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Copy path_targets_explode.R
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_targets_explode.R
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library(targets)
library(tarchetypes)
source("R/functions.R")
options(tidyverse.quiet = TRUE)
# Load packages -----------------------------------------------------------
tar_option_set(packages = c(
"ggplot2",
"scales",
"gridExtra",
"tibble",
"dplyr",
"stringr",
"tidyr",
"purrr",
"rlang",
"readr",
"magrittr",
"patchwork",
"vegan",
"patchwork",
"phyloseq",
"DECIPHER",
"Biostrings",
"ShortRead",
"savR",
"dada2",
"ngsReports",
"taxreturn",
"seqateurs"
),
imports =c(
"taxreturn",
"seqateurs"
),
workspace_on_error = TRUE)
# Source script files in R directory
tar_source()
# Targets pipeline
list(
# Parameter setup ---------------------------------------------------------
# Track input files
tar_file(samdf_file, "sample_data/Sample_info.csv"),
tar_file(params_file, "sample_data/loci_params.csv"),
# Load input tracking files
tar_target(samdf, {
# define default fields
default_samdf <- tibble::tibble(
sample_id = NA_character_,
sample_name = NA_character_,
extraction_rep = NA_integer_,
amp_rep = NA_integer_,
client_name = NA_character_,
experiment_name = NA_character_,
sample_type = NA_character_,
collection_method = NA_character_,
collection_location = NA_character_,
lat_lon = NA_character_,
environment = NA_character_,
collection_date = NA_character_,
operator_name = NA_character_,
description = NA_character_,
assay = NA_character_,
extraction_method = NA_character_,
amp_method = NA_character_,
target_gene = NA_character_,
pcr_primers = NA_character_,
for_primer_seq = NA_character_,
rev_primer_seq = NA_character_,
index_plate = NA_character_,
index_well = NA_character_,
i7_index_id = NA_character_,
i7_index = NA_character_,
i5_index_id = NA_character_,
i5_index = NA_character_,
seq_platform = NA_character_,
fcid = NA_character_,
for_read_length = NA_integer_,
rev_read_length = NA_integer_,
seq_run_id = NA_character_,
seq_id = NA_character_,
seq_date = NA_character_,
analysis_method = NA_character_,
notes = NA_character_
)
# Read in input samdf
input_samdf <- readr::read_csv(samdf_file, show_col_types = FALSE, col_types = cols(.default = "c"))
# Make sure all columns are present and same type using a special bind operation
samdf_checked <- new_bind(default_samdf %>% filter(FALSE), input_samdf)
# Check essential parameters are present
assertthat::assert_that(all(is.character(samdf_checked$sample_id)) & all(!is.na(samdf_checked$sample_id)),
msg = "All samples must have a sample_id in the sample_info.csv file")
assertthat::assert_that(all(is.character(samdf_checked$pcr_primers)) & all(!is.na(samdf_checked$pcr_primers)),
msg = "All samples must have pcr_primers in the sample_info.csv file")
assertthat::assert_that(all(is.character(samdf_checked$for_primer_seq)) & all(!is.na(samdf_checked$for_primer_seq)),
msg = "All samples must have a for_primer_seq in the sample_info.csv file")
assertthat::assert_that(all(is.character(samdf_checked$rev_primer_seq)) & all(!is.na(samdf_checked$rev_primer_seq)),
msg = "All samples must have a rev_primer_seq in the sample_info.csv file")
return(samdf_checked)
}),
tar_target(params, {
# define default params
default_params <- tibble::tibble(pcr_primers = NA_character_,
target_gene = NA_character_,
max_primer_mismatch = 0,
read_min_length = 20,
read_max_length = Inf,
read_max_ee = 1,
read_trunc_length = 0,
read_trim_left = 0,
read_trim_right = 0,
high_sensitivity = TRUE,
asv_min_length = 0,
asv_max_length = Inf,
concat_unmerged = FALSE,
genetic_code = NA_character_,
coding = FALSE,
phmm = NA_character_,
idtaxa_db = NA_character_,
ref_fasta = NA_character_,
idtaxa_confidence = 60,
run_blast = FALSE,
blast_min_identity = 97,
blast_min_coverage = 90,
target_kingdom = NA_character_,
target_phylum = NA_character_,
target_class = NA_character_,
target_order = NA_character_,
target_family = NA_character_,
target_genus = NA_character_,
target_species = NA_character_,
min_sample_reads = 0,
min_taxa_reads = 0,
min_taxa_ra = 0,
threads = 1
)
# Read in params file
input_params <- readr::read_csv(params_file, show_col_types = FALSE, col_types = cols(.default = "c"))
