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15_NetCoMi_Networks.R
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# Script for looking at microbiome networks
# Combined Data
set.seed(18)
library(tidyverse)
library(devtools)
#devtools::install_github("jbisanz/qiime2R")
library(qiime2R)
library(ggplot2)
library(phyloseq)
library(gridExtra)
library(vegan)
library(dplyr)
library(scales)
library(grid)
library(reshape2)
library(ggpubr)
#install_github("microbiome/microbiome")
library(microbiome)
#devtools::install_github('schuyler-smith/phylosmith')
library(phylosmith)
#library(DESeq2)
library(NetCoMi)
#turn off scientific notation
options(scipen=999)
setwd("//wsl.localhost/Ubuntu-18.04/home/crschul/IndigoAgMicrobiome")
load("phy_combined.rdata")
metadata <- read.csv("Metadata_Indigo_Clean.tsv", header = TRUE, sep = "\t")
phylum_combined <- tax_glom(phy_combined,"Phylum")
Soil_phy <- subset_samples(phylum_combined, tissue == "Soil")
RW_phy <- subset_samples(phylum_combined, tissue == "Root wash")
Root_phy <- subset_samples(phylum_combined, tissue == "Root")
Leaf_phy <- subset_samples(phylum_combined, tissue == "Leaf")
# cclasso, tss, pseudo, .8 look good <- USE ME
# sparcc, clr, pseudo, .8 look good
?plot.microNetProps # Try to find the answers to making better figs
# Get Colors
kingvec <- as.data.frame(tax_table(phylum_combined))$Kingdom
names(kingvec) <- as.data.frame(tax_table(phylum_combined))$Phylum
#### Combined data
combined_net <- netConstruct(phylum_combined,
measure = "cclasso",
normMethod = "TSS",
zeroMethod = "pseudo",
thresh = 0.8,
verbose = 2,
taxRank = "Phylum")
combined_anal <- netAnalyze(combined_net, clustMethod = "cluster_fast_greedy")
#summary(net_anal)
combined_plot <- plot(combined_anal,
title1 = "Network on Phylum level: All Tissues",
showTitle = TRUE,
cexTitle = 1.5,
rmSingles = TRUE,
labelScale = FALSE,
labelFont = 2,
cexLabels = .85,
posCol = "blue",
negCol = "orange",
highlightHubs=FALSE,
nodeSize = "normCounts",
hubBorderCol = "darkgrey",
cexNodes = 5,
edgeWidth = 1,
edgeTranspLow = 0,
edgeTranspHigh = 0,
cut = 0,
nodeColor = "feature",
featVecCol = kingvec,
colorVec = c("Fungi" = "tan","d__Bacteria" = "lightgreen", "d__Archaea" = "tan" )) # why does it call my fungi archea?
legend(.3, 1.1, cex = 1.2, title = "estimated correlation:",
legend = c("+","-"), lty = 1, lwd = 3, col = c("blue","darkorange"),
bty = "n", horiz = TRUE)
legend(.6, .9, cex = 1.2, pt.cex = 2.5, title = "Kingdom:",
legend=c("Fungi","Bacteria"), col = c("tan","lightgreen"), bty = "n", pch = 16)
##################################################### Soil data
combined_net <- netConstruct(Soil_phy,
measure = "cclasso",
normMethod = "TSS",
zeroMethod = "pseudo",
thresh = 0.8,
verbose = 2,
taxRank = "Phylum")
combined_anal <- netAnalyze(combined_net, clustMethod = "cluster_fast_greedy")
#summary(net_anal)
combined_plot <- plot(combined_anal,
title1 = "Network on Phylum level: Soil",
showTitle = TRUE,
cexTitle = 1.5,
rmSingles = TRUE,
labelScale = FALSE,
labelFont = 2,
cexLabels = .85,
posCol = "blue",
negCol = "orange",
highlightHubs=FALSE,
nodeSize = "normCounts",
hubBorderCol = "darkgrey",
cexNodes = 5,
edgeWidth = 1,
edgeTranspLow = 0,
edgeTranspHigh = 0,
cut = 0,
nodeColor = "feature",
featVecCol = kingvec,
colorVec = c("Fungi" = "tan","d__Bacteria" = "lightgreen", "d__Archaea" = "tan" )) # why does it call my fungi archea?
