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Starling2019.r
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Starling2019.r
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#Import######################
#Load packages
#most of the packages below can be installed with install.packages("package name here")
#For phyloseq and DeSeq2 you will have to use bioconductor, the easiest way is just to google the package name and install and follow the directions on the package's website
library(vegan)
library(MASS)
library(ggplot2)
library(plyr)
library(dplyr)
library(magrittr)
library(scales)
library(grid)
library(reshape2)
library(phyloseq)
library(randomForest)
library(knitr)
library(ape)
library(ggpubr)
library(DESeq2)
library(tiff)
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00","#CC79A7", "#000000") #Manually defines a color blind friendly palette
theme_set(theme_bw(base_size = 18)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())) #Sets plot options and font size ( to change font size change base_size=)
#To load files specify the direct paths
#running file.choose() will also let you pick the files you want without specifying the path to the file
biom=import_biom("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\table-with-taxonomy500.biom",parseFunction= parse_taxonomy_greengenes)
biom
set.seed(1117)
tax_table(biom)
biom=subset_taxa(biom, Kingdom== "Bacteria")
meta=read.csv("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingMeta1.16.18.csv",header=TRUE)
head(meta)
#meta$Sample.Type = factor(meta$Sample.Type, levels = c("Small Intestine","Ceca", "Large Intestine","Cloaca"))
meta
meta=sample_data(meta)
sample_names(meta)=meta$SampleID
tree=read.tree("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingTree7.31.19.nwk")
physeq=merge_phyloseq(biom,meta,tree)
physeq2=physeq
physeq=subset_taxa(physeq, Kingdom=="Bacteria")
physeq=subset_samples(physeq, Sample.Type != "Headgut Swab" & Sample.Type != "Small_Intestine_Contents") #Remove headgut swabs
#physeqRar=rarefy_even_depth(physeq, sample.size = 500) #Files rarefied in QIIME see StarlingRarefactionCurves3.1.19
sample_data(physeq)
physeq
#Deseq
biom=import_biom("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\table-with-taxonomyNoR.biom",parseFunction= parse_taxonomy_greengenes)
biom
set.seed(1117)
tax_table(biom)
biom=subset_taxa(biom, Kingdom== "Bacteria")
meta=read.csv("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingMeta1.16.18.csv",header=TRUE)
head(meta)
#meta$Sample.Type = factor(meta$Sample.Type, levels = c("Small Intestine","Ceca", "Large Intestine","Cloaca"))
meta
meta=sample_data(meta)
sample_names(meta)=meta$SampleID
tree=read.tree("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingTree7.31.19.nwk")
physeq2=merge_phyloseq(biom,meta,tree)
physeq2=subset_taxa(physeq2, Kingdom=="Bacteria")
physeq2=subset_samples(physeq2, Sample.Type != "Headgut Swab" & Sample.Type != "Small_Intestine_Contents") #Remove headgut swabs
#physeqRar=rarefy_even_depth(physeq, sample.size = 500) #Files rarefied in QIIME see StarlingRarefactionCurves3.1.19
sample_data(physeq2)
physeq2
########
#PiCRUSTImport#####
otufullPiCRUST=read.table("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingR\\StarlingPiCrustMatrixL2.txt",header=TRUE)
metadata=read.csv("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingMeta1.16.18.csv",header=TRUE)
metadata$Sample.Type = factor(metadata$Sample.Type, levels = c("Small Intestine","Ceca", "Large Intestine","Cloaca"))
groupPiCRUST=as.matrix(read.csv("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingR\\StarlingPiCrustGroupsL2.csv"))
head(groupPiCRUST)
OTU=otu_table(otufullPiCRUST, taxa_are_rows=TRUE)
#row.names(OTU)
TAX=tax_table(groupPiCRUST)