# Make sure all columns are present and same type using a special bind operation
params_df <- new_bind(default_params %>% filter(FALSE), input_params)
# Check columns arent NA
for(i in 1:ncol(default_params)){
param_to_check <- colnames(default_params)[i]
if(all(is.na(params_df %>% dplyr::pull(!!param_to_check))) & !param_to_check %in% colnames(input_params)){
warning(paste0("Parameter: ", param_to_check, " is NA, using default: ", default_params %>% dplyr::pull(!!param_to_check)))
params_df <- params_df %>%
dplyr::mutate(!!param_to_check := default_params %>% dplyr::pull(!!param_to_check))
}
}
# Check class of all columns
for(i in 1:ncol(default_params)){
param_to_check <- colnames(default_params)[i]
if(!class(default_params %>% dplyr::pull(!!param_to_check)) == class(params_df %>% dplyr::pull(!!param_to_check))){
stop(paste0("The column ", param_to_check, " in loci_params.csv file must be of class ", class(default_params %>% dplyr::pull(!!param_to_check))))
}
}
# Check idtaxa db exists - Needs to handle multiple dbs
check_paths <- params_df$idtaxa_db[!is.na(params_df$idtaxa_db)]%>%
stringr::str_split(pattern=";", n=Inf) %>%
unlist()
for(i in seq_along(check_paths)){
assertthat::is.readable(check_paths[i])
}
# Check fasta exists
check_paths <- params_df$ref_fasta[!is.na(params_df$ref_fasta)] %>%
stringr::str_split(pattern=";", n=Inf) %>%
unlist()
for(i in seq_along(check_paths)){
assertthat::is.readable(check_paths[i])
}
# Check phmm exists
check_paths <- params_df$phmm[!is.na(params_df$phmm)] %>%
stringr::str_split(pattern=";", n=Inf) %>%
unlist()
for(i in seq_along(check_paths)){
assertthat::is.readable(check_paths[i])
}
return(params_df)
}, tidy_eval = FALSE
),
# Create temporary params_primer file for tracking
tar_file(params_primer_path,{
out <- "output/temp/params_primer.csv"
params %>%
dplyr::select(pcr_primers, target_gene, max_primer_mismatch) %>%
write_csv(out)
return(out)
}),
tar_target(params_primer,read_csv(params_primer_path, show_col_types = FALSE)),
# Create temporary params_readfilter file for tracking
tar_file(params_readfilter_path,{
out <- "output/temp/params_readfilter.csv"
params %>%
dplyr::select(pcr_primers, target_gene, read_min_length, read_max_length, read_max_ee,
read_trunc_length, read_trim_left, read_trim_right) %>%
write_csv(out)
return(out)
}),
tar_target(params_readfilter, read_csv(params_readfilter_path, show_col_types = FALSE)),
# Create temporary params_dada file for tracking
tar_file(params_dada_path,{
out <- "output/temp/params_dada.csv"
params %>%
dplyr::select(pcr_primers, target_gene, concat_unmerged, high_sensitivity) %>%
write_csv(out)
return(out)
}),
tar_target(params_dada, read_csv(params_dada_path, show_col_types = FALSE)),
# Create temporary params_asvfilter file for tracking
tar_file(params_asvfilter_path,{
out <- "output/temp/params_asvfilter.csv"
params %>%
dplyr::select(pcr_primers, target_gene, asv_min_length, asv_max_length,
phmm, coding, genetic_code) %>%
write_csv(out)
return(out)
}),
tar_target(params_asvfilter, read_csv(params_asvfilter_path, show_col_types = FALSE)),
# Create temporary params_database file for tracking
tar_file(params_database_path,{
out <- "output/temp/params_database.csv"
params %>%
dplyr::select(pcr_primers, target_gene, idtaxa_db, idtaxa_confidence,
ref_fasta, blast_min_identity, blast_min_coverage, run_blast) %>%
write_csv(out)
return(out)
}),
tar_target(params_database, read_csv(params_database_path, show_col_types = FALSE)),
# Create temporary params_ps file for tracking
tar_file(params_ps_path,{
out <- "output/temp/params_ps.csv"
params %>%
dplyr::select(pcr_primers, target_gene, target_kingdom, target_phylum, target_class,
target_order, target_family, target_genus, target_species,
min_sample_reads, min_taxa_reads, min_taxa_ra) %>%
write_csv(out)
return(out)
}),
tar_target(params_ps, read_csv(params_ps_path, show_col_types = FALSE)),
# Create directories
tar_target(create_dirs, step_validate_folders(getwd())),
# Look for sequencing reads
tar_files(
fastq_path,
purrr::map(list.dirs("data", recursive=FALSE),
list.files, pattern="_R[12]_", full.names = TRUE) %>%
unlist()
),
tar_target(temp_samdf1, step_check_files(samdf, fastq_path, col_name="starting")),
tar_group_by(temp_samdf1_grouped, temp_samdf1, fcid),
