diff --git a/src/harpy/conda_deps.py b/src/harpy/conda_deps.py
index 6efa0b06d..fdc084d85 100644
--- a/src/harpy/conda_deps.py
+++ b/src/harpy/conda_deps.py
@@ -12,7 +12,7 @@ def generate_conda_deps():
"sv": ["bioconda::leviathan", "bioconda::naibr-plus"],
"phase" : ["bioconda::hapcut2", "bioconda::whatshap"],
"simulations" : ["conda-forge::perl", "bioconda::perl-math-random", "bioconda::perl-inline-c", "bioconda::perl-parse-recdescent", "conda-forge::numpy", "bioconda::dwgsim", "alienzj::msort"],
- "r" : ["conda-forge::r-xml2", "conda-forge::r-highcharter", "conda-forge::r-circlize", "r::r-biocircos", "conda-forge::r-dt", "conda-forge::r-flexdashboard", "conda-forge::r-ggplot2", "conda-forge::r-ggridges", "conda-forge::r-plotly", "conda-forge::r-tidyr"],
+ "r" : ["conda-forge::r-xml2", "conda-forge::r-highcharter", "conda-forge::r-circlize", "r::r-biocircos", "conda-forge::r-dt", "conda-forge::r-flexdashboard", "conda-forge::r-ggplot2", "conda-forge::r-plotly", "conda-forge::r-tidyr"],
"stitch" : ["bioconda::r-stitch=1.6.10"]
}
os.makedirs(".harpy_envs", exist_ok = True)
diff --git a/src/harpy/reports/HapCut2.Rmd b/src/harpy/reports/HapCut2.Rmd
index a7ffbf406..282ed6d21 100644
--- a/src/harpy/reports/HapCut2.Rmd
+++ b/src/harpy/reports/HapCut2.Rmd
@@ -23,25 +23,7 @@ using<-function(...) {
lapply(need,require,character.only=TRUE)
}
}
-using("flexdashboard","dplyr","ggplot2","DT","scales", "highcharter","ggridges")
-```
-
-```{css zoom-lib-src, echo = FALSE, message = FALSE, warning = FALSE}
-script src = "https://ajax.googleapis.com/ajax/libs/jquery/3.7.1/jquery.min.js"
-```
-```{js zoom-jquery, echo = FALSE, message = FALSE, warning = FALSE}
- $(document).ready(function() {
- $('body').prepend('');
- // onClick function for all plots (img's)
- $('img:not(.zoomImg)').click(function() {
- $('.zoomImg').attr('src', $(this).attr('src')).css({width: '100%'});
- $('.zoomDiv').css({opacity: '1', width: 'auto', border: '1px solid white', borderRadius: '5px', position: 'fixed', top: '50%', left: '50%', marginRight: '-50%', transform: 'translate(-50%, -50%)', boxShadow: '0px 0px 50px #888888', zIndex: '50', overflow: 'auto', maxHeight: '100%'});
- });
- // onClick function for zoomImg
- $('img.zoomImg').click(function() {
- $('.zoomDiv').css({opacity: '0', width: '0%'});
- });
- });
+using("flexdashboard","dplyr","DT","scales", "plotly", "highcharter")
```
```{r echo= FALSE, message = FALSE, warning = FALSE}
@@ -66,15 +48,15 @@ contigs <- group_by(df, contig) %>%
summarize(size = max(pos_end)) %>%
arrange(desc(size))
# limit the data to only the 30 largest contigs
-if (nrow(contigs) > 30){
- .contigs <- contigs[1:30, ]$contig
-} else {
- .contigs <- contigs$contig
-}
+#if (nrow(contigs) > 30){
+# .contigs <- contigs[1:30, ]$contig
+#} else {
+# .contigs <- contigs$contig
+#}
#pltheight <- round(1.2 * (length(.contigs)), digits = 0)
-ridgeheight <- 1 + round(0.7 * (length(.contigs)), digits = 0)
+#ridgeheight <- 1 + round(0.7 * (length(.contigs)), digits = 0)
#pltheight.samples <- <- round(1.2 * (length(levels(df$sample))), digits = 0)
-ridgeheight.samples <- 1 + round(0.7 * (length(levels(df$sample))), digits = 0)
+#ridgeheight.samples <- 1 + round(0.7 * (length(levels(df$sample))), digits = 0)
```
# General Stats
@@ -157,15 +139,31 @@ hchart(hs, "areaspline", hcaes(x = val, y = freq), color = "#7eb495", name = "co
### table per contig {.no-title}
Stats Per Contig
The plot below shows the distribution of haplotype length (in base pairs) for
-each contig, up to 30 of the largest contigs, the size of whom is inferred/assumed
-from the end position of the last haplotype. Phasing typically results in extreme
-right-tails in these distributions, therefore the plot presented below has **log-scaled lengths**
-to collapse the right tails for better readability.