colnames(TAX)=("KEGG.Pathway")
sampdat=sample_data(metadata)
sample_names(sampdat)=metadata$SampleID
row.names(OTU)=taxa_names(TAX)
PiCRUST=phyloseq(OTU,TAX,sampdat)
PiCRUST=subset_samples(PiCRUST, SampleID!="ST186"&SampleID!="ST189"&SampleID!="ST129") #remove samples that didn't pass taxonomy filtering
PiCRUST=subset_samples(PiCRUST, Sample.Type != "Headgut Swab" & Sample.Type != "Small_Intestine_Contents")
PiCRUST
head(sample_data(PiCRUST))
#Relative abundances
#######
#Taxonomic Filtering
########
GPr = transform_sample_counts(physeq, function(x) x / sum(x) ) #transform samples based on relative abundance
GPrPhylum=tax_glom(GPr,"Phylum")
PhylumLevel = filter_taxa(GPrPhylum, function(x) mean(x) > 1e-3, TRUE) #filter out any taxa lower tha 0.1%
#PhylumLevel = transform_sample_counts(PhylumLevel, function(x) x / sum(x) )
GPrFamily=tax_glom(GPr,"Family")
FamilyLevel = filter_taxa(GPrFamily, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
#FamilyLevel = transform_sample_counts(FamilyLevel, function(x) x / sum(x) )
GPrGenus=tax_glom(GPr,"Genus")
GenusLevel = filter_taxa(GPrGenus, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
GPrGenus=tax_glom(GPr,"Species")
SpeciesLevel = filter_taxa(GPrGenus, function(x) mean(x) > 1e-2, TRUE) #filter out any taxa lower tha 1%
###########################################################
##Percentages for top phyla across all samples
##########################################################
#Produce a table of samples N by hours and type
metadataNumbers<-subset_samples(meta, SampleID!="ST186"&SampleID!="ST189"&SampleID!="ST129") #remove samples with < 500 reads from metadata
metadataNumbers<-subset_samples(metadataNumbers, Sample.Type != "Headgut Swab" & Sample.Type != "Small_Intestine_Contents") #Remove headgut swabs and small intestine contents from metadata
SampleNumbers <- ddply(metadataNumbers, c("Hours","Sample.Type"), summarise,
N = length(LinkerSequence)
)
SampleNumbers
write.csv(SampleNumbers,"SampleNumbersStarling.csv")
#Phylum level
df <- psmelt(PhylumLevel)
df$Abundance=df$Abundance*100
PhylumRelAbu <- ddply(df, c("Phylum"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
PhylumRelAbu
#Family level
df <- psmelt(FamilyLevel)
df$Abundance=df$Abundance*100
FamilyRelAbuAll <- ddply(df, c("Family"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
FamilyRelAbuAll
#Relative Abus for antemortem samples
#Phylum
AntemortemPhylum=subset_samples(PhylumLevel, Anti_Post== "Antemortem")
sample_data(AntemortemPhylum)
df <- psmelt(AntemortemPhylum)
df$Abundance=df$Abundance*100
PhylumRelAbuAntemortem <- ddply(df, c("Phylum","Sample.Type"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
PhylumRelAbuAntemortem
write.csv(PhylumRelAbuAntemortem,"PhylumRelAbuAntemortem.csv")
#Phylum level All
df <- psmelt(PhylumLevel)
df$Abundance=df$Abundance*100
PhylumRelAbuAll <- ddply(df, c("Phylum","Sample.Type","Anti_Post"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
PhylumRelAbuAll
write.csv(FamilyRelAbuAll,"PhylumRelAbuAll.csv")
#Family level All
df <- psmelt(FamilyLevel)
df$Abundance=df$Abundance*100
FamilyRelAbuAll <- ddply(df, c("Family","Sample.Type","Anti_Post"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N)
)
head(FamilyRelAbuAll)
write.csv(FamilyRelAbuAll,"FamilyRelAbuAll.csv")
##################################################
# logFoldChange between Sample types antimortem phylum
##################################################
#Large vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Ceca")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestinePhylum <- subset_taxa(GPrIntestineAnti, Phylum != "NA")
GPrIntestinePhylum=tax_glom(GPrIntestinePhylum, "Phylum")
GPrIntestinePhylum = filter_taxa(GPrIntestinePhylum, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestinePhylum)
