In sponslerdb/vsearchR:
Load vsearch output
Philly 2017 pollen
trnL_PH_pollen <- load_blast6("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/pollen/")
ITS1_PH_pollen <- load_blast6("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/pollen/")
ITS2_PH_pollen <- load_blast6("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/pollen/")
write_csv(trnL_PH_pollen, "trnL_PH_pollen.csv")
write_csv(ITS1_PH_pollen, "ITS1_PH_pollen.csv")
write_csv(ITS2_PH_pollen, "ITS2_PH_pollen.csv")
trnL_PH_pollen <- read_csv("trnL_PH_pollen.csv", col_names = TRUE)
ITS1_PH_pollen <- read_csv("ITS1_PH_pollen.csv", col_names = TRUE)
ITS2_PH_pollen <- read_csv("ITS2_PH_pollen.csv", col_names = TRUE)
Philly 2017 honey
trnL_PH_honey <- load_blast6("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/honey/")
ITS1_PH_honey <- load_blast6("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/honey/")
ITS2_PH_honey <- load_blast6("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/honey/")
# write_csv(trnL_PH_honey, "trnL_PH_honey.csv")
# write_csv(ITS1_PH_honey, "ITS1_PH_honey.csv")
# write_csv(ITS2_PH_honey, "ITS2_PH_honey.csv")
trnL_PH_honey <- read_csv("trnL_PH_honey.csv", col_names = TRUE)
ITS1_PH_honey <- read_csv("ITS1_PH_honey.csv", col_names = TRUE)
ITS2_PH_honey <- read_csv("ITS2_PH_honey.csv", col_names = TRUE)
Philly jarred honey
trnL_PH_jarred_honey <- load_blast6_alt("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/jarred_honey/")
ITS1_PH_jarred_honey <- load_blast6_alt("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/jarred_honey/")
ITS2_PH_jarred_honey <- load_blast6_alt("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/jarred_honey/")
Philly 2017 blanks
trnL_PH_blanks <- load_blast6_alt("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/blanks/")
ITS1_PH_blanks <- load_blast6_alt("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/blanks/")
ITS2_PH_blanks <- load_blast6_alt("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/blanks/")
Load taxonomy tables
Philly 2017
trnL_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/trnL_PH_Amplicons2.tax")
ITS1_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/ITS1_PH_Amplicons.tax")
ITS2_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/ITS2_PH_Amplicons.tax")
Join vsearch output to taxonomy tables
Philly 2017 pollen
trnL_PH_pollen_join <- tax_join(trnL_PH_pollen, trnL_PH_tax, min_id = 97.0, min_len = 150)
ITS1_PH_pollen_join <- tax_join(ITS1_PH_pollen, ITS1_PH_tax, min_id = 95.0, min_len = 300)
ITS2_PH_pollen_join <- tax_join(ITS2_PH_pollen, ITS2_PH_tax, min_id = 95.0, min_len = 300)
write_csv(trnL_PH_pollen_join, "trnL_PH_pollen_join.csv")
write_csv(ITS1_PH_pollen_join, "ITS1_PH_pollen_join.csv")
write_csv(ITS2_PH_pollen_join, "ITS2_PH_pollen_join.csv")
#trnL_PH_pollen_join <- read_csv("trnL_PH_pollen_join.csv", col_names = TRUE)
#ITS1_PH_pollen_join <- read_csv("ITS1_PH_pollen_join.csv", col_names = TRUE)
#ITS2_PH_pollen_join <- read_csv("ITS2_PH_pollen_join.csv", col_names = TRUE)
Philly 2017 honey
trnL_PH_honey_join <- tax_join(trnL_PH_honey, trnL_PH_tax, min_id = 0.97)
ITS1_PH_honey_join <- tax_join(ITS1_PH_honey, ITS1_PH_tax, min_id = 0.95)
ITS2_PH_honey_join <- tax_join(ITS2_PH_honey, ITS2_PH_tax, min_id = 0.95)
# write_csv(trnL_PH_honey_join, "trnL_PH_honey_join.csv")
# write_csv(ITS1_PH_honey_join, "ITS1_PH_honey_join.csv")
# write_csv(ITS2_PH_honey_join, "ITS2_PH_honey_join.csv")
# trnL_PH_honey_join <- read_csv("trnL_PH_honey_join.csv", col_names = TRUE)
# ITS1_PH_honey_join <- read_csv("ITS1_PH_honey_join.csv", col_names = TRUE)
# ITS2_PH_honey_join <- read_csv("ITS2_PH_honey_join.csv", col_names = TRUE)
Philly 2017 jarred honey
trnL_PH_jarred_honey_join <- tax_join(trnL_PH_jarred_honey, trnL_PH_tax, min_id = 0.97)
ITS1_PH_jarred_honey_join <- tax_join(ITS1_PH_jarred_honey, ITS1_PH_tax, min_id = 0.95)