-The table below provides details on the outcome of haplotype phasing on a per-contig basis.
+each contig, up to 300 contigs. The dotted vertical bar represents the mean haplotype length.
+
+Phasing typically has a few very large haplotypes, resulting in extreme
+right-tails in these distributions, therefore, the haplotype lengths are **log-scaled lengths**
+to collapse the right tail for better readability.
+
+```{r, echo= FALSE, message = FALSE, warning = FALSE}
+dropdown_buttons <- function(COLUMN, cutoff){
+ buttonlist <- list()
+ categories <- unique(COLUMN)
+ idx <- 0
+ for(i in categories){
+ idx <- idx + 1
+ if(idx > cutoff) break
+ visibility <- as.list(rep(FALSE, length(categories)))
+ visibility[[idx]] <- TRUE
+ buttonlist[[idx]] <- list(method = "restyle", label = i, args = list("visible", visibility))
+ }
+ return( list(list(y = 1, buttons = buttonlist)) )
+}
+```
## Per contig data
### stats per contig {.no-title}
-```{r echo= FALSE, message = FALSE, warning = FALSE, out.width="100%"}
+```{r echo= FALSE, message = FALSE, warning = FALSE,paged.print=TRUE, out.width="100%"}
percontig <- df %>% group_by(contig) %>% summarise(
n_haplo = round(length(n_snp)),
mean_snps = round(mean(n_snp), digits = 0),
@@ -179,29 +177,42 @@ DT::datatable(
percontig,
rownames = F,
extensions = 'Buttons',
- options = list(dom = 'Brtip', buttons = c('csv'), scrollX = TRUE, pageLength = length(levels(df$contig))),
+ options = list(dom = 'Brtip', buttons = c('csv'), scrollX = TRUE),
colnames = c("Contig", "Total Haplotypes", "Mean SNPs", "Median SNPs", "Mean Haplotype Length", "Median Haplotype Length", "Largest Haplotype"),
- fillContainer = F
+ fillContainer = T
)
```
-## contig ridgeplot
### per contig plot {.no-title}
-```{r contig_ridgeplot, warning=FALSE, message=FALSE, echo= FALSE, fig.height=ridgeheight, fig.width=8}
-ggplot(df, aes(x = block_length, y = contig, fill = stat(x))) +
- geom_density_ridges_gradient() +
- scale_fill_viridis_c(option = "C", trans = "log10") +
- theme_minimal() +
- theme(legend.position = "none") +
- scale_x_log10(
- breaks = trans_breaks("log10", function(x) 10^x),
- labels = trans_format("log10", math_format(10^.x))
- ) +
- labs(title = "Distribution of Haplotype Lengths by Contig", fill = "Haplotype Length (bp)") +
- xlab("Haplotype Length (log scale)") +
- ylab("")
-```
+```{r contig_ridges, warning=FALSE, message=FALSE, echo= FALSE, out.width="100%"}
+fig <- plot_ly(hoverinfo = "none") %>%
+ layout(
+ xaxis = list(title = "Log-Scaled Haplotype Length (bp)", fixedrange = TRUE),#, type = "log"),
+ yaxis = list(fixedrange = TRUE),
+ title = "Distribution of Haplotype Lengths by Contig",
+ showlegend = F,
+ updatemenus = dropdown_buttons(df$contig, 300)
+ ) %>% config(displayModeBar = FALSE)
+# Loop over the categories
+.idx <- 0
+for (cont in unique(df$contig)) {
+ .idx <- .idx + 1
+ # stop after 300 contigs
+ if(.idx > 300) break
+ subset_cont <- df$block_length[df$contig == cont]
+ fig <- fig %>%
+ add_trace(
+ x = log(subset_cont),
+ type = "violin",
+ name = cont,
+ side = "positive",
+ meanline = list(visible = T),
+ visible = "legendonly"
+ )
+}
+fig
+```
# Per-Sample Stats
## per sample desc
@@ -209,12 +220,14 @@ ggplot(df, aes(x = block_length, y = contig, fill = stat(x))) +
Per-Sample Stats
Haplotype phasing occurs per-sample and the table below provides details on the outcome of haplotype phasing for each sample.
