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umccrise: add reactable table viewer
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@pdiakumis
pdiakumis committed Sep 10, 2024
1 parent 8baa52c commit e91187d
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4 changes: 2 additions & 2 deletions inst/rmd/umccr_workflows/umccrise/dl_and_tidy.R
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ lims <- lims_raw |>
subject_id, library_id, sample_id, sample_name,
external_subject_id, external_sample_id,
project_name, project_owner,
source, quality
source, quality, workflow
) |>
distinct()
table(lims$library_id %in% meta$LibraryID_tumor) # double-check
Expand All @@ -55,7 +55,7 @@ meta_lims <- meta |>
select(
rownum, wfr_id, version, end_status, start, end, portal_run_id, SubjectID, LibraryID_tumor, LibraryID_normal,
SampleID_tumor, SampleID_normal, gds_outdir_umccrise, gds_indir_dragen_somatic, external_subject_id, external_sample_id,
project_owner, project_name, source, quality
project_owner, project_name, source, quality, workflow
)
meta_lims |>
saveRDS(here(glue("inst/rmd/umccr_workflows/umccrise/nogit/meta/{start_date}_{end_date}.rds")))
Expand Down
189 changes: 167 additions & 22 deletions inst/rmd/umccr_workflows/umccrise/summary_umccrise.qmd
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Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,48 @@ date_end <- "2024-09-01"
d_raw <- readr::read_rds(here(glue("inst/rmd/umccr_workflows/umccrise/nogit/results_{date_start}_{date_end}.rds")))
```

```{r}
#| label: funcs
dt_view <- function(x, id, ...) {
htmltools::browsable(
htmltools::tagList(
htmltools::tags$button(
htmltools::tagList(fontawesome::fa("download"), "CSV"),
onclick = glue("Reactable.downloadDataCSV('{id}', '{id}.csv')")
),
x |>
reactable::reactable(
bordered = TRUE,
filterable = TRUE,
fullWidth = TRUE,
height = 800,
highlight = TRUE,
pagination = FALSE,
resizable = TRUE,
searchable = TRUE,
sortable = TRUE,
striped = TRUE,
wrap = FALSE,
elementId = id,
...
)
)
)
}
```

## Metadata

```{r}
#| label: metadata
meta <- d_raw |>
select(
rownum, portal_run_id, SubjectID, LibraryID_tumor, SampleID_tumor, external_subject_id, external_sample_id,
project_owner, project_name, source, quality, workflow
)
dt_view(meta, id = "metadata")
```

## Results

```{r}
Expand All @@ -66,10 +108,61 @@ d <- d_raw |>
tidyr::unnest_longer(res, indices_to = "filetype")
# main_cols <- c("rownum", "portal_run_id", "SubjectID", "LibraryID_tumor")
main_cols <- c("portal_run_id")
```

```{r}
#| label: qcsum
qcsum <- d |>
filter(filetype == "qcsummarytsv") |>
select(all_of(main_cols), res) |>
unnest_wider(res)
```

```{r}
#| label: pcgr
pcgr <- d |>
filter(filetype == "pcgrjson") |>
select(all_of(main_cols), res) |>
unnest_wider(res) |>
select(all_of(main_cols), qc_status_hmf:bpi_enabled)
rename(
msi_fraction_indels_pcgr = "fracIndels",
msi_pcgr = "predicted_class",
tmb_pcgr = "tmb_estimate",
n_tmb_pcgr = "n_tmb"
) |>
mutate(msi_pcgr = sub(" \\(.*\\)", "", msi_pcgr))
```

```{r}
#| label: conpair
sampleids <- d_raw |>
select(SampleID_tumor, SampleID_normal) |>
tidyr::pivot_longer(everything(), values_to = "sampleid") |>
mutate(phenotype = sub("SampleID_", "", .data$name)) |>
select(sampleid, phenotype) |>
distinct()
conpair_raw <- d |>
filter(filetype == "conpairmultiqc") |>
select(all_of(main_cols), res) |>
tidyr::unnest(res) |>
left_join(sampleids, by = "sampleid") |>
select(-sampleid)
conpair_tumor <- conpair_raw |>
filter(phenotype == "tumor")
conpair_normal <- conpair_raw |>
filter(phenotype == "normal") |>
select(portal_run_id, contamination)
conpair <- conpair_tumor |>
left_join(conpair_normal, by = "portal_run_id", suffix = c("_tumor", "_normal")) |>
select(portal_run_id,
contamination_tumor_conpair = "contamination_tumor",
contamination_normal_conpair = "contamination_normal",
concordance_conpair = "concordance"
)
```

```{r}
#| label: hrd
hrd_chord <- d |>
filter(filetype == "chordtsv") |>
unnest_wider(res) |>
Expand All @@ -84,6 +177,10 @@ hrd_hrdetect <- d |>
filter(filetype == "hrdetecttsv") |>
unnest_wider(res) |>
select(all_of(main_cols), hrdetect_prob = "Probability")
```