diagdds = phyloseq_to_deseq2(GPrIntestinePhylum, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
res
alpha = 0.05
sigtab = res[which(res$pvalue < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestinePhylum)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Large Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestinePhylum <- subset_taxa(GPrIntestineAnti, Phylum != "NA")
GPrIntestinePhylum=tax_glom(GPrIntestinePhylum, "Phylum")
GPrIntestinePhylum = filter_taxa(GPrIntestinePhylum, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestinePhylum)
diagdds = phyloseq_to_deseq2(GPrIntestinePhylum, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$pvalue < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestinePhylum)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs large intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Small Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestinePhylum <- subset_taxa(GPrIntestineAnti, Phylum != "NA")
GPrIntestinePhylum=tax_glom(GPrIntestinePhylum, "Phylum")
GPrIntestinePhylum = filter_taxa(GPrIntestinePhylum, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestinePhylum)
diagdds = phyloseq_to_deseq2(GPrIntestinePhylum, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestinePhylum)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
##################################################
# logFoldChange between Sample types antimortem phylum not filtered
##################################################
#Large vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Ceca")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestinePhylum <- subset_taxa(GPrIntestineAnti, Phylum != "NA")
GPrIntestinePhylum=tax_glom(GPrIntestinePhylum, "Phylum")
#GPrIntestinePhylum = filter_taxa(GPrIntestinePhylum, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestinePhylum)
diagdds = phyloseq_to_deseq2(GPrIntestinePhylum, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestinePhylum)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Large Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestinePhylum <- subset_taxa(GPrIntestineAnti, Phylum != "NA")
GPrIntestinePhylum=tax_glom(GPrIntestinePhylum, "Phylum")
#GPrIntestinePhylum = filter_taxa(GPrIntestinePhylum, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestinePhylum)
diagdds = phyloseq_to_deseq2(GPrIntestinePhylum, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestinePhylum)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs large intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Small Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestinePhylum <- subset_taxa(GPrIntestineAnti, Phylum != "NA")
GPrIntestinePhylum=tax_glom(GPrIntestinePhylum, "Phylum")
#GPrIntestinePhylum = filter_taxa(GPrIntestinePhylum, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestinePhylum)
diagdds = phyloseq_to_deseq2(GPrIntestinePhylum, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
res
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestinePhylum)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
##################################################
# logFoldChange between Sample types antimortem family all
##################################################
#Large vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Ceca")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestineFamily <- subset_taxa(GPrIntestineAnti, Family != "NA")
GPrIntestineFamily=tax_glom(GPrIntestineFamily, "Family")
#GPrIntestineFamily = filter_taxa(GPrIntestineFamily, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestineFamily)
