Workflow analysis of Maritz et al. (2019)

Wastewater from NYC.

Author

Will Bradshaw

Published

May 1, 2024

Continuing my analysis of datasets from the P2RA preprint, I analyzed the data from Maritz et al. (2019), a study that used DNA sequencing of wastewater samples to characterize protist diversity and temporal diversity in New York City. Samples for this study underwent direct DNA extraction without a dedicated concentration step, then underwent library prep and Illumina sequencing on a HiSeq Rapid Run (2x250bp).

The raw data

16 samples were collected from 14 treatment plants in NYC in November 2014. These samples yielded 8.6M-18.3M (mean 10.8M) reads per sample, for a total of 172M read pairs (84 gigabases of sequence). Read qualities were mostly high; adapter levels were moderate; inferred duplication levels were low.

Code
# Importing the data is a bit more complicated this time as the samples are split across three pipeline runs
data_dir <- "../data/2024-05-01_maritz"

# Data input paths
libraries_path <- file.path(data_dir, "sample-metadata.csv")
basic_stats_path <- file.path(data_dir, "qc_basic_stats.tsv.gz")
adapter_stats_path <- file.path(data_dir, "qc_adapter_stats.tsv.gz")
quality_base_stats_path <- file.path(data_dir, "qc_quality_base_stats.tsv.gz")
quality_seq_stats_path <- file.path(data_dir, "qc_quality_sequence_stats.tsv.gz")

# Import libraries and extract metadata from sample names
libraries_raw <- lapply(libraries_path, read_csv, show_col_types = FALSE) %>%
  bind_rows
libraries <- libraries_raw %>%
  mutate(sample = fct_inorder(sample))
Code
# Import QC data
stages <- c("raw_concat", "cleaned", "dedup", "ribo_initial", "ribo_secondary")
import_basic <- function(paths){
  lapply(paths, read_tsv, show_col_types = FALSE) %>% bind_rows %>%
    inner_join(libraries, by="sample") %>%
    arrange(sample) %>%
    mutate(stage = factor(stage, levels = stages),
           sample = fct_inorder(sample))
}
import_basic_paired <- function(paths){
  import_basic(paths) %>% arrange(read_pair) %>% 
    mutate(read_pair = fct_inorder(as.character(read_pair)))
}
basic_stats <- import_basic(basic_stats_path)
adapter_stats <- import_basic_paired(adapter_stats_path)
quality_base_stats <- import_basic_paired(quality_base_stats_path)
quality_seq_stats <- import_basic_paired(quality_seq_stats_path)

# Filter to raw data
basic_stats_raw <- basic_stats %>% filter(stage == "raw_concat")
adapter_stats_raw <- adapter_stats %>% filter(stage == "raw_concat")
quality_base_stats_raw <- quality_base_stats %>% filter(stage == "raw_concat")
quality_seq_stats_raw <- quality_seq_stats %>% filter(stage == "raw_concat")

# Get key values for readout
raw_read_counts <- basic_stats_raw %>% ungroup %>% 
  summarize(rmin = min(n_read_pairs), rmax=max(n_read_pairs),
            rmean=mean(n_read_pairs), 
            rtot = sum(n_read_pairs),
            btot = sum(n_bases_approx),
            dmin = min(percent_duplicates), dmax=max(percent_duplicates),
            dmean=mean(percent_duplicates), .groups = "drop")
Code
# Prepare data
basic_stats_raw_metrics <- basic_stats_raw %>%
  select(sample,
         `# Read pairs` = n_read_pairs,
         `Total base pairs\n(approx)` = n_bases_approx,
         `% Duplicates\n(FASTQC)` = percent_duplicates) %>%
  pivot_longer(-(sample), names_to = "metric", values_to = "value") %>%
  mutate(metric = fct_inorder(metric))

# Set up plot templates
g_basic <- ggplot(basic_stats_raw_metrics, aes(x=sample, y=value)) +
  geom_col(position = "dodge") +
  scale_y_continuous(expand=c(0,0)) +
  expand_limits(y=c(0,100)) +
  facet_grid(metric~., scales = "free", space="free_x", switch="y") +
  theme_kit + theme(
    axis.title.y = element_blank(),
    strip.text.y = element_text(face="plain")
  )
g_basic

Code
# Set up plotting templates
g_qual_raw <- ggplot(mapping=aes(linetype=read_pair, 
                         group=interaction(sample,read_pair))) + 
  scale_linetype_discrete(name = "Read Pair") +
  guides(color=guide_legend(nrow=2,byrow=TRUE),
         linetype = guide_legend(nrow=2,byrow=TRUE)) +
  theme_base

# Visualize adapters
g_adapters_raw <- g_qual_raw + 
  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats_raw) +
  scale_y_continuous(name="% Adapters", limits=c(0,NA),
                     breaks = seq(0,100,1), expand=c(0,0)) +
  scale_x_continuous(name="Position", limits=c(0,NA),
                     breaks=seq(0,500,20), expand=c(0,0)) +
  facet_grid(.~adapter)
g_adapters_raw

Code
# Visualize quality
g_quality_base_raw <- g_qual_raw +
  geom_hline(yintercept=25, linetype="dashed", color="red") +
  geom_hline(yintercept=30, linetype="dashed", color="red") +
  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats_raw) +
  scale_y_continuous(name="Mean Phred score", expand=c(0,0), limits=c(10,45)) +
  scale_x_continuous(name="Position", limits=c(0,NA),
                     breaks=seq(0,500,20), expand=c(0,0))
g_quality_base_raw

Code
g_quality_seq_raw <- g_qual_raw +
  geom_vline(xintercept=25, linetype="dashed", color="red") +
  geom_vline(xintercept=30, linetype="dashed", color="red") +
  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats_raw) +
  scale_x_continuous(name="Mean Phred score", expand=c(0,0)) +
  scale_y_continuous(name="# Sequences", expand=c(0,0))
g_quality_seq_raw

Preprocessing

About 6% of reads on average were lost during cleaning, and a further 2% during deduplication. Very few reads were lost during ribodepletion, as expected for DNA sequencing libraries.

