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Network_analysis_and_cond_compar.R
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Network_analysis_and_cond_compar.R
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# Import libraries
library(NetCoMi)
library(dplyr)
# Set seed to make results reproducible
set.seed(123)
# Load networks
nets_spring <- readRDS("Results/Networks/nets_spring_ColombRainforest.rds")
nets_cclasso <- readRDS("Results/Networks/nets_cclasso_ColombRainforest.rds")
###############################################################################
########################### Analyze networks ##################################
###############################################################################
props_spring_nets <- netAnalyze(nets_spring,
centrLCC = FALSE,
avDissIgnoreInf = TRUE,
sPathNorm = TRUE,
clustMethod = "cluster_fast_greedy",
hubPar = "degree", #change it to define hub def
hubQuant = 0.9,
lnormFit = TRUE,
normDeg = FALSE)
props_cclasso_nets <- netAnalyze(nets_cclasso,
centrLCC = FALSE,
avDissIgnoreInf = TRUE,
sPathNorm = TRUE,
clustMethod = "cluster_fast_greedy",
hubPar = "degree", #change it to define hub def
hubQuant = 0.9,
lnormFit = TRUE,
normDeg = FALSE)
# Calculate network properties for the networks
summ_props_spring_fam <- capture.output(summary(props_spring_nets,
groupNames = c("Forest", "Pasture"),
numbNodes = 5L, digits = 3L))
cat(summ_props_spring_fam, file="Results/Net_prop_summary/summ_props_spring_fam_betw.txt",
sep="\n",append=FALSE)
summ_props_cclasso_fam <- capture.output(summary(props_cclasso_nets,
groupNames = c("Forest", "Pasture"),
numbNodes = 5L, digits = 3L))
cat(summ_props_cclasso_fam, file="Results/Net_prop_summary/summ_props_cclasso_fam_betw.txt",
sep="\n",append=FALSE)
###############################################################################
########################### Visual comparison #################################
###############################################################################
# SPRING
# Degree as node size and hub definition
svg("Results/Figures/Family_nets/svg/spring_nets_family_deg.svg", width=20, height=10)
# png("Results/Figures/Family_nets/png/spring_nets_family_deg.png", width=1500, height=750)
spring_nets_plot <- plot(props_spring_nets,
layout = "spring",
sameLayout = FALSE,
layoutGroup = "union",
repulsion = 0.8,
nodeColor = "cluster",
edgeTranspLow = 0,
edgeTranspHigh = 40,
nodeSize = "degree",
posCol = "#ac1214",
negCol = "#0d1fad",
shortenLabels = "intelligent",
labelLength = 7,
labelScale = FALSE,
rmSingles = TRUE,
cexNodes = 1.8,
cexLabels = 0.9,
nodeSizeSpread = 2.5,
cexHubLabels = 1,
hubBorderCol = "snow4",
cexTitle = 2.5,
groupNames = c("A)", "B)"))
legend(-0.20, -0.68, title = "Estimated associations:", legend = c("+","-"),
col = c("#ac1214","#0d1fad"), inset = 0.02, cex = 1.5, lty = 1, lwd = 3.5,
bty = "n", horiz = TRUE, y.intersp = 0.7)
# Close device
dev.off()
# Betweenness as node size and hub definition
# svg("Results/Figures/Family_nets/svg/spring_nets_family_betw.svg", width=20, height=10)
png("Results/Figures/Family_nets/png/spring_nets_family_betw.png", width=1500, height=750)
spring_nets_plot <- plot(props_spring_nets,
layout = "spring",
sameLayout = FALSE,
layoutGroup = "union",
repulsion = 0.8,
nodeColor = "cluster",