ITS2_PH_jarred_honey_join <- tax_join(ITS2_PH_jarred_honey, ITS2_PH_tax, min_id = 0.95)
# write_csv(trnL_PH_jarred_honey_join, "trnL_PH_jarred_honey_join.csv")
# write_csv(ITS1_PH_jarred_honey_join, "ITS1_PH_jarred_honey_join.csv")
# write_csv(ITS2_PH_jarred_honey_join, "ITS2_PH_jarred_honey_join.csv")
# trnL_PH_jarred_honey_join <- read_csv("trnL_PH_jarred_honey_join.csv", col_names = TRUE)
# ITS1_PH_jarred_honey_join <- read_csv("ITS1_PH_jarred_honey_join.csv", col_names = TRUE)
# ITS2_PH_jarred_honey_join <- read_csv("ITS2_PH_jarred_honey_join.csv", col_names = TRUE)
Philly 2017 blanks
trnL_PH_blanks_join <- tax_join(trnL_PH_blanks, trnL_PH_tax, min_id = 0.97)
ITS1_PH_blanks_join <- tax_join(ITS1_PH_blanks, ITS1_PH_tax, min_id = 0.95)
ITS2_PH_blanks_join <- tax_join(ITS2_PH_blanks, ITS2_PH_tax, min_id = 0.95)
# write_csv(trnL_PH_blanks_join, "trnL_PH_blanks_join.csv")
# write_csv(ITS1_PH_blanks_join, "ITS1_PH_blanks_join.csv")
# write_csv(ITS2_PH_blanks_join, "ITS2_PH_blanks_join.csv")
# trnL_PH_blanks_join <- read_csv("trnL_PH_blanks_join.csv", col_names = TRUE)
# ITS1_PH_blanks_join <- read_csv("ITS1_PH_blanks_join.csv", col_names = TRUE)
# ITS2_PH_blanks_join <- read_csv("ITS2_PH_blanks_join.csv", col_names = TRUE)
Tally genera by sample
Philly 2017 pollen
trnL_PH_pollen_tally <- tally_gen(trnL_PH_pollen_join)
ITS1_PH_pollen_tally <- tally_gen(ITS1_PH_pollen_join)
ITS2_PH_pollen_tally <- tally_gen(ITS2_PH_pollen_join)
Philly 2017 honey
trnL_PH_honey_tally <- tally_gen(trnL_PH_honey_join)
ITS1_PH_honey_tally <- tally_gen(ITS1_PH_honey_join)
ITS2_PH_honey_tally <- tally_gen(ITS2_PH_honey_join)
Philly jarred honey
trnL_PH_jarred_honey_tally <- tally_gen(trnL_PH_jarred_honey_join)
ITS1_PH_jarred_honey_tally <- tally_gen(ITS1_PH_jarred_honey_join)
ITS2_PH_jarred_honey_tally <- tally_gen(ITS2_PH_jarred_honey_join)
Philly 2017 blanks
trnL_PH_blanks_tally <- tally_gen(trnL_PH_blanks_join)
ITS1_PH_blanks_tally <- tally_gen(ITS1_PH_blanks_join)
ITS2_PH_blanks_tally <- tally_gen(ITS2_PH_blanks_join)
Add sample metadata, create consensus data sets, create genus summary fields
Philly 2017 pollen
trnL_PH_pollen_final <- add_meta(trnL_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv")
ITS1_PH_pollen_final <- add_meta(ITS1_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv")
ITS2_PH_pollen_final <- add_meta(ITS2_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv")
PH_pollen_consensus <- consensus_xyz_gen(trnL_PH_pollen_final, ITS1_PH_pollen_final, ITS2_PH_pollen_final,
min_prop = 0.0005) %>%
add_meta("~/vsearchr/inst/extdata/PH_2017_key.csv") %>%
mutate(period = case_when(date > "2017-05-20" & date < "2017-06-15" ~ "May/June",
date > "2017-06-20" & date < "2017-07-15" ~ "June/July",
date > "2017-07-20" & date < "2017-08-15" ~ "Aug",
date > "2017-08-20" & date < "2017-09-15" ~ "Sept",
is.na(date) & site == "SHARE" ~ "June/July"))
PH_pollen_consensus_MayJune <- filter(PH_pollen_consensus, period == "May/June")
PH_pollen_consensus_JuneJuly <- filter(PH_pollen_consensus, period == "June/July")
PH_pollen_consensus_Aug <- filter(PH_pollen_consensus, period == "Aug")
PH_pollen_consensus_Sept <- filter(PH_pollen_consensus, period == "Sept")
PH_pollen_genus_summary <- PH_pollen_consensus %>%
group_by(genus) %>%
summarize(gen_freq = n())
PH_pollen_genus_summary_MayJune <- PH_pollen_consensus_MayJune %>%
group_by(genus) %>%
summarize(gen_freq = n(),
mean_prop = sum(scaled_prop)/8) # We only had 8 sites for the MayJune sample; we had 12 sites for all others
PH_pollen_genus_summary_JuneJuly <- PH_pollen_consensus_JuneJuly %>%
group_by(genus) %>%
summarize(gen_freq = n(),
mean_prop = sum(scaled_prop)/12)
PH_pollen_genus_summary_Aug <- PH_pollen_consensus_Aug %>%
group_by(genus) %>%
summarize(gen_freq = n(),
mean_prop = sum(scaled_prop)/12)
PH_pollen_genus_summary_Sept <- PH_pollen_consensus_Sept %>%
group_by(genus) %>%
summarize(gen_freq = n(),
mean_prop = sum(scaled_prop)/12)
