bioinfo.Rmd

---
title: "Bioinformatic and Biostatistic"
subtitle: "Life Sciences Research Unit"
author: "Aurélien Ginolhac"
date: "2019-07-04"
output:
  iosp::ioslides_plus:
    keep_md: no
    logo: ./img/uni.png
    footer: "[bioinfo core](http://wwwen.uni.lu/recherche/fstc/life_sciences_research_unit/core_positions/bioinformatics)"
    smaller: yes
    widescreen: yes
    self_contained: false
    box_colours:
      bg-question: [bg: "white", header_bg: "springgreen4", header_text: "#FFD700"]
      bg-uni: [bg: "grey95", header_bg: "#5a9fceff", header_text: "#bc3330ff"]
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggsignif)
library(survminer)
library(survival)
library(here) # devtools::install_github("krlmlr/here") 
```

```{css, echo = FALSE}
/* 
  to add negative vertical space (push content up)
  should be cleaned/fixed/enhanced in iosp...
  to be used for example to push a full slide screenshot close to the header (nvs1 or nvs2)
  https://gist.github.com/koncina/1c89e615654f123e879802e50291a0f6
*/

article.nvs1 {
  margin-top: -1em !important;
}

article.nvs2 {
  margin-top: -2em !important;
}

article.nvs3 {
  margin-top: -3em !important;
}

/* positive vertical space */

.vs1 {
  margin-top: 1em !important;
}

.vs2 {
  margin-top: 2em !important;
}

.vs3 {
  margin-top: 3em !important;
}

.center-pic img {
  display: block;
  margin: auto;
  margin-top: 5px;
  margin-bottom:5px;
}

.small-pic img {
  max-height: 100px;
}

.icon img {
  float: right;
  margin-left: 20px;
}
```


## Bioinformatic core position | activities {.build .vs1}


### Bioinformatics {.box-4 .bg-red .show}

- Next Generation Sequencing
    + QC
    + mapping
    + peak calling
    + CNV detection
- Microarray
- Functional enrichments

### Biostatistics / R {.box-4 .bg-green .stretch}

- which tests?
- Differential expression
- Unsupervised learning
- Survival analyses
- Data visualisation

### IT {.box-4 .bg-yellow .stretch}

- software licences
    + Graphpad (with LCSB)
    + EndNote
- High Performance Computer (gaia)
- set-up (mac)


### Teaching {.box-6 .bg-white .stretch}

- ChIP-seq, doctoral school
- HPC summer school, doctoral school


### Teaching {.box-6 .bg-white}

- BASV 5<sup>th</sup> semester
- _R_ workshop, now in doctoral school


## Bioinformatics | Next-Generation Sequencing {.center-pic .nvs1}

```{r, echo = FALSE, out.width = "110%"}
knitr::include_graphics(here("img/bioinfo_ngs.png"))
```


## Biostatistics {.nvs1}


### {.box-6}

```{r, echo = FALSE, fig.height = 4.5}
ggplot(iris, aes(x = Species, y = Sepal.Length)) + 
  geom_boxplot(size = 0.8) +
  ggbeeswarm::geom_quasirandom(size = 2, alpha = 0.6, colour = "grey60") +
  geom_signif(comparisons = list(c("setosa", "virginica")),
              map_signif_level = c("****" = 0.0001, "***" = 0.001, "**" = 0.01,  "*" = 0.05)) +
  theme_minimal(20) +
  labs(title = "Comparison",
       x = NULL,
       y = "Sepal length")
```

### {.box-6}