diagdds = phyloseq_to_deseq2(GPrIntestineFamily, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestineFamily)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Large Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestineFamily <- subset_taxa(GPrIntestineAnti, Family != "NA")
GPrIntestineFamily=tax_glom(GPrIntestineFamily, "Family")
#GPrIntestineFamily = filter_taxa(GPrIntestineFamily, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestineFamily)
diagdds = phyloseq_to_deseq2(GPrIntestineFamily, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestineFamily)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs large intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Small Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestineFamily <- subset_taxa(GPrIntestineAnti, Family != "NA")
GPrIntestineFamily=tax_glom(GPrIntestineFamily, "Family")
#GPrIntestineFamily = filter_taxa(GPrIntestineFamily, function(x) mean(x) > 1e-2, TRUE)
sample_data(GPrIntestineFamily)
diagdds = phyloseq_to_deseq2(GPrIntestineFamily, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
res
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestineFamily)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
##################################################
# logFoldChange between Sample types antimortem genus all
##################################################
#Large vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Ceca")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestineGenus <- subset_taxa(GPrIntestineAnti, Genus != "NA")
GPrIntestineGenus=tax_glom(GPrIntestineGenus, "Genus")
diagdds = phyloseq_to_deseq2(GPrIntestineGenus, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestineGenus)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs Small Intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Large Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestineGenus <- subset_taxa(GPrIntestineAnti, Genus != "NA")
GPrIntestineGenus=tax_glom(GPrIntestineGenus, "Genus")
diagdds = phyloseq_to_deseq2(GPrIntestineGenus, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestineGenus)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Ceca vs large intestine
GPrIntestine=subset_samples(physeq2, Sample.Type!="Cloaca"& Sample.Type != "Small Intestine")
GPrIntestineAnti=subset_samples(GPrIntestine, Anti_Post=="Antemortem")
GPrIntestineAnti
GPrIntestineGenus <- subset_taxa(GPrIntestineAnti, Genus != "NA")
GPrIntestineGenus=tax_glom(GPrIntestineGenus, "Genus")
#GPrIntestineFamily = filter_taxa(GPrIntestineFamily, function(x) mean(x) > 1e-2, TRUE)
diagdds = phyloseq_to_deseq2(GPrIntestineGenus, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
res
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(GPrIntestineGenus)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
############
#Taxonomic logfoldchange anti vs post all sample types combined
###########
AnteVSPostTax <- subset_taxa(physeq2, Phylum != "NA") #Change for other levels
AnteVSPostTax=tax_glom(AnteVSPostTax, "Phylum") #Change for other levels ex Family, Genus, Phylum
diagdds = phyloseq_to_deseq2(AnteVSPostTax, ~ Anti_Post) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(AnteVSPostTax)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
################
#Taxonomic logfoldchange antemortem vs post by sample type
###############
#Large Intestine
TaxGroup=subset_samples(physeq2, Sample.Type=="Large Intestine")
TaxGroup <- subset_taxa(TaxGroup, Genus != "NA")
TaxGroup=tax_glom(TaxGroup, "Genus")
sample_data(TaxGroup)
diagdds = phyloseq_to_deseq2(TaxGroup, ~ Anti_Post) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
#diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(TaxGroup)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Small Intestine
TaxGroup=subset_samples(physeq2, Sample.Type=="Small Intestine")