PH_pollen_final <- left_join(PH_pollen_consensus, PH_pollen_genus_summary, by = "genus")
PH_pollen_final_MayJune <- left_join(PH_pollen_consensus_MayJune, PH_pollen_genus_summary_MayJune, by = "genus")
PH_pollen_final_JuneJuly <- left_join(PH_pollen_consensus_JuneJuly, PH_pollen_genus_summary_JuneJuly, by = "genus")
PH_pollen_final_Aug <- left_join(PH_pollen_consensus_Aug, PH_pollen_genus_summary_Aug, by = "genus")
PH_pollen_final_Sept <- left_join(PH_pollen_consensus_Sept, PH_pollen_genus_summary_Sept, by = "genus")
Philly 2017 honey
trnL_PH_honey_final <- add_meta(trnL_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv")
ITS1_PH_honey_final <- add_meta(ITS1_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv")
ITS2_PH_honey_final <- add_meta(ITS2_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv")
PH_honey_consensus <- consensus_xyz_gen(trnL_PH_honey_final, ITS1_PH_honey_final, ITS2_PH_honey_final,
min_prop = 0.001, xname = "trnL", yname = "ITS1", zname = "ITS2") %>%
add_meta("~/vsearchr/inst/extdata/PH_2017_key.csv") %>%
mutate(period = case_when(date > "2017-05-20" & date < "2017-06-15" ~ "May/June",
date > "2017-06-20" & date < "2017-07-15" ~ "June/July",
date > "2017-07-20" & date < "2017-08-15" ~ "July/Aug",
date > "2017-08-20" & date < "2017-09-15" ~ "Aug/Sept",
is.na(date) & site == "SHARE" ~ "June/July"))
Philly 2017 jarred_honey -- no metadata to add
PH_jarred_honey_consensus <- consensus_xyz_gen(trnL_PH_jarred_honey_tally, ITS1_PH_jarred_honey_tally, ITS2_PH_jarred_honey_tally,
min_prop = 0.001, xname = "trnL", yname = "ITS1", zname = "ITS2")
Philly 2017 blanks -- no metadata to add
PH_blanks_consensus <- consensus_xyz_gen(trnL_PH_blanks_tally, ITS1_PH_blanks_tally, ITS2_PH_blanks_tally,
min_prop = 0.001, xname = "trnL", yname = "ITS1", zname = "ITS2")
Plot data
Philly 2017 pollen
trnL only
ggplot(filter(trnL_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_freq), fill = gen_prop)) +
geom_tile(width = 32, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
facet_grid(~site) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 12, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
facet_grid(~site) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 12, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
facet_grid(~site) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
# pdf("~/vsearchr/inst/extdata/figures/PH_final.pdf", height = 5, width = 8.5)
# ggplot(PH_pollen_final, aes(x = date, y = reorder(genus, gen_freq), fill = scaled_prop)) +
# geom_tile(width = 30, color = "gray40") +
# scale_fill_gradient(low = "gray95", high = "purple") +
# theme_bw(4) +
# ylab("Genus") +
# xlab("Sample") +
# labs(fill = "Proportional\nabundance") +
# facet_grid(~site) +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
# dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_mayjune.pdf", height = 11, width = 8.5)
ggplot(filter(PH_pollen_final_MayJune, period == "May/June"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_junejuly.pdf", height = 11, width = 8.5)
ggplot(filter(PH_pollen_final_JuneJuly, period == "June/July"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_aug.pdf", height = 11, width = 8.5)
ggplot(filter(PH_pollen_final_Aug, period == "Aug"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_sept.pdf", height = 11, width = 8.5)
ggplot(filter(PH_pollen_final_Sept, period == "Sept"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
# pdf("~/vsearchr/inst/extdata/figures/PH_final_period.pdf", height = 5, width = 8.5)
# ggplot(filter(PH_pollen_final_join, !is.na(date)),
# aes(x = reorder(period, date), y = reorder(genus, gen_freq), fill = scaled_prop)) +
# geom_tile(width = 0.5, color = "white") +
# scale_fill_gradient(low = "gray95", high = "purple") +
# theme_bw(8) +
# ylab("Genus") +
# xlab("Sample") +
# labs(fill = "Proportional\nabundance") +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
# dev.off()
############ Trimmed figures for presentation #################
