule Overview

The course will start at 9h15 each day and end around 17h00. There will be coffee breaks in the morning and afternoon and a lunch break for one hour. There will be further details on the first day.

Day 1

• exploratory data analysis (data types, graphics, data summaries)
• hypotheses and tests (z/t-tests, Wilcoxon, tests for counts, chi-squared and fisher exact test)

• ANOVA (1-way, 2-way, F test, post hoc tests, multiple testing, assumptions and how to check them, Kruskal-Wallis/Friedman)
• linear models (correlation, simple/multivariate linear regression, least squares, model checking, model selection, Simpson’s paradox)

• experimental design (confounding, batch effects, replication)
• model formulas, design matrices and contrasts with R
• modeling count data (comparison to transformations and non-parametric methods, normalization, shrinkage, offsets vs scaling, practical examples)

Day 4

• independent filtering, hypothesis weighting, multiple testing cont.
• generating and interpreting common visualizations for omics data
• performing and evaluating clustering ("what is the goal?", stability evaluation through bootstrapping, cluster quality measures, curse of dimensionality)

Location

Additional information

Coordination: Geoffrey Fucile

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For more information, please contact training@sib.swiss.