Add Figure 1

7.figures/nbconverted/Figure1_Workflow_and_Class_Chart.r

@@ -0,0 +1,111 @@
+suppressPackageStartupMessages(library(dplyr))
+suppressPackageStartupMessages(library(ggplot2))
+suppressPackageStartupMessages(library(grid))
+suppressPackageStartupMessages(library(patchwork))
+suppressPackageStartupMessages(library(RColorBrewer))
+
+# Load variables important for plotting (e.g., themes, phenotypes, etc.)
+source("themes.r")
+
+figure_dir <- "figures"
+output_main_figure_1 <- file.path(figure_dir, "main_figure_1_class_count_and_workflow.png")
+
+url <- "https://raw.githubusercontent.com/WayScience/mitocheck_data/b7abbdfda06d8dcc8e897cb9b89674b8d7a49c49/4.analyze_data/results/single_cell_class_counts.csv"
+class_count_df <- read.csv(url)
+
+# only take the rows from ic data type
+class_count_df <- subset(class_count_df, Dataset_Type == 'ic')
+
+# remove folded class as it isn't used the analysis
+class_count_df <- subset(class_count_df, Mitocheck_Phenotypic_Class != 'Folded')
+
+# add category as a column
+# Now phenotype_categories is available in your script
+class_count_df <- class_count_df %>%
+  mutate(Phenotype_Category = case_when(
+    Mitocheck_Phenotypic_Class %in% phenotype_categories$Interphase ~ "Interphase",
+    Mitocheck_Phenotypic_Class %in% phenotype_categories$Mitosis ~ "Mitosis",
+    Mitocheck_Phenotypic_Class %in% phenotype_categories$`Mitotic conseq.` ~ "Mitotic consequences",
+    Mitocheck_Phenotypic_Class %in% phenotype_categories$`Dynamic changes` ~ "Dynamic changes",
+    Mitocheck_Phenotypic_Class %in% phenotype_categories$Other ~ "Other",
+    TRUE ~ "Unknown"
+  ))
+
+dim(class_count_df)
+head(class_count_df)
+
+# Reorder Mitocheck_Phenotypic_Class based on the sum of Single_Cell_Counts
+class_count_df$Mitocheck_Phenotypic_Class <- reorder(class_count_df$Mitocheck_Phenotypic_Class, -class_count_df$Single_Cell_Counts)
+
+# Creating the bar chart
+class_counts_plot <-
+  ggplot(class_count_df, aes(x = Mitocheck_Phenotypic_Class, y = Single_Cell_Counts, fill = Phenotype_Category)) +
+  geom_bar(stat = "identity", position = "dodge") +
+
+  # Adding labels and title
+  labs(x = "MitoCheck phenotypic class", y = "Single cell counts", fill = "Phenotype category") +
+
+  theme_bw() + 
+
+  scale_fill_brewer(palette = "Dark2") +
+
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1, size = 16),  # Adjust font size for x-axis labels
+    axis.text.y = element_text(size = 16),  # Adjust font size for y-axis labels
+    axis.title.x = element_text(size = 20),  # Adjust font size for x-axis title
+    axis.title.y = element_text(size = 20),  # Adjust font size for y-axis title
+    legend.title = element_text(size = 20),  # Adjust font size for legend title
+    legend.text = element_text(size = 18),  # Adjust font size for legend text
+    legend.position = c(0.98, 0.98),  # Adjust the position of the legend within the plot
+    legend.justification = c("right", "top"),  # Align legend to the right and top
+    legend.box.just = "right",  # Justify the legend box to the right
+    legend.margin = margin(0, 0, 0, 0)  # Remove extra margin around the legend
+  ) +
+
+  # Adding annotations above each bar as integers in bold
+  geom_text(
+    aes(label = ifelse(Single_Cell_Counts != 0, as.character(Single_Cell_Counts), "")),
+    position = position_dodge(width = 0.9), vjust = -0.5,
+    size = 6  # Adjust font size for the count labels
+  ) +
+
+  # Set y-axis limit to 450
+  ylim(0, 450)
+
+class_counts_plot
+
+workflow_path = file.path("./figures/figure1_panelB.png")
+workflow_img = png::readPNG(workflow_path)
+
+# Get the dimensions of the image
+img_height <- nrow(workflow_img)
+img_width <- ncol(workflow_img)
+
+# Calculate the aspect ratio
+aspect_ratio <- img_height / img_width
+
+# Plot the workflow image from BioRender to a ggplot object
+workflow <- ggplot() +
+  annotation_custom(
+    rasterGrob(workflow_img, interpolate = TRUE),
+    xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf
+  ) +
+  theme_void() +
+  coord_fixed(ratio = aspect_ratio, clip = "off") +
+  theme(plot.margin = margin(0, 0, 0, 0, "cm"))  # Adjust margins as needed
+
+workflow
+
+align_plot_gg <- (
+    free(class_counts_plot) /
+    workflow
+) + plot_layout(heights = c(2, 2.25))
+
+fig_1_gg <- (
+  align_plot_gg
+) + plot_annotation(tag_levels = "A") & theme(plot.tag = element_text(size = 25))
+
+# Save or display the plot
+ggsave(output_main_figure_1, dpi = 500, height = 14, width = 14)
+
+fig_1_gg

README.md

@@ -1,5 +1,11 @@
 # Phenotypic Profiling Model
 
+## Dataset and analysis approach
+
+![main_figure_1](./7.figures/figures/main_figure_1_class_count_and_workflow.png)
+
+> Figure 1. Dataset and analysis approach. (A) Single-cell counts per labeled phenotype stratified by phenotype category. The labeled MitoCheck dataset included a total of 2,916 single nuclei. The original dataset contained labels for 16 classes, but we have removed “folded” because of low counts. Counts are not evenly distributed between the classes. (B) Our analysis pipeline incorporated image analysis, image-based profiling, and machine learning.
+
 ## Environment Setup
 
 Perform the following steps to set up the `phenotypic_profiling` environment necessary for processing data in this repository.
@@ -81,4 +87,4 @@ These intermediate data are available on the [Way Lab Zenodo page](https://zenod
 Specific code and steps used are available within each module folder.
 
 The [Way Lab](https://www.waysciencelab.com/) always strives for readable, reproducible computational biology analyses and workflows.
-If you struggle to understand or reproduce anything in this repository please [file an issue](https://github.com/WayScience/mitocheck_data/issues/new/choose)!
+If you struggle to understand or reproduce anything in this repository please [file an issue](https://github.com/WayScience/mitocheck_data/issues/new/choose)!