---
title: PGS Catalog Calculator (`pgsc_calc`) report
author: PGS Catalog team
date: today
title-block-banner: "#383434"
toc: true
format:
  html:
    code-fold: true
    anchor-sections: true
engine: knitr
params:
  log_pattern: "*_summary.csv"
  log_scorefiles: "log_scorefiles.json"
  score_path: ""
  sampleset: ""
  run_ancestry: false
  reference_panel_name: ""
css: logo.css
---

::: {.callout-note}
See the online [documentation](https://pgsc-calc.readthedocs.io/en/latest/output.html#report) for additional
explanation of the terms and data presented in this report.
:::

```{r setup, echo=FALSE, warning=FALSE, message=FALSE}
library(jsonlite)
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
```

```{r setup_logs, echo=FALSE}
log_paths <- list.files(pattern = params$log_pattern, full.names = TRUE)
read_log <- function(path) {
  log <- read.csv(path, stringsAsFactors = FALSE)
  
  return(
    log %>%
      mutate(
        is_multiallelic =  factor(is_multiallelic, levels = c("false", "true")),
        ambiguous = factor(ambiguous, levels = c("false", "true"))
      ) %>%
      rename(sampleset = dataset)
  )
}
log_df <- Reduce(dplyr::bind_rows, lapply(log_paths, read_log))
log_df$sampleset <- gsub("_", " ", log_df$sampleset)  # page breaking issues
```

# Pipeline command

```{bash}
cat command.txt | fold -w 80 -s | awk -F ' ' 'NR==1 { print "$", $0} NR>1 { print "    " $0}' | sed 's/$/\\/' | sed '$ s/.$//' 
```

# Scoring file metadata

## Scoring file summary

```{r load_scorefiles}
json_scorefiles <- read_json(params$log_scorefiles, simplifyVector=TRUE) 

link_traits <- function(trait_efo, mapped) {
  if (length(trait_efo) == 0) {
    return("")
  } else {
    return(purrr::map2_chr(trait_efo, mapped, ~ stringr::str_glue('<a href="http://www.ebi.ac.uk/efo/{.x}">{.y}</a>')))
  }
}

extract_traits <- function(x) {
  trait_efo <- purrr::map(json_scorefiles, ~ extract_chr_handle_null(.x, "trait_efo"))
  mapped <- purrr::map(json_scorefiles, ~ extract_chr_handle_null(.x, "trait_mapped"))
  trait_display <- purrr::map2(trait_efo, mapped, link_traits)
  mapped_trait_links <- purrr::map_chr(trait_display, ~ paste(.x, collapse = "<br />"))
  reported_traits <- purrr::map(json_scorefiles, ~ extract_chr_handle_null(.x, "trait_reported"))
  purrr::map2(reported_traits, mapped_trait_links, ~ {
    stringr::str_glue("<u>Reported trait:</u> {.x} <br /> <u>Mapped trait(s):</u> {.y}")
  })
}

extract_chr_handle_null <- function(x, field) {
  return(replace(x[[field]], is.null(x[[field]]), ""))
}

link_pgscatalog <- function(id, link_type) {
  if (id != "") {
    return(stringr::str_glue('<a href="https://www.pgscatalog.org/{link_type}/{id}">{id}</a>'))
  } else {
    return(id)
  }
}

add_note <- function(id, note) {
  if (id != "") {
    return(stringr::str_glue("{id} <br /> <small>{note}</small>"))
  } else {
    return(id)
  }
}

annotate_genome_build <- function(original_build, harmonised_build) {
  return(stringr::str_glue("<u>Original build:</u> {original_build} <br /> <u>Harmonised build:</u> {harmonised_build}"))
}

tibble::tibble(json = json_scorefiles) %>%
  # extract fields from json list
  mutate(pgs_id = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "pgs_id")),
         pgs_name = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "pgs_name")),
         pgp_id = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "pgp_id")),
         citation = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "citation")),
         # trait_efo = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "trait_efo")),
         # trait_reported = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "trait_reported")),
         # trait_mapped = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "trait_mapped")),
         trait_display = extract_traits(.),
         genome_build = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "genome_build")),
         harmonised_build = purrr::map_chr(json, ~ extract_chr_handle_null(.x, "HmPOS_build")),
         n_variants = purrr::map_chr(json, ~ .x$variants_number),
         accession = stringr::str_replace_all(names(json), "_", " ")
         ) %>%
  # add links to pgs catalog identifiers
  mutate(pgs_id = purrr::map_chr(pgs_id, ~ link_pgscatalog(.x, "score")),
         pgp_id = purrr::map_chr(pgp_id, ~ link_pgscatalog(.x, "publication"))) %>%
  # add notes
  mutate(pgp_id = purrr::map2_chr(pgp_id, citation, ~ add_note(.x, .y)),
         pgs_id = purrr::map2_chr(pgs_id, pgs_name, ~ add_note(.x, .y)),
         genome_build = purrr::map2_chr(genome_build, harmonised_build, ~ annotate_genome_build(.x, .y))) %>% 
  # pick columns
  select(accession, pgs_id, pgp_id, trait_display, n_variants, genome_build) -> scorefile_metadata
```