# Sequencing QC -------------------------------------------------------
tar_target(seq_qc, {
process <- temp_samdf1_grouped %>%
dplyr::group_by(fcid) %>%
tidyr::nest() %>%
dplyr::mutate(seq_qc = purrr::map(fcid, step_seq_qc, quiet=FALSE, write_all=FALSE))
out <- paste0("output/logs/", unique(process$fcid),"/",unique(process$fcid),"_flowcell_qc.pdf")
if(is.na(process$seq_qc[[1]]$reads_pf)){
pdf(file=out, paper="A4")
plot.new()
text(x=.5, y=.5, "ERROR: InterOp folder or RunInfo.xml not present")
try(dev.off(), silent=TRUE)
}
return(out)
}, pattern = map(temp_samdf1_grouped), format="file", iteration = "vector"),
tar_target(switching_qc,{
process <- temp_samdf1_grouped %>%
dplyr::group_by(fcid) %>%
tidyr::nest() %>%
dplyr::mutate(switching_qc = purrr::map(fcid, step_switching_calc, quiet=TRUE))
out <- paste0("output/logs/",unique(process$fcid),"/",unique(process$fcid),"_index_switching.pdf")
if(is.na(process$switching_qc[[1]]$switch_rate)){
pdf(file=out, paper="A4")
plot.new()
text(x=.5, y=.5, "ERROR: Undetermined reads file not found")
try(dev.off(), silent=TRUE)
}
return(out)
},pattern = map(temp_samdf1_grouped), format="file", iteration = "vector"),
# Demultiplex and trim primers --------------------------------------------
tar_target(primer_trim,
{
temp_samdf1 %>%
dplyr::left_join(params_primer, by = "pcr_primers") %>%
dplyr::mutate(primer_trim = purrr::pmap(dplyr::select(., sample_id, for_primer_seq, rev_primer_seq, pcr_primers, fcid, max_primer_mismatch),
.f = ~step_primer_trim(sample_id = ..1, for_primer_seq=..2, rev_primer_seq=..3, pcr_primers = ..4,
input_dir = paste0("data/",..5), output_dir = paste0("data/",..5,"/01_trimmed"),
qc_dir=paste0("output/logs/",..5),
max_mismatch=..6,
quiet = FALSE)))%>%
dplyr::select(sample_id, sample_name, fcid, primer_trim)
},
pattern = map(temp_samdf1), iteration = "vector"),
# Return filepath for tracking
tar_target(primer_trim_path,
{
outF <- primer_trim$primer_trim %>%
dplyr::bind_rows()%>%
dplyr::pull(fwd_out)
outR <- primer_trim$primer_trim %>%
dplyr::bind_rows()%>%
dplyr::pull(rev_out)
# Check for empty files
outF <- outF[file.exists(outF)]
outR <- outR[file.exists(outR)]
# Return list of completed path
return(c(outF,outR))
},
pattern = map(primer_trim), format="file", iteration = "vector"),
## Make temporary samdf
tar_target(temp_samdf2, {
temp_samdf1 %>%
dplyr::select(-where(is.list)) %>%
step_demux_samdf() %>%
step_check_files(primer_trim_path, col_name="trimmed")
}),
# Filter reads ------------------------------------------------------------
tar_target(read_filter,
{
temp_samdf2 %>%
dplyr::select(sample_id, sample_name, pcr_primers, fcid) %>%
dplyr::left_join(params_readfilter, by = "pcr_primers") %>%
dplyr::mutate(read_filter = purrr::pmap(dplyr::select(., sample_id, fcid, read_min_length, read_max_length, read_max_ee,
read_trunc_length, read_trim_left, read_trim_right),
.f = ~step_filter_reads(
sample_id = ..1,
input_dir = paste0("data/",..2,"/01_trimmed/"),
output_dir = paste0("data/",..2,"/02_filtered"),
min_length = ..3,
max_length = ..4,
max_ee = ..5,
trunc_length = ..6,
trim_left = ..7,
trim_right = ..8,
rm.lowcomplex = 0,
quiet = FALSE)))%>%
dplyr::select(sample_id, sample_name, fcid, read_filter)
},
pattern = map(temp_samdf2), iteration = "vector"),
# Return filepath for tracking
tar_target(read_filter_path,
{
outF <- read_filter$read_filter %>%
dplyr::bind_rows()%>%
dplyr::pull(fwd_out)
outR <- read_filter$read_filter %>%
dplyr::bind_rows()%>%
dplyr::pull(rev_out)
# Check for empty files
outF <- outF[file.exists(outF)]
outR <- outR[file.exists(outR)]
# Return list of completed path
return(c(outF,outR))
},
pattern = map(read_filter), format="file", iteration = "vector"),
# Make temporary samdf
tar_target(temp_samdf3, {
temp_samdf2 %>%
dplyr::select(-where(is.list)) %>%
step_check_files(read_filter_path, col_name="filtered")
}),
## Pre-filtering quality plots ---------------------------------------------
# Sample a random set of 5 samples for read quality plotting
#tar_target(prefilt_read_samples,{
# group_sizes <- temp_samdf2 %>%
# dplyr::group_by(fcid, pcr_primers) %>%
# dplyr::group_size()
# if(all(group_sizes > 5)){
# n_samples <- 5
# } else {
# n_samples = min(group_sizes)
# }
# out <- temp_samdf2 %>%
# dplyr::group_by(fcid, pcr_primers) %>%
# dplyr::slice_sample(n=n_samples)
#}),
