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updated integrate annotation and report #156

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102 changes: 90 additions & 12 deletions R/annotate_integrated_sce.R
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,8 @@ annotate_integrated_sce <- function(sce,
"\r\n"
)

sce@metadata$dataset_integration$annotation$input_reduced_dim <- input_reduced_dim
sce@metadata$dataset_integration$annotation$input_reduced_dim <-
input_reduced_dim

pca_reducedDim_plots <- list()
liger_reducedDim_plots <- list()
Expand All @@ -77,24 +78,22 @@ annotate_integrated_sce <- function(sce,
feature_dim = variable,
reduced_dim = sprintf("%s_PCA",
input_reduced_dim),
size = .1, alpha = 0.2)
size = 1, alpha = 0.3)
# reducedDim_pca <- plot_pca +
# ggtitle(sprintf("%s using PCA data", input_reduced_dim)) +
# theme(plot.title = element_text(size = 15, face = "bold", hjust = 0.8))
# pca_reducedDim_plots[[variable]] <- reducedDim_pca
plot_pca <- .grobify_ggplot(plot_pca)
pca_reducedDim_plots[[variable]] <- plot_pca

plot_liger <- plot_reduced_dim(sce,
feature_dim = variable,
reduced_dim = sprintf("%s_Liger",
input_reduced_dim),
size = 0.1, alpha = 0.2)
size = 0.75, alpha = 0.3)
# reducedDim_liger <- plot_liger +
# ggtitle(sprintf("%s using LIGER data", input_reduced_dim)) +
# theme(plot.title = element_text(size = 15, face = "bold", hjust = 0.8))
# liger_reducedDim_plots[[variable]] <- reducedDim_liger
plot_liger <- .grobify_ggplot(plot_liger)
liger_reducedDim_plots[[variable]] <- plot_liger

data <- SingleCellExperiment::reducedDim(sce, "PCA")
Expand Down Expand Up @@ -133,7 +132,6 @@ annotate_integrated_sce <- function(sce,
) +
ggtitle("kBET test results - PCA") +
theme(plot.title = element_text(hjust = 0.5, size = 15, face = "bold"))
kbet_pca <- .grobify_ggplot(kbet_pca)
pca_kbet_plots[[variable]] <- kbet_pca

data <- SingleCellExperiment::reducedDim(sce, "Liger")
Expand Down Expand Up @@ -174,7 +172,6 @@ annotate_integrated_sce <- function(sce,
) +
ggtitle("kBET test results - LIGER") +
theme(plot.title = element_text(hjust = 0.5, size = 15, face = "bold"))
kbet_liger <- .grobify_ggplot(kbet_liger)
liger_kbet_plots[[variable]] <- kbet_liger
}

Expand All @@ -196,35 +193,116 @@ annotate_integrated_sce <- function(sce,
)

cli::cli_text("Generating tSNE/UMAP plots for PCA and LIGER data...")
sce <- cluster_sce(sce, k = 50, reduction_method = sprintf("%s_PCA",
input_reduced_dim))
pca_clusters <- sce$clusters

plot_pca <- plot_reduced_dim(sce,
feature_dim = "clusters",
reduced_dim = sprintf("%s_PCA",
input_reduced_dim),
size = 0.1, alpha = 0.2, label_clusters = TRUE
size = 0.75, alpha = 0.3, label_clusters = TRUE
)
# cluster_pca <- plot_pca +
# ggtitle(sprintf("%s using PCA data (colored by cluster)",
# input_reduced_dim)) +
# theme(plot.title = element_text(size = 15, face = "bold"))

cluster_pca <- plot_pca
cluster_pca <- .grobify_ggplot(cluster_pca)
sce@metadata$dataset_integration$clustering_plots$cluster_pca <- cluster_pca

sce <- cluster_sce(sce, k = 50, reduction_method = sprintf("%s_Liger",
input_reduced_dim))

liger_clusters <- sce$clusters

plot_liger <- plot_reduced_dim(sce,
feature_dim = "clusters",
reduced_dim = sprintf("%s_Liger",
input_reduced_dim),
size = 0.1, alpha = 0.2,
size = 0.75, alpha = 0.3,
label_clusters = TRUE
)
# cluster_liger <- plot_liger +
# ggtitle(sprintf("%s using LIGER data (colored by cluster)",
# input_reduced_dim)) +
# theme(plot.title = element_text(size = 15, face = "bold"))
cluster_liger <- plot_liger
cluster_liger <- .grobify_ggplot(cluster_liger)

sce@metadata$dataset_integration$clustering_plots$cluster_liger <- cluster_liger
sce@metadata$dataset_integration$clustering_plots$cluster_liger <-
cluster_liger

sankey_data <- as.data.frame.matrix(table(pca_clusters, liger_clusters)) %>%
rownames_to_column %>%
tidyr::gather(key = "key", value = "value", -rowname) %>%
dplyr::filter(value > 0)
colnames(sankey_data) <- c("pca_cluster", "liger_cluster", "overlap")
sankey_data$liger_cluster <- paste(sankey_data$liger_cluster, " ", sep = "")
nodes <- data.frame(name = c(as.character(sankey_data$pca_cluster),
as.character(sankey_data$liger_cluster)) %>%
unique())
sankey_data$pca_cluster_id <-
match(sankey_data$pca_cluster, nodes$name) - 1
sankey_data$liger_cluster_id <-
match(sankey_data$liger_cluster, nodes$name) - 1
my_colour_scale <-
'd3.scaleOrdinal() .range(["red","cornflowerblue","green","blue","darkorange","grey"])'
sankey_plot <- networkD3::sankeyNetwork(Links = sankey_data, Nodes = nodes,
Source = "pca_cluster_id",
Target = "liger_cluster_id",
Value = "overlap", NodeID = "name",
sinksRight = TRUE,
colourScale = my_colour_scale,
nodeWidth = 80, fontSize = 13,
nodePadding = 20)

