Commit a0a2339e authored by Thomas Schwarzl's avatar Thomas Schwarzl

init statistical testing for high throguhput

parent 0396c4d0
---
title: "Statistical testing for high-throughput experiments"
author: "Thomas Schwarzl<br/>.... based on <br/>Modern Statistics for Modern Biology - Susan Holmes, Wolfgang Huber"
output:
slidy_presentation:
theme: paper
---
# design an experiment already with the analysis in mind
_"To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of"_ - R.A. Fisher
# start with the analysis as soon as you have acquired the first data
Don???t wait until everything is collected and it???s too late to troubleshoot
# Start writing the paper while you???re analyzing the data
once you???re writing, you realize what you should have done to properly support them
# Types of experiments
- measurements have limited precision and accuracy
-- preliminary data to estimate them
- directly or indirectly measurement
- side effects of treatment conditions
- interfering signals or ???background noise???
- limited sample sizes
# signal / noise ratio
_???Generally speaking, a well-designed experiment is one that is sufficiently powered and one in which technical artifacts and biological features that may systematically affect measurements are balanced, randomized or controlled in some other way in order to minimize opportunities for multiple explanations for the effect(s) under study.???_ - Bacher and Kendziorski 2016
# controlled experiment
- (model) system under study
- the environmental conditions
- the experimental readout.
_e.g. we could have a well-characterized cell line growing in laboratory conditions on defined media, temperature and atmosphere, we???ll administer a precise amount of a drug, and after 72h we measure the activity of a specific pathway reporter._
# study
important conditions that may affect the measured outcome are not under control of the researcher, usually because of ethical concerns or logistical constraints.
_e.g. in an ecological field study, this could be the weather, the availabilty of nutrition resources or the activity of predators_
# observational study
_e.g. in a clinical trial, this might be the assignment of the individual subjects to groups. Since there are many possibilities for confounding _
???correlation is not causation???
```{r}
```
```{r}
```
```{r}
```
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment