Last updated: 2021-07-03
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Knit directory: wildlife-bacteria/
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| File | Version | Author | Date | Message |
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| Rmd | af6ad5b | siobhon-egan | 2021-07-03 | general update |
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| Rmd | 6ebcc5d | siobhon-egan | 2021-04-24 | updates |
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| Rmd | ebe20d0 | siobhon-egan | 2021-02-26 | add qiime and phyloseq pages |
Visualization and analysis of microbiome data.
Note this was run using R version 4.0.3 and RStudo version 1.4. See R info for full details of R session.
See Visualization page for details on data visualization.
Install libraries if required
Only need to run this code once.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# phyloseq
source('http://bioconductor.org/biocLite.R')
biocLite('phyloseq')
#tidyverse
install. packages("tidyverse")
#ampvis2
install.packages("remotes")
remotes::install_github("MadsAlbertsen/ampvis2")
#ampvis2extras
install.packages("BiocManager")
BiocManager::install("kasperskytte/ampvis2extras")
#ggpubr
install.packages("ggpubr")
#agricolae
install.packages("agricolae")
install.packages("remotes")
remotes::install_github("DanielSprockett/reltools")
devtools::install_github('jsilve24/philr')
#decontam
BiocManager::install("decontam")
library(decontam)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Biostrings")Load libraries
pkgs <- c("qiime2R", "phyloseq", "tidyverse", "ampvis2", "ampvis2extras",
"ggpubr", "agricolae", "plotly", "viridis", "cowplot", "MicrobeR",
"microbiome", "reshape", "decontam", "data.table", "ape", "DESeq2",
"vegan", "microbiomeutilities", "knitr", "tibble", "dplyr",
"patchwork", "Biostrings")
lapply(pkgs, require, character.only = TRUE)Generate phyloseq object from spreadsheets.
Import ASV/OTU count data
dada2_ASVs <- read_tsv("data/dada2/phyloseq/feature-table.tsv", skip = 1)
dada2_ASVs_lab = column_to_rownames(dada2_ASVs, var = "#OTU ID")Import taxonomy data
taxonomy <- read_csv("data/dada2/phyloseq/taxonomy.csv")
taxonomy_lab = column_to_rownames(taxonomy, var = "#q2:types")Add ASV sequences by reading in the .fasta file.
# read sequence file
rep.seqs <- Biostrings::readDNAStringSet("data/dada2/phyloseq/all_rep-seqs.fasta", format = "fasta")Add metadata, importing gDNAID, SampleType and SampleCategory as factor to be able to merge later on and perform statistical analysis
library(readr)
metadata <- read_csv("data/dada2/phyloseq/metadata.csv",
col_types = cols(
SampleType = col_factor(levels =c("categorical","Sample", "Control", "EcolSample")),
SampleCategory = col_factor(levels = c("categorical", "Blood", "Tissue", "Tick")),
gDNAID = col_factor(levels = c("categorical","Bl_1","Bl_10","Bl_102","Bl_113","Bl_114","Bl_115","Bl_116","Bl_117","Bl_118","Bl_119","Bl_120","Bl_121","Bl_122","Bl_123","Bl_124","Bl_125","Bl_126","Bl_127","Bl_128","Bl_129","Bl_130","Bl_131","Bl_132","Bl_133","Bl_134","Bl_135","Bl_136","Bl_137","Bl_138","Bl_139","Bl_140","Bl_141","Bl_142","Bl_143","Bl_144","Bl_145","Bl_146","Bl_147","Bl_149","Bl_150","Bl_151","Bl_152","Bl_153","Bl_154","Bl_155","Bl_156","Bl_157","Bl_158","Bl_159","Bl_160","Bl_161","Bl_162","Bl_163","Bl_164","Bl_18","Bl_19","Bl_25","Bl_27","Bl_28","Bl_31","Bl_37","Bl_43","Bl_49","Bl_52","Bl_54","Bl_55","Bl_6","Bl_61","Bl_63","Bl_66","Bl_7","Bl_73","Bl_75","Bl_78","Bl_79","Bl_80","Bl_82","Bl_86","Bl_94","Bl_98","Bl_NTC1","Bl_NTC2","Bl_NTC3","Bl_NTC4","Bl_NTC45","Bl_NTC46","Bl_NTC47","Bl_100","Bl_101","Bl_103","Bl_104","Bl_105","Bl_106","Bl_108","Bl_109","Bl_11","Bl_110","Bl_111","Bl_112","Bl_12","Bl_13","Bl_14","Bl_15","Bl_16","Bl_17","Bl_2","Bl_20","Bl_21","Bl_22","Bl_23","Bl_24","Bl_26","Bl_29","Bl_3","Bl_30","Bl_32","Bl_33","Bl_34","Bl_35","Bl_36","Bl_38","Bl_39","Bl_4","Bl_40","Bl_41","Bl_42","Bl_44","Bl_45","Bl_46","Bl_47","Bl_48","Bl_5","Bl_50","Bl_51","Bl_53","Bl_56","Bl_57","Bl_58","Bl_59","Bl_60","Bl_62","Bl_64","Bl_65","Bl_67","Bl_68","Bl_69","Bl_70","Bl_71","Bl_72","Bl_74","Bl_76","Bl_77","Bl_8","Bl_81","Bl_83","Bl_84","Bl_85","Bl_87","Bl_88","Bl_89","Bl_9","Bl_90","Bl_91","Bl_92","Bl_93","Bl_95","Bl_96","Bl_97","Bl_99","Bl_C001Q","Bl_C002M","Bl_C002Q","Bl_C003M","Bl_C003Q","Bl_exbM","Bl_exbQ","Bl_Q001M","Bl_Q001Q","Tis_indexNTC1","TIS_indexNTC2","Tis_indexNTC3","Tis_NTC5","Tis_NTC54","Tis_NTC55","Tis_NTC57","Tis_NTC58","Tis_NTC59","Tis_NTC6","Tis_NTC60","Tis_NTC7","Tis_NTC8","Tis_104","Tis_105","Tis_106","Tis_107","Tis_108","Tis_109","Tis_110","Tis_111","Tis_112","Tis_113","Tis_114","Tis_115","Tis_116","Tis_117","Tis_118","Tis_119","Tis_120","Tis_121","Tis_122","Tis_123","Tis_124","Tis_125","Tis_126","Tis_127","Tis_128","Tis_129","Tis_130","Tis_131","Tis_132","Tis_133","Tis_134","Tis_135","Tis_136","Tis_137","Tis_138","Tis_139","Tis_140","Tis_141","Tis_142","Tis_143","Tis_144","Tis_145","Tis_146","Tis_147","Tis_148","Tis_149","Tis_15","Tis_150","Tis_151","Tis_152","Tis_153","Tis_154","Tis_155","Tis_156","Tis_157","Tis_158","Tis_159","Tis_160","Tis_161","Tis_162","Tis_163","Tis_164","Tis_165","Tis_166","Tis_167","Tis_168","Tis_169","Tis_170","Tis_171","Tis_172","Tis_173","Tis_174","Tis_175","Tis_176","Tis_177","Tis_178","Tis_179","Tis_21","Tis_43","Tis_5","Tis_55","Tis_56","Tis_58","Tis_62","Tis_68","Tis_70","Tis_72","Tis_80","Tis_84","Tis_94","Tis_95","Tis_96","Tis_97","Tis_SC009","Tis_1","Tis_10","Tis_100","Tis_101","Tis_102","Tis_103","Tis_11","Tis_12","Tis_13","Tis_14","Tis_16","Tis_17","Tis_18","Tis_19","Tis_2","Tis_20","Tis_22","Tis_23","Tis_24","Tis_25","Tis_26","Tis_27","Tis_28","