tar_target(prefilt_qualplots,
temp_samdf2 %>%
dplyr::mutate(prefilt_qualplots = purrr::pmap(list(sample_id, fcid),
.f = ~plot_read_quals(sample_id = ..1,
input_dir = paste0("data/",..2,"/01_trimmed/"),
truncLen=NULL, quiet = FALSE, n = 10000)
)),
pattern = map(temp_samdf2), iteration = "vector"),
## Write out prefilt qualplots
tar_target(write_prefilt_qualplots, {
prefilt_qualplots %>%
dplyr::group_by(fcid) %>%
tidyr::nest() %>%
purrr::pwalk(list(fcid, data),
.f = ~{
pdf(file=paste0("output/logs/",..1,"/", ..1,"_prefilt_qualplots.pdf"), width = 11, height = 8 , paper="a4r")
print(..2$prefilt_qualplots)
try(dev.off(), silent=TRUE)
})
out <- paste0("output/logs/",unique(prefilt_qualplots$fcid),"/", unique(prefilt_qualplots$fcid),"_prefilt_qualplots.pdf")
return(out)
}, format="file", iteration = "vector"),
## Post-filtering quality plots --------------------------------------------
# Get the same samples as the prefilt quality plots
#tar_target(postfilt_read_samples,{
# out <- temp_samdf3 %>%
# dplyr::group_by(fcid, pcr_primers) %>%
# filter(sample_id %in% prefilt_read_samples$sample_id)
#}),
tar_target(postfilt_qualplots,
temp_samdf3 %>%
dplyr::mutate(postfilt_qualplots = purrr::pmap(list(sample_id, fcid),
.f = ~plot_read_quals(sample_id = ..1,
input_dir = paste0("data/",..2,"/02_filtered/"), truncLen=NULL, quiet = FALSE, n = 10000)
)),
pattern = map(temp_samdf3), iteration = "vector"),
# Write out postfilt qualplots
tar_target(write_postfilt_qualplots, {
postfilt_qualplots %>%
dplyr::group_by(fcid) %>%
tidyr::nest() %>%
purrr::pwalk(list(fcid, data),
.f = ~{
pdf(file=paste0("output/logs/",..1,"/", ..1, "_postfilt_qualplots.pdf"), width = 11, height = 8 , paper="a4r")
print(..2$postfilt_qualplots)
try(dev.off(), silent=TRUE)
})
out <- paste0("output/logs/",unique(postfilt_qualplots$fcid),"/", unique(postfilt_qualplots$fcid),"_postfilt_qualplots.pdf")
return(out)
}, format="file", iteration = "vector"),
# Infer sequence variants with DADA2 --------------------------------------
# Group temporary samdf by fcid
tar_group_by(temp_samdf3_grouped, temp_samdf3, fcid, pcr_primers),
# Group temporary samdf by sample_ids
tar_group_by(temp_samdf3_grouped_sample, temp_samdf3, fcid, pcr_primers, sample_id),
# Error model for forward reads
tar_target(error_model_fwd,{
process <- temp_samdf3_grouped %>%
dplyr::group_by(fcid, pcr_primers) %>%
tidyr::nest() %>%
dplyr::mutate(error_model = purrr::pmap(dplyr::select(.,fcid, pcr_primers),
.f = ~step_errormodel(fcid = ..1,
input_dir = paste0("data/",..1,"/02_filtered"),
pcr_primers = ..2,
output = paste0("output/rds/",..1,"_",..2,"_errormodelF.rds"),
qc_dir = paste0("output/logs/",..1),
read="F",
nbases=1e+08,
randomize=FALSE,
multithread=FALSE,
quiet = FALSE,
write_all = FALSE)
))
return(paste0("output/rds/",unique(process$fcid),"_", unique(process$pcr_primers),"_errormodelF.rds"))
}, format="file", pattern = map(temp_samdf3_grouped), iteration = "vector"),
# Error model for reverse reads
tar_target(error_model_rev,{
process <- temp_samdf3_grouped %>%
dplyr::group_by(fcid, pcr_primers) %>%
tidyr::nest() %>%
dplyr::mutate(error_model = purrr::pmap(dplyr::select(.,fcid, pcr_primers),
.f = ~step_errormodel(fcid = ..1,
input_dir = paste0("data/",..1,"/02_filtered"),
pcr_primers = ..2,
output = paste0("output/rds/",..1,"_",..2,"_errormodelR.rds"),
qc_dir = paste0("output/logs/",..1),
read="R",
nbases=1e+08,
randomize=FALSE,
multithread=FALSE,
quiet = FALSE,
write_all = FALSE)
))
return(paste0("output/rds/",unique(process$fcid),"_", unique(process$pcr_primers),"_errormodelR.rds"))
}, format="file", pattern = map(temp_samdf3_grouped), iteration = "vector"),
# TODO:How to make it just redo one of the dadas if only one runs filtered file changed changed?
# first round denoising of forward reads
tar_target(denoise_fwd,{
process <- temp_samdf3_grouped_sample %>%
dplyr::group_by(fcid, pcr_primers, sample_id) %>%
tidyr::nest() %>%
dplyr::mutate(error_model = purrr::map2(fcid,pcr_primers, ~{
readRDS(error_model_fwd[stringr::str_detect(error_model_fwd, paste0(.x,"_",.y, "_errormodelF.rds"))])
})) %>%
dplyr::mutate(dada2 = purrr::pmap(dplyr::select(.,fcid, pcr_primers, error_model, sample_id),
.f = ~step_dada2_single2(fcid = ..1,
