Scatter plots of IL15 expression in bulk RNAseq data and their correlation with AD trais | 1,210 unique samples from DLFPC region

data_bulk <- load ("C:/Users/beker/OneDrive/Documentos/Mestrado/GitHub/Cytokines/bulk_RNAseq/bulk_DLPFC_2022.Rdata")
phenotype_dt <- pheno_DLPFC

Bulk

# Filtering dataset to IL15 expression
exprData_IL15_pheno <- as.data.frame(t(expr_target["IL15", ]))
exprData_IL15_pheno$projid <- rownames(exprData_IL15_pheno)
exprData_IL15_pheno <- merge(exprData_IL15_pheno, 
                             pheno_DLPFC[, c("projid", "cogng_demog_slope", "cogng_path_slope", "tangles_sqrt", "gpath", "cogdx_3grp")], 
                             by = "projid", 
                             all.x = TRUE)

exprData_IL15_pheno$cogdx_3grp[exprData_IL15_pheno$cogdx_3grp == 0] <- "NCI"
exprData_IL15_pheno$cogdx_3grp[exprData_IL15_pheno$cogdx_3grp == 1] <- "MCI"
exprData_IL15_pheno$cogdx_3grp[exprData_IL15_pheno$cogdx_3grp == 2] <- "AD"

exprData_IL15_pheno$cogdx_3grp <- as.factor(exprData_IL15_pheno$cogdx_3grp)
names(exprData_IL15_pheno)[names(exprData_IL15_pheno) == 'cogdx_3grp'] <- 'Diagnosis'

Cognitive decline

# scatter plot IL15
# 1º: cognitive decline
gg <- ggplot(na.omit(exprData_IL15_pheno), aes(x=cogng_demog_slope, y=IL15, color=Diagnosis, pch=Diagnosis)) + 
  geom_point() +
  stat_smooth(method = "lm", se=F) + # Add regression line
  stat_cor(method = "spearman", label.x.npc = "left", label.y.npc = "bottom", size = 6) + 
  labs(x = "Cognitive decline slope", y = "IL15 expression", title = "IL15 expression on bulk for cognitive decline", color = "Diagnosis", pch = "Diagnosis") +
  scale_color_manual(values = c("AD" = "#BB5566", "MCI" = "#DDAA33", "NCI" = "#004488")) + 
  theme_classic()+
  theme(
    text = element_text(size = 18), 
    axis.title = element_text(size = 16),
    axis.text = element_text(size = 18),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 14),
    legend.position = c(0.94, 0.145), 
    legend.background = element_rect(fill = "white", color = "black"),
    legend.box.background = element_rect(fill = "lightgray")
  )

print(gg)

# Save to PDF
pdf(file = paste0("scatterPlot_IL15_cogn_T045", ".pdf"), width = 10, height = 6)
print(gg)
dev.off()
## png 
##   2
# Save to PNG
png(file = paste0("scatterPlot_IL15_cogn_T045", ".png"), width = 3000, height = 1800, res = 300)
print(gg)
dev.off()
## png 
##   2

Resilience

# scatter plot IL15
# 2º: resilience
gg <- ggplot(na.omit(exprData_IL15_pheno), aes(x=cogng_path_slope, y=IL15, color=Diagnosis, pch=Diagnosis)) + 
  geom_point() +
  stat_smooth(method = "lm", se=F) + # Add regression line
  stat_cor(method = "spearman", label.x.npc = "left", label.y.npc = "bottom") + 
  labs(x = "Resilience", y = "IL15 expression", title = "IL15 expression on bulk for resilience") +
  scale_color_manual(values = c("AD" = "#BB5566", "MCI" = "#DDAA33", "NCI" = "#004488")) + 
  theme_classic()+
  theme(
    text = element_text(size = 18),
    axis.title = element_text(size = 20),
    axis.text = element_text(size = 18), 
    legend.title = element_text(size = 18),
    legend.text = element_text(size = 16)
  )

print(gg)

# Save to PDF
pdf(file = paste0("scatterPlot_IL15_resilience_T045", ".pdf"), width = 10, height = 6)
print(gg)
dev.off()
## png 
##   2
# Save to PNG
png(file = paste0("scatterPlot_IL15_resilience_T045", ".png"), width = 3000, height = 1800, res = 300)
print(gg)
dev.off()
## png 
##   2