Tis_29","Tis_3","Tis_30","Tis_31","Tis_32","Tis_33","Tis_34","Tis_35","Tis_36","Tis_37","Tis_38","Tis_39","Tis_4","Tis_40","Tis_41","Tis_42","Tis_44","Tis_45","Tis_46","Tis_47","Tis_48","Tis_49","Tis_50","Tis_51","Tis_52","Tis_53","Tis_54","Tis_57","Tis_59","Tis_6","Tis_60","Tis_61","Tis_63","Tis_64","Tis_65","Tis_66","Tis_67","Tis_69","Tis_7","Tis_71","Tis_73","Tis_74","Tis_75","Tis_76","Tis_77","Tis_78","Tis_79","Tis_8","Tis_81","Tis_82","Tis_83","Tis_85","Tis_86","Tis_87","Tis_88","Tis_89","Tis_9","Tis_90","Tis_91","Tis_92","Tis_93","Tis_98","Tis_99","Tis_SC010","Tis_SC011","Tis_SCexb","Tk_1","Tk_100","Tk_101","Tk_102","Tk_103","Tk_107","Tk_108","Tk_119","Tk_120","Tk_122","Tk_131","Tk_134","Tk_136","Tk_141","Tk_143","Tk_144","Tk_148","Tk_149","Tk_150","Tk_151","Tk_152","Tk_153","Tk_154","Tk_155","Tk_156","Tk_157","Tk_158","Tk_159","Tk_160","Tk_161","Tk_162","Tk_163","Tk_164","Tk_165","Tk_166","Tk_167","Tk_168","Tk_169","Tk_17","Tk_170","Tk_171","Tk_172","Tk_173","Tk_174","Tk_175","Tk_176","Tk_177","Tk_178","Tk_179","Tk_180","Tk_181","Tk_182","Tk_183","Tk_184","Tk_185","Tk_186","Tk_187","Tk_188","Tk_189","Tk_19","Tk_190","Tk_191","Tk_192","Tk_193","Tk_194","Tk_195","Tk_196","Tk_197","Tk_198","Tk_199","Tk_20","Tk_200","Tk_201","Tk_202","Tk_203","Tk_204","Tk_205","Tk_206","Tk_207","Tk_208","Tk_209","Tk_210","Tk_211","Tk_212","Tk_213","Tk_214","Tk_215","Tk_216","Tk_217","Tk_218","Tk_219","Tk_220","Tk_221","Tk_222","Tk_223","Tk_224","Tk_225","Tk_27","Tk_3","Tk_34","Tk_38","Tk_4","Tk_40","Tk_46","Tk_47","Tk_48","Tk_49","Tk_50","Tk_51","Tk_52","Tk_53","Tk_54","Tk_56","Tk_57","Tk_59","Tk_60","Tk_61","Tk_62","Tk_64","Tk_68","Tk_71","Tk_72","Tk_73","Tk_74","Tk_76","Tk_85","Tk_86","Tk_89","Tk_90","Tk_91","Tk_95","Tk_96","Tk_97","Tk_98","Tk_83","Tk_2","Tk_18","Tk_35","Tk_84","Tk_37","Tk_5","Tk_22","Tk_39","Tk_63","Tk_75","Tk_87","Tk_99","Tk_6","Tk_24","Tk_88","Tk_8","Tk_26","Tk_41","Tk_65","Tk_77","Tk_9","Tk_42","Tk_66","Tk_78","Tk_11","Tk_29","Tk_43","Tk_55","Tk_79","Tk_12","Tk_31","Tk_44","Tk_67","Tk_80","Tk_92","Tk_104","Tk_13","Tk_32","Tk_45","Tk_69","Tk_81","Tk_93","Tk_105","Tk_15","Tk_33","Tk_58","Tk_70","Tk_82","Tk_94","Tk_ntc14","Tk_106","Tk_109","Tk_110","Tk_111","Tk_112","Tk_113","Tk_114","Tk_115","Tk_116","Tk_117","Tk_118","Tk_121","Tk_123","Tk_124","Tk_125","Tk_126","Tk_127","Tk_128","Tk_129","Tk_130","Tk_132","Tk_133","Tk_135","Tk_137","Tk_138","Tk_139","Tk_140","Tk_142","Tk_145","Tk_146","Tk_147","Tk_Ecol1","Tk_Ecol10","Tk_Ecol11","Tk_Ecol12","Tk_Ecol13","Tk_Ecol14","Tk_Ecol15","Tk_Ecol16","Tk_Ecol17","Tk_Ecol18","Tk_Ecol19","Tk_Ecol2","Tk_Ecol20","Tk_Ecol21","Tk_Ecol22","Tk_Ecol23","Tk_Ecol24","Tk_Ecol25","Tk_Ecol3","Tk_Ecol4","Tk_Ecol5","Tk_Ecol6","Tk_Ecol7","Tk_Ecol8","Tk_Ecol9","Tk_ntc15","Tk_ntc16","Tk_ntcecol1","Tk_ntcecol2"))))
###Not using tsv import anymore