Code
n_reads_rel <- basic_stats %>% 
  select(sample, stage, 
         percent_duplicates, n_read_pairs) %>%
  group_by(sample) %>% arrange(sample, stage) %>%
  mutate(p_reads_retained = replace_na(n_read_pairs / lag(n_read_pairs), 0),
         p_reads_lost = 1 - p_reads_retained,
         p_reads_retained_abs = n_read_pairs / n_read_pairs[1],
         p_reads_lost_abs = 1-p_reads_retained_abs,
         p_reads_lost_abs_marginal = replace_na(p_reads_lost_abs - lag(p_reads_lost_abs), 0))
n_reads_rel_display <- n_reads_rel %>% 
  group_by(Stage=stage) %>% 
  summarize(`% Total Reads Lost (Cumulative)` = paste0(round(min(p_reads_lost_abs*100),1), "-", round(max(p_reads_lost_abs*100),1), " (mean ", round(mean(p_reads_lost_abs*100),1), ")"),
            `% Total Reads Lost (Marginal)` = paste0(round(min(p_reads_lost_abs_marginal*100),1), "-", round(max(p_reads_lost_abs_marginal*100),1), " (mean ", round(mean(p_reads_lost_abs_marginal*100),1), ")"), .groups="drop") %>% 
  filter(Stage != "raw_concat") %>%
  mutate(Stage = Stage %>% as.numeric %>% factor(labels=c("Trimming & filtering", "Deduplication", "Initial ribodepletion", "Secondary ribodepletion")))
n_reads_rel_display
Code
g_stage_base <- ggplot(mapping=aes(x=stage, group=sample)) +
  theme_kit

# Plot reads over preprocessing
g_reads_stages <- g_stage_base +
  geom_line(aes(y=n_read_pairs), data=basic_stats) +
  scale_y_continuous("# Read pairs", expand=c(0,0), limits=c(0,NA))
g_reads_stages

Code
# Plot relative read losses during preprocessing
g_reads_rel <- g_stage_base +
  geom_line(aes(y=p_reads_lost_abs_marginal), data=n_reads_rel) +
  scale_y_continuous("% Total Reads Lost", expand=c(0,0), 
                     labels = function(x) x*100)
g_reads_rel

Data cleaning was very successful at removing adapters and improving read qualities:

Code
g_qual <- ggplot(mapping=aes(linetype=read_pair, 
                         group=interaction(sample,read_pair))) + 
  scale_linetype_discrete(name = "Read Pair") +
  guides(color=guide_legend(nrow=2,byrow=TRUE),
         linetype = guide_legend(nrow=2,byrow=TRUE)) +
  theme_base

# Visualize adapters
g_adapters <- g_qual + 
  geom_line(aes(x=position, y=pc_adapters), data=adapter_stats) +
  scale_y_continuous(name="% Adapters", limits=c(0,20),
                     breaks = seq(0,50,10), expand=c(0,0)) +
  scale_x_continuous(name="Position", limits=c(0,NA),
                     breaks=seq(0,140,20), expand=c(0,0)) +
  facet_grid(stage~adapter)
g_adapters

Code
# Visualize quality
g_quality_base <- g_qual +
  geom_hline(yintercept=25, linetype="dashed", color="red") +
  geom_hline(yintercept=30, linetype="dashed", color="red") +
  geom_line(aes(x=position, y=mean_phred_score), data=quality_base_stats) +
  scale_y_continuous(name="Mean Phred score", expand=c(0,0), limits=c(10,45)) +
  scale_x_continuous(name="Position", limits=c(0,NA),
                     breaks=seq(0,140,20), expand=c(0,0)) +
  facet_grid(stage~.)
g_quality_base

Code
g_quality_seq <- g_qual +
  geom_vline(xintercept=25, linetype="dashed", color="red") +
  geom_vline(xintercept=30, linetype="dashed", color="red") +
  geom_line(aes(x=mean_phred_score, y=n_sequences), data=quality_seq_stats) +
  scale_x_continuous(name="Mean Phred score", expand=c(0,0)) +
  scale_y_continuous(name="# Sequences", expand=c(0,0)) +
  facet_grid(stage~.)
g_quality_seq

According to FASTQC, cleaning + deduplication was very effective at reducing measured duplicate levels in the few samples that required it:

Code
stage_dup <- basic_stats %>% group_by(stage) %>% 
  summarize(dmin = min(percent_duplicates), dmax=max(percent_duplicates),
            dmean=mean(percent_duplicates), .groups = "drop")

g_dup_stages <- g_stage_base +
  geom_line(aes(y=percent_duplicates), data=basic_stats) +
  scale_y_continuous("% Duplicates", limits=c(0,NA), expand=c(0,0))
g_dup_stages

Code
g_readlen_stages <- g_stage_base + 
  geom_line(aes(y=mean_seq_len), data=basic_stats) +
  scale_y_continuous("Mean read length (nt)", expand=c(0,0), limits=c(0,NA))
g_readlen_stages

High-level composition

As before, to assess the high-level composition of the reads, I ran the ribodepleted files through Kraken (using the Standard 16 database) and summarized the results with Bracken. Combining these results with the read counts above gives us a breakdown of the inferred composition of the samples:

Code
classifications <- c("Filtered", "Duplicate", "Ribosomal", "Unassigned",
                     "Bacterial", "Archaeal", "Viral", "Human")

# Import composition data
comp_path <- file.path(data_dir, "taxonomic_composition.tsv.gz")
comp <- read_tsv(comp_path, show_col_types = FALSE) %>%
  left_join(libraries, by="sample") %>%
  mutate(classification = factor(classification, levels = classifications))
  

# Summarize composition
read_comp_summ <- comp %>% 
  group_by(classification) %>%
  summarize(n_reads = sum(n_reads), .groups = "drop_last") %>%
  mutate(n_reads = replace_na(n_reads,0),
    p_reads = n_reads/sum(n_reads),
    pc_reads = p_reads*100)
Code
# Prepare plotting templates
g_comp_base <- ggplot(mapping=aes(x=sample, y=p_reads, fill=classification)) +
  theme_kit
scale_y_pc_reads <- purrr::partial(scale_y_continuous, name = "% Reads",
                                   expand = c(0,0), labels = function(y) y*100)

# Plot overall composition
g_comp <- g_comp_base + geom_col(data = comp, position = "stack", width=1) +
  scale_y_pc_reads(limits = c(0,1.01), breaks = seq(0,1,0.2)) +
  scale_fill_brewer(palette = "Set1", name = "Classification")
g_comp

Code
# Plot composition of minor components
comp_minor <- comp %>% 
  filter(classification %in% c("Archaeal", "Viral", "Human", "Other"))
palette_minor <- brewer.pal(9, "Set1")[6:9]
g_comp_minor <- g_comp_base + 
  geom_col(data=comp_minor, position = "stack", width=1) +
  scale_y_pc_reads() +
  scale_fill_manual(values=palette_minor, name = "Classification")
g_comp_minor

Code
p_reads_summ_group <- comp %>%
  mutate(classification = ifelse(classification %in% c("Filtered", "Duplicate", "Unassigned"), "Excluded", as.character(classification)),
         classification = fct_inorder(classification)) %>%
  group_by(classification, sample) %>%
  summarize(p_reads = sum(p_reads), .groups = "drop") %>%
  group_by(classification) %>%
  summarize(pc_min = min(p_reads)*100, pc_max = max(p_reads)*100, 
            pc_mean = mean(p_reads)*100, .groups = "drop")
p_reads_summ_prep <- p_reads_summ_group %>%
  mutate(classification = fct_inorder(classification),
         pc_min = pc_min %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
         pc_max = pc_max %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
         pc_mean = pc_mean %>% signif(digits=2) %>% sapply(format, scientific=FALSE, trim=TRUE, digits=2),
         display = paste0(pc_min, "-", pc_max, "% (mean ", pc_mean, "%)"))
p_reads_summ <- p_reads_summ_prep %>%
  select(Classification=classification, 
         `Read Fraction`=display) %>%
  arrange(Classification)
p_reads_summ

