script for DEG heatmap across celltype

library(dplyr)

# Family 1
family1 <- data.frame(
  no.up = c(4, 2),   # Number of up values for family 1
  no.down = c(1, 3)  # Number of down values for family 1
)

family1$is.DE <- case_when(
  family1$no.up >= 3 & family1$no.up - family1$no.down >= 2 ~ 1,
  family1$no.down >= 3 & family1$no.down - family1$no.up >= 2 ~ -1,
  TRUE ~ 0
)

family1$no.effective <- (family1$no.up - family1$no.down) * family1$is.DE

# Family 2
family2 <- data.frame(
  no.up = c(3, 1),   # Number of up values for family 2
  no.down = c(2, 4)  # Number of down values for family 2
)

family2$is.DE <- case_when(
  family2$no.up >= 3 & family2$no.up - family2$no.down >= 2 ~ 1,
  family2$no.down >= 3 & family2$no.down - family2$no.up >= 2 ~ -1,
  TRUE ~ 0
)

family2$no.effective <- (family2$no.up - family2$no.down) * family2$is.DE

# Print the results
print(family1)
print(family2)

# Create a bar plot
barplot(de_counts, col = c("green", "blue", "white", "orange", "red"),
        main = "DEG Categories",
        xlab = "Categories",
        ylab = "Count",
        legend = TRUE,
        names.arg = c("Up", "Up (not significant)", "Non-DEG", "Down (not significant)", "Down"),
        ylim = c(0, max(de_counts) * 1.1)
)

##script to plot DEG across celltypes based on avg_log2FC
x %>% 
    ggplot(aes(x = name, y = cell.type)) +
    geom_tile(aes(fill = avg_log2FC)) +
    scale_fill_gradient2(low = "blue", high = "red") +
    geom_text(aes(label = ifelse(name == cell.type, "x", "")), color = "white") +
    theme_minimal() +
    coord_flip() +  
    theme(
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        panel.grid = element_blank()
    ) +
    scale_color_manual(values = c("white", "black")) +
    guides(color = "none")
### Remove rows with NaN values in avg_log2FC column
cleaned_data <- x[!is.nan(as.numeric(as.character(x$avg_log2FC))), ]
# Your gene list
gene_list <- c("gene1", "gene2", "gene3")

# Intersect the gene list with the 'name' column in 'x'
intersected_data <- x[x$name %in% gene_list, ]

# Use the intersected data for further processing or visualization
intersected_data %>% 
    ggplot(aes(x = name, y = cell.type)) +
    geom_tile(aes(fill = avg_log2FC)) +
    scale_fill_gradient2(low = "blue", high = "red") +
    geom_text(aes(label = ifelse(name == cell.type, "x", "")), color = "white") +
    theme_minimal() +
    coord_flip() +
    theme(
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        panel.grid = element_blank()
    ) +
    scale_color_manual(values = c("white", "black")) +
    guides(color = "none") + ggtitle('dM_TD0')

##set diffexpressed column
# Set "NO" as the default value for diffexpressed column
resDF <- intersected_data 
resDF$diffexpressed <- "NO"
resDF$diffexpressed[resDF$lfc > 0.25 & resDF$adj_pval < 0.01] <- "UP"
resDF$diffexpressed[resDF$lfc < -0.25 & resDF$adj_pval < 0.01] <- "DOWN"
table(resDF$diffexpressed)

x %>% filter(avg_adj_pval < 0.01) %>% arrange(avg_log2FC) %>% ggplot(aes(x= name, y= cell.type)) + geom_tile(aes(fill= avg_log2FC, color = cell.type), size=.5) + scale_fill_gradient2(low="blue", high = "red") + geom_text(aes(label=ifelse(name==cell.type, "x", "")), color= "white") + coord_flip() + theme_minimal() + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust = 0.5)) + facet_wrap(~ sample.group) 
> 

library(ggplot2)
library(dplyr)
##dot plot codes
data %>%
    filter(avg_adj_pval < 0.01) %>%
    arrange(avg_log2FC) %>%
    ggplot(aes(x = name, y = cell.type)) +
    geom_point(aes(fill = avg_log2FC, color = sample.group), shape = 23, size = 4) +
    scale_fill_gradient2(low = "blue", high = "red") +
    geom_text(aes(label = ifelse(name == cell.type, "x", "")), color = "white") +
    coord_flip() +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
    facet_wrap(~ sample.group) +
    scale_color_manual(values = c("white", "black")) +
    guides(fill = "none", color = "none")

## Heatmap plot code without scale bar 
library(dplyr)
library(ggplot2)
#note x <- subset(intersected_data, grepl('_TD0', sample.group))
data %>%
    filter(avg_adj_pval < 0.01) %>%
    arrange(avg_log2FC) %>%
    ggplot(aes(x = name, y = cell.type)) +
    geom_tile(aes(fill = avg_log2FC)) +
    geom_text(aes(label = ifelse(name == cell.type, "x", "")), color = "white") +
    scale_fill_gradient2(low = "blue", high = "red") +
    coord_flip() +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
    facet_wrap(~ sample.group) +
    guides(fill = "none") +
    labs(color = NULL) +
    scale_color_manual(values = c("white", "black"))

library(dplyr)
library(ggplot2)
## Heatmap plot code with scale bar of avg_log2FC
data %>%
    filter(avg_adj_pval < 0.01) %>%
    arrange(avg_log2FC) %>%
    ggplot(aes(x = name, y = cell.type)) +
    geom_tile(aes(fill = avg_log2FC)) +
    geom_text(aes(label = ifelse(name == cell.type, "x", "")), color = "white", size = 3) +
    scale_fill_gradient2(low = "blue", high = "red") +
    coord_flip() +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 9),
          axis.text.y = element_text(size = 7)) +
    facet_wrap(~ sample.group) +
    guides(fill = guide_colorbar(title = "avg_log2FC")) +
    labs(color = NULL) +
    scale_color_manual(values = c("white", "black"))

##Dot plot final
data %>%
    filter(avg_adj_pval < 0.01) %>%
    arrange(avg_log2FC) %>%
    ggplot(aes(x = name, y = cell.type)) +
    geom_point(aes(color = avg_log2FC), size = 3) +
    geom_text(aes(label = ifelse(name == cell.type, "x", "")), color = "white", size = 3) +
    scale_color_gradient2(low = "#000080", high = "#8B0000") +  # Adjusted color palette to darker shades of blue and red
    coord_flip() +
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 9),
        axis.text.y = element_text(size = 7)
    ) +
    facet_wrap(~ sample.group) +
    labs(color = "avg_log2FC") +
    guides(color = guide_colorbar(title = "avg_log2FC")) +
    scale_fill_manual(values = "black")  # Set text color to black