TaxGroup <- subset_taxa(TaxGroup, Genus != "NA")
TaxGroup=tax_glom(TaxGroup, "Genus")
sample_data(TaxGroup)
diagdds = phyloseq_to_deseq2(TaxGroup, ~ Anti_Post) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(TaxGroup)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
#Cloaca
TaxGroup=subset_samples(physeq2, Sample.Type=="Cloaca")
TaxGroup <- subset_taxa(TaxGroup, Genus != "NA")
TaxGroup=tax_glom(TaxGroup, "Genus")
sample_data(TaxGroup)
diagdds = phyloseq_to_deseq2(TaxGroup, ~ Anti_Post) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
res = results(diagdds, cooksCutoff = FALSE)
#res$padj
#res
alpha = 0.05
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(TaxGroup)[rownames(sigtab), ], "matrix"))
#head(sigtab)
sigtab
##############
#####################
#Differences in alpha diversity antemortem
###################
#Shannon
Diversity=read.table("C:\\Users\\Joe Receveur\\Documents\\MSU data\\Starling\\StarlingMeta4.18WDiversity.txt",header=TRUE) #File from QIIME OUTPUT
head(Diversity)
AntimortemDiv <- subset(Diversity, Anti_Post == 'Anti_Mortem'& Sample_Type!="Cloacal_Swab")
(AntimortemDiv)
Trtdata <- ddply(AntimortemDiv, c("Sample_Type"), summarise,
N = length(shannon),
mean = mean(shannon),
sd = sd(shannon),
se = sd / sqrt(N))
Trtdata
labels <- c(Ceca = "Ceca", Cloacal_Swab = "Cloacal Swab",Large_Intestine_Tissue="Large Intestine Tissue", Small_Intestine_Contents="Small Intestine Contents",Headgut_Swab="Headgut Swab",Small_Intestine_Tissue="Small Intestine Tissue")
cdataplot=ggplot(Trtdata, aes(x=Sample_Type,y=mean))+geom_bar(aes(fill = Sample_Type),colour="black", stat="identity")+xlab("Sample Type")+ylab("Shannon Diversity") + theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+theme(axis.title.x=element_blank())+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_errorbar(aes(ymin=mean-se,ymax=mean+se),color="black",width=1) + scale_fill_manual(values=cbPalette)#+facet_wrap(~Sample_Type)#scale_x_discrete(labels=c("0 hrs", "24 hrs", "48 hrs","72 hrs"))+
cdataplot+guides(fill=FALSE)
kruskal.test(shannon~ Sample_Type, data=AntimortemDiv)
Means=compare_means(shannon ~ Sample_Type, data = AntimortemDiv, method= "wilcox",
, p.adjust.method = "fdr")
Means
#Faith's Diversity
head(Diversity)
Trtdata <- ddply(AntimortemDiv, c("Sample_Type"), summarise,
N = length(faith_pd),
mean = mean(faith_pd),
sd = sd(faith_pd),
se = sd / sqrt(N))
Trtdata
labels <- c(Ceca = "Ceca", Cloacal_Swab = "Cloacal Swab",Large_Intestine_Tissue="Large Intestine Tissue", Small_Intestine_Contents="Small Intestine Contents",Headgut_Swab="Headgut Swab",Small_Intestine_Tissue="Small Intestine Tissue")
cdataplot=ggplot(Trtdata, aes(x=Sample_Type,y=mean))+geom_bar(aes(fill = Sample_Type),colour="black", stat="identity")+xlab("Sample Type")+ylab("Faith PD (SEM)") + theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+theme(axis.title.x=element_blank())+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_errorbar(aes(ymin=mean-se,ymax=mean+se),color="black",width=1) + scale_fill_manual(values=cbPalette)#+facet_wrap(~Sample_Type)#scale_x_discrete(labels=c("0 hrs", "24 hrs", "48 hrs","72 hrs"))+
cdataplot+guides(fill=FALSE)
kruskal.test(faith_pd~ Sample_Type, data=AntimortemDiv)
Means=compare_means(shannon ~ Sample_Type, data = AntimortemDiv, method= "wilcox",
, p.adjust.method = "fdr")
Means
####################
#Differences in alpha diversity postmortem
###################
#Shannon By sample type
Diversity=read.table("StarlingMeta4.18WDiversity.txt",header=TRUE) #File from QIIME OUTPUT
head(Diversity)
PostmortemDiv <- subset(Diversity, Anti_Post == 'Post_Mortem')
head(PostmortemDiv)
kruskal.test(shannon~ Sample_Type, data=PostmortemDiv)
Means=compare_means(shannon ~ Sample_Type, data = AntimortemDiv, method= "wilcox",