As in previous DNA datasets, the vast majority of classified reads were bacterial in origin. Viral fraction averaged 0.13%, though one samples (NYC-08) reached almost 1%. As is common for DNA data, viral reads were overwhelmingly dominated by Caudoviricetes phages:

Code
# Get Kraken reports
reports_path <- file.path(data_dir, "kraken_reports.tsv.gz")
reports <- read_tsv(reports_path, show_col_types = FALSE)

# Get viral taxonomy
viral_taxa_path <- file.path(data_dir, "viral-taxids.tsv.gz")
viral_taxa <- read_tsv(viral_taxa_path, show_col_types = FALSE)

# Filter to viral taxa
kraken_reports_viral <- filter(reports, taxid %in% viral_taxa$taxid) %>%
  group_by(sample) %>%
  mutate(p_reads_viral = n_reads_clade/n_reads_clade[1])
kraken_reports_viral_cleaned <- kraken_reports_viral %>%
  inner_join(libraries, by="sample") %>%
  select(-pc_reads_total, -n_reads_direct, -contains("minimizers")) %>%
  select(name, taxid, p_reads_viral, n_reads_clade, everything())

viral_classes <- kraken_reports_viral_cleaned %>% filter(rank == "C")
viral_families <- kraken_reports_viral_cleaned %>% filter(rank == "F")
Code
major_threshold <- 0.02

# Identify major viral classes
viral_classes_major_tab <- viral_classes %>% 
  group_by(name, taxid) %>%
  summarize(p_reads_viral_max = max(p_reads_viral), .groups="drop") %>%
  filter(p_reads_viral_max >= major_threshold)
viral_classes_major_list <- viral_classes_major_tab %>% pull(name)
viral_classes_major <- viral_classes %>% 
  filter(name %in% viral_classes_major_list) %>%
  select(name, taxid, sample, p_reads_viral)
viral_classes_minor <- viral_classes_major %>% 
  group_by(sample) %>%
  summarize(p_reads_viral_major = sum(p_reads_viral), .groups = "drop") %>%
  mutate(name = "Other", taxid=NA, p_reads_viral = 1-p_reads_viral_major) %>%
  select(name, taxid, sample, p_reads_viral)
viral_classes_display <- bind_rows(viral_classes_major, viral_classes_minor) %>%
  arrange(desc(p_reads_viral)) %>% 
  mutate(name = factor(name, levels=c(viral_classes_major_list, "Other")),
         p_reads_viral = pmax(p_reads_viral, 0)) %>%
  rename(p_reads = p_reads_viral, classification=name)

palette_viral <- c(brewer.pal(12, "Set3"), brewer.pal(8, "Dark2"))
g_classes <- g_comp_base + 
  geom_col(data=viral_classes_display, position = "stack", width=1) +
  scale_y_continuous(name="% Viral Reads", limits=c(0,1.01), breaks = seq(0,1,0.2),
                     expand=c(0,0), labels = function(y) y*100) +
  scale_fill_manual(values=palette_viral, name = "Viral class")
  
g_classes

Human-infecting virus reads: validation

Next, I investigated the human-infecting virus read content of these unenriched samples. A grand total of 199 reads were identified as putatively human-viral:

Code
# Import HV read data
hv_reads_filtered_path <- file.path(data_dir, "hv_hits_putative_filtered.tsv.gz")
hv_reads_filtered <- lapply(hv_reads_filtered_path, read_tsv,
                            show_col_types = FALSE) %>%
  bind_rows() %>%
  left_join(libraries, by="sample")

# Count reads
n_hv_filtered <- hv_reads_filtered %>%
  group_by(sample, seq_id) %>% count %>%
  group_by(sample) %>% count %>% 
  inner_join(basic_stats %>% filter(stage == "ribo_initial") %>% 
               select(sample, n_read_pairs), by="sample") %>% 
  rename(n_putative = n, n_total = n_read_pairs) %>% 
  mutate(p_reads = n_putative/n_total, pc_reads = p_reads * 100)
n_hv_filtered_summ <- n_hv_filtered %>% ungroup %>%
  summarize(n_putative = sum(n_putative), n_total = sum(n_total), 
            .groups="drop") %>% 
  mutate(p_reads = n_putative/n_total, pc_reads = p_reads*100)
Code
# Collapse multi-entry sequences
rmax <- purrr::partial(max, na.rm = TRUE)
collapse <- function(x) ifelse(all(x == x[1]), x[1], paste(x, collapse="/"))
mrg <- hv_reads_filtered %>% 
  mutate(adj_score_max = pmax(adj_score_fwd, adj_score_rev, na.rm = TRUE)) %>%
  arrange(desc(adj_score_max)) %>%
  group_by(seq_id) %>%
  summarize(sample = collapse(sample),
            genome_id = collapse(genome_id),
            taxid_best = taxid[1],
            taxid = collapse(as.character(taxid)),
            best_alignment_score_fwd = rmax(best_alignment_score_fwd),
            best_alignment_score_rev = rmax(best_alignment_score_rev),
            query_len_fwd = rmax(query_len_fwd),
            query_len_rev = rmax(query_len_rev),
            query_seq_fwd = query_seq_fwd[!is.na(query_seq_fwd)][1],
            query_seq_rev = query_seq_rev[!is.na(query_seq_rev)][1],
            classified = rmax(classified),
            assigned_name = collapse(assigned_name),
            assigned_taxid_best = assigned_taxid[1],
            assigned_taxid = collapse(as.character(assigned_taxid)),
            assigned_hv = rmax(assigned_hv),
            hit_hv = rmax(hit_hv),
            encoded_hits = collapse(encoded_hits),
            adj_score_fwd = rmax(adj_score_fwd),
            adj_score_rev = rmax(adj_score_rev)
            ) %>%
  inner_join(libraries, by="sample") %>%
  mutate(kraken_label = ifelse(assigned_hv, "Kraken2 HV\nassignment",
                               ifelse(hit_hv, "Kraken2 HV\nhit",
                                      "No hit or\nassignment"))) %>%
  mutate(adj_score_max = pmax(adj_score_fwd, adj_score_rev),
         highscore = adj_score_max >= 20)

# Plot results
geom_vhist <- purrr::partial(geom_histogram, binwidth=5, boundary=0)
g_vhist_base <- ggplot(mapping=aes(x=adj_score_max)) +
  geom_vline(xintercept=20, linetype="dashed", color="red") +
  facet_wrap(~kraken_label, labeller = labeller(kit = label_wrap_gen(20)), scales = "free_y") +
  scale_x_continuous(name = "Maximum adjusted alignment score") + 
  scale_y_continuous(name="# Read pairs") + 
  theme_base 
g_vhist_0 <- g_vhist_base + geom_vhist(data=mrg)
g_vhist_0

BLASTing these reads against nt, we find that the pipeline performs well, with only a single high-scoring false-positive read:

Code
# Import paired BLAST results
blast_paired_path <- file.path(data_dir, "hv_hits_blast_paired.tsv.gz")
blast_paired <- read_tsv(blast_paired_path, show_col_types = FALSE)

