R/LabwareUpload_PNEUMO_AMR.R
In phac-nml/wade: Executes molecular typing on contig assemblies
Defines functions
labware_pneumo_amr
Documented in
labware_pneumo_amr
#' Labware Upload Formatter for PNEUMO AMR
#' February 5 2024, Walter Demczuk & Shelley Peterson
#' Run AMR first, then run the 23S allele counts,
#' Then run this analysis to combine data from AMR, 23S rRNA
#' to prepare full amr profile to upload to LabWare.
#'
#'
#' @param Org_id Organism to query: GAS, PNEUMO or GONO
#' @param curr_work_dir Start up directory from pipeline project to locate system file structure
#' @return A table frame containing the results of the query
#' @export
#-------------------------------------------------------------------------------
# For troubleshooting and debugging
#Org_id <- "PNEUMO"
#curr_work_dir <- "C:\\WADE\\"
#-------------------------------------------------------------------------------
labware_pneumo_amr <- function(Org_id, curr_work_dir) {
#-----------------------------------------------------------------------------
# get directory structure and remove previous output files
directorylist <- getdirectory(curr_work_dir, Org_id, "AMR")
#-----------------------------------------------------------------------------
# Load datafiles + combine them into one dataframe
AMR_Output.df <- as_tibble(read.csv(paste0(directorylist$output_dir, "output_profile_PNEUMO_AMR.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
rRNA23S_Output.df <- as_tibble(read.csv(paste0(directorylist$output_dir, "output_profile_PNEUMO_rRNA23S.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
Combined_Output.df <- full_join(AMR_Output.df, rRNA23S_Output.df, by = "SampleNo")
NumSamples <- dim(Combined_Output.df)[1]
NumLoci <- ((dim(Combined_Output.df)[2]-3) / 7)
# Load MIC chart for WGS MIC calculations
MIC_table <- as_tibble(read.csv(paste0(directorylist$system_dir, "PNEUMO/AMR/reference/MIC_table.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
m <- 1
for (m in 1L:NumSamples) #<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< Start of Sample Loop
{
############################################################################
# Build Molecular Profile
############################################################################
molec_profile <- NA
allele_profile <- NA
amr_profile <- NA
lw_CurrSampleNo <- as.character(Combined_Output.df[m, "SampleNo"])
cat("Sample", m, " of ", NumSamples, "\n")
if(Combined_Output.df[m,"SampleProfile"] == "Sample_Err")
{
sample_data.df <- tibble(lw_CurrSampleNo, lw_pbp1a = "Sample_Err",
lw_pbp2b = "Sample_Err", lw_pbp2x = "Sample_Err",
lw_23S_A2059G = "Sample_Err", lw_23S_C2611T <- "Sample_Err",
lw_ermB = "Sample_Err", lw_ermTR = "Sample_Err",
lw_mefAE = "Sample_Err", lw_folA = "Sample_Err",
lw_folP = "Sample_Err", lw_gyrA = "Sample_Err",
lw_parC = "Sample_Err", lw_tetM = "Sample_Err",
lw_tetO = "Sample_Err", lw_cat = "Sample_Err",
molec_profile = "Sample_Err",
pen_MIC = "Sample_Err", pen_interp = "Sample_Err",
cro_MIC = "Sample_Err", cro_interp = "Sample_Err",
cxm_MIC = "Sample_Err", cxm_interp = "Sample_Err",
azi_MIC = "Sample_Err", azi_interp = "Sample_Err",
ery_MIC = "Sample_Err", ery_interp = "Sample_Err",
cla_MIC = "Sample_Err", cla_interp = "Sample_Err",
cli_MIC = "Sample_Err", cli_interp = "Sample_Err",
chl_MIC = "Sample_Err", chl_interp = "Sample_Err",
lev_MIC = "Sample_Err", lev_interp = "Sample_Err",
mox_MIC = "Sample_Err", mox_interp = "Sample_Err",
tet_MIC = "Sample_Err", tet_interp = "Sample_Err",
dox_MIC = "Sample_Err", dox_interp = "Sample_Err",
sxt_MIC = "Sample_Err", sxt_interp = "Sample_Err",
amr_profile = "Sample_Err", lw_allele_profile = "Sample_Err")
} else #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Sample Not Error
{
##### pbp1a #####
pbp1a <- allele4SNPs(Combined_Output.df, "pbp1a", "pbp1a", "motifs", "motif1", "motif2", "motif3", "motif4", m)
pbp1a$v_pbp1a_motif1_SAMK <- ifelse(pbp1a$lw_pbp1a_motif1 == "SAMK", 1, 0)
pbp1a$v_pbp1a_motif1_SSMK <- ifelse(pbp1a$lw_pbp1a_motif1 == "SSMK", 1, 0)
pbp1a$molec_profile_pbp1a[!is.na(pbp1a$molec_profile_pbp1a)] <- paste("pbp1a", pbp1a$lw_pbp1a, sep = " ")
##### pbp2b #####
pbp2b <- allele4SNPs(Combined_Output.df, "pbp2b", "pbp2b", "motifs", "motif1", "motif2", "motif3", "motif4", m)
pbp2b$molec_profile_pbp2b[!is.na(pbp2b$molec_profile_pbp2b)] <- paste("pbp2b", pbp2b$lw_pbp2b, sep = " ")
