R/LabwareUpload_GAS_AMR.R
In phac-nml/wade: Executes molecular typing on contig assemblies
Defines functions
labware_gas_amr
Documented in
labware_gas_amr
#' Labware Upload Formatter for GAS AMR
#' February 5 2024, Walter Demczuk & Shelley Peterson
#' Run AMR first
#' Then run this analysis to combine data the 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 <- "GAS" #GAS, PNEUMO or GONO
# curr_work_dir <- "C:\\WADE\\"
#-------------------------------------------------------------------------------
labware_gas_amr <- function(Org_id, curr_work_dir) {
#-----------------------------------------------------------------------------
# get directory structure and remove previous output files
directorylist <- getdirectory(curr_work_dir, Org_id, "AMR")
#-----------------------------------------------------------------------------
# Load datafile
Output.df <- as_tibble(read.csv(paste0(directorylist$output_dir, "output_profile_GAS_AMR.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
NumSamples <- dim(Output.df)[1]
NumLoci <- ((dim(Output.df)[2]-3) / 7)
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(Output.df[m, "SampleNo"])
if(Output.df[m,"SampleProfile"] == "Sample_Err")
{
sample_data.df <- tibble(lw_CurrSampleNo, lw_ermA = "Sample_Err",
lw_ermB = "Sample_Err", lw_ermT = "Sample_Err",
lw_mefAE = "Sample_Err", lw_gyrA = "Sample_Err",
lw_parC = "Sample_Err", lw_tetM = "Sample_Err",
lw_tetO = "Sample_Err", lw_tetT = "Sample_Err",
lw_tetA = "Sample_Err", lw_tetB = "Sample_Err",
lw_cat = "Sample_Err", lw_dfrF = "Sample_Err",
lw_dfrG = "Sample_Err", lw_folA = "Sample_Err",
lw_folP = "Sample_Err", lw_pbp2x = "Sample_Err",
molec_profile = "Sample_Err",
ery_MIC = "Sample_Err", ery = "Sample_Err",
chl_MIC = "Sample_Err", chl = "Sample_Err",
lev_MIC = "Sample_Err", lev = "Sample_Err",
cli_MIC = "Sample_Err", cli = "Sample_Err",
tet_MIC = "Sample_Err", tet = "Sample_Err",
sxt_MIC = "Sample_Err", sxt = "Sample_Err",
pen_MIC = "Sample_Err", pen = "Sample_Err",
amr_profile = "Sample_Err", lw_allele_profile = "Sample_Err")
}else
{
##### ermA #####
lw_ermA <- as.character(Output.df[m, "ermA_result"])
lw_ermAp_allele <- NA
molec_profile_ermA <- NA
if (lw_ermA == "POS")
{
# ermA promoter analysis
lw_ermAp_result <- as.character(Output.df[m, "ermAp_result"]) #POS/NEG
lw_ermAp_allele <- as.character(Output.df[m, "ermAp_allele"]) #ID number
lw_ermAp_mutation <- as.character(Output.df[m, "ermAp_mutations"]) # differences from wildtype
lw_ermAp_comment <- as.character(Output.df[m, "ermAp_comments"]) #Susceptible(Inducible)/Resistant
lw_ermAp_motif <- as.character(Output.df[m, "ermAp_motifs"]) #amino acid substitutions from MasterBlastR
if (lw_ermAp_motif == "WT/WT") {lw_ermAp_motif <- ""}
if (lw_ermAp_result == "NEG")
{
molec_profile_ermA <- "ermA R"
lw_ermAp_allele <- "ermAp 0"
} else # susceptible promoter found, might have mutations for CLI-R
{
if (lw_ermAp_motif == "N90H/D97G")
{
molec_profile_ermA <- "ermA S N90H/D97G" # CLI-Inducible
lw_ermAp_allele <- paste0("ermAp ", lw_ermAp_allele)
}else
{
molec_profile_ermA <- "ermA R"
lw_ermAp_allele <-"ermA 0"
}
}
}
##### ermB #####
ermB <- posneg_gene(Output.df, "ermB", m)
ermBSNP <- allele1SNP(Output.df, "ermB", "motifs", m)
if(ermB$lw_ermB == "POS" & ermBSNP$lw_ermB == "N100S")
{
ermB$v_ermB <- 0
ermB$molec_profile_ermB <- "ermB N100S"
}
##### ermT #####
ermT <- posneg_gene(Output.df, "ermT", m)