edgeTranspLow = 0,
edgeTranspHigh = 40,
nodeSize = "betweenness",
posCol = "#ac1214",
negCol = "#0d1fad",
shortenLabels = "intelligent",
labelLength = 7,
labelScale = FALSE,
rmSingles = TRUE,
cexNodes = 1.5,
cexLabels = 0.9,
nodeSizeSpread = 3,
cexHubLabels = 1,
hubBorderCol = "snow4",
cexTitle = 2.5,
groupNames = c("", ""))
legend(-0.20, -0.68, title = "Estimated associations:", legend = c("+","-"),
col = c("#ac1214","#0d1fad"), inset = 0.02, cex = 1.5, lty = 1, lwd = 3.5,
bty = "n", horiz = TRUE, y.intersp = 0.7)
# Close device
dev.off()
# Create a dataframe with the shortened and full node names
forest_spring_node_names <- data.frame(Short_name = spring_nets_plot$labels$labels1)
forest_spring_node_names$Full_name <- row.names(spring_nets_plot$layout$layout1)
pasture_spring_node_names <- data.frame(Short_name = spring_nets_plot$labels$labels2)
pasture_spring_node_names$Full_name <- row.names(spring_nets_plot$layout$layout2)
# Load taxonomy df
tax_bact_familyrenamed <- read.csv("Data/tax_bact_familyrenamed.csv")
# Add a column with the species data
fam_sp_df <- tax_bact_familyrenamed %>%
select(Family, Species, Phylum) %>%
mutate(Species = ifelse(Species == "sBacteria", NA, Species)) %>%
rename(Full_name = Family)
forest_spring_node_names <- merge(forest_spring_node_names, fam_sp_df, by = "Full_name")
pasture_spring_node_names <- merge(pasture_spring_node_names, fam_sp_df, by = "Full_name")
# Export csv files
write.csv(forest_spring_node_names,
"Results/Figures/Family_nets/Node_names_in_nets/forest_spring_node_names.csv",
row.names = FALSE)
write.csv(pasture_spring_node_names,
"Results/Figures/Family_nets/Node_names_in_nets/pasture_spring_node_names.csv",
row.names = FALSE)
# CCLASSO - Both nets
# Degree as node size and hub definition
svg("Results/Figures/Family_nets/svg/cclasso_nets_family_deg.svg", width=20, height=10)
# png("Results/Figures/Family_nets/png/cclasso_nets_family_deg.png", width=1500, height=750)
cclasso_nets_plot <- plot(props_cclasso_nets,
layout = "spring",
sameLayout = FALSE,
layoutGroup = "union",
repulsion = 0.9,
nodeColor = "cluster",
edgeTranspLow = 0,
edgeTranspHigh = 40,
nodeSize = "degree",
posCol = "#ac1214",
negCol = "#0d1fad",
shortenLabels = "intelligent",
labelLength = 7,
labelScale = FALSE,
rmSingles = TRUE,
cexNodes = 2,
cexLabels = 0.9,
nodeSizeSpread = 3,
cexHubLabels = 1,
hubBorderCol = "snow4",
cexTitle = 2.5,
groupNames = c("A)", "B)"))
legend(-0.20, -0.68, title = "Estimated associations:", legend = c("+","-"),
col = c("#ac1214","#0d1fad"), inset = 0.02, cex = 1.5, lty = 1, lwd = 3.5,
bty = "n", horiz = TRUE, y.intersp = 0.7)
# Close device
dev.off()
# Betweenness as node size and hub definition
svg("Results/Figures/Family_nets/svg/cclasso_nets_family_betw.svg", width=20, height=10)
# png("Results/Figures/Family_nets/png/cclasso_nets_family_betw.png", width=1500, height=750)
cclasso_nets_plot <- plot(props_cclasso_nets,
layout = "spring",
sameLayout = FALSE,
layoutGroup = "union",
repulsion = 0.9,
nodeColor = "cluster",
edgeTranspLow = 0,
edgeTranspHigh = 40,
nodeSize = "betweenness",
posCol = "#ac1214",
negCol = "#0d1fad",
shortenLabels = "intelligent",
labelLength = 7,
labelScale = FALSE,
rmSingles = TRUE,
cexNodes = 2,
cexLabels = 0.9,
nodeSizeSpread = 4,
cexHubLabels = 1,