##### pbp2x #####
pbp2x <- allele4SNPs(Combined_Output.df, "pbp2x", "pbp2x", "motifs", "motif1", "motif2", "motif3", "motif4", m)
pbp2x$v_pbp2x_motif1_SAFK <- ifelse(pbp2x$lw_pbp2x_motif1 == "SAFK", 1, 0)
pbp2x$v_pbp2x_motif1_other <- ifelse(pbp2x$v_pbp2x_motif1_SAFK == 0 & pbp2x$v_pbp2x_motif1 == 1, 1, 0)
pbp2x$v_pbp2x_motif3_EDT <- ifelse(pbp2x$lw_pbp2x_motif3 == "EDT", 1, 0)
pbp2x$v_pbp2x_motif3_KEA <- ifelse(pbp2x$lw_pbp2x_motif3 == "KEA", 1, 0)
pbp2x$v_pbp2x_motif4_VKSG <- ifelse(pbp2x$lw_pbp2x_motif4 == "VKSG", 1, 0)
pbp2x$molec_profile_pbp2x[!is.na(pbp2x$molec_profile_pbp2x)] <- paste("pbp2x", pbp2x$lw_pbp2x, sep = " ")
##### rRNA 23S #####
rRNA23S <- tibble(lw_23S_A2059G = as.integer(Combined_Output.df[m, "A2059G"]),
lw_23S_C2611T = as.integer(Combined_Output.df[m, "C2611T"]),
v_23S_A2059G = as.integer(Combined_Output.df[m, "A2059G"]),
v_23S_C2611T = as.integer(Combined_Output.df[m, "C2611T"]),
lw_23S_prof = NA,
molec_profile_23S = NA)
rRNA23S$lw_23S_prof <- ifelse((is.na(rRNA23S$lw_23S_A2059G)|is.na(rRNA23S$lw_23S_C2611T)), "23S Err", NA)
rRNA23S$molec_profile_23S <- ifelse((is.na(rRNA23S$lw_23S_prof) & rRNA23S$lw_23S_A2059G > 0),
paste0("23S rRNA A2059G: ", rRNA23S$lw_23S_A2059G, "/4 "), NA)
rRNA23S$molec_profile_23S <- ifelse((is.na(rRNA23S$lw_23S_prof) & rRNA23S$lw_23S_C2611T > 0),
paste0(rRNA23S$molec_profile_23S, "23S rRNA C2611T: ",
rRNA23S$lw_23S_C2611T, "/4"), NA)
rRNA23S$molec_profile_23S <- sub("NA", "", rRNA23S$molec_profile_23S)
rRNA23S$lw_23S_prof[is.na(rRNA23S$lw_23S_prof)] <- rRNA23S$molec_profile_23S
##### ermB #####
ermB <- posneg_gene(Combined_Output.df, "ermB", m)
ermB$mutations_ermB <- Combined_Output.df[[m, "ermB_mutations"]]
ermB$allele_ermB <- Combined_Output.df[[m, "ermB_allele"]]
ermB$allele_ermB[ermB$allele_ermB == "NF"] <- "ermB +"
ermB$allele_ermB[ermB$lw_ermB == "NEG"] <- "ermB -"
ermB$lw_ermB[ermB$mutations_ermB == "S"] <- "NEG"
ermB$molec_profile_ermB[ermB$mutations_ermB == "S"] <- "ermB(S)"
##### ermTR #####
ermTR <- posneg_gene(Combined_Output.df, "ermTR", m)
##### mefAE #####
mefAE <- posneg_gene(Combined_Output.df, "mefAE", m)
##### folA #####
folA <- allele1SNP(Combined_Output.df, "folA", "motifs", m)
##### folP #####
folP <- allele1SNP(Combined_Output.df, "folP", "motifs", m)
##### gyrA #####
gyrA <- allele1SNP(Combined_Output.df, "gyrA", "motifs", m)
gyrA$v_gyrA_S81F <- ifelse(gyrA$lw_gyrA == "S81F", 1, 0)
gyrA$v_gyrA_S81Y <- ifelse(gyrA$lw_gyrA == "S81Y", 1, 0)
gyrA$v_gyrA_S81L <- ifelse(gyrA$lw_gyrA == "S81L", 1, 0)
##### parC #####
parC <- allele3SNPs(Combined_Output.df, "parC", "parC", "motifs", "S79", "D83", "N91", m)
##### tetM #####
tetM <- posneg_gene(Combined_Output.df, "tetM", m)
##### tetO #####
tetO <- posneg_gene(Combined_Output.df, "tetO", m)
##### cat #####
cat <- posneg_gene(Combined_Output.df, "cat", m)
# -------------------- Vancomycin - Not included in LW export, but is included in molec_profile
##### vanA #####
vanA <- posneg_gene(Combined_Output.df, "vanA", m)
##### vanB #####
vanB <- posneg_gene(Combined_Output.df, "vanB", m)
##### vanC #####
vanC <- posneg_gene(Combined_Output.df, "vanC", m)
##### vanD #####
vanD <- posneg_gene(Combined_Output.df, "vanD", m)
##### vanE #####
vanE <- posneg_gene(Combined_Output.df, "vanE", m)
##### vanG #####
vanG <- posneg_gene(Combined_Output.df, "vanG", m)
######################## Now put them all together #######################
molec_profile <- paste(ermB$molec_profile_ermB, ermTR$molec_profile_ermTR,
mefAE$molec_profile_mefAE, rRNA23S$molec_profile_23S,
tetM$molec_profile_tetM, tetO$molec_profile_tetO,
cat$molec_profile_cat, folA$molec_profile_folA,
folP$molec_profile_folP, pbp1a$molec_profile_pbp1a,
pbp2b$molec_profile_pbp2b, pbp2x$molec_profile_pbp2x,
gyrA$molec_profile_gyrA, parC$molec_profile_parC,
vanA$molec_profile_vanA, vanB$molec_profile_vanB,
vanC$molec_profile_vanC, vanD$molec_profile_vanD,
vanE$molec_profile_vanE, vanG$molec_profile_vanG, sep = "; ")
molec_profile <- gsub("NA; ", "", molec_profile)
molec_profile <- sub("; NA", "", molec_profile)
if(molec_profile == "NA") {molec_profile <- "Wild Type"}
lw_allele_profile <- paste(pbp1a$allele_pbp1a, pbp2b$allele_pbp2b,
pbp2x$allele_pbp2x, ermB$allele_ermB,
folA$allele_folA, folP$allele_folP,
gyrA$allele_gyrA, parC$allele_parC, sep = " : ")