input_dir = paste0("data/",..1,"/02_filtered"),
sample_id= ..4,
pcr_primers = ..2,
output = paste0("output/rds/", ..4,"_dada1F.rds"),
qc_dir = paste0("output/logs/",..1),
read = "F",
error_model = ..3,
multithread = FALSE,
quiet = FALSE)
))
return(paste0("output/rds/", unique(process$sample_id),"_dada1F.rds"))
}, format="file", pattern = map(temp_samdf3_grouped_sample), iteration = "vector"),
# first round denoising of reverse reads
tar_target(denoise_rev,{
process <- temp_samdf3_grouped_sample %>%
dplyr::group_by(fcid, pcr_primers, sample_id) %>%
tidyr::nest() %>%
dplyr::mutate(error_model = purrr::map2(fcid,pcr_primers, ~{
readRDS(error_model_rev[stringr::str_detect(error_model_rev, paste0(.x,"_",.y, "_errormodelR.rds"))])
})) %>%
dplyr::mutate(dada2 = purrr::pmap(dplyr::select(.,fcid, pcr_primers, error_model, sample_id),
.f = ~step_dada2_single2(fcid = ..1,
input_dir = paste0("data/",..1,"/02_filtered"),
sample_id= ..4,
pcr_primers = ..2,
output = paste0("output/rds/", ..4,"_dada1R.rds"),
qc_dir = paste0("output/logs/",..1),
read = "R",
error_model = ..3,
multithread = FALSE,
quiet = FALSE)
))
return(paste0("output/rds/", unique(process$sample_id),"_dada1R.rds"))
}, format="file", pattern = map(temp_samdf3_grouped_sample), iteration = "vector"),
# Extract priors from forward reads
tar_target(priors_fwd,{
process <- temp_samdf3_grouped_sample %>%
dplyr::group_by(fcid, pcr_primers, sample_id) %>%
tidyr::nest() %>%
dplyr::mutate(priors = purrr::map(sample_id, ~{
readRDS(denoise_fwd[stringr::str_detect(denoise_fwd, paste0(.x,"_dada1F.rds"))])$sequence
}))
# Only keep the ones that appear across more than one sample
priors <- unlist(process$priors)
priors <- names(table(priors))[table(priors) > 1]
# TODO: Merge with any input_priors
saveRDS(priors, "output/rds/priorsF.rds")
return("output/rds/priorsF.rds")
}, format="file"),
# Extract priors from reverse reads
tar_target(priors_rev,{
process <- temp_samdf3_grouped_sample %>%
dplyr::group_by(fcid, pcr_primers, sample_id) %>%
tidyr::nest() %>%
dplyr::mutate(priors = purrr::map(sample_id, ~{
readRDS(denoise_rev[stringr::str_detect(denoise_rev, paste0(.x,"_dada1R.rds"))])$sequence
}))
# Only keep the ones that appear across more than one sample
priors <- unlist(process$priors)
priors <- names(table(priors))[table(priors) > 1]
# TODO: Merge with any input_priors
saveRDS(priors, "output/rds/priorsR.rds")
return("output/rds/priorsR.rds")
}, format="file"),
# Run second round of forward read denoising using priors
tar_target(denoise2_fwd,{
process <- temp_samdf3_grouped_sample %>%
dplyr::select(-one_of("high_sensitivity"))%>%
dplyr::left_join(params_dada, by="pcr_primers") %>%
dplyr::group_by(fcid, pcr_primers, sample_id, high_sensitivity) %>%
tidyr::nest() %>%
dplyr::mutate(error_model = purrr::map2(fcid,pcr_primers, ~{
readRDS(error_model_fwd[stringr::str_detect(error_model_fwd, paste0(.x,"_",.y, "_errormodelF.rds"))])
})) %>%
dplyr::mutate(priors = list(readRDS(priors_fwd))) %>%
dplyr::mutate(dada2 = purrr::pmap(dplyr::select(.,fcid, pcr_primers, error_model, sample_id, priors, high_sensitivity),
.f = ~{
if(..6){
step_dada2_single2(fcid = ..1,
input_dir = paste0("data/",..1,"/02_filtered"),
sample_id= ..4,
pcr_primers = ..2,
output = paste0("output/rds/", ..4,"_dada2F.rds"),
qc_dir = paste0("output/logs/",..1),
read = "F",
priors = ..5,
error_model = ..3,
multithread = FALSE,
quiet = FALSE)
} else {
# Just copy over
saveRDS(readRDS(denoise_fwd[stringr::str_detect(denoise_fwd, paste0("output/rds/",..4,"_dada1F.rds"))]), paste0("output/rds/",..4,"_dada2F.rds"))
}
}
))
return(paste0("output/rds/", unique(process$sample_id),"_dada2F.rds"))
}, format="file", pattern = map(temp_samdf3_grouped_sample), iteration = "vector"),
# Run second round of reverse read denoising using priors
tar_target(denoise2_rev,{
process <- temp_samdf3_grouped_sample %>%
dplyr::select(-one_of("high_sensitivity"))%>%
dplyr::left_join(params_dada, by="pcr_primers") %>%
dplyr::group_by(fcid, pcr_primers, sample_id, high_sensitivity) %>%
tidyr::nest() %>%
dplyr::mutate(error_model = purrr::map2(fcid,pcr_primers, ~{
readRDS(error_model_rev[stringr::str_detect(error_model_rev, paste0(.x,"_",.y, "_errormodelR.rds"))])
})) %>%
dplyr::mutate(priors = list(readRDS(priors_rev))) %>%
