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---
title: "VIPERA report: `r params$name`"
subtitle: "Workflow version: `r params$workflow_version`"
date: last-modified
date-format: "YYYY-MM-DD"
title-block-banner: true
format:
html:
page-layout: article
embed-resources: true
smooth-scroll: true
theme: cosmo
toc: true
toc-expand: true
toc-location: left
toc-title: Summary
number-sections: true
css: config/report.styles.css
editor: visual
params:
ufboot_reps: ""
shalrt_reps: ""
min_ivar_freq: ""
workflow_version: ""
div: ""
freyja: ""
tree: ""
tempest: ""
SNV: ""
SNV_s: ""
evo: ""
div_value: ""
panel: ""
volcano: ""
tree_ml: ""
fig_cor_snp: ""
stats_lm: ""
table: ""
sum_nv: ""
heat_tab: ""
omega_plot: ""
name: ""
output-file: report.html
---
<head>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Montserrat&display=swap" rel="stylesheet">
</head>
```{r read, echo = F, message = F, include = F}
library(jsonlite)
library(gt)
library(tidyr)
library(dplyr)
library(heatmaply)
library(readr)
# Diversity
div_values <- fromJSON(params$div_value)
# Temporal signal
stats <- fromJSON(params$stats_lm)
correlation <- stats[["r2"]]
sub_rate <- stats[["sub_rate"]]
sub_rate <- round(sub_rate, digits = 2)
p_value_lm <- stats[["pvalue"]]
# NV counts
nv.counts <- fromJSON(params$sum_nv)
n_SNV <- nv.counts[["SNV"]]
n_INDELS <- nv.counts[["INDELS"]]
# Summary table
table <- read.csv(params$table)
# Heatmap
vcf <- read_csv(params$heat_tab)
row.names(vcf) <- vcf$`...1`
vcf <- vcf[-1]
cor.mat <- cor(vcf)
cor.mat[is.na(cor.mat)] <- 0
```
## Summary of the target samples dataset
```{r summary_table, echo = F,message = F}
#| label: tbl-summary
#| tbl-cap: Target dataset summary
table %>%
mutate(
DeltaTime = difftime(
as.Date(Collection_Date),
min(as.Date(Collection_Date), na.rm = TRUE), units = "days"
)
) %>%
gt %>%
cols_label(
Sample = "Sample",
Collection_Date = "Collection date",
Lineage = "Lineage",
DeltaTime = "Days after first sampling"
) %>%
tab_style(
style = list(
cell_text(weight = "bold")
),
location = cells_column_labels()
) %>%
cols_align(
align = "center",
columns = everything()
) %>%
opt_table_font(
font = google_font("Montserrat")
)
```
## Evidence for single, serially-sampled infection
### Lineage admixture
The estimated lineage admixture for each sample has been calculated
using [Freyja](https://github.com/andersen-lab/Freyja).
{#fig-freyja}
### Phylogenetic reconstruction
A maximum likelihood tree of the target and context samples has been
built using [IQTREE](http://www.iqtree.org/).
The target samples `r stats[["monophyly"]]` monophyletic. The clade
that contains all the target samples is supported by a UFBoot score of
$`r stats[["boot"]]`$% and a SH-aLRT score of $`r stats[["alrt"]]`$% (@fig-tree_ml).
{#fig-tree_ml}
### Nucleotide diversity comparison
Nucleotide diversity (π) has been calculated for $`r div_values[["boot.reps"]]`$ random
sample subsets of size $`r div_values[["sample.size"]]`$, extracted
from the context dataset. The distribution of the nuclotide diversity is assumed to
`r div_values[["norm.text"]]` be normal after performing a Shapiro-Wilk test
(p-value of $`r div_values[["normal.pvalue"]]`$).
The nucleotide diversity of the target samples is $`r div_values[["diversity"]] `$ (red line in @fig-div).
Assuming the independence of the context samples, the `r div_values[["type.test"]]`
p-value of the context samples having a nucleotide diversity (in orange in @fig-div)
as low as that of the target dataset is $`r div_values[["p.value"]]`$.