:::{.column-body-outset}

```{r, echo=FALSE}
DT::datatable(
  scorefile_metadata,
  rownames = FALSE,
  escape = FALSE,
  colnames = c(
    "Scoring file" = "accession",
    "Polygenic Score ID" = "pgs_id",
    "Publication" = "pgp_id",
    "Traits" = "trait_display",
    "Number of variants" = "n_variants",
    "Genome build" = "genome_build"
  ),
  extensions = 'Buttons',
  options = list(dom = 'Bfrtip',
                 buttons = c('csv'))
)
```

:::

# Variant matching 

## Parameters

```{bash}
cat params.txt
```

```{asis, echo = params$run_ancestry}
## Reference matching summary
```

```{r, echo = FALSE, message = FALSE, eval=params$run_ancestry}
intersect_stats_files <- list.files(pattern = "intersect_counts*")
intersect_stats <- lapply(intersect_stats_files, function(x) scan(x, sep = "\n", what=integer()))

n_target <- purrr::map_int(intersect_stats, ~ .x[[1]])
n_ref <- purrr::map_int(intersect_stats, ~ .x[[2]])
n_match <- purrr::map_int(intersect_stats, ~ .x[[3]])

data.frame(reference = params$reference_panel_name, n_target = n_target, n_match = n_match, n_ref = n_ref) %>%
  group_by(reference) %>%
  summarise(n_target = sum(n_target), n_ref = sum(n_ref), n_match = sum(n_match)) %>%
  mutate("% matched" = round(n_match / n_ref * 100, 2)) %>%
  rename("n target" = n_target, "n (matched)" = n_match, "N variants in panel" = n_ref) %>%
  DT::datatable()
```

## Summary

```{r setup_matches}
log_df %>%
  mutate(match_status = forcats::fct_collapse(match_status, matched = "matched", other_level = "unmatched")) %>%
  group_by(sampleset, accession, match_status, score_pass) %>%
  count(wt = count) %>%
  group_by(sampleset, accession) %>%
  mutate(percent = round(n / sum(n) * 100, 1), n_variants = sum(n)) %>%
  arrange(accession, desc(percent)) %>%
  tidyr::pivot_wider(names_from = match_status, values_from = c(n, percent)) %>%
  replace(is.na(.), 0) -> compat
```

```{r match_table}
if (!"n_unmatched" %in% colnames(compat)) {
  # handle missing column if all PGS matches perfectly (e.g. no unmatched or excluded variants)
  compat <- compat %>%
    mutate(n_unmatched = 0) 
}

compat %>%
  select(sampleset, accession, n_variants, score_pass, percent_matched,
         n_matched, n_unmatched) %>%
  mutate(score_pass = as.logical(score_pass)) %>%
  DT::datatable(rownames = FALSE,
                extensions = 'Buttons',
    options = list(dom = 'Bfrtip',
                   buttons = c('csv')),
    colnames = c(
      "Sampleset" = "sampleset",
      "Scoring file" = "accession",
      "Number of variants" = "n_variants",
      "Passed matching" = "score_pass",
      "Match %" = "percent_matched",
      "Total matched" = "n_matched",
      "Total unmatched" = "n_unmatched"
    )) %>%
  DT::formatStyle('Scoring file', 
                  valueColumns = 'Passed matching',
                  backgroundColor = DT::styleEqual(c(FALSE, TRUE), c('#c2a5cf', '#a6dba0')))
```

