library(readr)
combined_Microglia <- read.csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/combined_Microglia_heatmap.scater.plot.file.csv", row.names = 1)
combined_Microglia <- as.data.frame(combined_Microglia)
head(combined_Microglia, 10)
## Microglia.PAP1vs.Control Microglia.Shkvs.PBS Direction Gene
## Pcdh15 -1.1890763 -0.3738315 Concordance.Down Pcdh15
## Tenm3 -0.4147850 -0.3644790 Concordance.Down Tenm3
## Csf1 0.4595667 0.5434152 Concordance.UP Csf1
## Ctsb 0.5244967 0.2939233 Concordance.UP Ctsb
## Egfem1 0.5381028 0.5534336 Concordance.UP Egfem1
## Fat3 0.4028172 0.2837006 Concordance.UP Fat3
## Fkbp5 1.2906097 0.3898174 Concordance.UP Fkbp5
## Fth1 1.1818786 1.4137779 Concordance.UP Fth1
## Glul 0.9517597 0.4476713 Concordance.UP Glul
## Grin2a 1.1738602 0.3650096 Concordance.UP Grin2a
library(ggplot2)
library(ggrepel) # For geom_text_repel if not already loaded
# Assuming 'Direction' has values like "positive" and "negative"
direction_colors <- c( "blue", "red", "darkgreen")
ggplot(combined_Microglia, aes(x = Microglia.PAP1vs.Control, y = Microglia.Shkvs.PBS , label = Gene, colour = Direction)) +
geom_point() +
scale_colour_manual(values = direction_colors) +
xlim(-2, 2) +
ylim(-2, 2) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
geom_text_repel() +
labs(color = "Direction") + ggtitle('Microglia')
## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Astrocytes_combined <- read_csv("Astrocytes_combined_scaterplot.csv")
## Rows: 166 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Gene, Direction
## dbl (2): Astrocytes.PAP1.vs.Control, Astrocytes.ShK.vs.PBS
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Astrocytes_combined <- as.data.frame(Astrocytes_combined)
head(Astrocytes_combined, 10)
## Gene Astrocytes.PAP1.vs.Control Astrocytes.ShK.vs.PBS
## 1 9630028H03Rik -0.4786760 -0.4984252
## 2 Adcy8 -0.6738847 -0.4088442
## 3 Atp13a4 -0.3001954 -0.4373517
## 4 B230323A14Rik -0.4421292 -0.5203727
## 5 Bcl2 -0.3309549 -0.3454662
## 6 Camk2d -0.4907768 -0.4311398
## 7 Csgalnact1 -0.3640388 -0.2875638
## 8 Fchsd2 -0.3664939 -0.3902031
## 9 Fry -0.3118635 -0.2887065
## 10 Gm11266 -0.3723703 -0.4212250
## Direction
## 1 Concordance.Down
## 2 Concordance.Down
## 3 Concordance.Down
## 4 Concordance.Down
## 5 Concordance.Down
## 6 Concordance.Down
## 7 Concordance.Down
## 8 Concordance.Down
## 9 Concordance.Down
## 10 Concordance.Down
library(ggplot2)
library(ggrepel) # For geom_text_repel if not already loaded
# Assuming 'Direction' has values like "positive" and "negative"
direction_colors <- c( "blue", "red", "darkgreen")
ggplot(Astrocytes_combined, aes(x = Astrocytes.PAP1.vs.Control, y = Astrocytes.ShK.vs.PBS , label = Gene, colour = Direction)) +
geom_point() +
scale_colour_manual(values = direction_colors) +
xlim(-2, 2) +
ylim(-2, 2) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
geom_text_repel() +
labs(color = "Direction") + ggtitle('Astrocytes')
## Warning: ggrepel: 147 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(readr)
Glutamatergic_combined <- read_csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/Significant.overlapped.Glutamatergic.PAP1.ShK.csv")