# Add viral status
blast_viral <- mutate(blast_paired, viral = staxid %in% viral_taxa$taxid) %>%
  mutate(viral_full = viral & n_reads == 2)

# Compare to Kraken & Bowtie assignments
match_taxid <- function(taxid_1, taxid_2){
  p1 <- mapply(grepl, paste0("/", taxid_1, "$"), taxid_2)
  p2 <- mapply(grepl, paste0("^", taxid_1, "/"), taxid_2)
  p3 <- mapply(grepl, paste0("^", taxid_1, "$"), taxid_2)
  out <- setNames(p1|p2|p3, NULL)
  return(out)
}
mrg_assign <- mrg %>% select(sample, seq_id, taxid, assigned_taxid, adj_score_max)
blast_assign <- inner_join(blast_viral, mrg_assign, by="seq_id") %>%
    mutate(taxid_match_bowtie = match_taxid(staxid, taxid),
           taxid_match_kraken = match_taxid(staxid, assigned_taxid),
           taxid_match_any = taxid_match_bowtie | taxid_match_kraken)
blast_out <- blast_assign %>%
  group_by(seq_id) %>%
  summarize(viral_status = ifelse(any(viral_full), 2,
                                  ifelse(any(taxid_match_any), 2,
                                             ifelse(any(viral), 1, 0))),
            .groups = "drop")
Code
# Merge BLAST results with unenriched read data
mrg_blast <- full_join(mrg, blast_out, by="seq_id") %>%
  mutate(viral_status = replace_na(viral_status, 0),
         viral_status_out = ifelse(viral_status == 0, FALSE, TRUE))

# Plot
g_vhist_1 <- g_vhist_base + geom_vhist(data=mrg_blast, mapping=aes(fill=viral_status_out)) +
  scale_fill_brewer(palette = "Set1", name = "Viral status")
g_vhist_1

My usual disjunctive score threshold of 20 gave precision, sensitivity, and F1 scores all >96%:

Code
test_sens_spec <- function(tab, score_threshold){
  tab_retained <- tab %>% 
    mutate(retain_score = (adj_score_fwd > score_threshold | adj_score_rev > score_threshold),
           retain = assigned_hv | retain_score) %>%
    group_by(viral_status_out, retain) %>% count
  pos_tru <- tab_retained %>% filter(viral_status_out == "TRUE", retain) %>% pull(n) %>% sum
  pos_fls <- tab_retained %>% filter(viral_status_out != "TRUE", retain) %>% pull(n) %>% sum
  neg_tru <- tab_retained %>% filter(viral_status_out != "TRUE", !retain) %>% pull(n) %>% sum
  neg_fls <- tab_retained %>% filter(viral_status_out == "TRUE", !retain) %>% pull(n) %>% sum
  sensitivity <- pos_tru / (pos_tru + neg_fls)
  specificity <- neg_tru / (neg_tru + pos_fls)
  precision   <- pos_tru / (pos_tru + pos_fls)
  f1 <- 2 * precision * sensitivity / (precision + sensitivity)
  out <- tibble(threshold=score_threshold, sensitivity=sensitivity, 
                specificity=specificity, precision=precision, f1=f1)
  return(out)
}
range_f1 <- function(intab, inrange=15:45){
  tss <- purrr::partial(test_sens_spec, tab=intab)
  stats <- lapply(inrange, tss) %>% bind_rows %>%
    pivot_longer(!threshold, names_to="metric", values_to="value")
  return(stats)
}
stats_0 <- range_f1(mrg_blast)
g_stats_0 <- ggplot(stats_0, aes(x=threshold, y=value, color=metric)) +
  geom_vline(xintercept=20, color = "red", linetype = "dashed") +
  geom_line() +
  scale_y_continuous(name = "Value", limits=c(0,1), breaks = seq(0,1,0.2), expand = c(0,0)) +
  scale_x_continuous(name = "Adjusted Score Threshold", expand = c(0,0)) +
  scale_color_brewer(palette="Dark2") +
  theme_base
g_stats_0

Code
stats_0 %>% filter(threshold == 20) %>% 
  select(Threshold=threshold, Metric=metric, Value=value)

Human-infecting viruses: overall relative abundance

Code
# Get raw read counts
read_counts_raw <- basic_stats_raw %>%
  select(sample, n_reads_raw = n_read_pairs)

# Get HV read counts
mrg_hv <- mrg %>% mutate(hv_status = assigned_hv | highscore) %>%
  rename(taxid_all = taxid, taxid = taxid_best)
read_counts_hv <- mrg_hv %>% filter(hv_status) %>% group_by(sample) %>% 
  count(name="n_reads_hv")
read_counts <- read_counts_raw %>% left_join(read_counts_hv, by="sample") %>%
  mutate(n_reads_hv = replace_na(n_reads_hv, 0))

# Aggregate
read_counts_grp <- read_counts %>%
  summarize(n_reads_raw = sum(n_reads_raw),
            n_reads_hv = sum(n_reads_hv), .groups="drop") %>%
  mutate(sample= "All samples")
read_counts_agg <- bind_rows(read_counts, read_counts_grp) %>%
  mutate(p_reads_hv = n_reads_hv/n_reads_raw,
         sample = factor(sample, levels=c(levels(libraries$sample), "All samples")))

Applying a disjunctive cutoff at S=20 identifies 162 read pairs as human-viral. This gives an overall relative HV abundance of \(9.42 \times 10^{-7}\); higher than Ng and Bengtsson-Palme but lower than most other datasets I’ve analyzed with this pipeline:

Code
# Visualize
g_phv_agg <- ggplot(read_counts_agg, aes(x=sample)) +
  geom_point(aes(y=p_reads_hv)) +
  scale_y_log10("Relative abundance of human virus reads") +
  theme_kit
g_phv_agg

Code
# Collate past RA values
ra_past <- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,
                   "Brumfield", 5e-5, "RNA", FALSE,
                   "Brumfield", 3.66e-7, "DNA", FALSE,
                   "Spurbeck", 5.44e-6, "RNA", FALSE,
                   "Yang", 3.62e-4, "RNA", FALSE,
                   "Rothman (unenriched)", 1.87e-5, "RNA", FALSE,
                   "Rothman (panel-enriched)", 3.3e-5, "RNA", TRUE,
                   "Crits-Christoph (unenriched)", 1.37e-5, "RNA", FALSE,
                   "Crits-Christoph (panel-enriched)", 1.26e-2, "RNA", TRUE,
                   "Prussin (non-control)", 1.63e-5, "RNA", FALSE,
                   "Prussin (non-control)", 4.16e-5, "DNA", FALSE,
                   "Rosario (non-control)", 1.21e-5, "RNA", FALSE,
                   "Rosario (non-control)", 1.50e-4, "DNA", FALSE,
                   "Leung", 1.73e-5, "DNA", FALSE,
                   "Brinch", 3.88e-6, "DNA", FALSE,
                   "Bengtsson-Palme", 8.86e-8, "DNA", FALSE,
                   "Ng", 2.90e-7, "DNA", FALSE
)

# Collate new RA values
ra_new <- tribble(~dataset, ~ra, ~na_type, ~panel_enriched,
                  "Maritz", 9.42e-7, "DNA", FALSE)


# Plot
scale_color_na <- purrr::partial(scale_color_brewer, palette="Set1",
                                 name="Nucleic acid type")
ra_comp <- bind_rows(ra_past, ra_new) %>% mutate(dataset = fct_inorder(dataset))
g_ra_comp <- ggplot(ra_comp, aes(y=dataset, x=ra, color=na_type)) +
  geom_point() +
  scale_color_na() +
  scale_x_log10(name="Relative abundance of human virus reads") +
  theme_base + theme(axis.title.y = element_blank())
g_ra_comp

Human-infecting viruses: taxonomy and composition

In investigating the taxonomy of human-infecting virus reads, I restricted my analysis to samples with more than 5 HV read pairs total across all viruses, to reduce noise arising from extremely low HV read counts in some samples. 10 samples met this criterion.

