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R_analysis_DNA_all_droughtmicrobiome-exp.R
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R_analysis_DNA_all_droughtmicrobiome-exp.R
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#load libraries
library(ggplot2)
library(phyloseq)
library(dplyr)
library(vegan)
library(reshape2)
library(scales)
library(grid)
otu = read.csv("otu_table.csv", sep=",", row.names=1)
tax = read.csv("taxonomy.csv", sep=",", row.names=1)
tax = as.matrix(tax)
metadata = read.csv("sample-metadata-bean.csv", sep=",", row.names=1)
OTU = otu_table(otu, taxa_are_rows = TRUE)
TAX = tax_table(tax)
meta = sample_data(metadata)
##rownames(meta) <- meta$sampleid (this is not needed to merge)
##merge
phyloseq_merged = phyloseq(OTU, TAX, meta)
phyloseq_merged
sample_names(phyloseq_merged)
##check column names of taxonomy table
colnames(tax_table(phyloseq_merged))
##remove mito, chloroplast and archaea, eukarya
phyloseq_merged_clean <- phyloseq_merged %>%
subset_taxa(
Domain == "d__Bacteria" &
Family != " f__Chloroplast" &
#Order != "o_Mitochondria" &
#Class != "c__Chloroplast"
# OTU != "0a3cf58d4ca062c13d42c9db4ebcbc53" &
# OTU != "22f1fa0bdcc19746dee080bcc12a1840"
Family != " f__Mitochondria"
)
phyloseq_merged_clean
##one of the chloroplast id'd to OTU 2383b3a3e7fc6bd37f44efe7104b3fe4
#was not removed after this step, others were removed
#phyloseq_merged_clean <- phyloseq_merged_clean1 %>%
#subset_taxa(
# Family != "f__Chloroplast"
#)
#phyloseq_merged_clean <- phyloseq_merged %>%
#subset_taxa( Domain == "d__Bacteria" &
# Family!= "f__Mitochondria" | is.na(Family) &
# Family!= "f__Chloroplast" | is.na(Family) )
# Make a data frame with a column for the read counts of each sample
sample_sum_df <- data.frame(sum = sample_sums(phyloseq_merged_clean))
ggplot(sample_sum_df, aes(x = sum)) +
geom_histogram(color = "black", fill = "indianred", binwidth = 2500) +
ggtitle("Distribution of sample sequencing depth") +
xlab("Read counts") +
theme(axis.title.y = element_blank())
View(sample_sum_df)
#write.csv(sample_sum_df, "samplesumdf_controls_mitochlo_removed.csv")
smin <- min(sample_sums(phyloseq_merged_clean))
smean <- mean(sample_sums(phyloseq_merged_clean))
smax <- max(sample_sums(phyloseq_merged_clean))
smin
smean
smax
sum <- sum(sample_sum_df$sum
)
sum
##20526147 reads to start with (check from table.qzv file interactive plot (minus the 8 positive controls and negative controls)
##19940569 reads remain after filtering out mitochondria and chloroplast, input file is from qiime post dada2 denoise
##10205803 reads total for switchgrass only samples after filtering chloroplast and mitochondria
##9734766 reads total for bean only samples after filtering chloroplast and mitochondria
sample_data(phyloseq_merged_clean)
##rarefaction curve
rarecurve(t(otu_table(phyloseq_merged_clean)), step=50)
##rarefy to 25,000 reads, removes a few samples based on where I set the sample.size to be
phyloseq_rarefy<- rarefy_even_depth(phyloseq_merged_clean, sample.size = 25000, rngseed = TRUE, trimOTUs=FALSE)
##bean
8 samples removedbecause they contained fewer reads than `sample.size`.
Up to first five removed samples are:
DNA_129DNA_146DNA_154DNA_155DNA_163
##switchgrass
6 samples removedbecause they contained fewer reads than `sample.size`.
Up to first five removed samples are:
DNA_116DNA_13DNA_37DNA_38DNA_61
##all experimental
14 samples removedbecause they contained fewer reads than `sample.size`.
