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Species_Niches.Rmd
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Species_Niches.Rmd
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---
title: "Species_Niches"
author: "Jackson Pullman"
date: "2023-02-13"
output: html_notebook
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
set.seed(1)
#Get social network clustering for each village
sn_cluster_1 <- cluster_louvain(sn_vil_graph_1)
sn_cluster_2 <- cluster_louvain(sn_vil_graph_2)
sn_cluster_3 <- cluster_louvain(sn_vil_graph_3)
sn_cluster_4 <- cluster_louvain(sn_vil_graph_4)
sn_cluster_5 <- cluster_louvain(sn_vil_graph_5)
sn_cluster_6 <- cluster_louvain(sn_vil_graph_6)
sn_cluster_7 <- cluster_louvain(sn_vil_graph_7)
sn_cluster_8 <- cluster_louvain(sn_vil_graph_8)
sn_cluster_9 <- cluster_louvain(sn_vil_graph_9)
sn_member_1 <- membership(sn_cluster_1) + 100
sn_member_2 <- membership(sn_cluster_2) + 200
sn_member_3 <- membership(sn_cluster_3) + 300
sn_member_4 <- membership(sn_cluster_4) + 400
sn_member_5 <- membership(sn_cluster_5) + 500
sn_member_6 <- membership(sn_cluster_6) + 600
sn_member_7 <- membership(sn_cluster_7) + 700
sn_member_8 <- membership(sn_cluster_8) + 800
sn_member_9 <- membership(sn_cluster_9) + 900
#KW approach
niche_frame_kw <- data.frame(matrix(NA, nrow = length(village_names), ncol = ncol(species_data)))
colnames(niche_frame_kw) <- colnames(species_data)
for(j in 1:ncol(species_data)){
species_for_niche <- species_data[,j]
for(i in 1:length(village_names)){
clusters <- get(paste0("sn_member_",i))
clusters_samp_size <- table(clusters)
#For Kruskal Wallis want communities to have more than 5
clusters_samp_size <- clusters_samp_size[clusters_samp_size>=5]
clusters <- clusters[clusters %in% names(clusters_samp_size)]
ordered_species <- species_for_niche[match(names(clusters), names(species_for_niche))]
#Randomization for p-values
#ordered_species <- sample(ordered_species)
if(sum(abs(ordered_species) >0) >=5){
test <- kruskal.test(ordered_species, as.vector(clusters))
niche_frame_kw[i,j] <- test$p.value
}
}
}
sum(!is.na(niche_frame_kw))
hist_dat_pvals <- data.frame(pvals = (niche_frame_kw %>% as.matrix %>% as.vector))
niche_hist <-
gghistogram(hist_dat_pvals, x = "pvals", fill = "#E7B800") +
theme_pubr() +
labs_pubr() +
xlab("P-Values") +
ylab("Frequency") +
ggtitle("P-Value Distribution of Species Niches") +
theme(plot.title = element_text(hjust = .5))
```
Randomized p-value distribution
```{r}
#KW approach
niche_frame_kw_rand <- data.frame(matrix(NA, nrow = length(village_names), ncol = ncol(species_data)))
colnames(niche_frame_kw_rand) <- colnames(species_data)
for(j in 1:ncol(species_data)){
species_for_niche <- species_data[,j]
for(i in 1:length(village_names)){
clusters <- get(paste0("sn_member_",i))
clusters_samp_size <- table(clusters)
clusters_samp_size <- clusters_samp_size[clusters_samp_size>=5]
clusters <- clusters[clusters %in% names(clusters_samp_size)]
ordered_species <- species_for_niche[match(names(clusters), names(species_for_niche))]
#Randomization for p-values
ordered_species <- sample(ordered_species)
if(sum(abs(ordered_species) >0) >=5){
test <- kruskal.test(ordered_species, as.vector(clusters))
niche_frame_kw_rand[i,j] <- test$p.value
}
}
}
sum(!is.na(niche_frame_kw_rand))
hist_dat_pvals_rand <- data.frame(pvals = (niche_frame_kw_rand %>% as.matrix %>% as.vector))
niche_hist_rand <-
gghistogram(hist_dat_pvals_rand, x = "pvals", fill = "#E7B800") +
theme_pubr() +
labs_pubr() +
xlab("P-Values") +