## Detailed log

:::{.column-body-outset}

```{r match_table_detailed, echo = FALSE, warning=FALSE}
if(params$run_ancestry == TRUE){
    # Include match_IDs in the groupby to account for
    log_df %>%
      group_by(sampleset, accession) %>%
      count(match_status, match_IDs, ambiguous, is_multiallelic, match_flipped, duplicate_best_match, duplicate_ID, wt = count) %>%
      rename(is_ambiguous = ambiguous) %>%
      mutate(percent = round(n / sum(n) * 100, 2),
             match_status = forcats::fct_relevel(match_status, "matched", "excluded", "unmatched")) %>%
      arrange(accession, match_status) %>%
      mutate(accession = stringr::str_replace_all(accession, "_", " ")) %>%
      DT::datatable(rownames=FALSE,
                    extensions = 'Buttons',
        options = list(dom = 'Bfrtip',
                       buttons = c('csv')),
        colnames = c(
          "Sampleset" = "sampleset",
          "Scoring file" = "accession",
          "Match type" = "match_status",
          "Variant in reference panel" = "match_IDs",
          "Multiple potential matches" = "duplicate_best_match",
          "Duplicated matched variants" = "duplicate_ID",
          "Ambiguous" = "is_ambiguous",
          "Multiallelic" = "is_multiallelic",
          "Matches strand flip" = "match_flipped",
          "%" = "percent"
        ))
} else{
    log_df %>%
      group_by(sampleset, accession) %>%
      count(match_status, ambiguous, is_multiallelic,match_flipped, duplicate_best_match, duplicate_ID, wt = count) %>%
      rename(is_ambiguous = ambiguous) %>%
      mutate(percent = round(n / sum(n) * 100, 2),
             match_status = forcats::fct_relevel(match_status, "matched", "excluded", "unmatched")) %>%
      arrange(accession, match_status) %>%
      mutate(accession = stringr::str_replace_all(accession, "_", " ")) %>%
      DT::datatable(rownames=FALSE,
                    extensions = 'Buttons',
        options = list(dom = 'Bfrtip',
                       buttons = c('csv')),
        colnames = c(
          "Sampleset" = "sampleset",
          "Scoring file" = "accession",
          "Match type" = "match_status",
          "Multiple potential matches" = "duplicate_best_match",
          "Duplicated matched variants" = "duplicate_ID",
          "Ambiguous" = "is_ambiguous",
          "Multiallelic" = "is_multiallelic",
          "Matches strand flip" = "match_flipped",
          "%" = "percent"
        ))
}
```