Up to first five removed samples are:
DNA_116DNA_129DNA_13DNA_146DNA_154
##check histogram
sample_sum_df <- data.frame(sum = sample_sums(phyloseq_rarefy))
ggplot(sample_sum_df, aes(x = sum)) +
geom_histogram(color = "black", fill = "indianred", binwidth = 2500) +
ggtitle("Distribution of sample sequencing depth") +
xlab("Read counts") +
theme(axis.title.y = element_blank())
View(sample_sum_df)
# melt to long format (for ggploting)
# prune out phyla below 2% in each sample
phyloseq_class <- phyloseq_rarefy %>%
tax_glom(taxrank = "Class") %>% # agglomerate at family/phylum level
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() %>% # Melt to long format
#filter(Abundance > 0.02) %>% # Filter out low abundance taxa
arrange(Class) # Sort data frame alphabetically by phylum/family etc
phyloseq_class$Class[phyloseq_class$Abundance < 0.01] <- "< 1% abund."
# Set colors for plotting
family_colors <- c(
"#CBD588", "#5F7FC7", "orange","#DA5724", "#508578", "#CD9BCD",
"#AD6F3B", "#673770","#D14285", "#652926", "#C84248",
"#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861", "black", "blue", "red", "violet", "yellow",
"purple", "pink", "magenta", "darkblue", "darkorchid", "green","darkgreen", "brown","khaki", "grey",
"cornsilk4","white","cornflowerblue"
)
#OR
install.packages("Polychrome")
library(Polychrome)
P52<- createPalette(52, c("#010101", "#ff0000"))
swatch(P52)
View(phyloseq_order)
View(phyloseq_family)
View(phyloseq_class)
# Plot
plot<- ggplot(phyloseq_class, aes(x= treatment, y = Abundance, fill =Class)) +
facet_wrap(drought~planted, scales="free") +
geom_bar(stat = "identity") +
scale_fill_manual(values=as.vector(P52))+
#scale_x_discrete(
#breaks = c("7/8", "8/4", "9/2", "10/6"),
#labels = c("Jul", "Aug", "Sep", "Oct"),
#drop = FALSE
#) +
# Remove x axis title
theme(axis.title.x = element_blank()) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
#
guides(fill = guide_legend(reverse = TRUE, keywidth = 1, keyheight = 1, ncol=1)) +
ylab("Relative Abundance (class > 1%) \n") +
ggtitle("Class Composition")
plot
ggsave(filename = "barplot_class_exp_bean_drought-planted.tiff", plot = plot,
width = 50,
height = 30, units = c("cm"),
dpi = 300)
##NMDS
set.seed(1)
# Ordinate
phyloseq_nmds <- ordinate(
physeq = phyloseq_rarefy,
method = "NMDS",
distance = "bray"
)
plot1<- plot_ordination(
physeq = phyloseq_rarefy ,
ordination = phyloseq_nmds,
color = "drought",
shape = "planted"
)+scale_shape_manual(values=c(15,19,17,18,20,23,25))+scale_fill_manual(values=c("#a65628", "red", "#ffae19",
"#4daf4a", "#1919ff", "darkorchid3", "magenta"))+
theme_classic()+
geom_point(aes(color = drought), size = 4) #+stat_ellipse(aes(color = drought, group=drought),type="norm")
plot1
ggsave(filename="NMDS-bean-drought-planted.TIFF", plot=plot1, width=6.8, height=6, units="in", dpi=720)
##adonis
set.seed(1)
# Calculate bray curtis distance matrix
phyloseq_bray <- phyloseq::distance(phyloseq_rarefy, method = "bray")
phyloseq_bray
##cant use the sample data from phyloseq merged clean because rarefaction removed 5-6 samples so there
##will be discrepancy in the naming. hence need to remove those samples from sampledf dataframe
sampledf <- data.frame(sample_data(phyloseq_merged_clean))
# Adonis test
##
sample_df <- data.frame(sample_data(phyloseq_rarefy))
View(sample_df)
adonis(phyloseq_bray ~ drought, data = sample_df)
##Bean by drought and planted treatments
Call:
adonis(formula = phyloseq_bray ~ drought, data = sample_df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
drought 2 0.6172 0.30860 2.5071 0.04821 0.001 ***
Residuals 99 12.1858 0.12309 0.95179
Total 101 12.8030 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
adonis(formula = phyloseq_bray ~ planted, data = sample_df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