legend(.55, 1.1, cex = 1.2, title = "estimated correlation:",
legend = c("+","-"), lty = 1, lwd = 3, col = c("blue","darkorange"),
bty = "n", horiz = TRUE)
legend(.8, .9, cex = 1.2, pt.cex = 2.5, title = "Kingdom:",
legend=c("Fungi","Bacteria"), col = c("tan","lightgreen"), bty = "n", pch = 16)
############################################# RootWash data
combined_net <- netConstruct(RW_phy,
measure = "cclasso",
normMethod = "TSS",
zeroMethod = "pseudo",
thresh = 0.8,
verbose = 2,
taxRank = "Phylum")
combined_anal <- netAnalyze(combined_net, clustMethod = "cluster_fast_greedy")
#summary(net_anal)
combined_plot <- plot(combined_anal,
title1 = "Network on Phylum level: Root Wash",
showTitle = TRUE,
cexTitle = 1.5,
rmSingles = TRUE,
labelScale = FALSE,
labelFont = 2,
cexLabels = .85,
posCol = "blue",
negCol = "orange",
highlightHubs=FALSE,
nodeSize = "normCounts",
hubBorderCol = "darkgrey",
cexNodes = 5,
edgeWidth = 1,
edgeTranspLow = 0,
edgeTranspHigh = 0,
cut = 0,
nodeColor = "feature",
featVecCol = kingvec,
colorVec = c("Fungi" = "purple","d__Bacteria" = "lightgreen", "d__Archaea" = "tan" )) # why does it call my fungi archea?
legend(.5, 1.1, cex = 1.2, title = "estimated correlation:",
legend = c("+","-"), lty = 1, lwd = 3, col = c("blue","darkorange"),
bty = "n", horiz = TRUE)
legend(.9, .9, cex = 1.0, pt.cex = 2.5, title = "Kingdom:",
legend=c("Fungi","Bacteria","Archaea"), col = c("tan","lightgreen","purple"), bty = "n", pch = 16)
#### Root data
combined_net <- netConstruct(Root_phy,
measure = "cclasso",
normMethod = "TSS",
zeroMethod = "pseudo",
thresh = 0.8,
verbose = 2,
taxRank = "Phylum")
combined_anal <- netAnalyze(combined_net, clustMethod = "cluster_fast_greedy")
#summary(net_anal)
#summary(net_anal)
combined_plot <- plot(combined_anal,
title1 = "Network on Phylum level: Roots",
showTitle = TRUE,
cexTitle = 1.5,
rmSingles = TRUE,
labelScale = FALSE,
labelFont = 2,
cexLabels = .85,
posCol = "blue",
negCol = "orange",
highlightHubs=FALSE,
nodeSize = "normCounts",
hubBorderCol = "darkgrey",
cexNodes = 5,
edgeWidth = 1,
edgeTranspLow = 0,
edgeTranspHigh = 0,
cut = 0,
nodeColor = "feature",
featVecCol = kingvec,
colorVec = c("Fungi" = "purple","d__Bacteria" = "lightgreen", "d__Archaea" = "tan" )) # why does it call my fungi archea?