lw_allele_profile <- gsub("NA : ", "", lw_allele_profile)
lw_allele_profile <- sub(" : NA", "", lw_allele_profile)
##########################################################################
# Calculate MICs
##########################################################################
##### Levofloxacin (LEV) #####
lev <- NA
if(str_detect(gyrA$lw_gyrA, "Err") | str_detect(parC$lw_parC, "Err"))
{
lev <- tibble(lev_MIC = "Error",
lev_interp = "Error",
lev_profile = "Error")
}else
{
lev_MIC_calc <- round(-0.218 +
(gyrA$v_gyrA_S81F * 2.028)+
(gyrA$v_gyrA_S81Y * 1.564)+
(gyrA$v_gyrA_S81L * 3.564)+
(parC$v_parC_S79 * 1.654)+
(parC$v_parC_D83 * 0.834))
lev <- MICcalc(MIC_table, "lev", lev_MIC_calc, 0L, 5L)
}
##### Moxifloxacin (MOX) #####
mox <- NA
if(str_detect(gyrA$lw_gyrA, "Err") | str_detect(parC$lw_parC, "Err"))
{
mox <- tibble(mox_MIC = "Error",
mox_interp = "Error",
mox_profile = "Error")
}else
{
mox_MIC_calc <- round(-2.819 +
(gyrA$v_gyrA_S81F * 3.130)+
(gyrA$v_gyrA_S81Y * 3.907)+
(gyrA$v_gyrA_S81L * 4.907)+
(parC$v_parC_S79 * 0.911))
mox <- MICcalc(MIC_table, "mox", mox_MIC_calc, -3, 5L)
}
##### Penicillin (PEN) #####
pen <- NA
if(str_detect(pbp1a$lw_pbp1a, "Err") | str_detect(pbp2b$lw_pbp2b, "Err")|
str_detect(pbp2x$lw_pbp2x, "Err"))
{
pen <- tibble(pen_MIC = "Error",
pen_interp = "Error",
pen_profile = "Error")
}else
{
pen_MIC_calc <- round(-4.6099 +
(pbp1a$v_pbp1a_motif1 * 1.5466)+
(pbp1a$v_pbp1a_motif4 * 0.9491)+
(pbp2b$v_pbp2b_motif2 * 1.2025)+
(pbp2b$v_pbp2b_motif3 * 0.3563)+
(pbp2x$v_pbp2x_motif1_SAFK * 1.6259)+
(pbp2x$v_pbp2x_motif3_EDT * 1.5476)+
(pbp2x$v_pbp2x_motif3_KEA * 0.6760)+
(pbp2x$v_pbp2x_motif4_VKSG * 0.7536))
pen <- MICcalc(MIC_table, "pen", pen_MIC_calc, -5, 2L)
pen$pen_MIC[pen$pen_MIC == "<= 0.031 ug/ml"] <- "<= 0.03 ug/ml"
}
##### Cephalosporins: Ceftriaxone (CRO), Cefuroxime (CXM)
cro <- NA
cxm <- NA
if(str_detect(pbp1a$lw_pbp1a, "Err") | str_detect(pbp2x$lw_pbp2x, "Err"))
{
cro <- tibble(cro_MIC = "Error",
cro_interp = "Error",
cro_profile = "Error")
cxm <- tibble(cxm_MIC = "Error",
cxm_interp = "Error",
cxm_profile = "Error")
}else
{
cro_MIC_calc <- round(-2.709 +
(pbp1a$v_pbp1a_motif1 * 1.25)+
(pbp2x$v_pbp2x_motif1_SAFK * 2.72)+
(pbp2x$v_pbp2x_motif3_EDT * 0.76)+
(pbp2x$v_pbp2x_motif4_VKSG * 0.989))
cro <- MICcalc(MIC_table, "cro", cro_MIC_calc, -3, 2L)
cxm_MIC_calc <- round(-1.018 +
(pbp1a$v_pbp1a_motif1_SAMK * 1.509)+
(pbp1a$v_pbp1a_motif1_SSMK * 2.170)+
(pbp2x$v_pbp2x_motif1_SAFK * 2.322)+
(pbp2x$v_pbp2x_motif1_other * 0.256)+
(pbp2x$v_pbp2x_motif4_VKSG * 1.026))
cxm <- MICcalc(MIC_table, "cxm", cxm_MIC_calc, -1, 4L)
}
##### Trimethoprim/Sulfamethoxazole (SXT) #####
sxt <- NA
if(str_detect(folA$lw_folA, "Err") | str_detect(folP$lw_folP, "Err"))
{
sxt <- tibble(sxt_MIC = "Error",
sxt_interp = "Error",
sxt_profile = "Error")
}else
{
sxt_MIC_calc <- round(-2.265 +
(folA$v_folA * 2.113)+
(folP$v_folP * 2.668))
sxt <- MICcalc(MIC_table, "sxt", sxt_MIC_calc, -1, 4L)
sxt$sxt_MIC[sxt_MIC_calc < 0] <- "<= 0.5/9.5 ug/ml"
sxt$sxt_MIC[sxt_MIC_calc > 1] <- ">= 4/76 ug/ml"
}
##### Macrolides: Erythromycin (ERY), Azithromycin (AZI) #####
##### Clarithromycin (CLA), Clindamycin (CLI) #####
ery <- NA
azi <- NA
cla <- NA
cli <- NA
if(str_detect(rRNA23S$lw_23S_A2059G, "Err") | str_detect(rRNA23S$lw_23S_C2611T, "Err")|
str_detect(ermB$lw_ermB, "Err") | str_detect(mefAE$lw_mefAE, "Err"))
{
ery <- tibble(ery_MIC = "Error",
ery_interp = "Error",
ery_profile = "Error")
azi <- tibble(azi_MIC = "Error",
azi_interp = "Error",
azi_profile = "Error")
cla <- tibble(cla_MIC = "Error",
cla_interp = "Error",
cla_profile = "Error")
cli <- tibble(cli_MIC = "Error",
cli_interp = "Error",
cli_profile = "Error")
}else
{
# if both ermB and mef are positive, only use ermB value to calculate
mefAE$v_mefAE[1] <- ifelse(ermB$lw_ermB[1] == "POS", 0, mefAE$v_mefAE[1])
ery_MIC_calc <- round(-2.975 +
(rRNA23S$v_23S_A2059G * 1.993)+
(ermB$v_ermB * 7.680)+
(mefAE$v_mefAE * 4.808))
ery <- MICcalc(MIC_table, "ery", ery_MIC_calc, -3, 5L)
azi_MIC_calc <- round(-1.9722 +
(rRNA23S$v_23S_A2059G * 1.1204)+
(rRNA23S$v_23S_C2611T * 0.9917)+
(ermB$v_ermB * 3.9722)+
(mefAE$v_mefAE * 3.8122))
azi <- MICcalc(MIC_table, "azi", azi_MIC_calc, -2, 1L)
cla_MIC_calc <- round(-4.984 +
(rRNA23S$v_23S_A2059G * 1.819)+
(rRNA23S$v_23S_C2611T * 1.246)+
(ermB$v_ermB * 10.820)+