hubBorderCol = "snow4",
cexTitle = 2.5,
groupNames = c("", ""))
legend(-0.20, -0.68, title = "Estimated associations:", legend = c("+","-"),
col = c("#ac1214","#0d1fad"), inset = 0.02, cex = 1.5, lty = 1, lwd = 3.5,
bty = "n", horiz = TRUE, y.intersp = 0.7)
# Close device
dev.off()
# Create a dataframe with the shortened and full node names
forest_cclasso_node_names <- data.frame(Short_name = cclasso_nets_plot$labels$labels1)
forest_cclasso_node_names$Full_name <- row.names(cclasso_nets_plot$layout$layout1)
pasture_cclasso_node_names <- data.frame(Short_name = cclasso_nets_plot$labels$labels2)
pasture_cclasso_node_names$Full_name <- row.names(cclasso_nets_plot$layout$layout2)
# Add a column with the species data
forest_cclasso_node_names <- merge(forest_cclasso_node_names, fam_sp_df, by = "Full_name")
pasture_cclasso_node_names <- merge(pasture_cclasso_node_names, fam_sp_df, by = "Full_name")
# Export csv files
write.csv(forest_cclasso_node_names,
"Results/Figures/Family_nets/Node_names_in_nets/forest_cclasso_node_names.csv",
row.names = FALSE)
write.csv(pasture_cclasso_node_names,
"Results/Figures/Family_nets/Node_names_in_nets/pasture_cclasoo_node_names.csv",
row.names = FALSE)
###############################################################################
########################### Quantitative comparison ###########################
###############################################################################
# Calculate differences
spring_nets_comp <- netCompare(props_spring_nets,
permTest = TRUE,
adjust = "adaptBH",
nPerm = 100L,
cores = 12L,
storeCountsPerm = TRUE,
fileStoreCountsPerm = c("Results/Quantit_net_comparison/Spring/AdaptBH_adj/countsPerm1_spring",
"Results/Quantit_net_comparison/Spring/AdaptBH_adj/countsPerm2_spring"),
storeAssoPerm = TRUE,
fileStoreAssoPerm = "Results/Quantit_net_comparison/Spring/AdaptBH_adj/assoPerm_spring",
seed = 123)
cclasso_nets_comp <- netCompare(props_cclasso_nets,
permTest = TRUE,
adjust = "adaptBH",
nPerm = 100L,
cores = 12L,
storeCountsPerm = TRUE,
fileStoreCountsPerm = c("Results/Quantit_net_comparison/Cclasso/countsPerm1_cclasso",
"Results/Quantit_net_comparison/Cclasso/countsPerm2_cclasso"),
storeAssoPerm = TRUE,
fileStoreAssoPerm = "Results/Quantit_net_comparison/Cclasso/assoPerm_cclasso",
seed = 123)
# Save the results into txt files
summ_spring_nets_comp <- capture.output(summary(spring_nets_comp,
groupNames = c("Forest", "Pasture"),
numbNodes = 5L, digits = 3L))
cat(summ_spring_nets_comp, file="Results/Quantit_net_comparison/Spring/AdaptBH_adj/summ_spring_nets_comp_deg.txt",
sep="\n",append=TRUE)
summ_cclasso_nets_comp <- capture.output(summary(cclasso_nets_comp,
groupNames = c("Forest", "Pasture"),
numbNodes = 5L, digits = 3L))
cat(summ_cclasso_nets_comp, file="Results/Quantit_net_comparison/Cclasso/summ_cclasso_nets_comp_deg.txt",
sep="\n",append=TRUE)
###############################################################################
######################## Differential networks ################################
###############################################################################
# Differential network construction
diff_spring_nets <- diffnet(nets_spring,
diffMethod = "fisherTest",
adjust = "lfdr")
diff_cclasso_nets <- diffnet(nets_cclasso,
diffMethod = "fisherTest",
adjust = "lfdr")