dplyr::mutate(dada2 = purrr::pmap(dplyr::select(.,fcid, pcr_primers, error_model, sample_id, priors, high_sensitivity),
.f = ~{
if(..6){
step_dada2_single2(fcid = ..1,
input_dir = paste0("data/",..1,"/02_filtered"),
sample_id= ..4,
pcr_primers = ..2,
output = paste0("output/rds/", ..4,"_dada2R.rds"),
qc_dir = paste0("output/logs/",..1),
read = "R",
priors = ..5,
error_model = ..3,
multithread = FALSE,
quiet = FALSE)
} else {
# Just copy over
saveRDS(readRDS(denoise_rev[stringr::str_detect(denoise_rev, paste0("output/rds/",..4,"_dada1R.rds"))]), paste0("output/rds/",..4,"_dada2R.rds"))
}
}
))
return(paste0("output/rds/", unique(process$sample_id),"_dada2R.rds"))
}, format="file", pattern = map(temp_samdf3_grouped_sample), iteration = "vector"),
# Merge reads and create seqtab by pcr_primers
tar_target(dada,{
process <- temp_samdf3_grouped %>%
dplyr::select(-one_of("concat_unmerged"))%>%
dplyr::left_join(params_dada, by="pcr_primers") %>%
dplyr::group_by(fcid, pcr_primers, concat_unmerged) %>%
tidyr::nest() %>%
dplyr::mutate(dada = purrr::map2(fcid, pcr_primers, ~{
fwd_to_read <- denoise2_fwd[str_detect(denoise2_fwd, paste0(.x, ".*",.y))]
dada_fwd <- fwd_to_read %>%
purrr::map(function(x){readRDS(x)})
names(dada_fwd) <- basename(fwd_to_read) %>% stringr::str_remove("_dada2F.rds")
rev_to_read <- denoise2_rev[str_detect(denoise2_rev, paste0(.x, ".*",.y))]
dada_rev <- rev_to_read %>%
purrr::map(function(x){readRDS(x)})
names(dada_rev) <- basename(rev_to_read) %>% stringr::str_remove("_dada2R.rds")
return(list(dada_fwd =dada_fwd, dada_rev = dada_rev))
})) %>%
dplyr::mutate(dada2 = purrr::pmap(dplyr::select(.,fcid, pcr_primers, concat_unmerged, dada),
.f = ~step_mergereads(fcid = ..1,
input_dir = paste0("data/",..1,"/02_filtered"),
pcr_primers = ..2,
output = paste0("output/rds/",..1,"_", ..2,"_seqtab.rds"),
qc_dir = paste0("output/logs/",..1),
quiet = FALSE,
write_all = FALSE,
concat_unmerged=..3,
dada = ..4)
))
},
pattern = map(temp_samdf3_grouped), iteration = "vector"),
# Return filepath for tracking
tar_target(dada_path,
{
return(paste0("output/rds/",unique(dada$fcid), "_", unique(dada$pcr_primers),"_seqtab.rds"))
},
pattern = map(dada), format="file", iteration = "vector"),
# Filter ASVs -------------------------------------------------
# Filter by primer
tar_target(filtered_seqtab, {
temp_samdf3_grouped %>%
dplyr::select(-one_of("asv_min_length", "asv_max_length", "phmm", "coding", "genetic_code"))%>%
dplyr::left_join(params_asvfilter, by="pcr_primers") %>%
dplyr::group_by(fcid, pcr_primers, asv_min_length, asv_max_length, phmm, coding, genetic_code, for_primer_seq, rev_primer_seq) %>%
tidyr::nest() %>%
dplyr::mutate(subset_seqtab = purrr::map2(fcid, pcr_primers, ~{
readRDS(dada_path[stringr::str_detect(dada_path, paste0(.x, ".*",.y, "_seqtab.rds"))])
})) %>%
dplyr::ungroup()%>%
dplyr::mutate(filtered_seqtab = purrr::pmap(dplyr::select(., fcid, pcr_primers, subset_seqtab, asv_min_length, asv_max_length, phmm, coding, genetic_code, for_primer_seq, rev_primer_seq),
.f = ~step_filter_asvs(
seqtab = ..3,
pcr_primers = ..2,
output = paste0("output/rds/",..1,"_", ..2,"_seqtab.cleaned.rds"),
qc_dir = "output/logs/",
min_length = ..4,
max_length = ..5,
phmm = ..6,
check_frame = ..7,
genetic_code = ..8,
primers = c(..9, ..10),
multithread = FALSE,
quiet = FALSE)
)) %>%
unnest_wider(filtered_seqtab) %>%
dplyr::mutate(filtered_asvs = purrr::map(filtered_asvs, ~{
.x %>%
dplyr::select(-sample_id) # remove sample_id from the nested column to avoid duplicate name columns
})) %>%
tidyr::unnest(c(data, filtered_asvs))%>%
dplyr::select(sample_id, sample_name, fcid, reads_starting, reads_chimerafilt, pcr_primers, reads_lengthfilt,
reads_phmmfilt, reads_framefilt, reads_final, plot, cleanup_summary)%>%
dplyr::mutate(path = paste0("output/rds/",fcid, "_", pcr_primers,"_seqtab.cleaned.rds"))
}, pattern = map(temp_samdf3_grouped), iteration = "vector"),
# Return filepath for tracking
tar_target(filtered_seqtab_path,
{
return(unique(filtered_seqtab$path))
}, format="file"),
# Write out seqtab filtering summary csv
tar_target(write_seqtab_summary, {
bind_rows(unique(filtered_seqtab$cleanup_summary)) %>%
write_csv("output/logs/ASV_cleanup_summary.csv")
out <- "output/logs/ASV_cleanup_summary.csv"
return(out)
}, format="file", iteration = "vector"),