# pdf("~/vsearchr/inst/extdata/figures/PH_final.pdf", height = 5, width = 8.5)
# ggplot(PH_pollen_final, aes(x = date, y = reorder(genus, gen_freq), fill = scaled_prop)) +
# geom_tile(width = 30, color = "gray40") +
# scale_fill_gradient(low = "gray95", high = "purple") +
# theme_bw(4) +
# ylab("Genus") +
# xlab("Sample") +
# labs(fill = "Proportional\nabundance") +
# facet_grid(~site) +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
# dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_mayjune_PresTrim.pdf", height = 6, width = 6.5)
ggplot(filter(PH_pollen_final_MayJune, period == "May/June", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_junejuly_PresTrim.pdf", height = 6, width = 6.5)
ggplot(filter(PH_pollen_final_JuneJuly, period == "June/July", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_aug_PresTrim.pdf", height = 5, width = 6.5)
ggplot(filter(PH_pollen_final_Aug, period == "Aug", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
pdf("~/vsearchr/inst/extdata/figures/PH_final_sept_PresTrim.pdf", height = 5, width = 6.5)
ggplot(filter(PH_pollen_final_Sept, period == "Sept", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
geom_tile(color = "gray40") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Site") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()
# pdf("~/vsearchr/inst/extdata/figures/PH_final_period.pdf", height = 5, width = 8.5)
# ggplot(filter(PH_pollen_final_join, !is.na(date)),
# aes(x = reorder(period, date), y = reorder(genus, gen_freq), fill = scaled_prop)) +
# geom_tile(width = 0.5, color = "white") +
# scale_fill_gradient(low = "gray95", high = "purple") +
# theme_bw(8) +
# ylab("Genus") +
# xlab("Sample") +
# labs(fill = "Proportional\nabundance") +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
# dev.off()
Philly 2017 honey
trnL only
ggplot(filter(trnL_PH_honey_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 32, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
facet_grid(~site) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_honey_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 12, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
facet_grid(~site) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_honey_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 12, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
facet_grid(~site) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
ggplot(filter(PH_honey_consensus, scaled_prop >= 0.025), aes(x = date, y = reorder(genus, scaled_prop), fill = scaled_prop)) +
geom_tile(width = 32, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
facet_grid(~site) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Philly jarred_honey
trnL only
ggplot(filter(trnL_PH_jarred_honey_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_jarred_honey_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_jarred_honey_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
ggplot(filter(PH_jarred_honey_consensus, scaled_prop >= 0.025), aes(x = sample, y = reorder(genus, scaled_prop), fill = scaled_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +``
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Philly 2017 blanks
trnL only
ggplot(filter(trnL_PH_blanks_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_blanks_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_blanks_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
ggplot(filter(PH_blanks_consensus, scaled_prop >= 0.025), aes(x = sample, y = reorder(genus, scaled_prop), fill = scaled_prop)) +
geom_tile(width = 1, color = "white") +
scale_fill_gradient(low = "gray95", high = "purple") +
theme_bw(12) +
ylab("Genus") +
xlab("Sample") +
labs(fill = "Proportional\nabundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
sponslerdb/vsearchR documentation built on June 19, 2020, 10:01 a.m.
