03-G3.rmd

# IGHV1-24 - G3
```{r}
source("functions.R")
```

```{r, echo=FALSE}
load("data.rda")

chain = "IGH"
func <-  data.frame(allele = names(vgerms[[chain]]), functionality = !grepl("(ORF|P)",sapply(seqinr::getAnnot(vgerms_full[[chain]]), function(x) unlist(strsplit(x,"[|]"))[4])), sign = sapply(seqinr::getAnnot(vgerms_full[[chain]]), function(x) unlist(strsplit(x,"[|]"))[4]), stringsAsFactors = F)

mat <- mat_list$IGH$functional$nonsingle$all$`318`

load("data_frac_new2.rda")
data <- setDT(data_frac$IGH$functional$nonsingle$all$`318`$complete$`95`)
data[,v_call:=paste0(v_gene,"*",v_allele)]
load("alleles_dbs.rda")
allele_db <- alleles_dbs$IGH$functional$nonsingle$all$`318`$complete$`95`
allele_db <- allele_db %>% dplyr::rowwise() %>% dplyr::mutate(gene = alakazam::getGene(or_allele, strip_d = F, omit_nl = F), group = strsplit(gsub(gene, "", new_allele),"[*]")[[1]][1], gene_group = alakazam::getGene(new_allele, strip_d = F, omit_nl = F))
load("functional_groups.rda")
func_groups <- functional_groups$IGH$functional$nonsingle$all$`318`$complete$`95`

cols <- c("#FAAB18", "#1380A1","#990000", "#588300")

pal <- cols %>% 
  newpal(names = c("orangy", "bluish", "redish", "greeny"))

edit_links <- readLines("edit_links.txt")
share_links <- readLines("share_links.txt")
```

```{r,echo=FALSE}
g_group = 'IGHV1-24G3'
group = names(func_groups)[func_groups==g_group]
gr <- allele_db %>% filter(gene_group == g_group) %>% pull(group) %>% unique()
g <- allele_db %>% filter(gene_group == g_group) %>% pull(gene) %>% unique()
```

## Allele appearnce 

The group of `r group` includes `r length(grep(g_group,allele_db$new_allele,value=T))` alleles, `r sum(func$functionality[func$allele %in% allele_db$or_allele[grep(g_group,allele_db$new_allele)]])` out of the alleles are functional.

For each allele we counted the number of appearances across the population, any appearance was considered valid. 

```{r}
allele_appearance(data, g_group, allele_db)
```

## Sequence depth

To examine the potential cutoff we observed the sequence depth for each allele

```{r}
tagList(sequence_depth(data, g_group, allele_db))
```

## Observations

For this group we've seen a single allele, hence the general summary statistics do not apply here and were omitted.

This section is editable by clicking on the edit button below. To refresh the section click on the refresh button

You can access the file also from [here](`r edit_links[grep(g_group,edit_links)]`){target="_blank"}

```{r, message=F, echo=F, warning=F}
knitr::include_url(paste0("https://peresay.shinyapps.io/G2_group/?group=",g_group))
```

## Conclusions

Because we observed a single allele in this group we set the cutoff to $0.01%$ absolute threshold (the relative repertoire frequency). The plot below shows the frequency of the group above the threshold line.

```{r, out.height= "100%", out.width="100%"}
m <- list(
    l = 50,
    r = 50,
    b = 100,
    t = 100,
    pad = 0.5
)
heatmap_alleles(data, g_group, allele_db, func)%>%
  layout(autosize = F, width = 800, height = 1300)
```