In gfragiadakis/FDA-library: R package for the cytometry data analysis created for the FDA dataset

library(dplyr)
library(reshape2)
library(ggplot2)
library(FDAlibrary)

Here's the analysis all together:

compiled_df <- read.csv("~/Documents/FDAlibrary/saved_data_structures/human_signaling_fold_asinh_0.2.csv", row.names = 1)

all_df <- compiled_df %>%
  dplyr::select(Donor, Condition_Feature, value) %>%
  reshape2::dcast(., Donor ~ Condition_Feature) %>%
  dplyr::select(-Donor)

all_cormat <- cor(all_df)
dd <- as.dist((1-all_cormat)/2)
hc <- hclust(dd)

all_cormat <- all_cormat[hc$order, hc$order]

cormat_melted <- reshape2::melt(all_cormat)

cor_plot <- ggplot(data = cormat_melted, aes(Var1, Var2, fill = value)) + geom_tile(colour = "white")+
  scale_fill_gradient2(low = "red", high = "blue", mid = "white", midpoint = 0, limits=c(-1, 1)) + theme(axis.text = element_blank()) + coord_fixed(ratio = 1) + ggtitle("Human immune correlations")

print(cor_plot)

Adjacencies:

adj <- make_adjacency(all_cormat, cor_threshold = 0.5)

adj_melted <- reshape2::melt(adj)

  adj_plot <- ggplot(data = adj_melted, aes(Var1, Var2, fill = value)) + geom_tile() +
    scale_fill_gradient2(low = "red", high = "blue", mid = "white", midpoint = 0, limits=c(-1, 1)) + theme(axis.text = element_blank()) + coord_fixed(ratio = 1) + ggtitle("Human immune adjacency matrix")

print(adj_plot)

gfragiadakis/FDA-library documentation built on May 17, 2019, 2:13 a.m.