R Package Documentation
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Load vsearch output
Philly 2017 pollen
trnL_PH_pollen <- load_blast6("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/pollen/") ITS1_PH_pollen <- load_blast6("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/pollen/") ITS2_PH_pollen <- load_blast6("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/pollen/") write_csv(trnL_PH_pollen, "trnL_PH_pollen.csv") write_csv(ITS1_PH_pollen, "ITS1_PH_pollen.csv") write_csv(ITS2_PH_pollen, "ITS2_PH_pollen.csv") trnL_PH_pollen <- read_csv("trnL_PH_pollen.csv", col_names = TRUE) ITS1_PH_pollen <- read_csv("ITS1_PH_pollen.csv", col_names = TRUE) ITS2_PH_pollen <- read_csv("ITS2_PH_pollen.csv", col_names = TRUE)
Philly 2017 honey
trnL_PH_honey <- load_blast6("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/honey/") ITS1_PH_honey <- load_blast6("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/honey/") ITS2_PH_honey <- load_blast6("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/honey/") # write_csv(trnL_PH_honey, "trnL_PH_honey.csv") # write_csv(ITS1_PH_honey, "ITS1_PH_honey.csv") # write_csv(ITS2_PH_honey, "ITS2_PH_honey.csv") trnL_PH_honey <- read_csv("trnL_PH_honey.csv", col_names = TRUE) ITS1_PH_honey <- read_csv("ITS1_PH_honey.csv", col_names = TRUE) ITS2_PH_honey <- read_csv("ITS2_PH_honey.csv", col_names = TRUE)
Philly jarred honey
trnL_PH_jarred_honey <- load_blast6_alt("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/jarred_honey/") ITS1_PH_jarred_honey <- load_blast6_alt("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/jarred_honey/") ITS2_PH_jarred_honey <- load_blast6_alt("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/jarred_honey/")
Philly 2017 blanks
trnL_PH_blanks <- load_blast6_alt("~/vsearchr/inst/extdata/trnL_vsearch_philly.output/blanks/") ITS1_PH_blanks <- load_blast6_alt("~/vsearchr/inst/extdata/ITS1_vsearch_philly.output/blanks/") ITS2_PH_blanks <- load_blast6_alt("~/vsearchr/inst/extdata/ITS2_vsearch_philly.output/blanks/")
Load taxonomy tables
Philly 2017
trnL_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/trnL_PH_Amplicons2.tax") ITS1_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/ITS1_PH_Amplicons.tax") ITS2_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/ITS2_PH_Amplicons.tax")
Join vsearch output to taxonomy tables
Philly 2017 pollen
trnL_PH_pollen_join <- tax_join(trnL_PH_pollen, trnL_PH_tax, min_id = 97.0, min_len = 150) ITS1_PH_pollen_join <- tax_join(ITS1_PH_pollen, ITS1_PH_tax, min_id = 95.0, min_len = 300) ITS2_PH_pollen_join <- tax_join(ITS2_PH_pollen, ITS2_PH_tax, min_id = 95.0, min_len = 300) write_csv(trnL_PH_pollen_join, "trnL_PH_pollen_join.csv") write_csv(ITS1_PH_pollen_join, "ITS1_PH_pollen_join.csv") write_csv(ITS2_PH_pollen_join, "ITS2_PH_pollen_join.csv") #trnL_PH_pollen_join <- read_csv("trnL_PH_pollen_join.csv", col_names = TRUE) #ITS1_PH_pollen_join <- read_csv("ITS1_PH_pollen_join.csv", col_names = TRUE) #ITS2_PH_pollen_join <- read_csv("ITS2_PH_pollen_join.csv", col_names = TRUE)
Philly 2017 honey
trnL_PH_honey_join <- tax_join(trnL_PH_honey, trnL_PH_tax, min_id = 0.97) ITS1_PH_honey_join <- tax_join(ITS1_PH_honey, ITS1_PH_tax, min_id = 0.95) ITS2_PH_honey_join <- tax_join(ITS2_PH_honey, ITS2_PH_tax, min_id = 0.95) # write_csv(trnL_PH_honey_join, "trnL_PH_honey_join.csv") # write_csv(ITS1_PH_honey_join, "ITS1_PH_honey_join.csv") # write_csv(ITS2_PH_honey_join, "ITS2_PH_honey_join.csv") # trnL_PH_honey_join <- read_csv("trnL_PH_honey_join.csv", col_names = TRUE) # ITS1_PH_honey_join <- read_csv("ITS1_PH_honey_join.csv", col_names = TRUE) # ITS2_PH_honey_join <- read_csv("ITS2_PH_honey_join.csv", col_names = TRUE)
Philly 2017 jarred honey