At the family level, most samples were dominated by Adenoviridae, Polyomaviridae and Papillomaviridae. However, one sample, NYC-03, was overwhelmingly dominated by Herpesviridae:

Code
# Get viral taxon names for putative HV reads
viral_taxa$name[viral_taxa$taxid == 249588] <- "Mamastrovirus"
viral_taxa$name[viral_taxa$taxid == 194960] <- "Kobuvirus"
viral_taxa$name[viral_taxa$taxid == 688449] <- "Salivirus"
viral_taxa$name[viral_taxa$taxid == 585893] <- "Picobirnaviridae"
viral_taxa$name[viral_taxa$taxid == 333922] <- "Betapapillomavirus"
viral_taxa$name[viral_taxa$taxid == 334207] <- "Betapapillomavirus 3"
viral_taxa$name[viral_taxa$taxid == 369960] <- "Porcine type-C oncovirus"
viral_taxa$name[viral_taxa$taxid == 333924] <- "Betapapillomavirus 2"
viral_taxa$name[viral_taxa$taxid == 687329] <- "Anelloviridae"
viral_taxa$name[viral_taxa$taxid == 325455] <- "Gammapapillomavirus"
viral_taxa$name[viral_taxa$taxid == 333750] <- "Alphapapillomavirus"
viral_taxa$name[viral_taxa$taxid == 694002] <- "Betacoronavirus"
viral_taxa$name[viral_taxa$taxid == 334202] <- "Mupapillomavirus"
viral_taxa$name[viral_taxa$taxid == 197911] <- "Alphainfluenzavirus"
viral_taxa$name[viral_taxa$taxid == 186938] <- "Respirovirus"
viral_taxa$name[viral_taxa$taxid == 333926] <- "Gammapapillomavirus 1"
viral_taxa$name[viral_taxa$taxid == 337051] <- "Betapapillomavirus 1"
viral_taxa$name[viral_taxa$taxid == 337043] <- "Alphapapillomavirus 4"
viral_taxa$name[viral_taxa$taxid == 694003] <- "Betacoronavirus 1"
viral_taxa$name[viral_taxa$taxid == 334204] <- "Mupapillomavirus 2"
viral_taxa$name[viral_taxa$taxid == 334208] <- "Betapapillomavirus 4"
viral_taxa$name[viral_taxa$taxid == 333928] <- "Gammapapillomavirus 2"
viral_taxa$name[viral_taxa$taxid == 337039] <- "Alphapapillomavirus 2"
viral_taxa$name[viral_taxa$taxid == 333929] <- "Gammapapillomavirus 3"
viral_taxa$name[viral_taxa$taxid == 337042] <- "Alphapapillomavirus 7"
viral_taxa$name[viral_taxa$taxid == 334203] <- "Mupapillomavirus 1"
viral_taxa$name[viral_taxa$taxid == 333757] <- "Alphapapillomavirus 8"
viral_taxa$name[viral_taxa$taxid == 337050] <- "Alphapapillomavirus 6"
viral_taxa$name[viral_taxa$taxid == 333767] <- "Alphapapillomavirus 3"
viral_taxa$name[viral_taxa$taxid == 333754] <- "Alphapapillomavirus 10"
viral_taxa$name[viral_taxa$taxid == 687363] <- "Torque teno virus 24"
viral_taxa$name[viral_taxa$taxid == 687342] <- "Torque teno virus 3"
viral_taxa$name[viral_taxa$taxid == 687359] <- "Torque teno virus 20"
viral_taxa$name[viral_taxa$taxid == 194441] <- "Primate T-lymphotropic virus 2"
viral_taxa$name[viral_taxa$taxid == 334209] <- "Betapapillomavirus 5"
viral_taxa$name[viral_taxa$taxid == 194965] <- "Aichivirus B"
viral_taxa$name[viral_taxa$taxid == 333930] <- "Gammapapillomavirus 4"
viral_taxa$name[viral_taxa$taxid == 337048] <- "Alphapapillomavirus 1"
viral_taxa$name[viral_taxa$taxid == 337041] <- "Alphapapillomavirus 9"
viral_taxa$name[viral_taxa$taxid == 337049] <- "Alphapapillomavirus 11"
viral_taxa$name[viral_taxa$taxid == 337044] <- "Alphapapillomavirus 5"

# Filter samples and add viral taxa information
samples_keep <- read_counts %>% filter(n_reads_hv > 5) %>% pull(sample)
mrg_hv_named <- mrg_hv %>% filter(sample %in% samples_keep, hv_status) %>% left_join(viral_taxa, by="taxid") 

# Discover viral species & genera for HV reads
raise_rank <- function(read_db, taxid_db, out_rank = "species", verbose = FALSE){
  # Get higher ranks than search rank
  ranks <- c("subspecies", "species", "subgenus", "genus", "subfamily", "family", "suborder", "order", "class", "subphylum", "phylum", "kingdom", "superkingdom")
  rank_match <- which.max(ranks == out_rank)
  high_ranks <- ranks[rank_match:length(ranks)]
  # Merge read DB and taxid DB
  reads <- read_db %>% select(-parent_taxid, -rank, -name) %>%
    left_join(taxid_db, by="taxid")
  # Extract sequences that are already at appropriate rank
  reads_rank <- filter(reads, rank == out_rank)
  # Drop sequences at a higher rank and return unclassified sequences
  reads_norank <- reads %>% filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))
  while(nrow(reads_norank) > 0){ # As long as there are unclassified sequences...
    # Promote read taxids and re-merge with taxid DB, then re-classify and filter
    reads_remaining <- reads_norank %>% mutate(taxid = parent_taxid) %>%
      select(-parent_taxid, -rank, -name) %>%
      left_join(taxid_db, by="taxid")
    reads_rank <- reads_remaining %>% filter(rank == out_rank) %>%
      bind_rows(reads_rank)
    reads_norank <- reads_remaining %>%
      filter(rank != out_rank, !rank %in% high_ranks, !is.na(taxid))
  }
  # Finally, extract and append reads that were excluded during the process
  reads_dropped <- reads %>% filter(!seq_id %in% reads_rank$seq_id)
  reads_out <- reads_rank %>% bind_rows(reads_dropped) %>%
    select(-parent_taxid, -rank, -name) %>%
    left_join(taxid_db, by="taxid")
  return(reads_out)
}
hv_reads_species <- raise_rank(mrg_hv_named, viral_taxa, "species")
hv_reads_genus <- raise_rank(mrg_hv_named, viral_taxa, "genus")
hv_reads_family <- raise_rank(mrg_hv_named, viral_taxa, "family")
Code
threshold_major_family <- 0.02