(mefAE$v_mefAE * 6.064))
cla <- MICcalc(MIC_table, "cla", cla_MIC_calc, -5, 6L)
cla$cla_MIC[cla$cla_MIC == "<= 0.031 ug/ml"] <- "<= 0.03 ug/ml"
cli_MIC_calc <- round(-2.8145 +
(rRNA23S$v_23S_A2059G * 0.456)+
(ermB$v_ermB * 9.048))
cli <- MICcalc(MIC_table, "cli", cli_MIC_calc, -3, 6L)
cli$cli_interp <- ifelse(ermTR$lw_ermTR == "POS", "Inducible", cli$cli_interp)
if(cli$cli_interp == "Inducible")
{
cli$cli_profile <- "CLI-Ind"
}
}
##### Chloramphenicol (CHL) #####
chl <- NA
if(str_detect(cat$lw_cat, "Err"))
{
chl <- tibble(chl_MIC = "Error",
chl_interp = "Error",
chl_profile = "Error")
}else if(cat$lw_cat == "POS")
{
chl <- tibble(chl_MIC = ">= 8 ug/ml",
chl_interp = "Resistant",
chl_profile = "CHL-R")
}else
{
chl <- tibble(chl_MIC = "<= 4 ug/ml",
chl_interp = "Susceptible",
chl_profile = NA)
}
##### Tetracycline (TET), Doxycycline (DOX) #####
tet <- NA
dox <- NA
if(str_detect(tetM$lw_tetM, "Err"))
{
tet <- tibble(tet_MIC = "Error",
tet_interp = "Error",
tet_profile = "Error")
dox <- tibble(dox_MIC = "Error",
dox_interp = "Error",
dox_profile = "Error")
}else if(tetM$lw_tetM == "POS")
{
tet <- tibble(tet_MIC = ">= 16 ug/ml",
tet_interp = "Resistant",
tet_profile = "TET-R")
dox <- tibble(dox_MIC = ">= 4 ug/ml",
dox_interp = "Resistant",
dox_profile = "DOX-R")
}else
{
tet <- tibble(tet_MIC = "<= 1 ug/ml",
tet_interp = "Susceptible",
tet_profile = NA)
dox <- tibble(dox_MIC = "<= 0.25 ug/ml",
dox_interp = "Susceptible",
dox_profile = NA)
}
##### Vancomycin (VAN) #####
van <- NA
if(str_detect(molec_profile, paste(c("vanA", "vanB", "vanC", "vanD", "vanE", "vanG"), collapse = '|')))
{
van <- tibble(van_MIC = ">= 2 ug/ml",
van_interp = "Resistant",
van_profile = "VAN-R")
}else
{
van <- tibble(van_MIC = "<= 1 ug/ml",
van_interp = "Susceptible",
van_profile = NA)
}
###################### Combine them for AMR profile ######################
amr_profile <- paste(pen$pen_profile, cro$cro_profile, cxm$cxm_profile,
ery$ery_profile, azi$azi_profile, cla$cla_profile,
cli$cli_profile, chl$chl_profile, lev$lev_profile,
mox$mox_profile, tet$tet_profile, dox$dox_profile,
sxt$sxt_profile, van$van_profile, sep = "/")
amr_profile <- gsub("NA/", "", amr_profile)
amr_profile <- gsub("NA", "", amr_profile)
amr_profile <- sub("\\/$", "", amr_profile)
amr_profile <- ifelse(amr_profile == "", "Susceptible", amr_profile)
##########################################################################
# Put all data together
##########################################################################
sample_data.df <- tibble(lw_CurrSampleNo, pbp1a$lw_pbp1a, pbp2b$lw_pbp2b, pbp2x$lw_pbp2x,
as.character(rRNA23S$lw_23S_A2059G),
as.character(rRNA23S$lw_23S_C2611T), ermB$lw_ermB,
ermTR$lw_ermTR, mefAE$lw_mefAE, folA$lw_folA, folP$lw_folP,
gyrA$lw_gyrA, parC$lw_parC, tetM$lw_tetM, tetO$lw_tetO,
cat$lw_cat, molec_profile, pen$pen_MIC, pen$pen_interp,
cro$cro_MIC, cro$cro_interp, cxm$cxm_MIC, cxm$cxm_interp,
azi$azi_MIC, azi$azi_interp, ery$ery_MIC, ery$ery_interp,
cla$cla_MIC, cla$cla_interp, cli$cli_MIC, cli$cli_interp,
chl$chl_MIC, chl$chl_interp, lev$lev_MIC, lev$lev_interp,
mox$mox_MIC, mox$mox_interp, tet$tet_MIC, tet$tet_interp,
dox$dox_MIC, dox$dox_interp, sxt$sxt_MIC, sxt$sxt_interp,
amr_profile, lw_allele_profile)
sample_data.df <- sample_data.df %>% rename_at(vars(contains("$")), ~sub(".*\\$","",.))
} #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End of Sample Not Error loop
if(m==1)
{
lw_output.df <- tibble(sample_data.df)
}else
{
lw_output.df <- bind_rows(lw_output.df, sample_data.df)
}
} #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> End of sample loop
lw_output_bad.df <- filter(lw_output.df, amr_profile == "Error")
lw_output_good.df <- filter(lw_output.df, amr_profile != "Error")
write.csv(lw_output.df, paste0(directorylist$output_dir, "LabWareUpload_PNEUMO_AMR.csv"), quote = FALSE, row.names = FALSE)
write.csv(lw_output_good.df, paste0(directorylist$output_dir, "LabWareUpload_PNEUMO_AMR_good.csv"), quote = FALSE, row.names = FALSE)
write.csv(lw_output_bad.df, paste0(directorylist$output_dir, "LabWareUpload_PNEUMO_AMR_bad.csv"), quote = FALSE, row.names = FALSE)
cat("\n\nDone! ", directorylist$output_dir, "LabWareUpload_PNEUMO_AMR.csv is ready in output folder", "\n\n\n", sep = "")
return(lw_output.df)
}
phac-nml/wade documentation built on March 16, 2024, 8:32 a.m.
