In sponslerdb/vsearchR:

Load vsearch output

trnL_PH_honey_2018 <- load_blast6("~/vsearchr/inst/extdata/trnL_vsearch_philly_2018.output/honey/")
ITS1_PH_honey_2018 <- load_blast6("~/vsearchr/inst/extdata/ITS1_vsearch_philly_2018.output/honey/")
ITS2_PH_honey_2018 <- load_blast6("~/vsearchr/inst/extdata/ITS2_vsearch_philly_2018.output/honey/")
write_csv(trnL_PH_honey_2018, "~/vsearchr/inst/extdata/trnL_PH_honey_2018.csv")
write_csv(ITS1_PH_honey_2018, "~/vsearchr/inst/extdata/ITS1_PH_honey_2018.csv")
write_csv(ITS2_PH_honey_2018, "~/vsearchr/inst/extdata/ITS2_PH_honey_2018.csv")
trnL_PH_honey_2018 <- read_csv("~/vsearchr/inst/extdata/trnL_PH_honey_2018.csv", col_names = TRUE)
ITS1_PH_honey_2018 <- read_csv("~/vsearchr/inst/extdata/ITS1_PH_honey_2018.csv", col_names = TRUE)
ITS2_PH_honey_2018 <- read_csv("~/vsearchr/inst/extdata/ITS2_PH_honey_2018.csv", col_names = TRUE)

Load taxonomy tables

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

trnL_PH_honey_2018_join <- tax_join(trnL_PH_honey_2018, trnL_PH_tax, min_id = 97.0, min_len = 150)
ITS1_PH_honey_2018_join <- tax_join(ITS1_PH_honey_2018, ITS1_PH_tax, min_id = 95.0, min_len = 300)
ITS2_PH_honey_2018_join <- tax_join(ITS2_PH_honey_2018, ITS2_PH_tax, min_id = 95.0, min_len = 300)
write_csv(trnL_PH_honey_2018_join, "~/vsearchr/inst/extdata/trnL_PH_honey_join_2018.csv")
write_csv(ITS1_PH_honey_2018_join, "~/vsearchr/inst/extdata/ITS1_PH_honey_join_2018.csv")
write_csv(ITS2_PH_honey_2018_join, "~/vsearchr/inst/extdata/ITS2_PH_honey_join_2018.csv")
# trnL_PH_honey_join <- read_csv("trnL_PH_honey_join_2018.csv", col_names = TRUE)
# ITS1_PH_honey_join <- read_csv("ITS1_PH_honey_join_2018.csv", col_names = TRUE)
# ITS2_PH_honey_join <- read_csv("ITS2_PH_honey_join_2018.csv", col_names = TRUE)

Evaluate coverage

trnL_cov <- trnL_PH_honey_2018 %>%
  group_by(sample) %>%
  summarize(reads = n()) %>%
  rename("trnL_reads" = "reads")

ITS1_cov <- ITS1_PH_honey_2018 %>%
  group_by(sample) %>%
  summarize(reads = n()) %>%
  rename("ITS1_reads" = "reads")

ITS2_cov <- ITS2_PH_honey_2018 %>%
  group_by(sample) %>%
  summarize(reads = n()) %>%
  rename("ITS2_reads" = "reads")

Philly_honey_2018_coverage <- full_join(trnL_cov, ITS1_cov, by = "sample") %>%
  full_join(ITS2_cov, by = "sample")

write_csv(Philly_honey_2018_coverage, "~/vsearchr/inst/extdata/output_2018/honey_2018_coverage.csv")

Tally genera by sample

trnL_PH_honey_tally <- tally_gen(trnL_PH_honey_2018_join)
ITS1_PH_honey_tally <- tally_gen(ITS1_PH_honey_2018_join)
ITS2_PH_honey_tally <- tally_gen(ITS2_PH_honey_2018_join)

Add sample metadata

trnL_PH_honey_final <- add_meta(trnL_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2018_key.csv")
ITS1_PH_honey_final <- add_meta(ITS1_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2018_key.csv")
ITS2_PH_honey_final <- add_meta(ITS2_PH_honey_tally, "~/vsearchr/inst/extdata/PH_2018_key.csv")

