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Visualization and analysis of trypanosome NGS data 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.
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)
theme_set(theme_bw())Generate phyloseq object from spreadsheets.
Import ASV/OTU count data
count_data <- read_csv("data/tryp-phyloseq/count_data_cleaned.csv")
# use first column as label for rows
count_data_lab = column_to_rownames(count_data, var = "#Zotu ID")
# Make matrix
otumat <- as.matrix(count_data_lab)Import taxonomy data
taxonomy <- read_csv("data/tryp-phyloseq/taxonomy.csv",
col_types = cols(Accession_description = col_skip(),
`Accession no.` = col_skip(), evalue = col_skip(),
`per. Ident` = col_skip(), taxid = col_skip()))
# use first column as label for rows
taxonomy_lab = column_to_rownames(taxonomy, var = "#Zotu ID")
taxmat <- as.matrix(taxonomy_lab)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)Add metadata and sequence data
Add sequences to phyloseq object
# read sequence file
rep.seqs <- Biostrings::readDNAStringSet("data/tryp-phyloseq/unoise_zotus.fasta", format = "fasta")Add metadata, importing gDNAID as factor to be able to merge later on
metadata <- read_csv("data/tryp-phyloseq/sampledata.csv")
metadata_lab = column_to_rownames(metadata, var = "SampleID")
sampledata = sample_data(data.frame(metadata_lab))
sampledataCreate final phyloseq object
Now you can merge your data to create a final phyloseq object
ps_raw_tryp = merge_phyloseq(physeq, sampledata, rep.seqs)Preliminary subset
Remove samples with NA values or not part of final data set,
ps_raw_tryp <- subset_samples(ps_raw_tryp, !SampleType=="SampleEcol")
ps_samp_tryp <- subset_samples(ps_raw_tryp, SampleType=="Sample").RDataSave R data for phyloseq object - saving “raw data” which inc controls (ps_raw_tryp) and “sample only data” (ps_samp_tryp)
save(ps_raw_tryp, file = "data/Rdata/ps_raw_tryp.RData")
save(ps_samp_tryp, file = "data/Rdata/ps_samp_tryp.RData")To load raw and sample data quickly from .RData format.
load("data/Rdata/ps_raw_tryp.RData")
load("data/Rdata/ps_samp_tryp.RData")An easy way to view the tables is using Nice Tables
Nice.Table(ps_raw_tryp@sam_data)Nice.Table(ps_raw_tryp@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_tryp)) # Put sample_data into a ggplot-friendly data.frame
df$LibrarySize <- sample_sums(ps_raw_tryp)
df <- df[order(df$LibrarySize),]
df$Index <- seq(nrow(df))
libQC <- ggplot(data=df, aes(x=Index, y=LibrarySize, color=SampleType)) + geom_point() + theme_bw() + scale_colour_brewer(palette = "Set1")
ggsave("libQC.pdf", plot = libQC, path = "output/plots/trypNGS", width = 15, height = 10, units = "cm")Make html plot with plotly
libQCplotly <- ggplotly(libQC)
htmlwidgets::saveWidget(libQCplotly, "output/plots/libQCplotly.html")Make distribution plot of reads using microbiomeutilities
distrib <- plot_read_distribution(ps_raw_tryp, groups = "SampleCategory",
plot.type = "density") + xlab("Reads per sample") + ylab("Density")
distrib <- distrib + geom_density(alpha = 0.5, fill = "grey") + theme(axis.text.x = element_text(angle = 45, size=10, vjust = 1))
ggsave("distrib.pdf", plot = distrib, path = "output/plots/trypNGS", width = 15, height = 10, units = "cm")QC <- ggarrange(libQC, distrib,
labels = c("A", "B"),
ncol = 1, nrow = 2)
ggsave("QC.pdf", plot = QC, path = "output/plots/trypNGS", width = 20, height = 20, units = "cm")Subset phyloseq object based on sample types
# samples and positive controls
ps_tryp_sampcon = subset_samples(ps_raw_tryp, SampleType=="Sample" | SampleType=="ControlPos")
# Blood samples only
ps_tryp_bl = subset_samples(ps_samp_tryp, SampleCategory=="Blood")
# Tissue samples only
ps_tryp_tis = subset_samples(ps_samp_tryp, SampleCategory=="Tissue")
# Tick samples only
ps_tryp_tick = subset_samples(ps_samp_tryp, SampleCategory=="Tick")Subset phyloseq object based on host species
# Black rat
ps_BR = subset_samples(ps_samp_tryp, species=="Black rat")
# Brush tail possum
ps_BTP = subset_samples(ps_samp_tryp, species=="Brush tail possum")
# Chuditch
ps_chud = subset_samples(ps_samp_tryp, species=="Chuditch")
# Long-nosed bandicoot
ps_LNB = subset_samples(ps_samp_tryp, 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
tryp_amp <- phyloseq_to_ampvis2(ps_samp_tryp)Subset ampvis2 object based on sample category
#remove controls
amp_samp <- amp_subset_samples(tryp_amp,
!SampleType %in% c("ControlPos"),
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)Make heat map using ampvis2
First filter taxa
# order Trypanosomatida
tax_vector1 <- c(
"Trypanosomatida"
)
amp_samp_otry <- amp_subset_taxa(amp_samp,
tax_vector = tax_vector1)
# Trypanosoma species of interest
tax_vector2 <- c(
"Trypanosoma gilletti",
"Trypanosoma sp. (cyclops-like)",
"Trypanosoma vegrandis",
"Trypanosoma noyesi",
"Trypanosoma sp. (lewisi-like)",
"Lotmaria passim"
)
amp_samp_targettryp <- amp_subset_taxa(amp_samp,
tax_vector = tax_vector2)Relative abundance
Heatmap of subsetted data using relative abundance
# Relative abundance order level
heatmap_rel1 <- amp_heatmap(amp_samp_otry,
facet_by = "SampleCategory",
group_by = "species",
tax_aggregate = "Species",
tax_show = 10,
normalise = TRUE,
plot_values = FALSE,
plot_values_size = 3,
round = 0, color_vector = c("white", "#e5ba52", "#ab7ca3", "#9d02d7", "#0030bf"), plot_colorscale = "log10") +
theme(axis.text.x = element_text(angle = 45, size=10, vjust = 1),
axis.text.y = element_text(size=10),
legend.position="right")
# Relative abundance trypanosoma species of interest
heatmap_rel2 <- amp_heatmap(amp_samp_targettryp,
facet_by = "SampleCategory",
group_by = "species",
tax_aggregate = "Species",
tax_show = 10,
normalise = TRUE,
plot_values = FALSE,
plot_values_size = 3,
round = 0, color_vector = c("white", "#e5ba52", "#ab7ca3", "#9d02d7", "#0030bf"), plot_colorscale = "log10") +
theme(axis.text.x = element_text(angle = 45, size=10, vjust = 1),
axis.text.y = element_text(size=10),
legend.position="right")Save PDF of plots
ggsave("heatmap_rel1.pdf", plot = heatmap_rel1, path = "output/plots/trypNGS", width = 25, height = 15, units = "cm")
ggsave("heatmap_rel2.pdf", plot = heatmap_rel2, path = "output/plots/trypNGS", width = 25, height = 15, units = "cm")Count of sequences
heatmap_count <- amp_heatmap(amp_samp,
facet_by = "SampleCategory",
group_by = "species",
tax_aggregate = "Family",
tax_show = 40,
normalise = FALSE,
plot_values = FALSE,
plot_values_size = 3,
round = 0, color_vector = c("white", "#e5ba52", "#ab7ca3", "#9d02d7", "#0030bf"), plot_colorscale = "log10") + theme(axis.text.x = element_text(angle = 45, size=10, vjust = 1), axis.text.y = element_text(size=10), legend.position="right")Heatmap using microbiomeutilities
Subset taxa
ps_tryp_otry = subset_taxa(ps_samp_tryp, Order=="Trypanosomatida")
ps_tryp_subtry = subset_taxa(ps_samp_tryp, Species=="Trypanosoma gilletti" | Species=="Trypanosoma sp. (cyclops-like)" | Species =="Trypanosoma vegrandis" | Species=="Trypanosoma noyesi" | Species=="Trypanosoma sp. (lewisi-like)" | Species =="Lotmaria passim")# create a gradient color palette for abundance
#grad_ab <- colorRampPalette(c("#faf3dd","#f7d486" ,"#5e6472"))
color_vector = colorRampPalette(c("#faf3dd", "#e5ba52", "#ab7ca3", "#9d02d7", "#0030bf"))
grad_ab_pal <- color_vector(10)
# create a color palette for varaibles of interest
meta_colors = list(c("Blood" = "#7a255d", "Tick" = "#9fd0cb", "Tissue" = "#7566ff"),
c("Brush tail possum" = "#440154FF", "Black rat" = "#482878FF", "Swamp rat"="#3E4A89FF", "Long-nosed bandicoot" ="#31688EFF", "Bush rat"="#26828EFF", "Brown antechinus" = "#1F9E89FF", "Rabbit"="#35B779FF", "Chuditch"= "#6DCD59FF", "Quenda" = "#B4DE2CFF", "Deer" = "#FDE725FF" ))
# add labels for pheatmap to detect
names(meta_colors) <- c("SampleCategory", "species")
ph_heatmap <- plot_taxa_heatmap(ps_tryp_subtry,
subset.top = 50,
VariableA = c("SampleCategory","species"),
heatcolors = grad_ab_pal, #rev(brewer.pal(6, "RdPu")),
transformation = "log10",
cluster_rows = T,
cluster_cols = F,
show_colnames = F,
annotation_colors=meta_colors, fontsize = 8,)Save PDF of plots
ggsave("ph_heatmap.pdf", plot = ph_heatmap$plot, path = "output/plots/trypNGS", width = 25, height = 15, units = "cm")
sessionInfo()