Enhance documentation and automate R package management
Update documentation to reflect that all tools are provided via a builder Docker image - Simplify prerequisites section in Readme.md - Add detailed explanation of the builder image and its role - Document R package caching mechanism for faster builds - Update start-jupyterhub.sh to build and use the builder image - Add Dockerfile.builder to provide the build environment - Implement automatic R dependency detection and installation - Update Slides.qmd to use gt package for better table formatting
This commit is contained in:
Eric Coissac
@@ -3,22 +3,31 @@ title: "Biodiversity metrics \ and metabarcoding"
|
||||
author: "Eric Coissac"
|
||||
date: "02/02/2024"
|
||||
bibliography: inst/REFERENCES.bib
|
||||
format:
|
||||
format:
|
||||
revealjs:
|
||||
css: ../../slides.css
|
||||
transition: slide
|
||||
scrollable: true
|
||||
theme: beige
|
||||
theme: beige
|
||||
html-math-method: mathjax
|
||||
embed-resources: true
|
||||
editor: visual
|
||||
---
|
||||
|
||||
```{r setup, include=FALSE}
|
||||
library(knitr)
|
||||
library(knitr)
|
||||
library(Rdpack)
|
||||
library(tidyverse)
|
||||
library(kableExtra)
|
||||
library(gt)
|
||||
library(latex2exp)
|
||||
|
||||
# Install MetabarSchool if not available
|
||||
if (!requireNamespace("MetabarSchool", quietly = TRUE)) {
|
||||
if (!requireNamespace("remotes", quietly = TRUE)) {
|
||||
install.packages("remotes", dependencies = TRUE)
|
||||
}
|
||||
remotes::install_git('https://forge.metabarcoding.org/MetabarcodingSchool/biodiversity-metrics.git')
|
||||
}
|
||||
library(MetabarSchool)
|
||||
|
||||
opts_chunk$set(echo = FALSE,
|
||||
@@ -49,7 +58,7 @@ install.packages("devtools",dependencies = TRUE)
|
||||
Then you can install *MetabarSchool*
|
||||
|
||||
```{r eval=FALSE, echo=TRUE}
|
||||
devtools::install_git("https://git.metabarcoding.org/MetabarcodingSchool/biodiversity-metrics.git")
|
||||
remotes::install_git('https://forge.metabarcoding.org/MetabarcodingSchool/biodiversity-metrics.git')
|
||||
```
|
||||
|
||||
You will also need the *vegan* package
|
||||
@@ -68,11 +77,15 @@ A 16 plants mock community
|
||||
data("plants.16")
|
||||
x = cbind(` ` =seq_len(nrow(plants.16)),plants.16)
|
||||
x$`Relative aboundance`=paste0('1/',1/x$dilution)
|
||||
knitr::kable(x[,-(4:5)],
|
||||
format = "html",
|
||||
row.names = FALSE,
|
||||
align = "rlrr") %>%
|
||||
kable_styling(position = "center")
|
||||
x[,-(4:5)] %>%
|
||||
gt() %>%
|
||||
cols_align(align = "center", columns = 1) %>%
|
||||
cols_align(align = "left", columns = 2) %>%
|
||||
cols_align(align = "right", columns = c(3, 4)) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
## The experiment {.flexbox .vcenter}
|
||||
@@ -100,11 +113,14 @@ data("positive.motus")
|
||||
- `positive.count` read count matrix $`r nrow(positive.count)` \; PCRs \; \times \; `r ncol(positive.count)` \; MOTUs$
|
||||
|
||||
```{r}
|
||||
knitr::kable(positive.count[1:5,1:5],
|
||||
format="html",
|
||||
align = 'rc') %>%
|
||||
kable_styling(position = "center") %>%
|
||||
row_spec(0, angle = -45)
|
||||
as.data.frame(positive.count[1:5,1:5]) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "right", columns = 1) %>%
