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20-G20.rmd
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# IGHV3-30-5/IGHV3-30/IGHV3-33/IGHV3-30-3 - G20
```{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 = 'IGHV3-30-3G20'
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)
```
## Group alignment
Based on the viewd alleles, we calculated the distance between the germline sequences.
```{r}
v_calls <- unique(data[grepl(g_group,v_gene),v_call])
seq_align(v_calls, allele_db, vgerms, chain, mat, g_group)
```
```{r, fig.width=18,fig.height=30}
v_calls <- unique(data[grepl(g_group,v_gene),v_call])
seq_align2(v_calls, allele_db, vgerms, chain, mat, g_group)
```
## Sequence depth
To examine the potential cutoff we observed the sequence depth for each allele
```{r}
tagList(sequence_depth(data, g_group, allele_db))
```
## Absolute cutoff
We set an initial cutoff of $0.5\%$ to determine the potential genotype priors. For this cutoff we examined the zygousity state, such as homozygousity, heterozygousity and so on.
```{r}
tmp_allele_db <-
allele_db %>% dplyr::filter(grepl(as.character(g_group), new_allele)) %>%
dplyr::group_by(new_allele) %>% dplyr::summarise(or_allele = paste0(or_allele, collapse = "/"))
or_allele <-
setNames(gsub(chain, "", as.character(tmp_allele_db$or_allele)), as.character(gsub(
paste0(g_group, "[*]"),
"",
tmp_allele_db$new_allele
)))
allele_thresh = 0.5
tmp <- data_cutoff(data, func_g_groups, g_group, allele_thresh, or_allele)
```
With the selected cutoff we saw that there are `r length(unique(tmp %>% filter(is.na(j_call)) %>% arrange(zygousity_state) %>% pull(zygousity_state)))` zygousity states.
```{r eval=knitr::is_html_output(excludes = 'epub'), results = 'asis', echo = F}
source_haplo_usage(g_group, allele_thresh)
```
## Observations
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))
# # Authenticate and save token for later use2
# token <- drop_auth(rdstoken = "dropbox_token.rds")
#
# # Retrieveing your file is as simple as
# drop_download("public/conclusions/IGHV1-2.docx", local_path = "docs/IGHV1-2.docx",
# overwrite = TRUE, verbose = FALSE, progress = FALSE)
# drop_df <- textreadr::read_docx("docs/IGHV1-2.docx")
# invisible(rmarkdown::pandoc_convert(input = "IGHV1-2.docx", to = "markdown", output = "IGHV1-2_docx.md", options = c("--wrap=none","--reference-links","--metadata-file=metadata.yaml","--standalone"), wd = "docs", verbose = FALSE))
#
# ui <- fluidPage(
#
# # Application title
# mainPanel(
# shiny::actionButton(inputId='ab1', label="Edit text",
# icon = icon("edit"),
# onclick ="window.open('https://www.dropbox.com/scl/fi/crphl3u49fmkw8wnb5v71/IGHV1-2.docx?dl=0&rlkey=l54oqhrlxx8wyp6bywr224sx0')"),
#
# shiny::actionButton(inputId='ab2', label="Reload text",
# icon = icon("sync-alt")),
#
# #htmlOutput("df_output"),
# uiOutput('markdown')
# )
# )
#
# server <- function(input, output) {
#
# observeEvent(input$ab2, {
# drop_download("public/conclusions/IGHV1-2.docx", local_path = "docs/IGHV1-2.docx",
# overwrite = TRUE, verbose = F)
# #drop_df <- textreadr::read_docx("docs/IGHV1-2.docx")
#
# rmarkdown::pandoc_convert(input = "IGHV1-2.docx", to = "markdown", output = "IGHV1-2_docx.md", options = c("--wrap=none","--reference-links","--metadata-file=metadata.yaml","--standalone"), wd = "docs", verbose = F)
# output$markdown <- renderUI({
# #withMathJax(includeMarkdown(knitr::knit('IGHV1-2_docx.md', quiet = TRUE)))
# HTML(markdown::markdownToHTML(knitr::knit('docs/IGHV1-2_docx.md', quiet = TRUE)))
# })
#
# })
#
# # output$df_output <- renderUI({
# # if(input$ab2) return()
# # HTML(paste(drop_df, collapse = "<br/><br/>"))
# # })
#
# output$markdown <- renderUI({
# #withMathJax(includeMarkdown(knitr::knit('docs/IGHV1-2_docx.md', quiet = TRUE)))
# HTML(markdown::markdownToHTML(knitr::knit('docs/IGHV1-2_docx.md', quiet = TRUE)))
# })
# }
#
# shinyApp(ui = ui, server = server)
```
## Conclusions
From the results we believe that the cutoff for this group should be
```{r}
DT::datatable(data.frame(thresholds = absolute_thresholds_dict[[g_group]]))
```
and for the adjusted states the allele combinations and the relations are stated in the table below.
### Allele specific cutoff
```{r}
tableData <- data_cutoff(data, func_groups, g_group, 5, or_allele) %>% dplyr::group_by(zygousity_state, v_allele) %>% dplyr::summarise(mean_freq = paste0(round(quantile(freq2, 3/4),3),":", round(quantile(freq2, 1/4),3)), v_alleles_abc = unique(v_allele_axis)) %>% dplyr::group_by(zygousity_state, v_alleles_abc) %>% dplyr::summarise(fractions = paste0(mean_freq, collapse = ";"))
DT::datatable(
tableData,
options = list(dom = "tipr"),
selection = 'none',
colnames = c(
"Zygousity state" = "zygousity_state",
"V\nallele" = "v_alleles_abc",
"IQR range" = "fractions"
)
)
# column names change to Zygousity state, alleles combinations, IQR
```
```{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)
```