PHF tau tangles

# scatter plot IL15
# 3º: PHF tau tangles
gg <- ggplot(na.omit(exprData_IL15_pheno), aes(x=tangles_sqrt, y=IL15, color=Diagnosis,  pch=Diagnosis)) + 
  geom_point() +
  stat_smooth(method = "lm", se=F) + # Add regression line
  stat_cor(method = "spearman", label.x.npc = "left", label.y.npc = "bottom") + 
  labs(x = "PHF tau tangles on brain parenchyma", y = "IL15 expression", title = "IL15 expression on bulk for PHF tau tangles") +
  scale_color_manual(values = c("AD" = "#BB5566", "MCI" = "#DDAA33", "NCI" = "#004488")) + 
  theme_classic()+
  theme(
    text = element_text(size = 18),
    axis.title = element_text(size = 20),
    axis.text = element_text(size = 18),
    legend.title = element_text(size = 18),
    legend.text = element_text(size = 16) 
  )

print(gg)

# Save to PDF
pdf(file = paste0("scatterPlot_IL15_tangles_T045", ".pdf"), width = 10, height = 6)
print(gg)
dev.off()
## png 
##   2
# Save to PNG
png(file = paste0("scatterPlot_IL15_tangles_T045", ".png"), width = 3000, height = 1800, res = 300)
print(gg)
dev.off()
## png 
##   2

Global AD burden

# scatter plot IL15
# 4º: global ad burden
gg <- ggplot(na.omit(exprData_IL15_pheno), aes(x=gpath, y=IL15, color=Diagnosis, pch=Diagnosis)) + 
    geom_point()+
  stat_smooth(method = "lm", se=F) + # Add regression line
  # stat_regline_equation(aes(label = ..adj.rr.label..), show.legend = T) + # Add Rsquare 
  stat_cor(method = "spearman", label.x.npc = "left", label.y.npc = "bottom") +
  easy_labs(x = "Global AD burden", y = "IL15 expression", title = "IL15 expression on bulk for global AD burden") +
  scale_color_manual(values = c("AD" = "#BB5566", "MCI" = "#DDAA33", "NCI" = "#004488")) + 
  theme_classic()+
  theme(
    text = element_text(size = 18),
    axis.title = element_text(size = 20),
    axis.text = element_text(size = 18), 
    legend.title = element_text(size = 18),
    legend.text = element_text(size = 16) 
  )

print(gg)

# Save to PDF
pdf(file = paste0("scatterPlot_IL15_gpath_T045", ".pdf"), width = 10, height = 6)
print(gg)
dev.off()
## png 
##   2
# Save to PNG
png(file = paste0("scatterPlot_IL15_gpath_T045", ".png"), width = 3000, height = 1800, res = 300)
print(gg)
dev.off()
## png 
##   2

Session info

sessionInfo()
## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 26100)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
## [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
## [5] LC_TIME=Portuguese_Brazil.utf8    
## 
## time zone: America/Sao_Paulo
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggeasy_0.1.4    ggpubr_0.6.0    lubridate_1.9.3 forcats_1.0.0  
##  [5] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
##  [9] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.0   tidyverse_2.0.0
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    rstatix_0.7.2    
##  [5] lattice_0.21-9    stringi_1.8.3     hms_1.1.3         digest_0.6.36    
##  [9] magrittr_2.0.3    evaluate_0.24.0   grid_4.3.2        timechange_0.3.0 
## [13] fastmap_1.2.0     Matrix_1.6-5      jsonlite_1.8.8    backports_1.4.1  
## [17] mgcv_1.9-0        fansi_1.0.6       scales_1.3.0      jquerylib_0.1.4  
## [21] abind_1.4-5       cli_3.6.2         rlang_1.1.3       splines_4.3.2    
## [25] munsell_0.5.1     withr_3.0.1       cachem_1.1.0      yaml_2.3.10      
## [29] tools_4.3.2       tzdb_0.4.0        ggsignif_0.6.4    colorspace_2.1-0 
## [33] broom_1.0.6       vctrs_0.6.5       R6_2.5.1          lifecycle_1.0.4  
## [37] car_3.1-2         pkgconfig_2.0.3   pillar_1.9.0      bslib_0.8.0      
## [41] gtable_0.3.5      glue_1.7.0        highr_0.11        xfun_0.46        
## [45] tidyselect_1.2.1  rstudioapi_0.16.0 knitr_1.48        farver_2.1.1     
## [49] nlme_3.1-163      htmltools_0.5.8.1 labeling_0.4.3    rmarkdown_2.27   
## [53] carData_3.0-5     compiler_4.3.2