Overview(1) - Experimental designs, linear models and biomarkers.qmd

---
title: "Linear Models for Differential Expression"
author: "Alex Sanchez"
date: "`r Sys.Date()`"
format:
  html:
    theme:
      light: cerulean
      dark: textmate
    toc: true
    toc-depth: 2
---

```{r setup, include=FALSE}
require(knitr)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, comment = NA, prompt = TRUE, tidy = FALSE, fig.width = 7, fig.height = 7, fig_caption = TRUE,cache=FALSE)
Sys.setlocale("LC_TIME", "C")
```

```{r packages, include=FALSE}
# require(devtools)
# if(!require(installifnot)) install_github("uebvhir/installifnot")

library("bibtex")
```

# Introduction

An overview of the topics and their relation

# Experiments and Experimental design

## Experimental studies. Basic concepts and goals

## The principles of experimental design

## Types of experimental design (Introducing linear models)

# Analizing designed experiments

## The Analysis of the variance (ANOVA)

## The general linear model

### The design matrix and the contrast matrix

# Biomarkers (1): Building biomarkers

## Building biomarkers from tests

### From difference between groups to cutoffs. The ROC curve

## Building biomarkers using high throughput data

### Linear models for microarrays (or other continuous features)

### Multiple testin adjustments are important here!

## Biomarkers (2): Validating biomarkers

### Performance measures:

Sensitivity, Specificity, PPV. NPV, The ROC curve and AUC

### Using biomarkers to build classifiers/predictors

### Validating classifiers/predictors

-   Test/ Train
-   Crossvalidation

# Some references

-   [HarvardX Biomedical Data Science Open Online Training](http://rafalab.github.io/pages/harvardx.html)
-   [Common statistical tests are linear models](https://lindeloev.github.io/tests-as-linear/)
-   [Penn State Course's om Genomics Data Analysis, Lesson 7: Linear Models for Differential Expression in Microarray Studies](https://online.stat.psu.edu/stat555/node/12/)
-   [Introduction 2 Data Science](https://rafalab.github.io/dsbook/linear-models.html#lse)