## Rows: 236 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Gene, Direction
## dbl (2): Glutamatergic.Pap.vs.Control, Glutamatergic.ShK.vs.PBS
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Glutamatergic_combined <- as.data.frame(Glutamatergic_combined)
head(Glutamatergic_combined, 10)
## Gene Glutamatergic.Pap.vs.Control Glutamatergic.ShK.vs.PBS
## 1 2010300C02Rik 0.3415591 0.3205547
## 2 4930444A19Rik -0.4210505 -0.6235831
## 3 4930517O19Rik -0.2601487 -0.5290947
## 4 4930555F03Rik -0.3509839 -0.5472962
## 5 4932443L11Rik -0.3110655 -0.3452029
## 6 4933411O13Rik 0.4625059 -0.4546592
## 7 6330403K07Rik -0.2524713 -0.4005946
## 8 9630028H03Rik 0.2731180 -0.3262431
## 9 A330015K06Rik 0.4961092 -0.5951169
## 10 A330076H08Rik -0.2542697 0.2631306
## Direction
## 1 Concordance.UP
## 2 Concordance.Down
## 3 Concordance.Down
## 4 Concordance.Down
## 5 Concordance.Down
## 6 Discordant
## 7 Concordance.Down
## 8 Discordant
## 9 Discordant
## 10 Discordant
library(ggplot2)
library(ggrepel) # For geom_text_repel if not already loaded
# Assuming 'Direction' has values like "positive" and "negative"
direction_colors <- c( "blue", "red", "darkgreen")
ggplot(Glutamatergic_combined, aes(x = Glutamatergic.Pap.vs.Control, y = Glutamatergic.ShK.vs.PBS , label = Gene, colour = Direction)) +
geom_point() +
scale_colour_manual(values = direction_colors) +
xlim(-2, 2) +
ylim(-2, 2) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
geom_text_repel() +
labs(color = "Direction") + ggtitle('Glutamatergic Neuron')
## Warning: ggrepel: 227 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Oligodendrocyte_combined <- read_csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/Oligodendrocyte_combined.scater.plot.file.csv")
## Rows: 158 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Gene, Direction
## dbl (2): Oligodendrocyte.Pap1.vs.Control, Oligodendrocyte.ShK.vs.PBS
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Oligodendrocyte_combined <- as.data.frame(Oligodendrocyte_combined)
head(Oligodendrocyte_combined, 10)
## Gene Oligodendrocyte.Pap1.vs.Control Oligodendrocyte.ShK.vs.PBS
## 1 9330111N05Rik 0.3720454 0.3545807
## 2 9330199G10Rik 0.5379547 0.3059190
## 3 Adipor2 0.6313067 0.3817877
## 4 Arhgap21 0.4291029 0.2910258
## 5 Arid5b 0.5295041 0.3214507
## 6 Bcl2l1 0.6538686 0.3100963
## 7 Clasrp 0.2503947 0.2679935
## 8 Dennd5b 0.3610142 0.3267114
## 9 Fth1 0.8828162 0.6296450
## 10 Itpk1 0.3206527 0.5102906
## Direction
## 1 Concordance.Up
## 2 Concordance.Up
## 3 Concordance.Up
## 4 Concordance.Up
## 5 Concordance.Up
## 6 Concordance.Up
## 7 Concordance.Up
## 8 Concordance.Up
## 9 Concordance.Up
## 10 Concordance.Up
library(ggplot2)
library(ggrepel) # For geom_text_repel if not already loaded
# Assuming 'Direction' has values like "positive" and "negative"
direction_colors <- c( "blue", "red", "darkgreen")
ggplot(Oligodendrocyte_combined, aes(x = Oligodendrocyte.Pap1.vs.Control, y = Oligodendrocyte.ShK.vs.PBS , label = Gene, colour = Direction)) +
geom_point() +
scale_colour_manual(values = direction_colors) +
xlim(-2, 2) +
ylim(-2, 2) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
geom_text_repel() +
labs(color = "Direction") + ggtitle('Oligodendrocyte')
## Warning: ggrepel: 146 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(readr)
GABAergic_Combined <- read_csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/GABAergic_Combined.scater.plot.csv")