legend(.5, 1.1, cex = 1.2, title = "estimated correlation:",
legend = c("+","-"), lty = 1, lwd = 3, col = c("blue","darkorange"),
bty = "n", horiz = TRUE)
legend(.7, -.5, cex = 1.2, pt.cex = 2.5, title = "Kingdom:",
legend=c("Fungi","Bacteria"), col = c("tan","lightgreen"), bty = "n", pch = 16)
#### Leaf data
combined_net <- netConstruct(Leaf_phy,
measure = "cclasso",
normMethod = "TSS",
zeroMethod = "pseudo",
thresh = 0.8,
verbose = 2,
taxRank = "Phylum")
combined_anal <- netAnalyze(combined_net, clustMethod = "cluster_fast_greedy")
#summary(net_anal)
combined_plot <- plot(combined_anal,
title1 = "Network on Phylum level: Leaf",
showTitle = TRUE,
cexTitle = 1.5,
rmSingles = TRUE,
labelScale = FALSE,
labelFont = 2,
cexLabels = .85,
posCol = "blue",
negCol = "orange",
highlightHubs=FALSE,
nodeSize = "normCounts",
hubBorderCol = "darkgrey",
cexNodes = 5,
edgeWidth = 1,
edgeTranspLow = 0,
edgeTranspHigh = 0,
cut = 0,
nodeColor = "feature",
featVecCol = kingvec,
colorVec = c("Fungi" = "tan","d__Bacteria" = "lightgreen", "d__Archaea" = "tan" )) # why does it call my fungi archea?
legend(.4, 1.1, cex = 1.2, title = "estimated correlation:",
legend = c("+","-"), lty = 1, lwd = 3, col = c("blue","darkorange"),
bty = "n", horiz = TRUE)
legend(.4, .9, cex = 1.2, pt.cex = 2.5, title = "Kingdom:",
legend=c("Fungi","Bacteria"), col = c("tan","lightgreen"), bty = "n", pch = 16)
###### Testing below: ignore!!!
################################################### compare both methods!
#### Leaf data - NetCoMi
combined_net <- netConstruct(Leaf_phy,
measure = "spearman",
normMethod = "TSS",
zeroMethod = "pseudo",
thresh = 0.8,
verbose = 2,
taxRank = "Phylum")
combined_anal <- netAnalyze(combined_net, clustMethod = "cluster_fast_greedy")
#summary(net_anal)
combined_plot <- plot(combined_anal,
nodeColor = "grey",
title1 = "Network on Phylum level: Leaf",
showTitle = TRUE,
rmSingles = TRUE,
labelScale = FALSE,
cexLabels = .85,
posCol = "blue",
negCol = "darkorange",
highlightHubs=TRUE,
nodeSize = "normCounts")
legend(.6, 1.1, cex = 1.2, title = "estimated correlation:",
legend = c("+","-"), lty = 1, lwd = 3, col = c("blue","darkorange"),
bty = "n", horiz = TRUE)
##### Leaf Data PhyloSmith
# pseudo counts + relative abundance: 20,000 things (suspicious)
# loose relative abundance: CLR, VST (DESEQ)
#phy_pseudo <- transform_sample_counts(phy_combined, function(OTU) OTU +1)
# TSS is just relative abundance
phy_filtered = transform_sample_counts(phy_combined, function(x) (x / sum(x) * 10000))
phy_pseudo <- transform_sample_counts(phy_filtered, function(OTU) OTU +1)
#phy_clr <- microbiome::transform(phy_pseudo, "clr")