trnL_PH_jarred_honey_join <- tax_join(trnL_PH_jarred_honey, trnL_PH_tax, min_id = 0.97) ITS1_PH_jarred_honey_join <- tax_join(ITS1_PH_jarred_honey, ITS1_PH_tax, min_id = 0.95) ITS2_PH_jarred_honey_join <- tax_join(ITS2_PH_jarred_honey, ITS2_PH_tax, min_id = 0.95) # write_csv(trnL_PH_jarred_honey_join, "trnL_PH_jarred_honey_join.csv") # write_csv(ITS1_PH_jarred_honey_join, "ITS1_PH_jarred_honey_join.csv") # write_csv(ITS2_PH_jarred_honey_join, "ITS2_PH_jarred_honey_join.csv") # trnL_PH_jarred_honey_join <- read_csv("trnL_PH_jarred_honey_join.csv", col_names = TRUE) # ITS1_PH_jarred_honey_join <- read_csv("ITS1_PH_jarred_honey_join.csv", col_names = TRUE) # ITS2_PH_jarred_honey_join <- read_csv("ITS2_PH_jarred_honey_join.csv", col_names = TRUE)
Philly 2017 blanks
trnL_PH_blanks_join <- tax_join(trnL_PH_blanks, trnL_PH_tax, min_id = 0.97) ITS1_PH_blanks_join <- tax_join(ITS1_PH_blanks, ITS1_PH_tax, min_id = 0.95) ITS2_PH_blanks_join <- tax_join(ITS2_PH_blanks, ITS2_PH_tax, min_id = 0.95) # write_csv(trnL_PH_blanks_join, "trnL_PH_blanks_join.csv") # write_csv(ITS1_PH_blanks_join, "ITS1_PH_blanks_join.csv") # write_csv(ITS2_PH_blanks_join, "ITS2_PH_blanks_join.csv") # trnL_PH_blanks_join <- read_csv("trnL_PH_blanks_join.csv", col_names = TRUE) # ITS1_PH_blanks_join <- read_csv("ITS1_PH_blanks_join.csv", col_names = TRUE) # ITS2_PH_blanks_join <- read_csv("ITS2_PH_blanks_join.csv", col_names = TRUE)
Tally genera by sample
Philly 2017 pollen
trnL_PH_pollen_tally <- tally_gen(trnL_PH_pollen_join) ITS1_PH_pollen_tally <- tally_gen(ITS1_PH_pollen_join) ITS2_PH_pollen_tally <- tally_gen(ITS2_PH_pollen_join)
Philly 2017 honey
trnL_PH_honey_tally <- tally_gen(trnL_PH_honey_join) ITS1_PH_honey_tally <- tally_gen(ITS1_PH_honey_join) ITS2_PH_honey_tally <- tally_gen(ITS2_PH_honey_join)
Philly jarred honey
trnL_PH_jarred_honey_tally <- tally_gen(trnL_PH_jarred_honey_join) ITS1_PH_jarred_honey_tally <- tally_gen(ITS1_PH_jarred_honey_join) ITS2_PH_jarred_honey_tally <- tally_gen(ITS2_PH_jarred_honey_join)
Philly 2017 blanks
trnL_PH_blanks_tally <- tally_gen(trnL_PH_blanks_join) ITS1_PH_blanks_tally <- tally_gen(ITS1_PH_blanks_join) ITS2_PH_blanks_tally <- tally_gen(ITS2_PH_blanks_join)
Add sample metadata, create consensus data sets, create genus summary fields
Philly 2017 pollen
trnL_PH_pollen_final <- add_meta(trnL_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv") ITS1_PH_pollen_final <- add_meta(ITS1_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv") ITS2_PH_pollen_final <- add_meta(ITS2_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv") PH_pollen_consensus <- consensus_xyz_gen(trnL_PH_pollen_final, ITS1_PH_pollen_final, ITS2_PH_pollen_final, min_prop = 0.0005) %>% add_meta("~/vsearchr/inst/extdata/PH_2017_key.csv") %>% mutate(period = case_when(date > "2017-05-20" & date < "2017-06-15" ~ "May/June", date > "2017-06-20" & date < "2017-07-15" ~ "June/July", date > "2017-07-20" & date < "2017-08-15" ~ "Aug", date > "2017-08-20" & date < "2017-09-15" ~ "Sept", is.na(date) & site == "SHARE" ~ "June/July")) PH_pollen_consensus_MayJune <- filter(PH_pollen_consensus, period == "May/June") PH_pollen_consensus_JuneJuly <- filter(PH_pollen_consensus, period == "June/July") PH_pollen_consensus_Aug <- filter(PH_pollen_consensus, period == "Aug") PH_pollen_consensus_Sept <- filter(PH_pollen_consensus, period == "Sept") PH_pollen_genus_summary <- PH_pollen_consensus %>% group_by(genus) %>% summarize(gen_freq = n()) PH_pollen_genus_summary_MayJune <- PH_pollen_consensus_MayJune %>% group_by(genus) %>% summarize(gen_freq = n(), mean_prop = sum(scaled_prop)/8) # We only had 8 sites for the MayJune sample; we had 12 sites for all others PH_pollen_genus_summary_JuneJuly <- PH_pollen_consensus_JuneJuly %>% group_by(genus) %>% summarize(gen_freq = n(), mean_prop = sum(scaled_prop)/12) PH_pollen_genus_summary_Aug <- PH_pollen_consensus_Aug %>% group_by(genus) %>% summarize(gen_freq = n(), mean_prop = sum(scaled_prop)/12) PH_pollen_genus_summary_Sept <- PH_pollen_consensus_Sept %>% group_by(genus) %>% summarize(gen_freq = n(), mean_prop = sum(scaled_prop)/12) PH_pollen_final <- left_join(PH_pollen_consensus, PH_pollen_genus_summary, by = "genus") PH_pollen_final_MayJune <- left_join(PH_pollen_consensus_MayJune, PH_pollen_genus_summary_MayJune, by = "genus") PH_pollen_final_JuneJuly <- left_join(PH_pollen_consensus_JuneJuly, PH_pollen_genus_summary_JuneJuly, by = "genus") PH_pollen_final_Aug <- left_join(PH_pollen_consensus_Aug, PH_pollen_genus_summary_Aug, by = "genus") PH_pollen_final_Sept <- left_join(PH_pollen_consensus_Sept, PH_pollen_genus_summary_Sept, by = "genus")