, p.adjust.method = "fdr")
Means
#Faith's Diversity by sample type
head(Diversity)
kruskal.test(faith_pd~ Sample_Type, data=AntimortemDiv)
Means=compare_means(shannon ~ Sample_Type, data = AntimortemDiv, method= "wilcox",
, p.adjust.method = "fdr")
Means
#Shannon By hour Large Intestine
PostmortemDiv <- subset(Diversity, Anti_Post == 'Post_Mortem'&Sample_Type=="Large_Intestine_Tissue")
head(PostmortemDiv)
kruskal.test(shannon~ Hours, data=PostmortemDiv)
#Faith's Diversity by hour Large Intestine
kruskal.test(faith_pd~ Hours, data=PostmortemDiv)
#Shannon By hour Small Intestine
PostmortemDiv <- subset(Diversity, Anti_Post == 'Post_Mortem'&Sample_Type=="Small_Intestine_Tissue")
head(PostmortemDiv)
kruskal.test(shannon~ Hours, data=PostmortemDiv)
#Faith's Diversity by hour Small Intestine
kruskal.test(faith_pd~ Hours, data=PostmortemDiv)
#Shannon By hour Ceca
PostmortemDiv <- subset(Diversity, Anti_Post == 'Post_Mortem'&Sample_Type=="Ceca")
head(PostmortemDiv)
kruskal.test(shannon~ Hours, data=PostmortemDiv)
#Faith's Diversity by hour Ceca
kruskal.test(faith_pd~ Hours, data=PostmortemDiv)
#Shannon By hour Cloaca
PostmortemDiv <- subset(Diversity, Anti_Post == 'Post_Mortem'&Sample_Type=="Cloacal_Swab")
head(PostmortemDiv)
kruskal.test(shannon~ Hours, data=PostmortemDiv)
#Faith's Diversity by hour Cloaca
kruskal.test(faith_pd~ Hours, data=PostmortemDiv)
######################
#Differences in alpha diversity antemortem vs post split by sample type
###################
#Shannon Ante-Post Large Intestine
PostmortemDiv <- subset(Diversity,Sample_Type=="Large_Intestine_Tissue")
head(PostmortemDiv)
kruskal.test(shannon~ Anti_Post, data=PostmortemDiv)
Trtdata <- ddply(PostmortemDiv, c("Anti_Post"), summarise,
N = length(shannon),
mean = mean(shannon),
sd = sd(shannon),
se = sd / sqrt(N))
Trtdata
#Faith's Diversity Ante-Post Large Intestine
kruskal.test(faith_pd~ Anti_Post, data=PostmortemDiv)
Trtdata <- ddply(PostmortemDiv, c("Anti_Post"), summarise,
N = length(faith_pd),
mean = mean(faith_pd),
sd = sd(faith_pd),
se = sd / sqrt(N))
Trtdata
#Shannon By Amte-Post Small Intestine
PostmortemDiv <- subset(Diversity, Sample_Type=="Small_Intestine_Tissue")
head(PostmortemDiv)
kruskal.test(shannon~ Anti_Post, data=PostmortemDiv)
Trtdata <- ddply(PostmortemDiv, c("Anti_Post"), summarise,
N = length(shannon),
mean = mean(shannon),
sd = sd(shannon),
se = sd / sqrt(N))
Trtdata
#Faith's Diversity by Ante-Post Small Intestine
kruskal.test(faith_pd~ Anti_Post, data=PostmortemDiv)
Trtdata <- ddply(PostmortemDiv, c("Anti_Post"), summarise,
N = length(faith_pd),
mean = mean(faith_pd),
sd = sd(faith_pd),
se = sd / sqrt(N))
Trtdata
#Shannon By Ante-Post Ceca
PostmortemDiv <- subset(Diversity, Sample_Type=="Ceca")
head(PostmortemDiv)
kruskal.test(shannon~ Anti_Post, data=PostmortemDiv)
Trtdata <- ddply(PostmortemDiv, c("Anti_Post"), summarise,
N = length(shannon),
mean = mean(shannon),
sd = sd(shannon),
se = sd / sqrt(N))
Trtdata
#Faith's Diversity by Ante-Post Ceca
kruskal.test(faith_pd~ Anti_Post, data=PostmortemDiv)
Trtdata <- ddply(PostmortemDiv, c("Anti_Post"), summarise,
N = length(faith_pd),
mean = mean(faith_pd),
sd = sd(faith_pd),
se = sd / sqrt(N))
Trtdata
#######################
#Beta dispersion split by sample type
#########################
#All grouped Antemortem vs post
GPdist=phyloseq::distance(physeq, "jaccard")
beta=betadisper(GPdist, sample_data(physeq)$Anti_Post)
permutest(beta)
boxplot(beta)
beta
TukeyHSD(beta, which = "group", ordered = FALSE,
conf.level = 0.95)
#Large Intestine
Subset<-subset_samples(physeq,Sample.Type=="Large Intestine")
Subset
GPdist=phyloseq::distance(Subset, "jaccard")
beta=betadisper(GPdist, sample_data(Subset)$Anti_Post)
permutest(beta)
boxplot(beta)
beta