# Write out seqtab filtering plots
tar_target(write_seqtab_qualplots, {
pdf("output/logs/ASV_cleanup_summary.pdf", width = 11, height = 8 , paper="a4r")
print(unique(filtered_seqtab$plot))
try(dev.off(), silent=TRUE)
out <- "output/logs/ASV_cleanup_summary.pdf"
return(out)
}, format="file", iteration = "vector"),
## Merge all loci into final seqtab ----------------------------------------
tar_target(merged_seqtab_path, {
process <- temp_samdf3 %>%
tidyr::nest(data=everything()) %>%
dplyr::mutate(final_seqtab = purrr::map(data, ~{
seqtabs <- filtered_seqtab_path
seqtabs <- seqtabs[seqtabs %>%
purrr::map_lgl(function(y){
any(stringr::str_detect(y, paste0(unique(.x$pcr_primers), "_seqtab.cleaned.rds")))
})]
# Remove empty seqtabs
empty <- seqtabs[purrr::map_lgl(seqtabs, function(z){
!ncol(readRDS(z)) > 0
})]
if(length(empty) > 0 ){
warning(paste0("No sequences in: ", empty, " skipping"))
}
if(length(seqtabs[!seqtabs %in% empty]) > 1){
st.all <- dada2::mergeSequenceTables(tables=seqtabs[!seqtabs %in% empty])
} else if(length(seqtabs[!seqtabs %in% empty]) == 1) {
st.all <- readRDS(seqtabs[!seqtabs %in% empty])
}
saveRDS(st.all, "output/rds/seqtab_final.rds")
return(TRUE)
})) %>%
tidyr::unnest(data)
out <- paste0("output/rds/seqtab_final.rds")
return(out)
}, format="file", iteration = "vector"),
# Assign taxonomy ---------------------------------------------------------
tar_file(idtaxa_db_tracked,{
out <- params_database %>%
dplyr::pull(idtaxa_db) %>%
unique()%>%
stringr::str_split(pattern=";", n=Inf) %>%
unlist()
return(out)
}
),
tar_file(ref_fasta_tracked,{
out <- params_database %>%
dplyr::pull(ref_fasta) %>%
unique() %>%
stringr::str_split(pattern=";", n=Inf) %>%
unlist()
return(out)
}
),
## IDTAXA -------------------------------------------------------------------
tar_target(tax_idtaxa,{
temp_samdf3_grouped %>%
dplyr::select(-one_of("target_gene", "idtaxa_db"))%>%
dplyr::left_join(params_database %>% dplyr::select(pcr_primers, target_gene, idtaxa_db, idtaxa_confidence)) %>%
tidyr::separate_rows(idtaxa_db, sep=";") %>%
dplyr::group_by(fcid, target_gene, pcr_primers, idtaxa_db, idtaxa_confidence) %>%
tidyr::nest() %>%
dplyr::mutate(idtaxa_db2 = purrr::map(idtaxa_db, ~{
idtaxa_db_tracked[stringr::str_detect(idtaxa_db_tracked, .x)]
})) %>%
unnest(idtaxa_db2)%>%
dplyr::mutate(filtered_seqtab = purrr::map2(fcid, pcr_primers, ~{
readRDS(filtered_seqtab_path[stringr::str_detect(filtered_seqtab_path, paste0(.x,"_", .y, "_seqtab.cleaned.rds"))])
})) %>%
dplyr::mutate(idtaxa = purrr::pmap(list(target_gene, pcr_primers, filtered_seqtab, idtaxa_db2, idtaxa_confidence),
.f = ~step_idtaxa(
seqtab = ..3,
database = ..4,
ranks = c("Root","Kingdom", "Phylum","Class", "Order", "Family", "Genus","Species"),
qc_dir = "output/logs/",
threshold = ..5,
multithread = FALSE,
quiet = FALSE,
return_ids = TRUE)
)) %>%
mutate(idtaxa_ids = purrr::map(idtaxa,~{
.x$ids
})) %>%
mutate(idtaxa = purrr::map(idtaxa,~{
.x$tax
}))
}, pattern = map(temp_samdf3_grouped), iteration = "vector"),
# Write out idtaxa objects
tar_target(idtaxa_path, {
process <- tax_idtaxa %>%
mutate(output = purrr::pmap(list(fcid, pcr_primers, idtaxa_db, idtaxa), ~{
# Write out RDS of the tax table
saveRDS(..4, paste0("output/rds/",..1,"_",..2,"_",basename(..3) %>% stringr::str_remove("\\..*$"),"_taxtab.rds"))
}))
out <- unique(paste0("output/rds/",process$fcid,"_",process$pcr_primers,"_",basename(process$idtaxa_db %>% stringr::str_remove("\\..*$")),"_taxtab.rds"))
return(out)
}, format="file", iteration = "vector"),
## BLAST -------------------------------------------------------------------
tar_target(tax_blast_path,
{
process <- temp_samdf3_grouped %>%
dplyr::select(-one_of("target_gene", "ref_fasta"))%>%
dplyr::left_join(params_database %>% dplyr::select(pcr_primers, target_gene, ref_fasta, blast_min_identity, blast_min_coverage, run_blast)) %>%
tidyr::separate_rows(ref_fasta, sep=";") %>%
dplyr::group_by(fcid, target_gene, pcr_primers, blast_min_identity, blast_min_coverage, ref_fasta, run_blast) %>%
tidyr::nest() %>%
dplyr::mutate(ref_fasta2 = purrr::map(ref_fasta, ~{
ref_fasta_tracked[stringr::str_detect(ref_fasta_tracked, .x)]
})) %>%
unnest(ref_fasta2) %>%
dplyr::mutate(filtered_seqtab = purrr::map2(fcid, pcr_primers, ~{