##### mefAE #####
mefAE <- posneg_gene(Output.df, "mefAE", m)
##### msrD #####
msrD <- posneg_gene(Output.df, "msrD", m) ################################ Not included in labware export, but is included in molec_profile
##### gyrA #####
gyrA <- allele1SNP(Output.df, "gyrA", "motifs",m)
##### parC #####
parC <- allele3SNPs(Output.df, "parC", "parC", "motifs", "D78", "S79", "D83", m)
##### tetM #####
tetM <- posneg_gene(Output.df, "tetM", m)
##### tetO #####
tetO <- posneg_gene(Output.df, "tetO", m)
##### tetT #####
tetT <- posneg_gene(Output.df, "tetT", m)
##### tetA #####
tetA <- posneg_gene(Output.df, "tetA", m)
##### tetB #####
tetB <- posneg_gene(Output.df, "tetB", m)
##### cat #####
cat <- posneg_gene(Output.df, "cat", m)
##### catQ #####
catQ <- posneg_gene(Output.df, "catQ", m)
##### dfrF #####
dfrF <- posneg_gene(Output.df, "dfrF", m)
##### dfrG #####
dfrG <- posneg_gene(Output.df, "dfrG", m)
##### folA #####
folA <- allele1SNP(Output.df, "folA", "motifs", m)
##### folP #####
folP <- allele1SNP(Output.df, "folP", "motifs", m)
##### pbp2x #####
pbp2x <- allele1SNP(Output.df, "pbp2x", "motifs", m)
# -------------------- Vancomycin - Not included in LW export, but is included in molec_profile
##### vanA #####
vanA <- posneg_gene(Output.df, "vanA", m)
##### vanB #####
vanB <- posneg_gene(Output.df, "vanB", m)
##### vanC #####
vanC <- posneg_gene(Output.df, "vanC", m)
##### vanD #####
vanD <- posneg_gene(Output.df, "vanD", m)
##### vanE #####
vanE <- posneg_gene(Output.df, "vanE", m)
##### vanG #####
vanG <- posneg_gene(Output.df, "vanG", m)
######################## Now put them all together #######################
molec_profile <- paste(molec_profile_ermA, ermB$molec_profile_ermB,
ermT$molec_profile_ermT, mefAE$molec_profile_mefAE,
msrD$molec_profile_msrD, gyrA$molec_profile_gyrA,
parC$molec_profile_parC, tetM$molec_profile_tetM,
tetO$molec_profile_tetO, tetT$molec_profile_tetT,
tetA$molec_profile_tetA, tetB$molec_profile_tetB,
cat$molec_profile_cat, catQ$molec_profile_catQ,
dfrF$molec_profile_dfrF, dfrG$molec_profile_dfrG,
folA$molec_profile_folA, folP$molec_profile_folP,
pbp2x$molec_profile_pbp2x, vanA$molec_profile_vanA,
vanB$molec_profile_vanB, vanC$molec_profile_vanC,
vanD$molec_profile_vanD, vanE$molec_profile_vanE,
vanG$molec_profile_vanG, sep = "; ")
molec_profile <- gsub("NA; ", "", molec_profile)
molec_profile <- sub("; NA", "", molec_profile)
if(molec_profile == "NA") {molec_profile <- "Wild Type"}
lw_allele_profile <- paste(lw_ermAp_allele, ermB$allele_ermB, ermT$allele_ermT,
mefAE$allele_mefAE, msrD$allele_msrD, gyrA$allele_gyrA,
parC$allele_parC, tetM$allele_tetM, tetO$allele_tetO,
tetT$allele_tetT, tetA$allele_tetA, tetB$allele_tetB,
cat$allele_cat, catQ$allele_catQ, dfrF$allele_dfrF,
dfrG$allele_dfrG, folA$allele_folA, folP$allele_folP,
pbp2x$allele_pbp2x, vanA$allele_vanA, vanB$allele_vanB,
vanC$allele_vanC, vanD$allele_vanD, vanE$allele_vanE,
vanG$allele_vanG, sep = ":")
lw_allele_profile <- gsub("NA:", "", lw_allele_profile)
lw_allele_profile <- sub(":NA", "", lw_allele_profile)
##########################################################################
# Calculate MICs
##########################################################################
##### Erythromycin (ERY) #####
if(str_detect(molec_profile, paste(c("ermA", "ermB", "ermT", "mefAE"),collapse = '|')))