## Rows: 137 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Gene, Direction
## dbl (2): GABAergic.PAP1.vs.Control, GABAergic.ShK.vs.PBS
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
GABAergic_Combined <- as.data.frame(GABAergic_Combined)
head(GABAergic_Combined, 10)
## Gene GABAergic.PAP1.vs.Control GABAergic.ShK.vs.PBS
## 1 6330403K07Rik -0.2745266 -0.4201151
## 2 Aifm3 -0.3931608 -0.3087359
## 3 Angpt2 -0.2836565 -0.4518208
## 4 Ankfn1 -0.5450917 -0.4405324
## 5 Atxn2l -0.2945982 -0.3766158
## 6 B230217J21Rik -0.2945627 -0.4674012
## 7 B430010I23Rik -0.2618529 -0.3988186
## 8 Dcakd -0.2813577 -0.3357226
## 9 Dpyd -0.3757872 -0.2688052
## 10 Fbxo10 -0.2547471 -0.4507361
## Direction
## 1 Concordance.Down
## 2 Concordance.Down
## 3 Concordance.Down
## 4 Concordance.Down
## 5 Concordance.Down
## 6 Concordance.Down
## 7 Concordance.Down
## 8 Concordance.Down
## 9 Concordance.Down
## 10 Concordance.Down
library(ggplot2)
library(ggrepel) # For geom_text_repel if not already loaded
# Assuming 'Direction' has values like "positive" and "negative"
direction_colors <- c( "blue", "red", "darkgreen")
ggplot(GABAergic_Combined, aes(x = GABAergic.PAP1.vs.Control, y = GABAergic.ShK.vs.PBS , label = Gene, colour = Direction)) +
geom_point() +
scale_colour_manual(values = direction_colors) +
xlim(-2, 2) +
ylim(-2, 2) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
geom_text_repel() +
labs(color = "Direction") + ggtitle('GABAergic.Neuron')
## Warning: ggrepel: 133 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(readr)
Oligodendrocyte_PrecursorCell_Combined <- read_csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/Oligodendrocyte_PrecursorCell_Combined.scater.main.csv")
## Rows: 80 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Gene, Direction
## dbl (2): OPC.PAP1.vs.Control, OPC.ShK.vs.PBS
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Oligodendrocyte_PrecursorCell_Combined <- as.data.frame(Oligodendrocyte_PrecursorCell_Combined)
head(Oligodendrocyte_PrecursorCell_Combined, 10)
## Gene OPC.PAP1.vs.Control OPC.ShK.vs.PBS Direction
## 1 Cnksr2 0.4322768 0.6248864 Concordance.UP
## 2 Dip2a 0.5291506 0.5122841 Concordance.UP
## 3 Efr3a 0.4149760 0.4985686 Concordance.UP
## 4 Grin1 0.4784360 0.7426865 Concordance.UP
## 5 Grin2a 0.4882243 0.8477109 Concordance.UP
## 6 Hlf 0.4617496 0.5220250 Concordance.UP
## 7 Itpr1 0.3596582 0.8988930 Concordance.UP
## 8 Kctd16 0.3098695 0.3957652 Concordance.UP
## 9 Mthfd1l 0.4396143 0.6349368 Concordance.UP
## 10 Nrg1 0.5190646 0.3829384 Concordance.UP
library(ggplot2)
library(ggrepel) # For geom_text_repel if not already loaded
# Assuming 'Direction' has values like "positive" and "negative"
direction_colors <- c( "blue", "red", "darkgreen")
ggplot(Oligodendrocyte_PrecursorCell_Combined, aes(x = OPC.PAP1.vs.Control, y = OPC.ShK.vs.PBS , label = Gene, colour = Direction)) +
geom_point() +
scale_colour_manual(values = direction_colors) +
xlim(-2, 2) +
ylim(-2, 2) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
geom_text_repel() +
labs(color = "Direction") + ggtitle('Oligodendrocyte_PrecursorCell')
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_text_repel()`).