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Defines functions labware_pneumo_amr
Documented in labware_pneumo_amr
#' Labware Upload Formatter for PNEUMO AMR
#' February 5 2024, Walter Demczuk & Shelley Peterson
#' Run AMR first, then run the 23S allele counts,
#' Then run this analysis to combine data from AMR, 23S rRNA
#' to prepare full amr profile to upload to LabWare.
#'
#'
#' @param Org_id Organism to query: GAS, PNEUMO or GONO
#' @param curr_work_dir Start up directory from pipeline project to locate system file structure
#' @return A table frame containing the results of the query
#' @export
#-------------------------------------------------------------------------------
# For troubleshooting and debugging
#Org_id <- "PNEUMO"
#curr_work_dir <- "C:\\WADE\\"
#-------------------------------------------------------------------------------
labware_pneumo_amr <- function(Org_id, curr_work_dir) {
#-----------------------------------------------------------------------------
# get directory structure and remove previous output files
directorylist <- getdirectory(curr_work_dir, Org_id, "AMR")
#-----------------------------------------------------------------------------
# Load datafiles + combine them into one dataframe
AMR_Output.df <- as_tibble(read.csv(paste0(directorylist$output_dir, "output_profile_PNEUMO_AMR.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
rRNA23S_Output.df <- as_tibble(read.csv(paste0(directorylist$output_dir, "output_profile_PNEUMO_rRNA23S.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
Combined_Output.df <- full_join(AMR_Output.df, rRNA23S_Output.df, by = "SampleNo")
NumSamples <- dim(Combined_Output.df)[1]
NumLoci <- ((dim(Combined_Output.df)[2]-3) / 7)
# Load MIC chart for WGS MIC calculations
MIC_table <- as_tibble(read.csv(paste0(directorylist$system_dir, "PNEUMO/AMR/reference/MIC_table.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
m <- 1
for (m in 1L:NumSamples) #<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< Start of Sample Loop
{
############################################################################
# Build Molecular Profile
############################################################################
molec_profile <- NA
allele_profile <- NA
amr_profile <- NA
lw_CurrSampleNo <- as.character(Combined_Output.df[m, "SampleNo"])
cat("Sample", m, " of ", NumSamples, "\n")
if(Combined_Output.df[m,"SampleProfile"] == "Sample_Err")
{
sample_data.df <- tibble(lw_CurrSampleNo, lw_pbp1a = "Sample_Err",
lw_pbp2b = "Sample_Err", lw_pbp2x = "Sample_Err",
lw_23S_A2059G = "Sample_Err", lw_23S_C2611T <- "Sample_Err",
lw_ermB = "Sample_Err", lw_ermTR = "Sample_Err",
lw_mefAE = "Sample_Err", lw_folA = "Sample_Err",
lw_folP = "Sample_Err", lw_gyrA = "Sample_Err",
lw_parC = "Sample_Err", lw_tetM = "Sample_Err",
lw_tetO = "Sample_Err", lw_cat = "Sample_Err",
molec_profile = "Sample_Err",
pen_MIC = "Sample_Err", pen_interp = "Sample_Err",
cro_MIC = "Sample_Err", cro_interp = "Sample_Err",
cxm_MIC = "Sample_Err", cxm_interp = "Sample_Err",
azi_MIC = "Sample_Err", azi_interp = "Sample_Err",
ery_MIC = "Sample_Err", ery_interp = "Sample_Err",
cla_MIC = "Sample_Err", cla_interp = "Sample_Err",
cli_MIC = "Sample_Err", cli_interp = "Sample_Err",
chl_MIC = "Sample_Err", chl_interp = "Sample_Err",
lev_MIC = "Sample_Err", lev_interp = "Sample_Err",
mox_MIC = "Sample_Err", mox_interp = "Sample_Err",
tet_MIC = "Sample_Err", tet_interp = "Sample_Err",
dox_MIC = "Sample_Err", dox_interp = "Sample_Err",
sxt_MIC = "Sample_Err", sxt_interp = "Sample_Err",
amr_profile = "Sample_Err", lw_allele_profile = "Sample_Err")
} else #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Sample Not Error
{
##### pbp1a #####
pbp1a <- allele4SNPs(Combined_Output.df, "pbp1a", "pbp1a", "motifs", "motif1", "motif2", "motif3", "motif4", m)
pbp1a$v_pbp1a_motif1_SAMK <- ifelse(pbp1a$lw_pbp1a_motif1 == "SAMK", 1, 0)
pbp1a$v_pbp1a_motif1_SSMK <- ifelse(pbp1a$lw_pbp1a_motif1 == "SSMK", 1, 0)
pbp1a$molec_profile_pbp1a[!is.na(pbp1a$molec_profile_pbp1a)] <- paste("pbp1a", pbp1a$lw_pbp1a, sep = " ")
##### pbp2b #####
pbp2b <- allele4SNPs(Combined_Output.df, "pbp2b", "pbp2b", "motifs", "motif1", "motif2", "motif3", "motif4", m)
pbp2b$molec_profile_pbp2b[!is.na(pbp2b$molec_profile_pbp2b)] <- paste("pbp2b", pbp2b$lw_pbp2b, sep = " ")