:::

```{asis, echo = params$run_ancestry}
# Genetic Ancestry
```

```{r colour_palette, echo = FALSE, eval=params$run_ancestry}
# source: https://github.com/PGScatalog/PGS_Catalog/blob/master/catalog/static/catalog/pgs.scss#L2493-L2520
# $ancestry_colours
thousand_genomes_colours <- c("#FFD900", "#E41A1C", "#B15928", "#4DAF4A", 
                              "#377EB8", "#00CED1", "#984EA3", "#A6CEE3", 
                              "#FF7F00", "#BBB", "#999")
names(thousand_genomes_colours) <- c("AFR", "AMR", "ASN", "EAS", "EUR", "GME",
                                     "SAS", "MAE", "MAO", "NR", "OTH")
thousand_genomes_palette <- scale_colour_manual(name = "Populations", values = thousand_genomes_colours)
```

```{r, echo = FALSE, message = FALSE, eval=params$run_ancestry}
popsim <- readr::read_tsv(gsub("_pgs.", "_popsimilarity.", params$score_path))
head(popsim)

new_label_target = paste({params$sampleset}, '(Most Similar Population)')
new_label_ref = paste0('reference (', {params$reference_panel_name}, ' populations)')

popsim$slabel <- new_label_ref
popsim[popsim$sampleset == {params$sampleset}, 'slabel'] <- new_label_target

map_shapes <- c(1, 16)
map_shapes <- setNames(map_shapes, c(new_label_target, new_label_ref))

# Placeholder figure: needs better legend labelling
for(pc in seq.int(1,5,2)){
  pcY = paste('PC', pc, sep='')
  pcX = paste('PC', pc+1, sep='')
  if (pcX %in% colnames(popsim)){
    p_pca <- ggplot(popsim[popsim$REFERENCE == TRUE,], aes(x=!!sym(pcX), y=!!sym(pcY))) + geom_point(aes(colour=SuperPop, shape=slabel), alpha=0.25)
    p_pca <- p_pca + geom_point(data=popsim[popsim$REFERENCE != TRUE,], aes(color=MostSimilarPop, shape=slabel))
    p_pca <- p_pca + theme_bw() + thousand_genomes_palette + scale_shape_manual(values=map_shapes, name='sampleset')
    print(p_pca)
  }
}
```

```{asis, echo = params$run_ancestry}
## Population similarity summary
```

```{r, echo = FALSE, message = FALSE, eval=params$run_ancestry}
popsim %>% 
  filter(sampleset == "reference") %>%
  group_by(sampleset) %>%
  count(`Most similar population` = SuperPop ) %>%
  mutate(percent = round(n / sum(n) * 100, 2)) -> ref_count

popsim %>%
  filter(sampleset != "reference") %>%
  group_by(sampleset) %>%
  count(`Most similar population` = MostSimilarPop) %>%
  mutate(percent = round(n / sum(n) * 100, 2)) %>%
  bind_rows(ref_count) %>%
  mutate(count = stringr::str_glue("{n} ({percent}%)")) %>%
  select(-n, -percent) %>%
  tidyr::pivot_wider(names_from = sampleset, values_from = count) -> pop_summary

write.csv(pop_summary, "pop_summary.csv", quote=FALSE, row.names = FALSE)

pop_summary %>%
  DT::datatable()
```

# Scores

```{r, echo = FALSE, message = FALSE, eval=!params$run_ancestry}
# problem: aggregated_scores.txt.gz  has a different structure to the ancestry outputs
# solution: pivot the table in the report, but fix in pgscatalog_utils in a future release
# problem: big scores can take a lot of memory
# use data.table as a temporary solution to pivoting datasets
scores <- data.table::fread(params$score_path)
n_scores <- sum(grepl("*_SUM$", colnames(scores)))
n_samples <- nrow(scores)

id_cols <- c("sampleset", "IID")
melted_scores <- data.table::melt(scores, id.vars=id_cols, measure.vars=patterns(SUM="*_SUM$", DENOM="DENOM", AVG="*_AVG$"), variable.name = "PGS")

# annoying fix for scores getting converted to a factor level integer
# fixed in data.table 1.14.9 but not released yet
score_names <- sub("_.*", "", colnames(scores)[grepl("_SUM$", colnames(scores))])
setattr(melted_scores$PGS, "levels", score_names)
data.table::fwrite(melted_scores, params$score_path, sep="\t", compress="gzip")
```

```{r, echo = FALSE, message = FALSE, eval=params$run_ancestry}
scores <- readr::read_tsv(params$score_path) 
n_scores <- length(unique(scores$PGS))
n_samples <- length(unique(scores$IID))
print(n_samples)
```


```{asis, echo = any(table(scores$sampleset) < 50)}

::: {.callout-important title="Warning: small sampleset size (n < 50) detected"}
* plink2 uses allele frequency data to [mean impute](https://www.cog-genomics.org/plink/2.0/score) the dosages of missing genotypes
* Currently `pgsc_calc` disables mean-imputation in these small sample sets to make sure that the calculated PGS is as consistent with the genotype data as possible
* With a small sample size, the resulting score sums may be inconsistent between samples
* The average `([scorename]_AVG)` may be more applicable as it calculates an average weighting over all genotypes present

In the future mean-imputation will be supported in small samplesets using ancestry-matched reference samplesets to ensure consistent calculation of score sums (e.g. 1000G Genomes).
:::