readRDS(filtered_seqtab_path[stringr::str_detect(filtered_seqtab_path, paste0(.x,"_", .y, "_seqtab.cleaned.rds"))])
})) %>%
dplyr::mutate(blast = purrr::pmap(list(fcid, target_gene, pcr_primers, filtered_seqtab, ref_fasta2, blast_min_identity, blast_min_coverage, run_blast),
.f = ~{
if(isTRUE(..8)){
blast_res <- step_blast_tophit(
seqtab = ..4,
database = ..5,
ranks = c("Root","Kingdom", "Phylum","Class", "Order", "Family", "Genus","Species") ,
output = paste0("output/rds/",..1,"_",..3,"_",basename(..5) %>% stringr::str_remove("\\..*$"),"_blast.rds"),
qc_dir = "output/logs/",
identity = ..6,
coverage=..7,
evalue=1e06,
max_target_seqs=5,
max_hsp=5,
multithread = FALSE,
quiet = FALSE)
return(blast_res)
} else {
blast_res <- tibble::enframe(getSequences(..4), name=NULL, value="OTU") %>%
dplyr::mutate(Genus = NA_character_, Species = NA_character_) %>%
column_to_rownames("OTU") %>%
as.matrix()
saveRDS(blast_res, paste0("output/rds/",..1,"_",..3,"_",basename(..5) %>% stringr::str_remove("\\..*$"),"_blast.rds"))
return(blast_res)
}
}
))
out <- unique(paste0("output/rds/",process$fcid, "_",process$pcr_primers, "_",basename(process$ref_fasta) %>% stringr::str_remove("\\..*$"),"_blast.rds"))
return(out)
}, pattern = map(temp_samdf3_grouped), format="file", iteration = "vector"),
## Aggregate taxonomic assignment methods-----------------------------------------------
tar_target(joint_tax, {
process <- temp_samdf3_grouped %>%
dplyr::select(-one_of("target_gene"))%>%
dplyr::left_join(params_database %>% dplyr::select(pcr_primers, target_gene, idtaxa_db, ref_fasta)) %>%
dplyr::group_by(fcid, pcr_primers) %>%
tidyr::nest() %>%
dplyr::mutate(filtered_seqtab = purrr::map2(fcid, pcr_primers, ~{
readRDS(filtered_seqtab_path[stringr::str_detect(filtered_seqtab_path, paste0(.x,"_", .y, "_seqtab.cleaned.rds"))])
}))%>%
dplyr::mutate(idtaxa = purrr::pmap(list(data, fcid, pcr_primers, filtered_seqtab),
.f = ~{
idtaxa_dbs <- ..1 %>%
tidyr::separate_rows(idtaxa_db, sep=";") %>%
dplyr::pull(idtaxa_db) %>%
unique() %>%
basename() %>%
stringr::str_remove("\\..*$")
taxtabs <- idtaxa_path[stringr::str_detect(idtaxa_path, idtaxa_dbs)& stringr::str_detect(idtaxa_path, paste0(..2, "_",..3,"_"))] %>%
purrr::map(readRDS)
if(length(taxtabs) == 1){
out <- taxtabs[[1]]
} else if(length(taxtabs) == 2){
out <- coalesce_tax(taxtabs[[1]], taxtabs[[2]])
} else if(length(taxtabs) == 3){
temptax <- coalesce_tax(taxtabs[[1]], taxtabs[[2]])
out <- coalesce_tax(temptax, taxtabs[[3]])
}
# Check that output dimensions match input
if(!all(rownames(out) %in% colnames(..4))){
stop("Number of ASVs classified does not match the number of input ASVs")
}
return(out)
}),
blast = purrr::pmap(list(data, fcid, pcr_primers, filtered_seqtab),
.f = ~{
ref_fastas <- ..1 %>%
tidyr::separate_rows(ref_fasta, sep=";") %>%
dplyr::pull(ref_fasta) %>%
unique() %>%
basename() %>%
stringr::str_remove("\\..*$")
taxtabs <- tax_blast_path[stringr::str_detect(tax_blast_path, ref_fastas) & stringr::str_detect(tax_blast_path, paste0(..2, "_",..3,"_"))]%>%
purrr::map(readRDS)
if(length(taxtabs) == 1){
out <- taxtabs[[1]]
} else if(length(taxtabs) == 2){
out <- coalesce_tax(taxtabs[[1]], taxtabs[[2]])
} else if(length(taxtabs) == 3){
temptax <- coalesce_tax(taxtabs[[1]], taxtabs[[2]])
out <- coalesce_tax(temptax, taxtabs[[3]])
}
# Check that output dimensions match input
if(!all(rownames(out) %in% colnames(..4))){
stop("Number of ASVs classified does not match the number of input ASVs")
}
return(out)
})) %>%
mutate(joint_tax = purrr::pmap(list(fcid, pcr_primers, idtaxa, blast, filtered_seqtab),
.f = ~{
out <- step_join_tax_blast(
tax = ..3,
blast_spp = ..4,
ranks = c("Root","Kingdom", "Phylum","Class", "Order", "Family", "Genus","Species") ,
output = paste0("output/rds/",..1,"_",..2,"_taxblast.rds"),
propagate_tax = TRUE)
# Check that output dimensions match input
if(!all(rownames(out) %in% colnames(..5))){
stop("Number of ASVs classified does not match the number of input ASVs")
}
return(out)
}
)) %>%
tidyr::unnest(data)
out <- unique(paste0("output/rds/",process$fcid, "_", process$pcr_primers,"_taxblast.rds"))
return(out)
}, pattern = map(temp_samdf3_grouped), format="file", iteration = "vector"),
## Merge taxonomy tables -----------------------------------------------------
tar_target(merged_tax,