##### pbp2x #####
pbp2x <- allele4SNPs(Combined_Output.df, "pbp2x", "pbp2x", "motifs", "motif1", "motif2", "motif3", "motif4", m)
pbp2x$v_pbp2x_motif1_SAFK <- ifelse(pbp2x$lw_pbp2x_motif1 == "SAFK", 1, 0)
pbp2x$v_pbp2x_motif1_other <- ifelse(pbp2x$v_pbp2x_motif1_SAFK == 0 & pbp2x$v_pbp2x_motif1 == 1, 1, 0)
pbp2x$v_pbp2x_motif3_EDT <- ifelse(pbp2x$lw_pbp2x_motif3 == "EDT", 1, 0)
pbp2x$v_pbp2x_motif3_KEA <- ifelse(pbp2x$lw_pbp2x_motif3 == "KEA", 1, 0)
pbp2x$v_pbp2x_motif4_VKSG <- ifelse(pbp2x$lw_pbp2x_motif4 == "VKSG", 1, 0)
pbp2x$molec_profile_pbp2x[!is.na(pbp2x$molec_profile_pbp2x)] <- paste("pbp2x", pbp2x$lw_pbp2x, sep = " ")
##### rRNA 23S #####
rRNA23S <- tibble(lw_23S_A2059G = as.integer(Combined_Output.df[m, "A2059G"]),
lw_23S_C2611T = as.integer(Combined_Output.df[m, "C2611T"]),
v_23S_A2059G = as.integer(Combined_Output.df[m, "A2059G"]),
v_23S_C2611T = as.integer(Combined_Output.df[m, "C2611T"]),
lw_23S_prof = NA,
molec_profile_23S = NA)
rRNA23S$lw_23S_prof <- ifelse((is.na(rRNA23S$lw_23S_A2059G)|is.na(rRNA23S$lw_23S_C2611T)), "23S Err", NA)
rRNA23S$molec_profile_23S <- ifelse((is.na(rRNA23S$lw_23S_prof) & rRNA23S$lw_23S_A2059G > 0),
paste0("23S rRNA A2059G: ", rRNA23S$lw_23S_A2059G, "/4 "), NA)
rRNA23S$molec_profile_23S <- ifelse((is.na(rRNA23S$lw_23S_prof) & rRNA23S$lw_23S_C2611T > 0),
paste0(rRNA23S$molec_profile_23S, "23S rRNA C2611T: ",
rRNA23S$lw_23S_C2611T, "/4"), NA)
rRNA23S$molec_profile_23S <- sub("NA", "", rRNA23S$molec_profile_23S)
rRNA23S$lw_23S_prof[is.na(rRNA23S$lw_23S_prof)] <- rRNA23S$molec_profile_23S
##### ermB #####
ermB <- posneg_gene(Combined_Output.df, "ermB", m)
ermB$mutations_ermB <- Combined_Output.df[[m, "ermB_mutations"]]
ermB$allele_ermB <- Combined_Output.df[[m, "ermB_allele"]]
ermB$allele_ermB[ermB$allele_ermB == "NF"] <- "ermB +"
ermB$allele_ermB[ermB$lw_ermB == "NEG"] <- "ermB -"
ermB$lw_ermB[ermB$mutations_ermB == "S"] <- "NEG"
ermB$molec_profile_ermB[ermB$mutations_ermB == "S"] <- "ermB(S)"
##### ermTR #####
ermTR <- posneg_gene(Combined_Output.df, "ermTR", m)
##### mefAE #####
mefAE <- posneg_gene(Combined_Output.df, "mefAE", m)
##### folA #####
folA <- allele1SNP(Combined_Output.df, "folA", "motifs", m)
##### folP #####
folP <- allele1SNP(Combined_Output.df, "folP", "motifs", m)
##### gyrA #####
gyrA <- allele1SNP(Combined_Output.df, "gyrA", "motifs", m)
gyrA$v_gyrA_S81F <- ifelse(gyrA$lw_gyrA == "S81F", 1, 0)
gyrA$v_gyrA_S81Y <- ifelse(gyrA$lw_gyrA == "S81Y", 1, 0)
gyrA$v_gyrA_S81L <- ifelse(gyrA$lw_gyrA == "S81L", 1, 0)
##### parC #####
parC <- allele3SNPs(Combined_Output.df, "parC", "parC", "motifs", "S79", "D83", "N91", m)
##### tetM #####
tetM <- posneg_gene(Combined_Output.df, "tetM", m)
##### tetO #####
tetO <- posneg_gene(Combined_Output.df, "tetO", m)
##### cat #####
cat <- posneg_gene(Combined_Output.df, "cat", m)
# -------------------- Vancomycin - Not included in LW export, but is included in molec_profile
##### vanA #####
vanA <- posneg_gene(Combined_Output.df, "vanA", m)
##### vanB #####
vanB <- posneg_gene(Combined_Output.df, "vanB", m)
##### vanC #####
vanC <- posneg_gene(Combined_Output.df, "vanC", m)
##### vanD #####
vanD <- posneg_gene(Combined_Output.df, "vanD", m)
##### vanE #####
vanE <- posneg_gene(Combined_Output.df, "vanE", m)
##### vanG #####
vanG <- posneg_gene(Combined_Output.df, "vanG", m)
######################## Now put them all together #######################
molec_profile <- paste(ermB$molec_profile_ermB, ermTR$molec_profile_ermTR,
mefAE$molec_profile_mefAE, rRNA23S$molec_profile_23S,
tetM$molec_profile_tetM, tetO$molec_profile_tetO,
cat$molec_profile_cat, folA$molec_profile_folA,
folP$molec_profile_folP, pbp1a$molec_profile_pbp1a,
pbp2b$molec_profile_pbp2b, pbp2x$molec_profile_pbp2x,
gyrA$molec_profile_gyrA, parC$molec_profile_parC,
vanA$molec_profile_vanA, vanB$molec_profile_vanB,
vanC$molec_profile_vanC, vanD$molec_profile_vanD,
vanE$molec_profile_vanE, vanG$molec_profile_vanG, sep = "; ")
molec_profile <- gsub("NA; ", "", molec_profile)
molec_profile <- sub("; NA", "", molec_profile)
if(molec_profile == "NA") {molec_profile <- "Wild Type"}
lw_allele_profile <- paste(pbp1a$allele_pbp1a, pbp2b$allele_pbp2b,
pbp2x$allele_pbp2x, ermB$allele_ermB,
folA$allele_folA, folP$allele_folP,
gyrA$allele_gyrA, parC$allele_parC, sep = " : ")