# Count reads for each human-viral family
hv_family_counts <- hv_reads_family %>% 
  group_by(sample, name, taxid) %>%
  count(name = "n_reads_hv") %>%
  group_by(sample) %>%
  mutate(p_reads_hv = n_reads_hv/sum(n_reads_hv))

# Identify high-ranking families and group others
hv_family_major_tab <- hv_family_counts %>% group_by(name) %>% 
  filter(p_reads_hv == max(p_reads_hv)) %>% filter(row_number() == 1) %>%
  arrange(desc(p_reads_hv)) %>% filter(p_reads_hv > threshold_major_family)
hv_family_counts_major <- hv_family_counts %>%
  mutate(name_display = ifelse(name %in% hv_family_major_tab$name, name, "Other")) %>%
  group_by(sample, name_display) %>%
  summarize(n_reads_hv = sum(n_reads_hv), p_reads_hv = sum(p_reads_hv), 
            .groups="drop") %>%
  mutate(name_display = factor(name_display, 
                               levels = c(hv_family_major_tab$name, "Other")))
hv_family_counts_display <- hv_family_counts_major %>%
  rename(p_reads = p_reads_hv, classification = name_display)

# Plot
g_hv_family <- g_comp_base + 
  geom_col(data=hv_family_counts_display, position = "stack", width=1) +
  scale_y_continuous(name="% HV Reads", limits=c(0,1.01), 
                     breaks = seq(0,1,0.2),
                     expand=c(0,0), labels = function(y) y*100) +
  scale_fill_manual(values=palette_viral, name = "Viral family") +
  labs(title="Family composition of human-viral reads") +
  guides(fill=guide_legend(ncol=4)) +
  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))
g_hv_family

Code
# Get most prominent families for text
hv_family_collate <- hv_family_counts %>% group_by(name, taxid) %>% 
  summarize(n_reads_tot = sum(n_reads_hv),
            p_reads_max = max(p_reads_hv), .groups="drop") %>% 
  arrange(desc(n_reads_tot))

In investigating individual viral families, to avoid distortions from a few rare reads, I restricted myself to samples where that family made up at least 10% of human-viral reads:

Code
threshold_major_species <- 0.05
taxid_adeno <- 10508

# Get set of adenoviridae reads
adeno_samples <- hv_family_counts %>% filter(taxid == taxid_adeno) %>%
  filter(p_reads_hv >= 0.1) %>%
  pull(sample)
adeno_ids <- hv_reads_family %>% 
  filter(taxid == taxid_adeno, sample %in% adeno_samples) %>%
  pull(seq_id)

# Count reads for each adenoviridae species
adeno_species_counts <- hv_reads_species %>%
  filter(seq_id %in% adeno_ids) %>%
  group_by(sample, name, taxid) %>%
  count(name = "n_reads_hv") %>%
  group_by(sample) %>%
  mutate(p_reads_adeno = n_reads_hv/sum(n_reads_hv))

# Identify high-ranking families and group others
adeno_species_major_tab <- adeno_species_counts %>% group_by(name) %>% 
  filter(p_reads_adeno == max(p_reads_adeno)) %>% 
  filter(row_number() == 1) %>%
  arrange(desc(p_reads_adeno)) %>% 
  filter(p_reads_adeno > threshold_major_species)
adeno_species_counts_major <- adeno_species_counts %>%
  mutate(name_display = ifelse(name %in% adeno_species_major_tab$name, 
                               name, "Other")) %>%
  group_by(sample, name_display) %>%
  summarize(n_reads_adeno = sum(n_reads_hv),
            p_reads_adeno = sum(p_reads_adeno), 
            .groups="drop") %>%
  mutate(name_display = factor(name_display, 
                               levels = c(adeno_species_major_tab$name, "Other")))
adeno_species_counts_display <- adeno_species_counts_major %>%
  rename(p_reads = p_reads_adeno, classification = name_display)

# Plot
g_adeno_species <- g_comp_base + 
  geom_col(data=adeno_species_counts_display, position = "stack", width=1) +
  scale_y_continuous(name="% Adenoviridae Reads", limits=c(0,1.01), 
                     breaks = seq(0,1,0.2),
                     expand=c(0,0), labels = function(y) y*100) +
  scale_fill_manual(values=palette_viral, name = "Viral species") +
  labs(title="Species composition of Adenoviridae reads") +
  guides(fill=guide_legend(ncol=3)) +
  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))

g_adeno_species

Code
# Get most prominent species for text
adeno_species_collate <- adeno_species_counts %>% group_by(name, taxid) %>% 
  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_adeno), .groups="drop") %>% 
  arrange(desc(n_reads_tot))
Code
threshold_major_species <- 0.1
taxid_polyoma <- 151341

# Get set of polyomaviridae reads
polyoma_samples <- hv_family_counts %>% filter(taxid == taxid_polyoma) %>%
  filter(p_reads_hv >= 0.1) %>%
  pull(sample)
polyoma_ids <- hv_reads_family %>% 
  filter(taxid == taxid_polyoma, sample %in% polyoma_samples) %>%
  pull(seq_id)

# Count reads for each polyomaviridae species
polyoma_species_counts <- hv_reads_species %>%
  filter(seq_id %in% polyoma_ids) %>%
  group_by(sample, name, taxid) %>%
  count(name = "n_reads_hv") %>%
  group_by(sample) %>%
  mutate(p_reads_polyoma = n_reads_hv/sum(n_reads_hv))

# Identify high-ranking families and group others
polyoma_species_major_tab <- polyoma_species_counts %>% group_by(name) %>% 
  filter(p_reads_polyoma == max(p_reads_polyoma)) %>% 
  filter(row_number() == 1) %>%
  arrange(desc(p_reads_polyoma)) %>% 
  filter(p_reads_polyoma > threshold_major_species)
polyoma_species_counts_major <- polyoma_species_counts %>%
  mutate(name_display = ifelse(name %in% polyoma_species_major_tab$name, 
                               name, "Other")) %>%
  group_by(sample, name_display) %>%
  summarize(n_reads_polyoma = sum(n_reads_hv),
            p_reads_polyoma = sum(p_reads_polyoma), 
            .groups="drop") %>%
  mutate(name_display = factor(name_display, 
                               levels = c(polyoma_species_major_tab$name, "Other")))
polyoma_species_counts_display <- polyoma_species_counts_major %>%
  rename(p_reads = p_reads_polyoma, classification = name_display)