![Analysis of the nucleotide diversity (π). The orange line describes
a normal distribution with the same mean and standard deviation as the distribution
of π from $`r div_values[["boot.reps"]]`$ subsets of $`r div_values[["sample.size"]]`$
sequences from the context. The red vertical line indicates the π value of the
target samples.](`r params$div`){#fig-div}
## Evolutionary trajectory of the serially-sampled SARS-CoV-2 infection
### Number of polymorphic sites
Sites with minor allele frequency $> `r params$min_ivar_freq`$ are considered polymorphic.
The linear association between the collection date of the samples and the number of
polymorphic sites has an $R^2$ of $`r nv.counts[["r2"]]`$ and a p-value of
$`r nv.counts[["value"]]`$ (@fig-fig_cor_snp).
{#fig-fig_cor_snp}
### Description of intra-host nucleotide variants
A total of $`r n_SNV`$ different single nucleotide variants (SNV) and $`r n_INDELS`$
insertions and deletions (indels) have been detected along the genome (@fig-SNV).
::: {.panel-tabset}
## Whole genome
![Summary of the intra-host accumulation of nucleotide variants,
using the reconstructed dataset ancestor as reference. A) Nucleotide
variants per site along the SARS-CoV-2 genome. Relative abundance of NVs is calculated
with a sliding window of width $`r nv.counts[["window"]]`$ nucleotides and a step of
$`r nv.counts[["step"]]`$. Labels indicate the coding regions of the non structural
proteins (NSP) within ORF1ab. B) Genome variation along the genome for each sample.
The Y-axis displays samples in chronological order, with the earliest collection date
at the bottom, and the latest, at the top.](`r params$SNV`){#fig-SNV}
## Spike ORF
![Summary of the intra-host accumulation of nucleotide variants
in the spike sequence, using the reconstructed dataset ancestor as reference. A) Nucleotide
variants per site along the S gene. Relative abundance of NVs is calculated
with a sliding window of width $`r nv.counts[["window"]]`$ nucleotides and a step of
$`r nv.counts[["step"]]`$. B) Genome variation along the S gene for each sample.
The Y-axis displays samples in chronological order, with the earliest collection date
at the bottom, and the latest, at the top.](`r params$SNV_s`){#fig-SNV_s}
:::
### Temporal signal of the intra-host mutations
The correlation of the allele frequency of each NV with the time since the
initial sampling has been calculated (@fig-volcano).
{#fig-volcano}
Significantly correlated nucleotide variants are described in more detail in @fig-panel.
{#fig-panel}
A neighbor-joining tree has been constructed using pairwise distances
between target samples (@fig-tree), based on the allele frequencies measured from
read mappings.
{#fig-tree}
To estimate the evolutionary rate, root-to-tip distances measured on the previous
tree (@fig-tree) have been correlated with time, obtaining a $R^2$ of
$`r correlation`$ and a p-value of $`r p_value_lm`$. The estimated evolutionary
rate is $`r sub_rate`$ number of changes per year (@fig-tempest).
{#fig-tempest}
### Correlation between alternative alleles
To detect possible interactions between mutations, pairwise correlation between allele
frequencies are calculated (@fig-heatmap). The heatmap is an interactive figure that allows
zooming in on specific regions.
```{r heatmap, echo = F, message = F, fig.align = 'center'}
#| label: fig-heatmap
#| fig-cap: "Interactive hierarchically clustered heatmap of the pairwise Pearson’s correlation coefficients between the time series of allele frequencies in the case study."
heatmaply_cor(
cor.mat,
fontsize_row = 8,
fontsize_col = 8,
column_text_angle = 90
)
```
### Non-synonymous and synonymous substitution rate over time
To track selection footprints, the substitutions per synonymous site ($dS$) and
per non-synonymous site ($dN$) for each sample with respect to the reconstructed
ancestral sequence have been calculated (@fig-evo), as well as their ratio ($\omega = dN/dS$; @fig-omega).
::: {.panel-tabset}
## $dN$ and $dS$
{#fig-evo}
## $\omega$ ($dN/dS$)
{#fig-omega}
:::