lw_allele_profile <- gsub("NA : ", "", lw_allele_profile)
lw_allele_profile <- sub(" : NA", "", lw_allele_profile)
##########################################################################
# Calculate MICs
##########################################################################
##### Levofloxacin (LEV) #####
lev <- NA
if(str_detect(gyrA$lw_gyrA, "Err") | str_detect(parC$lw_parC, "Err"))
{
lev <- tibble(lev_MIC = "Error",
lev_interp = "Error",
lev_profile = "Error")
}else
{
lev_MIC_calc <- round(-0.218 +
(gyrA$v_gyrA_S81F * 2.028)+
(gyrA$v_gyrA_S81Y * 1.564)+
(gyrA$v_gyrA_S81L * 3.564)+
(parC$v_parC_S79 * 1.654)+
(parC$v_parC_D83 * 0.834))
lev <- MICcalc(MIC_table, "lev", lev_MIC_calc, 0L, 5L)
}
##### Moxifloxacin (MOX) #####
mox <- NA
if(str_detect(gyrA$lw_gyrA, "Err") | str_detect(parC$lw_parC, "Err"))
{
mox <- tibble(mox_MIC = "Error",
mox_interp = "Error",
mox_profile = "Error")
}else
{
mox_MIC_calc <- round(-2.819 +
(gyrA$v_gyrA_S81F * 3.130)+
(gyrA$v_gyrA_S81Y * 3.907)+
(gyrA$v_gyrA_S81L * 4.907)+
(parC$v_parC_S79 * 0.911))
mox <- MICcalc(MIC_table, "mox", mox_MIC_calc, -3, 5L)
}
##### Penicillin (PEN) #####
pen <- NA
if(str_detect(pbp1a$lw_pbp1a, "Err") | str_detect(pbp2b$lw_pbp2b, "Err")|
str_detect(pbp2x$lw_pbp2x, "Err"))
{
pen <- tibble(pen_MIC = "Error",
pen_interp = "Error",
pen_profile = "Error")
}else
{
pen_MIC_calc <- round(-4.6099 +
(pbp1a$v_pbp1a_motif1 * 1.5466)+
(pbp1a$v_pbp1a_motif4 * 0.9491)+
(pbp2b$v_pbp2b_motif2 * 1.2025)+
(pbp2b$v_pbp2b_motif3 * 0.3563)+
(pbp2x$v_pbp2x_motif1_SAFK * 1.6259)+
(pbp2x$v_pbp2x_motif3_EDT * 1.5476)+
(pbp2x$v_pbp2x_motif3_KEA * 0.6760)+
(pbp2x$v_pbp2x_motif4_VKSG * 0.7536))
pen <- MICcalc(MIC_table, "pen", pen_MIC_calc, -5, 2L)
pen$pen_MIC[pen$pen_MIC == "<= 0.031 ug/ml"] <- "<= 0.03 ug/ml"
}
##### Cephalosporins: Ceftriaxone (CRO), Cefuroxime (CXM)
cro <- NA
cxm <- NA
if(str_detect(pbp1a$lw_pbp1a, "Err") | str_detect(pbp2x$lw_pbp2x, "Err"))
{
cro <- tibble(cro_MIC = "Error",
cro_interp = "Error",
cro_profile = "Error")
cxm <- tibble(cxm_MIC = "Error",
cxm_interp = "Error",
cxm_profile = "Error")
}else
{
cro_MIC_calc <- round(-2.709 +
(pbp1a$v_pbp1a_motif1 * 1.25)+
(pbp2x$v_pbp2x_motif1_SAFK * 2.72)+
(pbp2x$v_pbp2x_motif3_EDT * 0.76)+
(pbp2x$v_pbp2x_motif4_VKSG * 0.989))
cro <- MICcalc(MIC_table, "cro", cro_MIC_calc, -3, 2L)
cxm_MIC_calc <- round(-1.018 +
(pbp1a$v_pbp1a_motif1_SAMK * 1.509)+
(pbp1a$v_pbp1a_motif1_SSMK * 2.170)+
(pbp2x$v_pbp2x_motif1_SAFK * 2.322)+
(pbp2x$v_pbp2x_motif1_other * 0.256)+
(pbp2x$v_pbp2x_motif4_VKSG * 1.026))
cxm <- MICcalc(MIC_table, "cxm", cxm_MIC_calc, -1, 4L)
}
##### Trimethoprim/Sulfamethoxazole (SXT) #####
sxt <- NA
if(str_detect(folA$lw_folA, "Err") | str_detect(folP$lw_folP, "Err"))
{
sxt <- tibble(sxt_MIC = "Error",
sxt_interp = "Error",
sxt_profile = "Error")
}else
{
sxt_MIC_calc <- round(-2.265 +
(folA$v_folA * 2.113)+
(folP$v_folP * 2.668))
sxt <- MICcalc(MIC_table, "sxt", sxt_MIC_calc, -1, 4L)
sxt$sxt_MIC[sxt_MIC_calc < 0] <- "<= 0.5/9.5 ug/ml"
sxt$sxt_MIC[sxt_MIC_calc > 1] <- ">= 4/76 ug/ml"
}
##### Macrolides: Erythromycin (ERY), Azithromycin (AZI) #####
##### Clarithromycin (CLA), Clindamycin (CLI) #####
ery <- NA
azi <- NA
cla <- NA
cli <- NA
if(str_detect(rRNA23S$lw_23S_A2059G, "Err") | str_detect(rRNA23S$lw_23S_C2611T, "Err")|
str_detect(ermB$lw_ermB, "Err") | str_detect(mefAE$lw_mefAE, "Err"))
{
ery <- tibble(ery_MIC = "Error",
ery_interp = "Error",
ery_profile = "Error")
azi <- tibble(azi_MIC = "Error",
azi_interp = "Error",
azi_profile = "Error")
cla <- tibble(cla_MIC = "Error",
cla_interp = "Error",
cla_profile = "Error")
cli <- tibble(cli_MIC = "Error",
cli_interp = "Error",
cli_profile = "Error")
}else
{
# if both ermB and mef are positive, only use ermB value to calculate
mefAE$v_mefAE[1] <- ifelse(ermB$lw_ermB[1] == "POS", 0, mefAE$v_mefAE[1])
ery_MIC_calc <- round(-2.975 +
(rRNA23S$v_23S_A2059G * 1.993)+
(ermB$v_ermB * 7.680)+
(mefAE$v_mefAE * 4.808))
ery <- MICcalc(MIC_table, "ery", ery_MIC_calc, -3, 5L)
azi_MIC_calc <- round(-1.9722 +
(rRNA23S$v_23S_A2059G * 1.1204)+
(rRNA23S$v_23S_C2611T * 0.9917)+
(ermB$v_ermB * 3.9722)+
(mefAE$v_mefAE * 3.8122))
azi <- MICcalc(MIC_table, "azi", azi_MIC_calc, -2, 1L)
cla_MIC_calc <- round(-4.984 +
(rRNA23S$v_23S_A2059G * 1.819)+
(rRNA23S$v_23S_C2611T * 1.246)+
(ermB$v_ermB * 10.820)+
