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0.-Biomarkers-ExperimentalDesign-LinearModels-OmicsData-Validation.html
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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Biomarkers, Experimental Designs, Linear Models, Omics Data and Diagnostic measures and Validation</title>
<meta charset="utf-8" />
<meta name="author" content="Alex Sanchez-Pla" />
<meta name="date" content="2024-02-19" />
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class: center, middle, inverse, title-slide
.title[
# Biomarkers, Experimental Designs, Linear Models, Omics Data and Diagnostic measures and Validation
]
.subtitle[
## One approach to meet them all
]
.author[
### Alex Sanchez-Pla
]
.institute[
### Departament de Genètica, Microbiología y Estadística. <br> Universitat de Barcelona (UB)
]
.date[
### 2024-02-19
]
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# A typical scenario
A common situation in biomedicine and other fields:
- An individual can be in one of two states: healthy or unhealthy
- Goal: Detect unhealthy state as son as posible so that preventive actions can be taken.
- Unhealthyness is not always obvious, particularly at early stages.
This situation appears in many contexts, not only nutrition:
- In nutritional studies it is necessary to find out if people have eaten a given nutrient or if they adhere to a certain type of diet, even if there are no nutritional questionaire available.
- In this case it is common to try to find it out from the analysis of metabolomic data.
---
# Building biomarkers
- A (bio)markers for the *unhealthy state*, is *anything*, whose values are different among healthy and unhealthy.
- Healthy/Unhealthy is only one possibility!
- Not anything that differs is a useful biomarker: The difference between conditions must be *consistent enough* to be used to distinguish each other group, not only on the samples used to build the biomarker but in (any) other independent datasets.
- It should happen whith *error rates* *as small as possible* in both
- False positive errors: Declaring unhealthiness in healthy cases
- False negative errors : Declaring health in unhealthy cases
<!-- - A couple of details: -->
<!-- - The biomarker is aimed at finding individuals in unhealthy state. -->
<!-- - Healthy/Unhealthy is only one possibility. A nutritional biomarker may, for instance, be used to distinguish people who has taken wine from those who haven't. -->
---
# The road to biomarkers
- Given a hypothesis about how a certain condition or disease works,
- An experiment must be designed to identify its potential biomarkers.
- Statistical analysis of the experiment results can provide a list of candidate biomarkers. This may require
- Different statistical methods: Each experimental design may have an ANOVA model suitable for its analysis.
- If *omics* data are used, high dimensionality and, often small sample size has to be accounted for.
- The candidate biomarkers list must be validate against an independent dataset to determine their suitability as biomarkers
---
# The road to biomarkers
<img src="images/From_Biomarkers_to_Diagnostic_Tests.png" width="80%" />
---
class: inverse, middle, center
# Let's go for it!
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