#metadata <- read_tsv("../data/dada2/phyloseq/metadata.tsv")
# Remove second row which contains column data for QIIME2 format
metadata <- metadata[-c(1), ]
metadata_lab = column_to_rownames(metadata, var = "new_sample-id")
sampledata = sample_data(data.frame(metadata_lab))
sampledataRenaming sample IDs
Renaming sample-ids for consistency and matching to ENA accession numbers.
OldName <- colnames(dada2_ASVs_lab)
NewName <- rownames(metadata_lab)
dada2_ASVs_lab_renamed <- dada2_ASVs_lab %>% rename_at(vars(OldName), ~ NewName)
# Make OTU matrix
otumat <- as.matrix(dada2_ASVs_lab_renamed)
# Make Tax matrix
taxmat <- as.matrix(taxonomy_lab)Create phyloseq object
Check the class of the otumat and taxmat objects, they MUST be in matrix format. Then we can great a phyloseq object called physeq from the otu and taxonomy tables and check the sample names.
class(otumat)
class(taxmat)
OTU = otu_table(otumat, taxa_are_rows = TRUE)
TAX = tax_table(taxmat)
physeq = phyloseq(OTU, TAX)
physeq
sample_names(physeq)Now you can merge your sequence and metadata to create a final phyloseq object
ps_raw_bact = merge_phyloseq(physeq, sampledata, rep.seqs)Remove samples with NA values or not part of final data set,
ps_raw_bact <- subset_samples(ps_raw_bact, !SampleType=="EcolSample")An easy way to view the tables is using Nice Tables
Nice.Table(ps_raw_bact@sam_data)Nice.Table(ps_bact_samp@tax_table)R package decontam to assess contaminating OTUs, tutorial.
The CRAN version only works on R version <4. To install for R versions >4 install from bioconductor using the following
Make plot of library size of Samples vs Controls
df <- as.data.frame(sample_data(ps_raw_bact)) # Put sample_data into a ggplot-friendly data.frame
df$LibrarySize <- sample_sums(ps_raw_bact)
df <- df[order(df$LibrarySize),]
df$Index <- seq(nrow(df))
libQC <- ggplot(data=df, aes(x=Index, y=LibrarySize, color=SampleType)) + geom_point()
ggsave("libQC.pdf", plot = libQC, path = "output/plots", width = 10, height = 10, units = "cm")Make html plot with plotly
libQCplotly <- ggplotly(libQC)
htmlwidgets::saveWidget(libQCplotly, "output/plots/libQCplotly.html")Identify contaminating ASVs - define control samples and threshold (e.g. 0.05)
df <- as.data.frame(sample_data(ps_raw_bact)) # Put sample_data into a ggplot-friendly data.frame
sample_data(ps_raw_bact)$is.neg <- sample_data(ps_raw_bact)$SampleType == "Control"
contamdf.prev <- isContaminant(ps_raw_bact, method="prevalence", neg="is.neg", threshold = 0.05)Identify contaminants
table(contamdf.prev$contaminant)
head(which(contamdf.prev$contaminant))
table(contamdf.prev$contaminant)
con_ASVs <- contamdf.prev %>%
filter(contaminant == "TRUE")
con_ASVs <- rownames(con_ASVs)Take a look at the number of times several of these taxa were observed in negative controls and positive samples.