(mefAE$v_mefAE * 6.064))
cla <- MICcalc(MIC_table, "cla", cla_MIC_calc, -5, 6L)
cla$cla_MIC[cla$cla_MIC == "<= 0.031 ug/ml"] <- "<= 0.03 ug/ml"
cli_MIC_calc <- round(-2.8145 +
(rRNA23S$v_23S_A2059G * 0.456)+
(ermB$v_ermB * 9.048))
cli <- MICcalc(MIC_table, "cli", cli_MIC_calc, -3, 6L)
cli$cli_interp <- ifelse(ermTR$lw_ermTR == "POS", "Inducible", cli$cli_interp)
if(cli$cli_interp == "Inducible")
{
cli$cli_profile <- "CLI-Ind"
}
}
##### Chloramphenicol (CHL) #####
chl <- NA
if(str_detect(cat$lw_cat, "Err"))
{
chl <- tibble(chl_MIC = "Error",
chl_interp = "Error",
chl_profile = "Error")
}else if(cat$lw_cat == "POS")
{
chl <- tibble(chl_MIC = ">= 8 ug/ml",
chl_interp = "Resistant",
chl_profile = "CHL-R")
}else
{
chl <- tibble(chl_MIC = "<= 4 ug/ml",
chl_interp = "Susceptible",
chl_profile = NA)
}
##### Tetracycline (TET), Doxycycline (DOX) #####
tet <- NA
dox <- NA
if(str_detect(tetM$lw_tetM, "Err"))
{
tet <- tibble(tet_MIC = "Error",
tet_interp = "Error",
tet_profile = "Error")
dox <- tibble(dox_MIC = "Error",
dox_interp = "Error",
dox_profile = "Error")
}else if(tetM$lw_tetM == "POS")
{
tet <- tibble(tet_MIC = ">= 16 ug/ml",
tet_interp = "Resistant",
tet_profile = "TET-R")
dox <- tibble(dox_MIC = ">= 4 ug/ml",
dox_interp = "Resistant",
dox_profile = "DOX-R")
}else
{
tet <- tibble(tet_MIC = "<= 1 ug/ml",
tet_interp = "Susceptible",
tet_profile = NA)
dox <- tibble(dox_MIC = "<= 0.25 ug/ml",
dox_interp = "Susceptible",
dox_profile = NA)
}
##### Vancomycin (VAN) #####
van <- NA
if(str_detect(molec_profile, paste(c("vanA", "vanB", "vanC", "vanD", "vanE", "vanG"), collapse = '|')))
{
van <- tibble(van_MIC = ">= 2 ug/ml",
van_interp = "Resistant",
van_profile = "VAN-R")
}else
{
van <- tibble(van_MIC = "<= 1 ug/ml",
van_interp = "Susceptible",
van_profile = NA)
}
###################### Combine them for AMR profile ######################
amr_profile <- paste(pen$pen_profile, cro$cro_profile, cxm$cxm_profile,
ery$ery_profile, azi$azi_profile, cla$cla_profile,
cli$cli_profile, chl$chl_profile, lev$lev_profile,
mox$mox_profile, tet$tet_profile, dox$dox_profile,
sxt$sxt_profile, van$van_profile, sep = "/")
amr_profile <- gsub("NA/", "", amr_profile)
amr_profile <- gsub("NA", "", amr_profile)
amr_profile <- sub("\\/$", "", amr_profile)
amr_profile <- ifelse(amr_profile == "", "Susceptible", amr_profile)
##########################################################################
# Put all data together
##########################################################################
sample_data.df <- tibble(lw_CurrSampleNo, pbp1a$lw_pbp1a, pbp2b$lw_pbp2b, pbp2x$lw_pbp2x,
as.character(rRNA23S$lw_23S_A2059G),
as.character(rRNA23S$lw_23S_C2611T), ermB$lw_ermB,
ermTR$lw_ermTR, mefAE$lw_mefAE, folA$lw_folA, folP$lw_folP,
gyrA$lw_gyrA, parC$lw_parC, tetM$lw_tetM, tetO$lw_tetO,
cat$lw_cat, molec_profile, pen$pen_MIC, pen$pen_interp,
cro$cro_MIC, cro$cro_interp, cxm$cxm_MIC, cxm$cxm_interp,
azi$azi_MIC, azi$azi_interp, ery$ery_MIC, ery$ery_interp,
cla$cla_MIC, cla$cla_interp, cli$cli_MIC, cli$cli_interp,
chl$chl_MIC, chl$chl_interp, lev$lev_MIC, lev$lev_interp,
mox$mox_MIC, mox$mox_interp, tet$tet_MIC, tet$tet_interp,
dox$dox_MIC, dox$dox_interp, sxt$sxt_MIC, sxt$sxt_interp,
amr_profile, lw_allele_profile)
sample_data.df <- sample_data.df %>% rename_at(vars(contains("$")), ~sub(".*\\$","",.))
} #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End of Sample Not Error loop
if(m==1)
{
lw_output.df <- tibble(sample_data.df)
}else
{
lw_output.df <- bind_rows(lw_output.df, sample_data.df)
}
} #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> End of sample loop
lw_output_bad.df <- filter(lw_output.df, amr_profile == "Error")
lw_output_good.df <- filter(lw_output.df, amr_profile != "Error")
write.csv(lw_output.df, paste0(directorylist$output_dir, "LabWareUpload_PNEUMO_AMR.csv"), quote = FALSE, row.names = FALSE)
write.csv(lw_output_good.df, paste0(directorylist$output_dir, "LabWareUpload_PNEUMO_AMR_good.csv"), quote = FALSE, row.names = FALSE)
write.csv(lw_output_bad.df, paste0(directorylist$output_dir, "LabWareUpload_PNEUMO_AMR_bad.csv"), quote = FALSE, row.names = FALSE)
cat("\n\nDone! ", directorylist$output_dir, "LabWareUpload_PNEUMO_AMR.csv is ready in output folder", "\n\n\n", sep = "")
return(lw_output.df)
}
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