|
||||
cols_align(align = "center", columns = 2:ncol(positive.count[1:5,1:5])) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
<br>
|
||||
@@ -126,10 +142,14 @@ data("positive.motus")
|
||||
- `positive.samples` a `r nrow(positive.samples)` rows `data.frame` of `r ncol(positive.samples)` columns describing each PCR
|
||||
|
||||
```{r}
|
||||
knitr::kable(head(positive.samples,n=3),
|
||||
format="html",
|
||||
align = 'rc') %>%
|
||||
kable_styling(position = "center")
|
||||
head(positive.samples,n=3) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "right", columns = 1) %>%
|
||||
cols_align(align = "center", columns = 2:ncol(head(positive.samples,n=3))) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
<br>
|
||||
@@ -151,10 +171,16 @@ data("positive.motus")
|
||||
- `positive.motus` : a `r nrow(positive.motus)` rows `data.frame` of `r ncol(positive.motus)` columns describing each MOTU
|
||||
|
||||
```{r}
|
||||
knitr::kable(head(positive.motus,n=3),
|
||||
format = "html",
|
||||
align = 'rlrc') %>%
|
||||
kable_styling(position = "center")
|
||||
head(positive.motus,n=3) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "right", columns = 1) %>%
|
||||
cols_align(align = "left", columns = 2) %>%
|
||||
cols_align(align = "right", columns = 3) %>%
|
||||
cols_align(align = "center", columns = 4) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
<br>
|
||||
@@ -172,10 +198,17 @@ table(colSums(positive.count) == 1)
|
||||
```
|
||||
|
||||
```{r}
|
||||
kable(t(table(colSums(positive.count) == 1)),
|
||||
format = "html") %>%
|
||||
kable_styling(position = "center") %>%
|
||||
row_spec(0, align = 'c')
|
||||
as.data.frame(t(table(colSums(positive.count) == 1))) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "center", columns = everything()) %>%
|
||||
tab_style(
|
||||
style = cell_text(align = "center"),
|
||||
locations = cells_column_labels()
|
||||
) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
<br>
|
||||
@@ -195,7 +228,7 @@ positive.motus = positive.motus[are.not.singleton,]
|
||||
Despite all standardization efforts
|
||||
|
||||
```{r fig.height=3}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
hist(rowSums(positive.count),
|
||||
breaks = 15,
|
||||
xlab="Read counts",
|
||||
@@ -209,7 +242,7 @@ Is it related to the amount of DNA in the extract ?
|
||||
## What do the reading numbers per PCR mean? {.smaller}
|
||||
|
||||
```{r echo=TRUE, fig.height=4}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
boxplot(rowSums(positive.count) ~ positive.samples$dilution,log="y")
|
||||
abline(h = median(rowSums(positive.count)),lw=2,col="red",lty=2)
|
||||
```
|
||||
@@ -288,7 +321,7 @@ table(are.still.present)
|
||||
## Rarefying read count (4) {.flexbox .vcenter}
|
||||
|
||||
```{r echo=TRUE, fig.height=3.5}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
boxplot(colSums(positive.count) ~ are.still.present, log="y")
|
||||
```
|
||||
|
||||
@@ -360,10 +393,13 @@ knitr::include_graphics("figures/alpha_diversity.svg")
|
||||
E1 = c(A=0.25,B=0.25,C=0.25,D=0.25,E=0,F=0,G=0)
|
||||
E2 = c(A=0.55,B=0.07,C=0.02,D=0.17,E=0.07,F=0.07,G=0.03)
|
||||