# Make phyloseq object of presence-absence in negative controls and true samples
ps.pa <- transform_sample_counts(ps_raw_bact, function(abund) 1*(abund>0))
ps.pa.neg <- prune_samples(sample_data(ps_raw_bact)$SampleType == "Control", ps.pa)
ps.pa.pos <- prune_samples(sample_data(ps.pa)$SampleType == "Sample", ps.pa)
# Make data.frame of prevalence in positive and negative samples
df.pa <- data.frame(pa.pos=taxa_sums(ps.pa.pos), pa.neg=taxa_sums(ps.pa.neg),
contaminant=contamdf.prev$contaminant)
# Make plot and save as pdf
deconplot <- ggplot(data=df.pa, aes(x=pa.neg, y=pa.pos, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")
ggsave("deconplot.pdf", plot = deconplot, path = "output/plots", width = 10, height = 10, units = "cm")Make distribution plot of reads using microbiomeutilities
distrib <- plot_read_distribution(ps_raw_bact, groups = "SampleCategory",
plot.type = "density") + xlab("Reads per sample") + ylab("Density")
distrib <- distrib + geom_density(alpha = 0.5, fill = "grey")
ggsave("distrib.pdf", plot = distrib, path = "output/plots", width = 15, height = 10, units = "cm")As determined by decontam methods identify contaminant ASVs and threshold for positive reads. Then transform otu count data.
ps_dec_bact <- prune_taxa(!contamdf.prev$contaminant, ps_raw_bact)
ps_dec_bact@otu_table [, 1:702][ps_dec_bact@otu_table [, 1:702] < 100] <- 0Save R data for phyloseq object - saving “raw data” (ps_raw_bact) and “decontaminated data” (ps_dec_bact)
save(ps_raw_bact, file = "RData/ps_raw_bact.RData")
save(ps_dec_bact, file = "RData/ps_dec_bact.RData")To load raw and decon data quickly from .RData format.
load("RData/ps_raw_bact.RData")
load("RData/ps_dec_bact.RData")Subset phyloseq object based on sample types
# Exclude controls
ps_bact_samp = subset_samples(ps_dec_bact, !SampleType=="Control")
# Blood samples only
ps_bact_bl = subset_samples(ps_bact_samp, SampleCategory=="Blood")
# Tissue samples only
ps_bact_tis = subset_samples(ps_bact_samp, SampleCategory=="Tissue")
# Tick samples only
ps_bact_tick = subset_samples(ps_bact_samp, SampleCategory=="Tick")Subset for taxa of interest - family level
# taxa of interest
ps_toi_fam = subset_taxa(ps_bact_samp, Family=="Coxiellaceae" | Family=="Mycoplasmataceae" | Family=="Bartonellaceae" | Family=="Francisellaceae" | Family=="Borreliaceae" | Family=="Anaplasmataceae" | Family=="Midichloriaceae" | Family =="Mycobacteriaceae" | Family=="Rickettsiaceae")
ps_toi_fam = prune_taxa(taxa_sums(ps_toi_fam) > 0, ps_toi_fam)
writeXStringSet(ps_toi_fam@refseq, "data/dada2_tois/ps_toi_fam.fasta")
melt_toi_fam = psmelt(ps_toi_fam)
melt_toi_fam = subset(melt_toi_fam, Abundance > 0)
write.csv(melt_toi_fam, "data/dada2_tois/melt_toi_fam.csv")Subset for taxa of interest - genus