ylab("Frequency") +
ggtitle("P-Value Distribution of Randomized Species Niches") +
theme(plot.title = element_text(hjust = .5))
```
MTC
```{r}
#Multiple testing correction
niche_frame_kw_adj <- niche_frame_kw %>%
as.matrix %>%
as.vector %>%
p.adjust(method = 'BH') %>%
matrix(nrow = length(village_names))
sum(niche_frame_kw_adj<.05, na.rm = TRUE)
sum(!is.na(niche_frame_kw_adj))
hist(niche_frame_kw_adj %>% as.matrix %>% as.vector)
colnames(niche_frame_kw)[colSums(niche_frame_kw_adj<.05, na.rm = TRUE)>0]
hist(colSums(niche_frame_kw_adj<.05))
plot(niche_frame_kw_adj[1,],niche_frame_kw_adj[7,])
niche_frame_kw_adj_2 <- niche_frame_kw %>%
as.matrix %>%
as.vector %>%
p.adjust(method = 'bonferroni') %>%
matrix(nrow = length(village_names))
sum(niche_frame_kw_adj_2<.05, na.rm = TRUE)
species_for_niche <- species_data[,j]
```
Illustrative Relative abundance graphs
```{r}
vil_graph <- sn_vil_graph_15
#Get species
species_for_niche <- unlist(species_data["k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Enterococcaceae|g__Enterococcus|s__Enterococcus_faecium|t__SGB7967",])
#Get village clusters
clusters <- sn_member_15
#Filter species to village members
ordered_species <-
species_for_niche[match(names(clusters), names(species_for_niche))]
pal <- colorRampPalette(c("red", "yellow"))
my_pal <- pal(2)
test_cols <- my_pal[ceiling(ordered_species)+1]
V(vil_graph)$color <- test_cols[match(names(V(vil_graph)),
names(ordered_species))]
V(vil_graph)$size <- log2(ordered_species*100000+5)+2
set.seed(1)
par(mar= c(0,0,0,0))
plot(
vil_graph,
vertex.size = log2(ordered_species*100000+5)+2,
vertex.label = NA
)
title(paste0("Differentially Abundant Species: Enterococcus_faecium (SGB7967)"),
cex.main=1,
line = -1)
legend(
"bottomleft",
legend = c("Species Present",
"Species Absent"),
pt.bg = rev(my_pal),
pch = 21,
cex = 1,
border = NA,
bty = "n"
)
set.seed(1)
species_da_1 <- as.ggplot(expression(plot(
vil_graph,
vertex.label = NA
),
title(paste0("Differentially Abundant Species:\nEnterococcus_faecium (SGB7967)"),
cex.main=1,
line = -1),
legend(
'bottomleft',
legend = c("Species Present",
"Species Absent"),
pt.bg = rev(my_pal),
pch = 21,
cex = 1,
border = NA,
bty = "n"
)))
```
```{r}
vil_graph <- sn_vil_graph_15
#Get species
species_for_niche <- unlist(species_data["k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae|g__GGB9379|s__GGB9379_SGB14372|t__SGB14372",])
#Get village clusters
clusters <- sn_member_15
#Filter species to village members
ordered_species <-
species_for_niche[match(names(clusters), names(species_for_niche))]
pal <- colorRampPalette(c("red", "yellow"))
my_pal <- pal(2)
test_cols <- my_pal[rank(ordered_species, ties.method = "min")]
test_cols <- my_pal[ceiling(ordered_species)+1]
V(vil_graph)$color <- test_cols[match(names(V(vil_graph)),
names(ordered_species))]
V(vil_graph)$size <- log2(ordered_species*100000+5)+2
set.seed(1)
plot(vil_graph,
vertex.label = NA
)
title(
paste0(
"Differentially Abundant Species: Coriobacteriaceae (GGB9379_SGB14372)"
),
cex.main = 1,
line = -1
)
legend(
'bottomleft',
legend = c("Species Present",
"Species Absent"),
pt.bg = rev(my_pal),
pch = 21,
cex = 1,
border = NA,
bty = "n"
)
set.seed(1)
species_da_2 <- as.ggplot(expression(plot(vil_graph,
vertex.label = NA
),
title(
paste0(
"Differentially Abundant Species:\nCoriobacteriaceae (GGB9379_SGB14372)"
),
cex.main = 1,
line = -1
),
legend(
'bottomleft',
legend = c("Species Present",
"Species Absent"),
pt.bg = rev(my_pal),
pch = 21,
cex = 1,
border = NA,
bty = "n"
)))
```