Create consensus data sets, create genus summary fields

PH_honey_consensus <- consensus_xyz_gen(trnL_PH_honey_final, ITS1_PH_honey_final, ITS2_PH_honey_final,
                                         min_prop = 0.0005) %>%
  add_meta("~/vsearchr/inst/extdata/PH_2018_key.csv") %>%
  mutate(period = case_when(date > "2018-05-01" & date < "2018-06-01" ~ "May",
                            date > "2018-06-01" & date < "2018-07-01" ~ "June",
                            date > "2018-07-01" & date < "2018-08-01" ~ "July",
                            date > "2018-08-01" & date < "2018-09-01" ~ "Aug",
                            date > "2018-09-01" & date < "2018-10-01" ~ "Sept",
                            date > "2018-10-01" & date < "2018-11-01" ~ "Oct",
                            is.na(date) & site == "Frankford" ~ "Sept",
                            is.na(date) & site == "Mayfair" ~ "Sept"))

PH_honey_consensus_May <- filter(PH_honey_consensus, period == "May")
PH_honey_consensus_June <- filter(PH_honey_consensus, period == "June")
PH_honey_consensus_July <- filter(PH_honey_consensus, period == "July")
PH_honey_consensus_Aug <- filter(PH_honey_consensus, period == "Aug")
PH_honey_consensus_Sept <- filter(PH_honey_consensus, period == "Sept")
PH_honey_consensus_Oct <- filter(PH_honey_consensus, period == "Oct")

PH_honey_genus_summary <- PH_honey_consensus %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            max_prop = max(scaled_prop))

PH_honey_genus_summary_May <- PH_honey_consensus_May %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_honey_genus_summary_June <- PH_honey_consensus_June %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_honey_genus_summary_July <- PH_honey_consensus_July %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_honey_genus_summary_Aug <- PH_honey_consensus_Aug %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_honey_genus_summary_Sept <- PH_honey_consensus_Sept %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_honey_genus_summary_Oct <- PH_honey_consensus_Oct %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_honey_final <- left_join(PH_honey_consensus, PH_honey_genus_summary, by = "genus")
PH_honey_final_May <- left_join(PH_honey_consensus_May, PH_honey_genus_summary_May, by = "genus")
PH_honey_final_June <- left_join(PH_honey_consensus_June, PH_honey_genus_summary_June, by = "genus")
PH_honey_final_July <- left_join(PH_honey_consensus_July, PH_honey_genus_summary_July, by = "genus")
PH_honey_final_Aug <- left_join(PH_honey_consensus_Aug, PH_honey_genus_summary_Aug, by = "genus")
PH_honey_final_Sept <- left_join(PH_honey_consensus_Sept, PH_honey_genus_summary_Sept, by = "genus")
PH_honey_final_Oct <- left_join(PH_honey_consensus_Oct, PH_honey_genus_summary_Oct, by = "genus")

write_csv(PH_honey_final, "~/vsearchr/inst/extdata/output_2018/PH_2018_honey.csv")

Plot data

trnL only

ggplot(filter(trnL_PH_honey_final, gen_prop >= 0.01), aes(x = date, y = genus, 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 = genus, 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 = genus, 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(PH_honey_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))

ggplot(PH_honey_final_May, 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))

ggplot(PH_honey_final_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))

ggplot(PH_honey_final_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))

ggplot(PH_honey_final_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))

ggplot(PH_honey_final_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))

ggplot(PH_honey_final_Oct, 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))

Truncated for presentation

ggplot(filter(PH_honey_final_May, max_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))
ggsave("~/Desktop/may2018_honey.png", device = "png")

ggplot(filter(PH_honey_final_June, max_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))
ggsave("~/Desktop/June2018_honey.png", device = "png")


ggplot(filter(PH_honey_final_July, max_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))
ggsave("~/Desktop/july2018_honey.png", device = "png")


ggplot(filter(PH_honey_final_Aug, max_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))
ggsave("~/Desktop/august2018_honey.png", device = "png")


ggplot(filter(PH_honey_final_Sept, max_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))
ggsave("~/Desktop/sept2018_honey.png", device = "png")


ggplot(filter(PH_honey_final_Oct, max_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))
ggsave("~/Desktop/october2018_honey.png", device = "png")