## Warning: ggrepel: 64 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(ggplot2)
library(pheatmap)
combined_Microglia <- read.csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/combined_Microglia_heatmap.scater.plot.file.csv", row.names = 1)
table(combined_Microglia$Direction)
##
## Concordance.Down Concordance.UP Discordant
## 2 11 30
Microglia <- combined_Microglia %>% select(-c('Direction' , 'Gene'))
Microglia <- as.matrix(Microglia)
# Step 2: Set up the color palette (optional)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 100)
# Step 3: Create the heatmap using pheatmap
pheatmap(Microglia, color = my_palette, cluster_rows = TRUE, cluster_cols = FALSE, main = 'Microglia', display_numbers = TRUE)
library(readr)
Oligodendrocytes_Concordance_up_down <- read.csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/Oligodendrocytes.Concordance.up.down.csv", row.names = 1)
Oligodendrocytes_Concordance_up_down <- Oligodendrocytes_Concordance_up_down %>% select(-c('Direction'))
Oligodendrocytes_Concordance_up_down <- as.matrix(Oligodendrocytes_Concordance_up_down)
# Define the range of logFC values you want to emphasize
low_logFC <- -2 # Adjust this threshold as needed
# Adjust the color palette to emphasize low fold change values
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 100)
my_palette <- colorRampPalette(c("blue", "white"))(n = 50) # Change midpoint color
my_palette <- c(my_palette, colorRampPalette(c("white", "red"))(n = 50))
# Create the heatmap using pheatmap with the modified color palette
pheatmap(Oligodendrocytes_Concordance_up_down, color = my_palette,
cluster_rows = TRUE, cluster_cols = FALSE,
main = ' Oligodendrocytes_Concordance_up_down',
display_numbers = TRUE)
library(readr)
Oligodendrocytes_Discordant <- read.csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/Oligodendrocytes.Discordant.csv", row.names = 1)
Oligodendrocytes_Discordant <- Oligodendrocytes_Discordant %>% select(-c('Direction'))
Oligodendrocytes_Discordant <- as.matrix(Oligodendrocytes_Discordant)
# Define the range of logFC values you want to emphasize
low_logFC <- -2 # Adjust this threshold as needed
# Adjust the color palette to emphasize low fold change values
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 100)
my_palette <- colorRampPalette(c("blue", "white"))(n = 50) # Change midpoint color
my_palette <- c(my_palette, colorRampPalette(c("white", "red"))(n = 50))
# Create the heatmap using pheatmap with the modified color palette
pheatmap(Oligodendrocytes_Discordant, color = my_palette,
cluster_rows = TRUE, cluster_cols = FALSE,
main = 'Oligodendrocytes_Discordant',
display_numbers = TRUE)
#Astrocytes_combined.concordance <- subset(Astrocytes_combined, Direction!= "Discordant")
library(readr)
Astrocytes_Concordance <- read.csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/Astrocytes.Concordance.csv", row.names = 1)
Astrocytes_Concordance <- Astrocytes_Concordance %>% select(-c('Direction'))
Astrocytes_Concordance <- as.matrix(Astrocytes_Concordance)
# Step 2: Set up the color palette (optional)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 100)
# Step 3: Create the heatmap using pheatmap
pheatmap(Astrocytes_Concordance, color = my_palette, cluster_rows = TRUE, cluster_cols = FALSE, main = 'Astrocytes_Concordance', display_numbers = TRUE)
library(readr)
Astrocytes_Discordant <- read.csv("/Users/usri/Desktop/Mureen.dec6/Celltypes.concordance/Astrocytes.Discordant.csv", row.names = 1)
Astrocytes_Discordant <- Astrocytes_Discordant %>% select(-c('Direction'))
Astrocytes_Discordant <- as.matrix( Astrocytes_Discordant)
# Step 2: Set up the color palette (optional)
my_palette <- colorRampPalette(c("blue", "white", "red"))(n = 100)
# Step 3: Create the heatmap using pheatmap
pheatmap(Astrocytes_Discordant, color = my_palette, cluster_rows = TRUE, cluster_cols = FALSE, main = 'Astrocytes_Discordant', display_numbers = TRUE)