environments = t(data.frame(`Environment 1` = E1,`Environment 2` = E2))
|
||||
kable(environments,
|
||||
format="html",
|
||||
align = 'rr') %>%
|
||||
kable_styling(position = "center")
|
||||
as.data.frame(environments) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "right", columns = everything()) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
## Richness {.flexbox .vcenter}
|
||||
@@ -379,10 +415,13 @@ S = rowSums(environments > 0)
|
||||
```
|
||||
|
||||
```{r}
|
||||
kable(data.frame(S=S),
|
||||
format="html",
|
||||
align = 'rr') %>%
|
||||
kable_styling(position = "center")
|
||||
data.frame(S=S) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "right", columns = everything()) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
## Gini-Simpson's index {.smaller}
|
||||
@@ -414,10 +453,13 @@ GS = 1 - rowSums(environments^2)
|
||||
```
|
||||
|
||||
```{r}
|
||||
kable(data.frame(`Gini-Simpson`=GS),
|
||||
format="html",
|
||||
align = 'rr') %>%
|
||||
kable_styling(position = "center")
|
||||
data.frame(`Gini-Simpson`=GS) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "right", columns = everything()) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
## Shannon entropy {.smaller}
|
||||
@@ -443,10 +485,13 @@ H = - rowSums(environments * log(environments),na.rm = TRUE)
|
||||
```
|
||||
|
||||
```{r}
|
||||
kable(data.frame(`Shannon index`=H),
|
||||
format="html",
|
||||
align = 'rr') %>%
|
||||
kable_styling(position = "center")
|
||||
data.frame(`Shannon index`=H) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "right", columns = everything()) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
## Hill's number {.smaller}
|
||||
@@ -476,10 +521,17 @@ D2 = exp(- rowSums(environments * log(environments),na.rm = TRUE))
|
||||
```
|
||||
|
||||
```{r}
|
||||
kable(data.frame(`Hill Numbers`=D2),
|
||||
format="html",
|
||||
align = 'rr') %>%
|
||||
kable_styling(position = "center")
|
||||
data.frame(`Hill Numbers` = D2) %>%
|
||||
gt() %>%
|
||||
cols_align(align = "center") %>%
|
||||
tab_style(
|
||||
style = cell_text(weight = "bold"),
|
||||
locations = cells_column_labels()
|
||||
) %>%
|
||||
tab_options(
|
||||
table.align = "center",
|
||||
heading.align = "center"
|
||||
)
|
||||
```
|
||||
|
||||
## Generalized logaritmic function {.smaller}
|
||||
@@ -493,7 +545,7 @@ $$
|
||||
The function is not defined for $q=1$ but when $q \longrightarrow 1\;,\; ^q\log(x) \longrightarrow \log(x)$
|
||||
|
||||
$$
|
||||
^q\log(x) = \left\{
|
||||
^q\log(x) = \left\{
|
||||
\begin{align}
|
||||
\log(x),& \text{if } q = 1\\
|
||||
\frac{x^{(1-q)}-1}{1-q},& \text{otherwise}
|
||||
@@ -505,7 +557,7 @@ $$
|
||||
log_q = function(x,q=1) {
|
||||
if (q==1)
|
||||
log(x)
|
||||
else
|
||||
else
|
||||
(x^(1-q)-1)/(1-q)
|
||||
}
|
||||
```
|
||||
@@ -535,7 +587,7 @@ legend("topleft",legend = qs,fill = seq_along(qs),cex=1.5)
|
||||
## And its inverse function {.flexbox .vcenter}
|
||||
|
||||
$$
|
||||
^qe^x = \left\{
|
||||
^qe^x = \left\{
|
||||
\begin{align}
|
||||
e^x,& \text{if } x = 1 \\
|
||||
(1 + x(1-q))^{(\frac{1}{1-q})},& \text{otherwise}
|
||||
@@ -601,14 +653,14 @@ environments.dq = apply(environments,MARGIN = 1,D_spectrum,q=qs)
|
||||