```

```{asis, echo = (nrow(compat) == n_scores)}
:::{.callout-tip title="Success"}
* All requested scores were calculated successfully
:::
```

```{asis, echo = (nrow(compat) != n_scores)}
:::{.callout-caution}
* Some requested scores could not be calculated
* Please check the variant matching summary table to understand why
:::
```

`r n_scores` scores for `r n_samples` samples processed.

### Score data 

#### Score extract

::: {.callout-note}
Below is a summary of the aggregated scores, which might be useful for debugging. See here for an explanation of [plink2](https://www.cog-genomics.org/plink/2.0/formats#sscore) column names
:::

```{r, echo = FALSE}
scores %>%
  tibble::as_tibble(.)
```

#### Density plot(s)

::: {.callout-note}
The summary density plots show up to six scoring files
:::

```{r density_ancestry, echo=FALSE, message=FALSE, warning=FALSE, eval=params$run_ancestry}
# Select which PGS to plot
uscores <- unique(scores$PGS)
uscores_plot <- uscores[1:min(length(uscores), 6)] # plot max 6 PGS

# Plot multiple adjustment methods at once per PGS
for(current_pgs in uscores_plot){
  long_scores <- scores %>% select(!percentile_MostSimilarPop) %>% filter(PGS == current_pgs) %>% gather(Method, score, -sampleset, -IID, -PGS)
  long_scores %>%
  ggplot(aes(x = score, fill = sampleset)) +
    geom_density(alpha = 0.3) +
    facet_wrap(~Method, scales = "free", nrow=1) +
    theme_bw() +
    labs(x = 'PGS', y = "Density", title = paste(current_pgs, '(adjusted distributions)')) -> p_dist
  print(p_dist)
}
```

```{r, echo = FALSE, message=FALSE, warning=FALSE, eval=!params$run_ancestry}
scores %>%
  ungroup() %>%
  select(IID, sampleset, ends_with("SUM")) %>%
  group_by(IID, sampleset) %>%
  tidyr::pivot_longer(cols = -group_cols()) %>%
  ungroup() %>%
  filter(name != "DENOM_SUM") %>%
  mutate(name = ifelse(
    stringr::str_detect(name, "^PGS"),
    stringr::str_extract(name, "^PGS[0-9]{6}"),
    name)) %>%
  group_by(sampleset, name) -> long_scores
group_keys(long_scores) %>%
  slice(1:6) -> score_keys
long_scores %>%
  inner_join(score_keys) %>%
  ggplot(aes(x = value, fill = sampleset)) +
      geom_density(alpha = 0.3) +
      facet_wrap(~name, ncol = 2, scales = "free") +
      theme_bw() +
      labs(x = "PGS (SUM)", y = "Density", title = "PGS Distribution(s)")
```

### Get all scores

All scores can be found in the results directory, at:

```{r, eval=params$run_ancestry, echo=FALSE}
stringr::str_glue("{params$sampleset}/score/{params$sampleset}_pgs.txt.gz")
```

```{r, eval=!params$run_ancestry, echo=FALSE}
stringr::str_glue("{params$sampleset}/score/aggregated_scores.txt.gz")
```

# Citations

::: {.callout-important}
For scores from the PGS Catalog, please remember to cite the original publications from which they came (these are listed in the metadata table.) 
:::

> PGS Catalog Calculator (in development). PGS Catalog Team. `https://github.com/PGScatalog/pgsc_calc`

> Lambert et al. (2021) The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nature Genetics. 53:420–425 doi:10.1038/s41588-021-00783-5.