sce@metadata$dataset_integration$clustering_plots$sankey_plot <- sankey_plot

pca_proportional_barplots <- list()
liger_proportional_barplots <- list()

for (variable in categorical_covariates) {

pca_barplot_data <-
data.frame("clusters" = pca_clusters, "group" = sce@colData[[variable]])
clusters <- pca_barplot_data$clusters
group <- pca_barplot_data$group

pca_barplot <- ggplot(pca_barplot_data, aes(x = clusters, fill = group)) +
geom_bar(position = "fill") + theme_bw() +
theme(
axis.text = element_text(size = 13),
axis.title = element_text(size = 13),
axis.line = element_line(colour = "black"),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.text = element_text(size = 13))

pca_proportional_barplots[[variable]] <- pca_barplot

liger_barplot_data <-
data.frame("clusters" = liger_clusters,
"group" = sce@colData[[variable]])
clusters <- liger_barplot_data$clusters
group <- liger_barplot_data$group

liger_barplot <-
ggplot(liger_barplot_data, aes(x = clusters, fill = group)) +
geom_bar(position = "fill") + theme_bw() +
theme(
axis.text = element_text(size = 13),
axis.title = element_text(size = 13),
axis.line = element_line(colour = "black"),
panel.grid.minor = element_blank(),
panel.background = element_blank()) +
theme(legend.text = element_text(size = 13))

liger_proportional_barplots[[variable]] <- liger_barplot
}
sce@metadata$dataset_integration$clustering_plots$pca_proportional_barplots <-
pca_proportional_barplots
sce@metadata$dataset_integration$clustering_plots$liger_proportional_barplots <-
liger_proportional_barplots

return(sce)
}
1 change: 1 addition & 0 deletions R/liger_preprocess.R
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Original file line number Diff line number Diff line change
Expand Up @@ -88,6 +88,7 @@ liger_preprocess <- function(sce,
manifests <- unique(sce@colData[, unique_id_var])
for (mnft in manifests) {
dataset_name <- paste0("dataset_", mnft)
cli::cli_text("Extracting {.val {dataset_name}}")
dataset_list[[dataset_name]] <-
sce[, sce[[unique_id_var]] == mnft]
mat_list[[dataset_name]] <-
Expand Down
16 changes: 8 additions & 8 deletions R/report_integrated_sce.R
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Original file line number Diff line number Diff line change
Expand Up @@ -44,14 +44,14 @@ report_integrated_sce <- function(sce,
"rmarkdown/templates/integrate/skeleton/skeleton.Rmd",
package = "scFlow"),
params = list(
metadata_path = metadata_tmp_path,
categorical_covariates = categorical_covariates
),
output_dir = report_folder_path,
output_file = report_file,
knit_root_dir = krd,
intermediates_dir = intd,
quiet = TRUE
metadata_path = metadata_tmp_path,
categorical_covariates = categorical_covariates
),
output_dir = report_folder_path,
output_file = report_file,
knit_root_dir = krd,
intermediates_dir = intd,
quiet = TRUE
)
cli::cli_text(c(
"Report successfully generated: ",
Expand Down
20 changes: 20 additions & 0 deletions inst/rmarkdown/templates/integrate/skeleton/categorical_covariates_2.Rmd
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Original file line number Diff line number Diff line change
@@ -0,0 +1,20 @@
## `r variable`

<div class = "row">
<div class="col-md-6">
<div class = "basicfig">
```{r, fig.align="center", fig.width=4.5, fig.height=4, fig.cap = sprintf("<b>Proportional barplot by %s</b>", variable)}
knitr::opts_chunk$set(echo = FALSE)
metadata$dataset_integration$clustering_plots$pca_proportional_barplots[[variable]]
```
</div>
</div>
<div class="col-md-6">
<div class = "basicfig">
```{r, fig.align="center", fig.width=4.5, fig.height=4, fig.cap = sprintf("<b>Proportional barplot (LIGER) by %s</b>", variable)}
knitr::opts_chunk$set(echo = FALSE)
metadata$dataset_integration$clustering_plots$liger_proportional_barplots[[variable]]
```
</div>
</div>
</div>
17 changes: 17 additions & 0 deletions inst/rmarkdown/templates/integrate/skeleton/skeleton.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,24 @@ paste(out, collapse='\n')

```{r conditional_clustering_block, child='clustering.Rmd', eval = !is.null(metadata$cluster_params)}
```
</div>
</div>
```{r, results='asis', include=FALSE, echo=FALSE}
knitr::opts_chunk$set(echo = FALSE)

show_plot <- function(plot_object) {
div(style="margin:auto;text-align:center", plot_object)
}

out <- NULL
for (variable in names(metadata$dataset_integration$clustering_plots$liger_proportional_barplots)) {
out <- c(out, knitr::knit_child("categorical_covariates_2.Rmd"))
}
paste(out, collapse='\n')
```
`r paste(out, collapse='\n')`
</div>
</div>
# References {-}
<div id="refs"></div>

Expand Down