# Plot
g_polyoma_species <- g_comp_base + 
  geom_col(data=polyoma_species_counts_display, position = "stack", width=1) +
  scale_y_continuous(name="% Polyomaviridae Reads", limits=c(0,1.01), 
                     breaks = seq(0,1,0.2),
                     expand=c(0,0), labels = function(y) y*100) +
  scale_fill_manual(values=palette_viral, name = "Viral species") +
  labs(title="Species composition of Polyomaviridae reads") +
  guides(fill=guide_legend(ncol=3)) +
  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))

g_polyoma_species

Code
# Get most prominent species for text
polyoma_species_collate <- polyoma_species_counts %>% group_by(name, taxid) %>% 
  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_polyoma), .groups="drop") %>% 
  arrange(desc(n_reads_tot))
Code
threshold_major_species <- 0.1
taxid_papilloma <- 151340

# Get set of papillomaviridae reads
papilloma_samples <- hv_family_counts %>% filter(taxid == taxid_papilloma) %>%
  filter(p_reads_hv >= 0.1) %>%
  pull(sample)
papilloma_ids <- hv_reads_family %>% 
  filter(taxid == taxid_papilloma, sample %in% papilloma_samples) %>%
  pull(seq_id)

# Count reads for each papillomaviridae species
papilloma_species_counts <- hv_reads_species %>%
  filter(seq_id %in% papilloma_ids) %>%
  group_by(sample, name, taxid) %>%
  count(name = "n_reads_hv") %>%
  group_by(sample) %>%
  mutate(p_reads_papilloma = n_reads_hv/sum(n_reads_hv))

# Identify high-ranking families and group others
papilloma_species_major_tab <- papilloma_species_counts %>% group_by(name) %>% 
  filter(p_reads_papilloma == max(p_reads_papilloma)) %>% 
  filter(row_number() == 1) %>%
  arrange(desc(p_reads_papilloma)) %>% 
  filter(p_reads_papilloma > threshold_major_species)
papilloma_species_counts_major <- papilloma_species_counts %>%
  mutate(name_display = ifelse(name %in% papilloma_species_major_tab$name, 
                               name, "Other")) %>%
  group_by(sample, name_display) %>%
  summarize(n_reads_papilloma = sum(n_reads_hv),
            p_reads_papilloma = sum(p_reads_papilloma), 
            .groups="drop") %>%
  mutate(name_display = factor(name_display, 
                               levels = c(papilloma_species_major_tab$name, "Other")))
papilloma_species_counts_display <- papilloma_species_counts_major %>%
  rename(p_reads = p_reads_papilloma, classification = name_display)

# Plot
g_papilloma_species <- g_comp_base + 
  geom_col(data=papilloma_species_counts_display, position = "stack", width=1) +
  scale_y_continuous(name="% Papillomaviridae Reads", limits=c(0,1.01), 
                     breaks = seq(0,1,0.2),
                     expand=c(0,0), labels = function(y) y*100) +
  scale_fill_manual(values=palette_viral, name = "Viral species") +
  labs(title="Species composition of Papillomaviridae reads") +
  guides(fill=guide_legend(ncol=3)) +
  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))

g_papilloma_species

Code
# Get most prominent species for text
papilloma_species_collate <- papilloma_species_counts %>% group_by(name, taxid) %>% 
  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_papilloma), .groups="drop") %>% 
  arrange(desc(n_reads_tot))
Code
threshold_major_species <- 0.1
taxid_herpes <- 10292

# Get set of herpesviridae reads
herpes_samples <- hv_family_counts %>% filter(taxid == taxid_herpes) %>%
  filter(p_reads_hv >= 0.1) %>%
  pull(sample)
herpes_ids <- hv_reads_family %>% 
  filter(taxid == taxid_herpes, sample %in% herpes_samples) %>%
  pull(seq_id)

# Count reads for each herpesviridae species
herpes_species_counts <- hv_reads_species %>%
  filter(seq_id %in% herpes_ids) %>%
  group_by(sample, name, taxid) %>%
  count(name = "n_reads_hv") %>%
  group_by(sample) %>%
  mutate(p_reads_herpes = n_reads_hv/sum(n_reads_hv))

# Identify high-ranking families and group others
herpes_species_major_tab <- herpes_species_counts %>% group_by(name) %>% 
  filter(p_reads_herpes == max(p_reads_herpes)) %>% 
  filter(row_number() == 1) %>%
  arrange(desc(p_reads_herpes)) %>% 
  filter(p_reads_herpes > threshold_major_species)
herpes_species_counts_major <- herpes_species_counts %>%
  mutate(name_display = ifelse(name %in% herpes_species_major_tab$name, 
                               name, "Other")) %>%
  group_by(sample, name_display) %>%
  summarize(n_reads_herpes = sum(n_reads_hv),
            p_reads_herpes = sum(p_reads_herpes), 
            .groups="drop") %>%
  mutate(name_display = factor(name_display, 
                               levels = c(herpes_species_major_tab$name, "Other")))
herpes_species_counts_display <- herpes_species_counts_major %>%
  rename(p_reads = p_reads_herpes, classification = name_display)

# Plot
g_herpes_species <- g_comp_base + 
  geom_col(data=herpes_species_counts_display, position = "stack", width=1) +
  scale_y_continuous(name="% Herpesviridae Reads", limits=c(0,1.01), 
                     breaks = seq(0,1,0.2),
                     expand=c(0,0), labels = function(y) y*100) +
  scale_fill_manual(values=palette_viral, name = "Viral species") +
  labs(title="Species composition of Herpesviridae reads") +
  guides(fill=guide_legend(ncol=3)) +
  theme(plot.title = element_text(size=rel(1.4), hjust=0, face="plain"))

g_herpes_species

Code
# Get most prominent species for text
herpes_species_collate <- herpes_species_counts %>% group_by(name, taxid) %>% 
  summarize(n_reads_tot = sum(n_reads_hv), p_reads_mean = mean(p_reads_herpes), .groups="drop") %>% 
  arrange(desc(n_reads_tot))

I was a bit suspicious of this last result, given that it only occurred in one sample, but according to BLASTN, at least, these human gammaherpesvirus 4 (a.k.a. EBV) matches are real:

Code
# Configure
ref_taxids_hv <- c(10376)
ref_names_hv <- sapply(ref_taxids_hv, function(x) viral_taxa %>% filter(taxid == x) %>% pull(name) %>% first)
p_threshold <- 0.1

# Get taxon names
tax_names_path <- file.path(data_dir, "taxid-names.tsv.gz")
tax_names <- read_tsv(tax_names_path, show_col_types = FALSE)