# taxa of interest
ps_toi_gen = subset_taxa(ps_bact_samp, Family=="Coxiellaceae" | Genus=="Mycoplasma" | Genus=="Bartonella" | Genus=="Francisella" | Genus=="Borrelia" | Genus=="Rickettsia" | Genus=="Neoehrlichia" | Genus=="Ehrlichia" | Genus=="Anaplasma" | Genus=="Candidatus Midichloria")
ps_toi_gen = prune_taxa(taxa_sums(ps_toi_gen) > 0, ps_toi_gen)
writeXStringSet(ps_toi_gen@refseq, "data/dada2_tois/ps_toi_gen.fasta")
melt_toi_gen = psmelt(ps_toi_gen)
melt_toi_gen = subset(melt_toi_gen, Abundance > 0)
write.csv(melt_toi_gen, "data/dada2_tois/melt_toi_gen.csv")Outside of RStudio
Align sequences and produce quick neighbour joining tree using muscle64. Code for command line terminal with muscle64 installed in the $PATH
# Family taxa of interest
muscle64 -in data/dada2_tois/ps_toi_fam.fasta -out data/dada2_tois/ps_toi_fam_musaln.fasta
muscle64 -maketree -in data/dada2_tois/ps_toi_fam_musaln.fasta -out data/dada2_tois/ps_toi_fam_tree.phy -cluster neighborjoining
# Genus taxa of interest
muscle64 -in data/dada2_tois/ps_toi_gen.fasta -out data/dada2_tois/ps_toi_gen_musaln.fasta
muscle64 -maketree -in data/dada2_tois/ps_toi_gen_musaln.fasta -out data/dada2_tois/ps_toi_gen_tree.phy -cluster neighborjoiningNow back to RStudio
Add tree for this taxa
tree_toi_fam
tree_toi_fam <- read_tree("../data/dada2_tois/ps_toi_fam_tree.phy")
# Merge tree object into phyloseq to create tree_toi_fam object
ps_toi_fam <- merge_phyloseq(ps_toi_fam, tree_toi_fam)tree_toi_gen
tree_toi_gen <- read_tree("../data/dada2_tois/ps_toi_gen_tree.phy")
# Merge tree object into phyloseq to create tree_toi_gen object
ps_toi_gen <- merge_phyloseq(ps_toi_gen, tree_toi_gen)More subsetting
Subset phyloseq object based on host species
# Black rat
ps_BR = subset_samples(ps_bact_samp, species=="Black rat")
# Brush tail possum
ps_BTP = subset_samples(ps_bact_samp, species=="Brush tail possum")
# Chuditch
ps_chud = subset_samples(ps_bact_samp, species=="Chuditch")
# Long-nosed bandicoot
ps_LNB = subset_samples(ps_bact_samp, species=="Long-nosed bandicoot")Make ampvis2 object for analysis
#require the devtools package to source gist
if(!require("devtools"))
install.packages("devtools")
#source the phyloseq_to_ampvis2() function from the gist
devtools::source_gist("8d0ca4206a66be7ff6d76fc4ab8e66c6")
# convert
amp_raw_bact <- phyloseq_to_ampvis2(ps_raw_bact)
amp_dec_bact <- phyloseq_to_ampvis2(ps_dec_bact)
amp_toi_fam <- phyloseq_to_ampvis2(ps_toi_fam)
amp_toi_gen <- phyloseq_to_ampvis2(ps_toi_gen)Save R data for ampvis2 object - saving “decontam ampvis2 data” (bact_amp)
# raw ampvis2
save(amp_raw_bact, file = "RData/amp_raw_bact.RData")
# decontam ampvis2
save(amp_dec_bact, file = "RData/amp_dec_bact.RData")Subset ampvis2 object based on sample category
#remove controls
amp_samp <- amp_subset_samples(amp_dec_bact,
!SampleType %in% c("Control"),
RemoveAbsents = TRUE)
#blood samples