-The ridgeline plot below shows the distribution of haplotype lengths (in base pairs) for each
+The plots below shows the distribution of haplotype lengths (in base pairs) for each
sample. While no Y-axis for these counts are provided, this plot is intended to be more of
a visual reference of the relative distributions of haplotype lengths among samples.
-Phasing typically results in extreme right-tails in these distributions, making regular
-visualization difficult to plot meaningfully. The plot presented below has
-**log-scaled lengths** to collapse the right tails for better readability.
+The dotted vertical bar represents the mean haplotype length.
+
+Phasing typically has a few very large haplotypes, resulting in extreme
+right-tails in these distributions, therefore, the haplotype lengths are
+**log-scaled lengths** to collapse the right tail for better readability.
## per sample
### stats per sample {.no-title}
@@ -232,25 +245,38 @@ DT::datatable(
persample,
rownames = F,
extensions = 'Buttons',
- options = list(dom = 'Brtip', buttons = c('csv'), scrollX = TRUE, pageLength = length(levels(df$sample))),
+ options = list(dom = 'Brtip', buttons = c('csv'), scrollX = TRUE),
colnames = c("Sample", "Haplotypes", "Mean SNPs", "Median SNPs", "Mean Haplotype Length", "Median Haplotype Length", "Largest Haplotype"),
- fillContainer = F
+ fillContainer = T
)
```
-## ridgeplot
### the ridgeplot {.no-title}
-```{r sample_ridgeplot, warning=FALSE, message=FALSE, echo= FALSE, fig.height=ridgeheight.samples, fig.width=8}
-ggplot(df, aes(x = block_length, y = sample, fill = stat(x))) +
- geom_density_ridges_gradient(scale = 0.1) +
- scale_fill_viridis_c(option = "G", trans = "log10") +
- theme_minimal() +
- theme(legend.position = "none") +
- scale_x_log10(
- breaks = trans_breaks("log10", function(x) 10^x),
- labels = trans_format("log10", math_format(10^.x))
- ) +
- labs(title = "Distribution of Haplotype Lengths by Sample", fill = "Haplotype Length (bp)") +
- xlab("Haplotype Length (log scale)") +
- ylab("")
-```
+```{r sample_ridgeplot, warning=FALSE, message=FALSE, echo= FALSE, out.width="100%"}
+fig <- plot_ly(hoverinfo = "none") %>%
+ layout(
+ xaxis = list(title = "Log-Scaled Haplotype Length (bp)", fixedrange = TRUE),#, type = "log"),
+ yaxis = list(fixedrange = TRUE),
+ title = "Distribution of Haplotype Lengths by Sample",
+ showlegend = F,
+ updatemenus = dropdown_buttons(df$sample, 600)
+ ) %>% config(displayModeBar = FALSE)
+# Loop over the categories
+.idx <- 0
+for (samp in unique(df$sample)) {
+ .idx <- .idx + 1
+ if(.idx > 600) break
+ subset_sample <- df$block_length[df$sample == samp]
+ fig <- fig %>%
+ add_trace(
+ x = log(subset_sample),
+ type = "violin",
+ name = samp,
+ side = "positive",
+ meanline = list(visible = T),
+ visible = "legendonly"
+ )
+}
+
+fig
+```
\ No newline at end of file