Philly 2017 honey
trnL_PH_honey_final <- add_meta(trnL_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv") ITS1_PH_honey_final <- add_meta(ITS1_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv") ITS2_PH_honey_final <- add_meta(ITS2_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2017_key.csv") PH_honey_consensus <- consensus_xyz_gen(trnL_PH_honey_final, ITS1_PH_honey_final, ITS2_PH_honey_final, min_prop = 0.001, xname = "trnL", yname = "ITS1", zname = "ITS2") %>% add_meta("~/vsearchr/inst/extdata/PH_2017_key.csv") %>% mutate(period = case_when(date > "2017-05-20" & date < "2017-06-15" ~ "May/June", date > "2017-06-20" & date < "2017-07-15" ~ "June/July", date > "2017-07-20" & date < "2017-08-15" ~ "July/Aug", date > "2017-08-20" & date < "2017-09-15" ~ "Aug/Sept", is.na(date) & site == "SHARE" ~ "June/July"))
Philly 2017 jarred_honey -- no metadata to add
PH_jarred_honey_consensus <- consensus_xyz_gen(trnL_PH_jarred_honey_tally, ITS1_PH_jarred_honey_tally, ITS2_PH_jarred_honey_tally, min_prop = 0.001, xname = "trnL", yname = "ITS1", zname = "ITS2")
Philly 2017 blanks -- no metadata to add
PH_blanks_consensus <- consensus_xyz_gen(trnL_PH_blanks_tally, ITS1_PH_blanks_tally, ITS2_PH_blanks_tally, min_prop = 0.001, xname = "trnL", yname = "ITS1", zname = "ITS2")
Plot data
Philly 2017 pollen
trnL only
ggplot(filter(trnL_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_freq), fill = gen_prop)) + geom_tile(width = 32, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + facet_grid(~site) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 12, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + facet_grid(~site) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 12, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + facet_grid(~site) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
# pdf("~/vsearchr/inst/extdata/figures/PH_final.pdf", height = 5, width = 8.5) # ggplot(PH_pollen_final, aes(x = date, y = reorder(genus, gen_freq), fill = scaled_prop)) + # geom_tile(width = 30, color = "gray40") + # scale_fill_gradient(low = "gray95", high = "purple") + # theme_bw(4) + # ylab("Genus") + # xlab("Sample") + # labs(fill = "Proportional\nabundance") + # facet_grid(~site) + # theme(axis.text.x = element_text(angle = 45, hjust = 1)) # dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_mayjune.pdf", height = 11, width = 8.5) ggplot(filter(PH_pollen_final_MayJune, period == "May/June"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_junejuly.pdf", height = 11, width = 8.5) ggplot(filter(PH_pollen_final_JuneJuly, period == "June/July"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_aug.pdf", height = 11, width = 8.5) ggplot(filter(PH_pollen_final_Aug, period == "Aug"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_sept.pdf", height = 11, width = 8.5) ggplot(filter(PH_pollen_final_Sept, period == "Sept"), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() # pdf("~/vsearchr/inst/extdata/figures/PH_final_period.pdf", height = 5, width = 8.5) # ggplot(filter(PH_pollen_final_join, !is.na(date)), # aes(x = reorder(period, date), y = reorder(genus, gen_freq), fill = scaled_prop)) + # geom_tile(width = 0.5, color = "white") + # scale_fill_gradient(low = "gray95", high = "purple") + # theme_bw(8) + # ylab("Genus") + # xlab("Sample") + # labs(fill = "Proportional\nabundance") + # theme(axis.text.x = element_text(angle = 45, hjust = 1)) # dev.off() ############ Trimmed figures for presentation ################# # pdf("~/vsearchr/inst/extdata/figures/PH_final.pdf", height = 5, width = 8.5) # ggplot(PH_pollen_final, aes(x = date, y = reorder(genus, gen_freq), fill = scaled_prop)) + # geom_tile(width = 30, color = "gray40") + # scale_fill_gradient(low = "gray95", high = "purple") + # theme_bw(4) + # ylab("Genus") + # xlab("Sample") + # labs(fill = "Proportional\nabundance") + # facet_grid(~site) + # theme(axis.text.x = element_text(angle = 45, hjust = 1)) # dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_mayjune_PresTrim.pdf", height = 6, width = 6.5) ggplot(filter(PH_pollen_final_MayJune, period == "May/June", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_junejuly_PresTrim.pdf", height = 6, width = 6.5) ggplot(filter(PH_pollen_final_JuneJuly, period == "June/July", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_aug_PresTrim.pdf", height = 5, width = 6.5) ggplot(filter(PH_pollen_final_Aug, period == "Aug", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() pdf("~/vsearchr/inst/extdata/figures/PH_final_sept_PresTrim.pdf", height = 5, width = 6.5) ggplot(filter(PH_pollen_final_Sept, period == "Sept", gen_freq > 1 | mean_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) + geom_tile(color = "gray40") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Site") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) dev.off() # pdf("~/vsearchr/inst/extdata/figures/PH_final_period.pdf", height = 5, width = 8.5) # ggplot(filter(PH_pollen_final_join, !is.na(date)), # aes(x = reorder(period, date), y = reorder(genus, gen_freq), fill = scaled_prop)) + # geom_tile(width = 0.5, color = "white") + # scale_fill_gradient(low = "gray95", high = "purple") + # theme_bw(8) + # ylab("Genus") + # xlab("Sample") + # labs(fill = "Proportional\nabundance") + # theme(axis.text.x = element_text(angle = 45, hjust = 1)) # dev.off()
Philly 2017 honey
trnL only
ggplot(filter(trnL_PH_honey_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 32, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + facet_grid(~site) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_honey_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 12, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + facet_grid(~site) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_honey_final, gen_prop >= 0.01), aes(x = date, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 12, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + facet_grid(~site) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
ggplot(filter(PH_honey_consensus, scaled_prop >= 0.025), aes(x = date, y = reorder(genus, scaled_prop), fill = scaled_prop)) + geom_tile(width = 32, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + facet_grid(~site) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Philly jarred_honey
trnL only
ggplot(filter(trnL_PH_jarred_honey_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_jarred_honey_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_jarred_honey_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
ggplot(filter(PH_jarred_honey_consensus, scaled_prop >= 0.025), aes(x = sample, y = reorder(genus, scaled_prop), fill = scaled_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) +`` ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Philly 2017 blanks
trnL only
ggplot(filter(trnL_PH_blanks_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS1 only
ggplot(filter(ITS1_PH_blanks_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
ITS2 only
ggplot(filter(ITS2_PH_blanks_tally, gen_prop >= 0.01), aes(x = sample, y = reorder(genus, gen_prop), fill = gen_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
2-of-3 consensus
ggplot(filter(PH_blanks_consensus, scaled_prop >= 0.025), aes(x = sample, y = reorder(genus, scaled_prop), fill = scaled_prop)) + geom_tile(width = 1, color = "white") + scale_fill_gradient(low = "gray95", high = "purple") + theme_bw(12) + ylab("Genus") + xlab("Sample") + labs(fill = "Proportional\nabundance") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
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