```{r}
#| label: sigs
sigs_snv2015 <- d |>
filter(filetype == "sigssnv2015tsv") |>
select(all_of(main_cols), res) |>
Expand All @@ -104,8 +201,6 @@ sigs_indel <- d |>
select(all_of(main_cols), res) |>
tidyr::unnest_wider(res) |>
tidyr::unnest_longer(col = c(Rank, Signature, Contribution, RelFreq))
# signatures
dsig <- bind_rows(
list(
snv2015 = sigs_snv2015, snv2020 = sigs_snv2020, dbs = sigs_dbs, indel = sigs_indel
Expand All @@ -128,20 +223,37 @@ dsig_filt <- dsig |>
select(Sig_group, portal_run_id, sig_rank, sig_summary) |>
tidyr::pivot_wider(names_from = sig_rank, values_from = sig_summary, names_prefix = "rank") |>
mutate(sig_top2 = paste(rank1, rank2, sep = ", ")) |>
select(portal_run_id, sig_top2)
select(Sig_group, portal_run_id, sig_top2) |>
tidyr::pivot_wider(names_from = Sig_group, values_from = sig_top2) |>
select(portal_run_id, snv2015, snv2020, dbs, indel)
```

```{r}
#| label: qc_all
dall <- d_raw |>
select(
rownum,
date_analysed = "start", portal_run_id,
SubjectID, LibraryID_tumor, SampleID_tumor,
external_subject_id, external_sample_id,
project_owner, project_name, source, quality
project_owner, project_name, source, quality, workflow
) |>
left_join(qcsum, by = "portal_run_id") |>
left_join(hrd_chord, by = "portal_run_id") |>
left_join(hrd_hrdetect, by = "portal_run_id")
left_join(hrd_hrdetect, by = "portal_run_id") |>
left_join(pcgr, by = "portal_run_id") |>
left_join(conpair, by = "portal_run_id")
```

### Summary Metrics

```{r}
#| label: summary_metrics
dt_view(dall, "summary_metrics")
```

### HRD Plot

```{r}
#| label: hrd_plot
#| fig-width: 15
Expand All @@ -159,27 +271,54 @@ p1 <- dall |>
plotly::ggplotly(p1)
```

### Signature Results
### Signatures

#### All (SNV, Indel, DBS)

```{r}
#| label: sig_results_all
dsig |>
left_join(meta |> select(rownum, portal_run_id, SubjectID, LibraryID_tumor),
by = "portal_run_id"
) |>
select(rownum, portal_run_id, SubjectID, LibraryID_tumor, everything()) |>
dt_view("sig_results_all")
```

#### Top 2

```{r}
#| label: sig_results_top2
dsig_filt |>
left_join(meta |> select(rownum, portal_run_id, SubjectID, LibraryID_tumor),
by = "portal_run_id"
) |>
select(rownum, portal_run_id, SubjectID, LibraryID_tumor, everything()) |>
arrange(rownum) |>
dt_view("sig_results_top2")
```

#### Top 3 SNV2015

```{r}
#| label: sig_results
#| fig-width: 15
#| fig-height: 65
sig_order2015 <- paste0("Sig", 1:30)
sig_order2020 <- paste0(
"SBS",
c(
1:6,
paste0(7, c("a", "b", "c", "d")),
8:9,
paste0(10, c("a", "b", "c", "d")),
11:16,
paste0(17, c("a", "b")),
18:60,
84:94
)
)
# sig_order2020 <- paste0(
# "SBS",
# c(
# 1:6,
# paste0(7, c("a", "b", "c", "d")),
# 8:9,
# paste0(10, c("a", "b", "c", "d")),
# 11:16,
# paste0(17, c("a", "b")),
# 18:60,
# 84:94
# )
# )
p2_prep <- dsig |>
filter(
Expand All @@ -189,12 +328,11 @@ p2_prep <- dsig |>
left_join(dall |> select(portal_run_id, date_analysed, SubjectID, LibraryID_tumor), by = "portal_run_id") |>
mutate(sbj = as.character(glue("{SubjectID}_{LibraryID_tumor}"))) |>
select(date_analysed, sbj, Sig_group, Rank, Signature, Contribution, RelFreq) |>
mutate(Signature = factor(Signature, levels = c(sig_order2015, sig_order2020)))
mutate(Signature = factor(Signature, levels = sig_order2015))
p2 <- p2_prep |>
ggplot2::ggplot(aes(x = Contribution, y = sbj, fill = Signature, text = sbj)) +
ggplot2::geom_bar(position = "fill", stat = "identity") +
ggplot2::theme_bw(base_size = 7)
# ggplot2::facet_wrap(~Sig_group, ncol = 1)
plotly::ggplotly(p2, tooltip = c("x", "text", "fill"))
```
Expand All @@ -219,6 +357,13 @@ dall |>
count(dall, source, quality) |> knitr::kable()
```

### Workflow

```{r}
#| label: workflow_summary
count(dall, workflow) |> knitr::kable()
```

:::

</div>

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