TukeyHSD(beta, which = "group", ordered = FALSE,
conf.level = 0.95)
#Small Intestine
Subset<-subset_samples(physeq,Sample.Type=="Small Intestine")
sample_data(Subset)
GPdist=phyloseq::distance(Subset, "jaccard")
beta=betadisper(GPdist, sample_data(Subset)$Anti_Post)
permutest(beta)
boxplot(beta)
beta
TukeyHSD(beta, which = "group", ordered = FALSE,
conf.level = 0.95)
#Ceca
Subset<-subset_samples(physeq,Sample.Type=="Ceca")
sample_data(Subset)
GPdist=phyloseq::distance(Subset, "jaccard")
beta=betadisper(GPdist, sample_data(Subset)$Anti_Post)
permutest(beta)
boxplot(beta)
beta
TukeyHSD(beta, which = "group", ordered = FALSE,
conf.level = 0.95)
########################
#Differences in beta diversity antemortem
#######################
Antimortem=subset_samples(physeq, Anti_Post== "Antemortem")
AntimortemJaccard=subset_samples(Antimortem, Sample.Type!= "Cloacal_Swab")
GPdistAntemortemJaccard=phyloseq::distance(AntimortemJaccard, "jaccard")
adonis(GPdistAntemortemJaccard ~ Sample.Type, as(sample_data(AntimortemJaccard), "data.frame"))
#F = 1.116, P = 0.125
#####################
#Differences in beta diversity antemortem vs postmortem by sample type
#####################
#Small Intestine
SmallIntestine=subset_samples(physeq,Sample.Type=="Small Intestine")
sample_data(SmallIntestine)
SmallIntestineOrd=phyloseq::distance(SmallIntestine, "jaccard") #Tree will not be a submultiple as some of the taxa are subset out
adonis(SmallIntestineOrd ~ Anti_Post, as(sample_data(SmallIntestine), "data.frame"))
#F= 1.87, P = 0.011
#Large Intestine
LargeIntestine=subset_samples(physeq,Sample.Type=="Large Intestine")
sample_data(LargeIntestine)
LargeIntestineOrd=phyloseq::distance(LargeIntestine, "jaccard")
adonis(LargeIntestineOrd ~ Anti_Post, as(sample_data(LargeIntestine), "data.frame"))
# F = 2.49, P = 0.001
#Cloaca Not run as Cloaca only has 2 antemortem samples
Cloaca=subset_samples(physeq,Sample.Type=="Cloaca")
sample_data(Cloaca)
CloacaOrd=phyloseq::distance(Cloaca, "jaccard")
adonis(CloacaOrd ~ Anti_Post, as(sample_data(Cloaca), "data.frame"))
#Ceca
Ceca=subset_samples(physeq,Sample.Type=="Ceca")
sample_data(Ceca)
CecaOrd=phyloseq::distance(Ceca, "jaccard")
adonis(CecaOrd ~ Anti_Post, as(sample_data(Ceca), "data.frame"))
#############################
#Picrust rel abundances antemortem
##############################
PicrustAntemortem<-subset_samples(PiCRUST, Anti_Post== "Antemortem")
PicrustAntemortem<- transform_sample_counts(PicrustAntemortem, function(x) x / sum(x) ) #transform samples based on relative abundance
head(sample_data(PicrustAntemortem))
df <- psmelt(PicrustAntemortem)
df$Abundance=df$Abundance*100
head(df)
Trtdata <- ddply(df, c("KEGG.Pathway","Sample.Type"), summarise,
N = length(Abundance),
mean = mean(Abundance),
sd = sd(Abundance),
se = sd / sqrt(N))
(Trtdata)
#write.csv(Trtdata, "AntemortemPiCrustByType.csv")
###########################
#PiCRUST logfold change sample types Antemortem
############################
#Large vs Small Intestine
PicrustAntemortem<-subset_samples(PiCRUST, Anti_Post== "Antemortem")
PicrustIntestinentemortem=subset_samples(PicrustAntemortem, Sample.Type!="Cloaca"& Sample.Type != "Ceca")
PicrustIntestinentemortem
PicrustIntestinentemortem <- subset_taxa(PicrustIntestinentemortem, KEGG.Pathway != "NA")
PicrustIntestinentemortem
diagdds = phyloseq_to_deseq2(PicrustIntestinentemortem, ~ Type_Hours) #Here is where you choose what variable you want to look at ex Anti_Post, log fold change can only deal with two groups so you will have to subset your samples
# calculate geometric means prior to estimate size factors
diagdds$TypeHours <- factor(paste0(diagdds$Sample_Type, diagdds$Hours))
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
geoMeans = apply(counts(diagdds), 1, gm_mean)