```{r, echo = FALSE}
library(FactoMineR)
pca    <- prcomp(iris[, 1:4], retx = TRUE, scale. = TRUE) # scaled pca [exclude species col]
pca_iris <- PCA(iris[, 1:4], graph = FALSE)
var_iris <- pca_iris$var$coord %>%
  as.data.frame() %>%
  rownames_to_column(var = "var") %>%
  separate(var, into = c("flower", "measure"), sep = "\\.") %>%
  as_tibble()
scores <- pca$x[, 1:3]                        # scores for first three PC's
# k-means clustering [assume 3 clusters]
km     <- kmeans(scores, centers = 3, nstart = 5)
ggdata <- data.frame(scores, Cluster = km$cluster, Species = iris$Species)
# get some custom colors
my_col_var <- ggsci::pal_npg("nrc")(4)
my_col_ell <- ggsci::pal_uchicago()(3)
ggplot(ggdata) +
  geom_point(aes(x = PC1, y = PC2, fill = factor(Species)), alpha = 0.8, size = 3, shape = 21, colour = "grey90") +
  geom_segment(data = var_iris, aes(x = 0, xend = Dim.1 * 2, colour = flower,
                                    y = 0, yend = Dim.2 * 2), size = 1.2, arrow = arrow(length = unit(0.03, "npc"))) +
  geom_text(data = var_iris, aes(x = Dim.1 * 2, colour = flower, label = measure,
                                 y = Dim.2 * 2), nudge_x = 0.2, nudge_y = 0.3, show.legend = FALSE) +
  scale_fill_manual(values = my_col_ell) +
  scale_colour_manual(values = my_col_var) +
  labs(fill = "cluster",
       title = "PCA",
       x = paste("PC1 (var:", round(pca_iris$eig[1, 2], 2), "%)"),
       y = paste("PC1 (var:", round(pca_iris$eig[2, 2], 2), "%)"),
       shape = "cluster",
       colour = "loadings") +
  theme_minimal(20)
```

###  {.box-6}

```{r, echo = FALSE}
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = 2) +
  geom_smooth(method = "loess") +
  theme_minimal(20) +
  labs(title = "Regression")
```



### {.box-6}

```{r, echo = FALSE, fig.height = 4.5}
fit <- survfit(Surv(time, status) ~ sex, data = lung)
ggsurvplot(
  fit, 
  data = lung, 
  size = 1,                 # change line size
  palette = 
    c("#E7B800", "#2E9FDF"),# custom color palettes
  conf.int = TRUE,          # Add confidence interval
  pval = TRUE,              # Add p-value
  risk.table = FALSE,        # Add risk table
  risk.table.col = "strata",# Risk table color by groups
  title = "Survival, Kaplan-Meier",
  legend.labs = 
    c("Male", "Female"),    # Change legend labels
  risk.table.height = 0.25, # Useful to change when you have multiple groups
  ggtheme = theme_minimal(20)      # Change ggplot2 theme
)
```





## Project | code versioning { .center-pic .nvs1}


```{r, echo = FALSE, out.width = "80%"}
knitr::include_graphics(here("img/git_commit.png"))
```

<span class = "small">Modified from Maria Mele's [blog post](http://www.marinamele.com/2014/07/git-tutorial-create-a-repository-commit-git-branches-and-bitbucket.html)</span>


## Project | collaborative work {.vs1}

### branches {.box-12 .icon .bg-white}

![](./img/tvcourse_branches.png)

[_R_ workshop repository](https://github.com/LSRU/tv_course)

- ginolhac
- <span class = "blue">krause</span>
- <span class = "green">koncina</span>

# R

## What is R? {.vs2}

### {.icon .col-12}

![](https://hpc.uni.lu/images/logo/logo_hpc-shool2017.png)

**R** is shorthand for ["GNU R"](https://www.r-project.org):

- An interactive programming language derived from **S** (**J. Chambers**, Bell Lab, 1976)
- Appeared in 1993, created by **R. Ihaka** and **R. Gentleman**, University of Auckland
- Focus on data analysis and plotting
- **R** is also shorthand for the ecosystem around this language
    + Book authors
    + Package developers
    + Ordinary useRs
  
Learning to use **R** will make you **more efficient** and **facilitate the use** of advanced data analysis tools

## Why use R? {.vs2}

- It's *free!*
- easy to install / maintain
- easy to process big files and analyse huge amounts of data
- integrated data visualization tools, *even dynamic* via [shiny](http://shiny.rstudio.com/)
- fast, and even faster with *C++* integration via [Rcpp](http://dirk.eddelbuettel.com/code/rcpp/Rcpp-attributes.pdf).
- easy to get help
    + [huge R community in the web](https://support.rstudio.com/hc/en-us/articles/200552336-Getting-Help-with-R)
    + [stackoverflow](http://stackoverflow.com/questions/tagged/r) with a lot of tags like **r**, **tidyverse**, **dplyr**, **ggplot2** etc.
    + [rbloggers](http://www.r-bloggers.com)