---
title: "ImmunoGenetics assignment"
author: "Student A and student B and student C"
date: "Due date: June 3rd, 2021"
output:
  html_document:
    code_folding: show
    number_sections: yes
    theme: united
    toc: yes
    toc_float:
      collapsed: yes
      smooth_scroll: yes
    df_print: paged
---

```{r setup, include=FALSE}
## DO NOT EDIT THIS CHUNCK
knitr::opts_chunk$set(echo = TRUE, fig.width = 7, fig.height = 5, cache = FALSE)
library(L1fdvGenetics) ## Homework data and helper functions()
data("hw_data")        ## Homework data: geno/phenotypes of the F2 mice
data("genetic_map")    ## Homework data: genetic map
```

# Goal 

You're doing an internship in an immunology lab working with mice. The lab developed a QTL panel (a dense genetic map) for LEWIS and BN lines and the PI (Principal Investigator) asked the previous intern to do a QTL analysis for IgG1 production. Unfortunately, the intern was super sloppy and his digital lab book is very messy with bits of code and comments. The PI tasked you to revisit and document the analysis, to produce a nicer report. 

<!-- replace {r} with {r, include = FALSE} to remove a chunck output from the report -->
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<!-- You can combine options such as {r, include = FALSE, echo = FALSE} -->

# Data

## Genetic data

**Instructions:** Give a brief description of the genetic data, with relevant information.

<!-- Edit here -->

```{r}
dim(hw_data)
colnames(hw_data)[-1]
summary(range(hw_data[ , "IgG1"]))
```

## Genetic map 

**Instructions:** Give a brief description of the genetic data, with relevant information.

<!-- Edit here -->

```{r}
genetic_map
## Nb markers
with(genetic_map, tapply(Location, Chromosome, length))
## dist between markers
with(genetic_map, tapply(Location, Chromosome, function(x) { mean(diff(x)) })) 
## Min/Max distance
with(genetic_map, tapply(Location, Chromosome, function(x) { range(diff(x)) }))
## Min/Max positions
with(genetic_map, tapply(Location, Chromosome, range)) 
```

# Analysis on chromosome 1 

**Instructions:** Document the analyses performed. Select, format and comment the relevant results. 

<!-- Edit here (or below, but preserve document structure) -->

```{r}
chr1_map <- subset(genetic_map, Chromosome == "Chr1")
chr1_markers <- chr1_map[ , "Marker"]
chr1_data <- hw_data[ , c("Organism", chr1_markers, "IgG1")]
for (marker in chr1_markers) {
  LOD(marker = marker, data = chr1_data) 
}
```

<!-- replace {r} with {r, fig.cap = "My caption"} to add a caption -->

```{r}
## LOD scores
positions  <- seq(from = 0, to = 100, by = 1)
lod_scores <- rep(0, length(positions)) 
for (i in 1:length(positions)) {
  lod_scores[i] <- LOD_position(position = positions[i], data = chr1_data, map = chr1_map)
}
lod_data <- data.frame(Position = positions, 
                       LOD      = lod_scores)
## Plot
plot(LOD ~ Position, data = lod_data, 
     type = "o",
     xlab = "Position along chromosome 1 (in cM)", 
     ylab = "LOD score"
     )
abline(v = chr1_map$Location, lty = 3, col = "grey80")
```

```{r}
## Positions
subset(lod_data, LOD >= 2)
```

# Analysis on chromosome 2 (DIY)

**This part is completely optional and will not count toward the final grade**. 

**Instructions:** Repeat and adapt the previous analysis to detect and position putative QTL located in Chromosome 2. 

<!-- Edit here -->

```{r}
## Write code here
```

# Conclusion 

**Instructions:** Write a small conclusion summarizing your findings. Add any information you find relevant. 

<!-- Edit here -->

# Perspective 

**Instructions:** How would you adapt the experimental and/or analytic strategy to account for an *additive* model?  

<!-- Edit here -->