{
ery_MIC <- ">= 2 ug/ml"
ery <- "Resistant"
ery_AMR <- "ERY-R"
}else
{
ery_MIC <- "<= 0.25 ug/ml"
ery <- "Susceptible"
ery_AMR <- NA
}
##### Clindamycin (CLI) #####
if(str_detect(molec_profile, paste(c("ermB", "ermA R"), collapse = '|')))
{
cli_MIC <- ">= 1 ug/ml"
cli <- "Resistant"
cli_AMR <- "CLI-R"
}else
{
cli_MIC <- "<= 0.12 ug/ml"
cli <-"Susceptible"
cli_AMR <- NA
}
if(str_detect(molec_profile, "ermB N100S"))
{
cli_MIC <- "<= 0.12 ug/ml"
cli <- "Susceptible"
cli_AMR <- NA
}
if(str_detect(molec_profile, "ermA S"))
{
cli_MIC <- "<= 0.12 ug/ml"
cli <- "Inducible"
cli_AMR <- "CLI-Ind"
}
##### Chloramphenicol (CHL) #####
if(str_detect(molec_profile, paste(c("cat", "catQ"),collapse = '|')))
{
chl_MIC <- ">= 32 ug/ml"
chl <- "Resistant"
chl_AMR <- "CHL-R"
}else
{
chl_MIC <- "<= 4 ug/ml"
chl <- "Susceptible"
chl_AMR <- NA
}
##### Levofloxacin (LEV) #####
if(gyrA$lw_gyrA == "Err" | parC$lw_parC == "Err")
{
lev_MIC <- "Error"
lev <- "Error"
lev_AMR <- "LEV-Err"
}else if(str_detect(molec_profile, "gyrA S81"))
{
lev_MIC <- ">= 8 ug/ml"
lev <- "Resistant"
lev_AMR <- "LEV-R"
}else if((str_detect(molec_profile, "D78")))
{
lev_MIC <- "1 ug/ml"
lev <- "Susceptible"
lev_AMR <- NA
}else if((str_detect(molec_profile, paste(c("S79", "D83"), collapse = '|'))))
{
lev_MIC <- "2 ug/ml"
lev <- "Susceptible"
lev_AMR <- NA
}else
{
lev_MIC <- "<= 0.5 ug/ml"
lev <- "Susceptible"
lev_AMR <- NA
}
##### Tetracycline (TET) #####
if(str_detect(molec_profile, paste(c("tetM", "tetO", "tetT", "tetA", "tetB"), collapse = '|')))
{
tet_MIC <- ">= 8 ug/ml"
tet <- "Resistant"
tet_AMR <- "TET-R"
}else
{
tet_MIC <- "<= 1 ug/ml"
tet <- "Susceptible"
tet_AMR <- NA
}
if(str_detect(molec_profile, "tetM Disrupted"))
{
tet_MIC <- "<= 1 ug/ml"
tet <- "Susceptible"
tet_AMR <- NA
}
##### Trimethoprim/Sulfamethoxazole (SXT) #####
if(folA$lw_folA == "Err" | folP$lw_folP == "Err")
{
sxt_MIC <- "Error"
sxt <- "Error"
sxt_AMR <- "SXT-Err"
}else if(str_detect(molec_profile, paste(c("dfrG", "dfrF", "folA", "folP"), collapse = '|')))
{
if(str_detect(molec_profile, paste(c("dfrG", "dfrF"), collapse = '|')))
{
sxt_MIC <- ">= 4 ug/ml" #=4/76
sxt <- "Resistant"
sxt_AMR <- "SXT-R"
} else if(str_detect(molec_profile, paste(c("folA", "folP"), collapse = '|')))
{
sxt_MIC <- "<= 0.5 ug/ml" #=0.5/9.5
sxt <- "Susceptible"
sxt_AMR <- NA
if((str_detect(molec_profile, "folA")) & (str_detect(molec_profile, "folP")))
{
sxt_MIC <- ">= 4 ug/ml"
sxt <- "Resistant"
sxt_AMR <- "SXT-R"
}
}
}else
{
sxt_MIC <- "<= 0.5 ug/ml"
sxt <- "Susceptible"
sxt_AMR <- NA
}
##### Penicillin (PEN) #####
if(pbp2x$lw_pbp2x == "Err")
{
pen_MIC <- "Error"
pen <- "Error"
pen_AMR <- "PEN-Err"
}else
{
if(str_detect(molec_profile, "pbp2x"))
{
pen_MIC <- "> 0.12 ug/ml"
pen <- "Unknown"
pen_AMR <- "pen-NS"
}else
{
pen_MIC <- "<= 0.12 ug/ml"
pen <- "Susceptible"
pen_AMR <- NA
}
}
##### Vancomycin (VAN) #####
if(str_detect(molec_profile, paste(c(
"vanA", "vanB", "vanC", "vanD", "vanE", "vanG"), collapse = '|')))
{
van_MIC <- ">= 2 ug/ml"
van <- "Resistant"
van_AMR <- "VAN-R"
}else
{
van_MIC <- "<= 1 ug/ml"
van <- "Susceptible"
van_AMR <- NA
}
###################### Combine them for AMR profile ######################
amr_profile <- paste(ery_AMR, cli_AMR, chl_AMR, lev_AMR, tet_AMR, sxt_AMR,
pen_AMR, van_AMR, sep = "/")
amr_profile <- gsub("NA/", "", amr_profile)
amr_profile <- sub("/NA", "", amr_profile)