# Add missing names
tax_names_new <- tribble(~staxid, ~name,
                         3050295, "Cytomegalovirus humanbeta5",
                         459231, "FLAG-tagging vector pFLAG97-TSR",
                         257877, "Macaca thibetana thibetana",
                         256321, "Lentiviral transfer vector pHsCXW",
                         419242, "Shuttle vector pLvCmvMYOCDHA",
                         419243, "Shuttle vector pLvCmvLacZ",
                         421868, "Cloning vector pLvCmvLacZ.Gfp",
                         421869, "Cloning vector pLvCmvMyocardin.Gfp",
                         426303, "Lentiviral vector pNL-GFP-RRE(SA)",
                         436015, "Lentiviral transfer vector pFTMGW",
                         454257, "Shuttle vector pLvCmvMYOCD2aHA",
                         476184, "Shuttle vector pLV.mMyoD::ERT2.eGFP",
                         476185, "Shuttle vector pLV.hMyoD.eGFP",
                         591936, "Piliocolobus tephrosceles",
                         627481, "Lentiviral transfer vector pFTM3GW",
                         680261, "Self-inactivating lentivirus vector pLV.C-EF1a.cyt-bGal.dCpG",
                         2952778, "Expression vector pLV[Exp]-EGFP:T2A:Puro-EF1A",
                         3022699, "Vector PAS_122122",
                         3025913, "Vector pSIN-WP-mPGK-GDNF",
                         3105863, "Vector pLKO.1-ZsGreen1",
                         3105864, "Vector pLKO.1-ZsGreen1 mouse Wfs1 shRNA",
                         3108001, "Cloning vector pLVSIN-CMV_Neo_v4.0",
                         3109234, "Vector pTwist+Kan+High",
                         3117662, "Cloning vector pLV[Exp]-CBA>P301L",
                         3117663, "Cloning vector pLV[Exp]-CBA>P301L:T2A:mRuby3",
                         3117664, "Cloning vector pLV[Exp]-CBA>hMAPT[NM_005910.6](ns):T2A:mRuby3",
                         3117665, "Cloning vector pLV[Exp]-CBA>mRuby3",
                         3117666, "Cloning vector pLV[Exp]-CBA>mRuby3/NFAT3 fusion protein",
                         3117667, "Cloning vector pLV[Exp]-Neo-mPGK>{EGFP-hSEPT6}",
                         438045, "Xenotropic MuLV-related virus",
                         447135, "Myodes glareolus",
                         590745, "Mus musculus mobilized endogenous polytropic provirus",
                         181858, "Murine AIDS virus-related provirus",
                         356663, "Xenotropic MuLV-related virus VP35",
                         356664, "Xenotropic MuLV-related virus VP42",
                         373193, "Xenotropic MuLV-related virus VP62",
                         286419, "Canis lupus dingo",
                         415978, "Sus scrofa scrofa",
                         494514, "Vulpes lagopus",
                         3082113, "Rangifer tarandus platyrhynchus",
                         3119969, "Bubalus kerabau")
tax_names <- bind_rows(tax_names, tax_names_new)

# Get matches
hv_blast_staxids <- hv_reads_species %>% filter(taxid %in% ref_taxids_hv) %>%
  group_by(taxid) %>% mutate(n_seq = n()) %>%
  left_join(blast_paired, by="seq_id") %>%
  mutate(staxid = as.integer(staxid)) %>%
  left_join(tax_names %>% rename(sname=name), by="staxid")

# Count matches
hv_blast_counts <- hv_blast_staxids %>%
  group_by(taxid, name, staxid, sname, n_seq) %>%
  count %>% mutate(p=n/n_seq)

# Subset to major matches
hv_blast_counts_major <- hv_blast_counts %>% 
  filter(n>1, p>p_threshold, !is.na(staxid)) %>%
  arrange(desc(p)) %>% group_by(taxid) %>%
  filter(row_number() <= 25) %>%
  mutate(name_display = ifelse(name == ref_names_hv[1], "EBV", name))

# Plot
g_hv_blast <- ggplot(hv_blast_counts_major, mapping=aes(x=p, y=sname)) +
  geom_col() +
  facet_grid(name_display~., scales="free_y", space="free_y") +
  scale_x_continuous(name="% mapped reads", limits=c(0,1), 
                     breaks=seq(0,1,0.2), expand=c(0,0)) +
  theme_base + theme(axis.title.y = element_blank())
g_hv_blast

Finally, here again are the overall relative abundances of the specific viral genera I picked out manually in my last entry:

Code
# Define reference genera
path_genera_rna <- c("Mamastrovirus", "Enterovirus", "Salivirus", "Kobuvirus", "Norovirus", "Sapovirus", "Rotavirus", "Alphacoronavirus", "Betacoronavirus", "Alphainfluenzavirus", "Betainfluenzavirus", "Lentivirus")
path_genera_dna <- c("Mastadenovirus", "Alphapolyomavirus", "Betapolyomavirus", "Alphapapillomavirus", "Betapapillomavirus", "Gammapapillomavirus", "Orthopoxvirus", "Simplexvirus",
                     "Lymphocryptovirus", "Cytomegalovirus", "Dependoparvovirus")
path_genera <- bind_rows(tibble(name=path_genera_rna, genome_type="RNA genome"),
                         tibble(name=path_genera_dna, genome_type="DNA genome")) %>%
  left_join(viral_taxa, by="name")

# Count in each sample
mrg_hv_named_all <- mrg_hv %>% left_join(viral_taxa, by="taxid")
hv_reads_genus_all <- raise_rank(mrg_hv_named_all, viral_taxa, "genus")
n_path_genera <- hv_reads_genus_all %>% 
  group_by(sample, name, taxid) %>% 
  count(name="n_reads_viral") %>% 
  inner_join(path_genera, by=c("name", "taxid")) %>%
  left_join(read_counts_raw, by=c("sample")) %>%
  mutate(p_reads_viral = n_reads_viral/n_reads_raw)

# Pivot out and back to add zero lines
n_path_genera_out <- n_path_genera %>% ungroup %>% select(sample, name, n_reads_viral) %>%
  pivot_wider(names_from="name", values_from="n_reads_viral", values_fill=0) %>%
  pivot_longer(-sample, names_to="name", values_to="n_reads_viral") %>%
  left_join(read_counts_raw, by="sample") %>%
  left_join(path_genera, by="name") %>%
  mutate(p_reads_viral = n_reads_viral/n_reads_raw)

## Aggregate across dates
n_path_genera_stype <- n_path_genera_out %>% 
  group_by(name, taxid, genome_type) %>%
  summarize(n_reads_raw = sum(n_reads_raw),
            n_reads_viral = sum(n_reads_viral), .groups = "drop") %>%
  mutate(sample="All samples", location="All locations",
         p_reads_viral = n_reads_viral/n_reads_raw,
         na_type = "DNA")

# Plot
g_path_genera <- ggplot(n_path_genera_stype,
                        aes(y=name, x=p_reads_viral)) +
  geom_point() +
  scale_x_log10(name="Relative abundance") +
  facet_grid(genome_type~., scales="free_y") +
  theme_base + theme(axis.title.y = element_blank())
g_path_genera

Conclusion

I’ve had trouble with this dataset previously, so I was surprised at how well this analysis went. It seems the improvements I’ve made to the pipeline over the last couple of months have really had an effect. Like other DNA wastewater datasets I’ve looked at recently, this one (a) has very low HV relative abundance overall, and (b) shows a very high preponderance of human mastadenovirus F. I have one more DNA dataset from the P2RA study to analyze with this pipeline, so we’ll see if this pattern persists there.