amp_bl <- amp_subset_samples(amp_samp,
SampleCategory %in% c("Blood"),
RemoveAbsents = TRUE)
#tissue samples
am_tis <- amp_subset_samples(amp_samp,
SampleCategory %in% c("Tissue"),
RemoveAbsents = TRUE)
#tick samples
amp_tick <- amp_subset_samples(amp_samp,
SampleCategory %in% c("Tick"),
RemoveAbsents = TRUE)
#tick samples
amp_tick_sub <- amp_subset_samples(amp_tick,
instar %in% c("Female", "Larvae", "Nymph"),
RemoveAbsents = TRUE)Ampvis2 have an easy way to merge replicate samples (i.e. duplicate PCR amplicons)
#merge by gDNAID
dmerged <- amp_mergereplicates(ps_bact_samp,
merge_var = "gDNAID",
round = "up"
)
dmerged$metadataFilter out low abundant samples
amp_samp1000 <- amp_subset_samples(amp_samp,
minreads = 1000,
RemoveAbsents = TRUE)
amp_samp1000Subset certain species and sites
chud <- amp_subset_samples(amp_samp,
species %in% c("Chuditch"),
!SampleCategory %in% c("Tick"),
RemoveAbsents = TRUE)
LNB <- amp_subset_samples(amp_samp,
species %in% c("Long-nosed bandicoot"),
RemoveAbsents = TRUE)
BTP <- amp_subset_samples(amp_samp,
species %in% c("Brush tail possum"),
RemoveAbsents = TRUE)
BR <- amp_subset_samples(amp_samp,
species %in% c("Black rat"),
RemoveAbsents = TRUE)Set colours when comparing blood, tick and tissue samples.
ColSampCat = c("#7a255d", "#9fd0cb", "#7566ff")Subset phyloseq object and convert to ‘long’ dataframe to quickly identify samples.
Identify unidentified taxa at kingdom level
# subset taxa to include those with no Kingdom assignment
kingdom_na = subset_taxa(ps_bact_samp, is.na(Kingdom))
# remove taxa with 0 abundance
kingdom_na <- prune_taxa(taxa_sums(kingdom_na) > 0, kingdom_na)
# write sequences to fasta file
writeXStringSet(kingdom_na@refseq, "data/dada2/unclassified_taxa/kingdom_na.fa")
# melt dataframe into long df
m_kingdom = psmelt(kingdom_na)
#Remove all rows were abundance is zero
m_kingdom2 = m_kingdom[m_kingdom$Abundance != 0, ]
# write melt data frame to csv file
write.csv(m_kingdom2, "data/dada2/unclassified_taxa/kingdom_na.csv")Taxa of interest - example
# subset taxa to include those with no Kingdom assignment
bart = subset_taxa(ps_bact_samp, Genus=="Rickettsia")
# remove taxa with 0 abundance
bart = prune_taxa(taxa_sums(bart) > 0, bart)
# melt dataframe into long df
m_bart= psmelt(bart)
#Remove all rows were abundance is zero
m_bart2 = m_bart[m_bart$Abundance != 0, ]
# identify number of sample positive (i.e. `gDNAID``)
n_distinct(m_bart2$gDNAID)
# identify number of animals positive (i.e. `animalID``)
n_distinct(m_bart2$animalID)
unique(m_bart2$species)
tmp = m_bart2 %>%
filter(SampleCategory == "Blood")
# identify number of sample positive (i.e. `gDNAID``)
n_distinct(tmp$gDNAID)
# identify number of animals positive (i.e. `animalID``)
n_distinct(tmp$animalID)
# List host species
unique(tmp$species)
unique(tmp$tick_species)
sessionInfo()