planted 1 2.5734 2.5734 25.157 0.201 0.001 ***
Residuals 100 10.2296 0.1023 0.799
Total 101 12.8030 1.000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
##Swicthgrass by planted and drought treatments
Call:
adonis(formula = phyloseq_bray ~ drought, data = sample_df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
drought 2 0.3072 0.15361 1.4031 0.02703 0.012 *
Residuals 101 11.0573 0.10948 0.97297
Total 103 11.3645 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
adonis(formula = phyloseq_bray ~ planted, data = sample_df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
planted 1 0.4397 0.43970 4.1053 0.03869 0.001 ***
Residuals 102 10.9248 0.10711 0.96131
Total 103 11.3645 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
##switchgrass and bean by crop
Call:
adonis(formula = phyloseq_bray ~ crop, data = sample_df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
crop 1 19.424 19.4242 164.04 0.44571 0.001 ***
Residuals 204 24.156 0.1184 0.55429
Total 205 43.580 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
library(devtools)
install_github("pmartinezarbizu/pairwiseAdonis/pairwiseAdonis")
library(pairwiseAdonis)
pairwise.adonis2(phyloseq_bray ~ drought, data = sample_df)
##switchgrass (planted vs unplanted)
$parent_call
[1] "phyloseq_bray ~ planted , strata = Null , permutations 999"
$planted_vs_unplanted
Df SumOfSqs R2 F Pr(>F)
planted 1 0.4397 0.03869 4.1053 0.001 ***
Residual 102 10.9248 0.96131
Total 103 11.3645 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
attr(,"class")
[1] "pwadstrata" "list"
##switchgrass (dr vs ww vs pre dr)
$parent_call
[1] "phyloseq_bray ~ drought , strata = Null , permutations 999"
$`pre-drought_vs_well-watered`
Df SumOfSqs R2 F Pr(>F)
drought 1 0.1500 0.02315 1.3509 0.051 .
Residual 57 6.3292 0.97685
Total 58 6.4792 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`pre-drought_vs_drought`
Df SumOfSqs R2 F Pr(>F)
drought 1 0.1077 0.0184 0.9935 0.436
Residual 53 5.7466 0.9816
Total 54 5.8544 1.0000
$`well-watered_vs_drought`
Df SumOfSqs R2 F Pr(>F)
drought 1 0.1827 0.01788 1.6746 0.005 **
Residual 92 10.0387 0.98212
Total 93 10.2215 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
attr(,"class")
[1] "pwadstrata" "list"
##bean planted versus unplanted
$parent_call
[1] "phyloseq_bray ~ planted , strata = Null , permutations 999"
$planted_vs_unplanted
Df SumOfSqs R2 F Pr(>F)
planted 1 2.5734 0.201 25.157 0.001 ***
Residual 100 10.2296 0.799
Total 101 12.8030 1.000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
attr(,"class")
[1] "pwadstrata" "list"
>
##bean (dr vs ww vs predr)
$parent_call
[1] "phyloseq_bray ~ drought , strata = Null , permutations 999"
$`pre-drought_vs_drought`
Df SumOfSqs R2 F Pr(>F)
drought 1 0.3132 0.04556 2.4346 0.008 **
Residual 51 6.5605 0.95444
Total 52 6.8737 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`pre-drought_vs_well-watered`
Df SumOfSqs R2 F Pr(>F)
drought 1 0.3555 0.05192 3.0667 0.001 ***
Residual 56 6.4910 0.94808
Total 57 6.8464 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$`drought_vs_well-watered`
Df SumOfSqs R2 F Pr(>F)
drought 1 0.2768 0.02386 2.2247 0.014 *
Residual 91 11.3202 0.97614
Total 92 11.5969 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
attr(,"class")
[1] "pwadstrata" "list"
>
#pcoa
phyloseq_pcoa <- ordinate(
physeq = phyloseq_rarefy,
method = "PCoA",
distance = "bray"
)
# Plot
plot_ordination(
physeq = phyloseq_rarefy,
ordination = phyloseq_pcoa,
color = "drought",
shape = "planted",
title = "PCoA"
) +
scale_color_manual(values = c("#a65628", "red", "#ffae19",
"#4daf4a", "#1919ff", "darkorchid3", "magenta", "black")
) +
geom_point(aes(color = drought), alpha = 0.7, size = 4) +
geom_point(colour = "grey90", size = 1.5)