# Test the whole network across everything!
combined_phylum <- conglomerate_taxa(phy_pseudo, "Phylum")
# Look at networks across Leafs
leaf_d <- subset_samples(combined_phylum, tissue == "Leaf")
# Correlation Table of all phylum, with a rho cutoff of .85
co_leaf <- co_occurrence(leaf_d, treatment = c('Location'), method = 'pearson',
cores = 4, rho = 0.2) # Drop because it just wont run
# The actual network layout
network_layout_leaf <- network_layout_ps(leaf_d,treatment = c("Location"),
co_occurrence_table = co_leaf,
algorithm = 'circle')
# filter abundant and rare taxa through col in network layout obj
# Abundant
leaf_abundant <- co_occurrence_network(leaf_d,treatment = c("Location"),
co_occurrence_table = co_leaf,
layout = network_layout_leaf,
negative_positive_colors = c('tomato3','grey22'), #Need to change if only positive or negative
classification = 'Phylum') +
ggtitle("Leaf Network")
leaf_abundant
####################### Figure out the color and shape with their data
# Load data sets from American Gut Project (from SpiecEasi package)
data("amgut1.filt")
# Network construction
amgut_net <- netConstruct(amgut1.filt, measure = "pearson",
filtTax = "highestVar",
filtTaxPar = list(highestVar = 50),
zeroMethod = "pseudoZO", normMethod = "clr",
sparsMethod = "threshold", thresh = 0.3)
# Network analysis
amgut_props <- netAnalyze(amgut_net)
### Network plots ###
# Clusters are used for node coloring:
plot(amgut_props,
nodeColor = "cluster")
# Remove singletons
plot(amgut_props,
nodeColor = "cluster",
rmSingles = TRUE)
# A higher repulsion places nodes with high edge weight closer together
plot(amgut_props,
nodeColor = "cluster",
rmSingles = TRUE,
repulsion = 1.2)
kingvec <- as.data.frame(tax_table(Leaf_phy))$Kingdom
names(kingvec) <- as.data.frame(tax_table(Leaf_phy))$Phylum
# A feature vector is used for node coloring
# (this could be a vector with phylum names of the ASVs)
set.seed(123456)
featVec <- sample(1:5, nrow(amgut1.filt), replace = TRUE)
# Names must be equal to ASV names
names(featVec) <- colnames(amgut1.filt)
featVec
plot(amgut_props,
rmSingles = TRUE,
nodeColor = "feature",
featVecCol = featVec,
colorVec = heat.colors(5))
# Use a further feature vector for node shapes
shapeVec <- sample(1:3, ncol(amgut1.filt), replace = TRUE)
names(shapeVec) <- colnames(amgut1.filt)
plot(amgut_props,
rmSingles = TRUE,
nodeColor = "feature",
featVecCol = featVec,
colorVec = heat.colors(5),
nodeShape = c("circle", "square", "diamond"),
featVecShape = shapeVec,
highlightHubs = FALSE)
############################## Try to convert this to ggnet
library(GGally)
library(network)
library(sna)
#### Leaf data - NetCoMi
leaf_net_test <- netConstruct(Leaf_phy,
measure = "cclasso",
normMethod = "TSS",
zeroMethod = "pseudo",
thresh = 0.8,
verbose = 2,
taxRank = "Phylum")
leaf_edge <- leaf_net_test$edgelist1
leaf_assoc <- leaf_net_test$assoMat1
leaf_adj <- leaf_net_test$adjaMat1
ggnet2(leaf_edge, label = TRUE)
leaf_net <- network(leaf_edge,
ignore.eval = FALSE)
taxtab <- as(tax_table(phylum_combined), "matrix")
phyla <- as.factor(gsub("p__", "", taxtab[, "Phylum"]))
names(phyla) <- taxtab[, "Kingdom"]
col = c("Ascomycota" = "tan", "Proteobacteria" = "orange","Firmicutes" = "darkgreen", "Basdiomycota" = "purple",
"Bacteroidota" = "darkgreen", "Verrucomicrobiota" = "darkgreen", "Acidobacteriota" = "darkgreen")
set.edge.attribute(leaf_net, "color", ifelse(leaf_net %e% "asso" > 0, "black", "red"))
set.edge.value(leaf_net, "weight", value = leaf_edge$asso)
ggnet2(leaf_net, label = TRUE, edge.color = "color",
edge.size = 5, node.color = col)
############# Theirs
# weighted adjacency matrix
net = rgraph(10, mode = "graph", tprob = 0.5)
net = network(net, directed = FALSE)
# vertex names
network.vertex.names(net) = letters[1:10]
net %v% "phono" = ifelse(letters[1:10] %in% c("a", "e", "i"), "vowel", "consonant")
ggnet2(net, color = "phono")