|
||||
```{r}
|
||||
par(mfrow=c(1,2),bg=NA)
|
||||
plot(qs,environments.hq[,2],type="l",col="red",
|
||||
plot(qs,environments.hq[,2],type="l",col="red",
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qH$'),
|
||||
xlim=c(-0.5,3.5),
|
||||
main="generalized entropy")
|
||||
points(qs,environments.hq[,1],type="l",col="blue")
|
||||
abline(v=c(0,1,2),lty=2,col=4:6)
|
||||
plot(qs,environments.dq[,2],type="l",col="red",
|
||||
plot(qs,environments.dq[,2],type="l",col="red",
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qD$'),
|
||||
main="Hill's number")
|
||||
@@ -657,7 +709,7 @@ plot(qs,H.mock,type="l",
|
||||
xlim=c(-0.5,3.5),
|
||||
main="generalized entropy")
|
||||
abline(v=c(0,1,2),lty=2,col=4:6)
|
||||
plot(qs,D.mock,type="l",
|
||||
plot(qs,D.mock,type="l",
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qD$'),
|
||||
main="Hill's number")
|
||||
@@ -674,7 +726,7 @@ positive.H = apply(positive.count.relfreq,
|
||||
```
|
||||
|
||||
```{r}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
boxplot(t(positive.H),
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qH$'),
|
||||
@@ -685,7 +737,7 @@ points(H.mock,col="red",type="l")
|
||||
## Biodiversity spectrum and metabarcoding (2) {.flexbox .vcenter .smaller}
|
||||
|
||||
```{r}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
boxplot(t(positive.H)[,11:31],
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qH$'),
|
||||
@@ -706,7 +758,7 @@ positive.D = apply(positive.count.relfreq,
|
||||
```
|
||||
|
||||
```{r}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
boxplot(t(positive.D),
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qD$'),
|
||||
@@ -753,7 +805,7 @@ positive.clean.H = apply(positive.clean.count.relfreq,
|
||||
```
|
||||
|
||||
```{r fig.height=3.5}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
boxplot(t(positive.clean.H),
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qH$'),
|
||||
@@ -771,7 +823,7 @@ positive.clean.D = apply(positive.clean.count.relfreq,
|
||||
```
|
||||
|
||||
```{r}
|
||||
par(bg=NA)
|
||||
par(bg=NA)
|
||||
boxplot(t(positive.clean.D),
|
||||
xlab=TeX('$q$'),
|
||||
ylab=TeX('$^qD$'),
|
||||
@@ -1069,7 +1121,7 @@ BC_{jk}=\frac{\sum _{i=1}^{p}(N_{ij} - min(N_{ij},N_{ik}) + (N_{ik} - min(N_{ij}
|
||||
$$
|
||||
|
||||
$$
|
||||
BC_{jk}=\frac{\sum _{i=1}^{p}|N_{ij} - N_{ik}|}{\sum _{i=1}^{p}N_{ij}+\sum _{i=1}^{p}N_{ik}}
|
||||
BC_{jk}=\frac{\sum _{i=1}^{p}|N_{ij} - N_{ik}|}{\sum _{i=1}^{p}N_{ij}+\sum _{i=1}^{p}N_{ik}}
|
||||
$$
|
||||
|
||||
$$
|
||||
@@ -1159,7 +1211,7 @@ legend("topleft",legend = levels(samples.type),fill = 1:4,cex=1.2)
|
||||
|
||||
````{=html}
|
||||
<!---
|
||||
## Computation of norms
|
||||
## Computation of norms
|
||||
|
||||
```{r guiana_norm, echo=TRUE}
|
||||
guiana.n1.dist = norm(guiana.relfreq.final,l=1)
|
||||
@@ -1168,7 +1220,7 @@ guiana.n3.dist = norm(guiana.relfreq.final^(1/3),l=3)
|
||||
guiana.n4.dist = norm(guiana.relfreq.final^(1/100),l=100)
|
||||
```
|
||||
|
||||
## pCoA on norms
|
||||
## pCoA on norms
|
||||
|
||||
```{r dependson="guiana_norm"}
|
||||
guiana.n1.pcoa = cmdscale(guiana.n1.dist,k=3,eig = TRUE)
|
||||
|
||||
Reference in New Issue
Block a user