if(amr_profile == "NA") {amr_profile <- "Susceptible"}
##########################################################################
# Put all data together
##########################################################################
# combine cat and catQ results into single column for labware. Specified will be in molecular profile.
lw_cat <- ifelse(str_detect(molec_profile, paste(c("cat", "catQ"),collapse = '|')), "POS", "NEG")
sample_data.df <- tibble(lw_CurrSampleNo, lw_ermA, ermB$lw_ermB,
ermT$lw_ermT,mefAE$lw_mefAE, gyrA$lw_gyrA,
parC$lw_parC, tetM$lw_tetM, tetO$lw_tetO,
tetT$lw_tetT, tetA$lw_tetA, tetB$lw_tetB,
cat$lw_cat, dfrF$lw_dfrF, dfrG$lw_dfrG,
folA$lw_folA, folP$lw_folP, pbp2x$lw_pbp2x,
molec_profile, ery_MIC, ery, chl_MIC, chl,
lev_MIC, lev, cli_MIC, cli, tet_MIC, tet,
sxt_MIC, sxt, pen_MIC, pen, amr_profile,
lw_allele_profile)
sample_data.df <- sample_data.df %>% rename_at(vars(contains("$")), ~sub(".*\\$","",.))
}
if(m==1) #if first sample make one row profile table, otherwise add new row to table
{
lw_Output.df <- tibble(sample_data.df)
}else
{
lw_Output.df <- rbind(lw_Output.df, sample_data.df)
}
} # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> End of sample loop
lw_output_bad.df <- filter(lw_Output.df, amr_profile == "Error")
write.csv(lw_Output.df, paste(directorylist$output_dir, "LabWareUpload_GAS_AMR.csv", sep = ""), quote = FALSE, row.names = FALSE)
write.csv(lw_output_bad.df, paste(directorylist$output_dir, "LabWareUpload_GAS_AMR_bad.csv", sep = ""), quote = FALSE, row.names = FALSE)
cat("\n\nDone! ", directorylist$output_dir, "LabWareUpload_GAS_AMR.csv is ready in output folder", "\n\n\n", sep = "")
return(lw_Output.df)
}
phac-nml/wade documentation built on March 16, 2024, 8:32 a.m.
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Defines functions labware_gas_amr
Documented in labware_gas_amr
#' Labware Upload Formatter for GAS AMR
#' February 5 2024, Walter Demczuk & Shelley Peterson
#' Run AMR first
#' Then run this analysis to combine data the full amr profile to upload to LabWare.
#'
#'
#' @param Org_id Organism to query: GAS, PNEUMO or GONO
#' @param curr_work_dir Start up directory from pipeline project to locate system file structure
#' @return A table frame containing the results of the query
#' @export
#-------------------------------------------------------------------------------
# For troubleshooting and debugging
# Org_id <- "GAS" #GAS, PNEUMO or GONO
# curr_work_dir <- "C:\\WADE\\"
#-------------------------------------------------------------------------------
labware_gas_amr <- function(Org_id, curr_work_dir) {
#-----------------------------------------------------------------------------
# get directory structure and remove previous output files
directorylist <- getdirectory(curr_work_dir, Org_id, "AMR")
#-----------------------------------------------------------------------------
# Load datafile
Output.df <- as_tibble(read.csv(paste0(directorylist$output_dir, "output_profile_GAS_AMR.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE))
NumSamples <- dim(Output.df)[1]
NumLoci <- ((dim(Output.df)[2]-3) / 7)
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(Output.df[m, "SampleNo"])
if(Output.df[m,"SampleProfile"] == "Sample_Err")
{
sample_data.df <- tibble(lw_CurrSampleNo, lw_ermA = "Sample_Err",
lw_ermB = "Sample_Err", lw_ermT = "Sample_Err",
lw_mefAE = "Sample_Err", lw_gyrA = "Sample_Err",
lw_parC = "Sample_Err", lw_tetM = "Sample_Err",
lw_tetO = "Sample_Err", lw_tetT = "Sample_Err",
lw_tetA = "Sample_Err", lw_tetB = "Sample_Err",
lw_cat = "Sample_Err", lw_dfrF = "Sample_Err",
lw_dfrG = "Sample_Err", lw_folA = "Sample_Err",
lw_folP = "Sample_Err", lw_pbp2x = "Sample_Err",
molec_profile = "Sample_Err",
ery_MIC = "Sample_Err", ery = "Sample_Err",
chl_MIC = "Sample_Err", chl = "Sample_Err",
lev_MIC = "Sample_Err", lev = "Sample_Err",
cli_MIC = "Sample_Err", cli = "Sample_Err",
tet_MIC = "Sample_Err", tet = "Sample_Err",
sxt_MIC = "Sample_Err", sxt = "Sample_Err",
pen_MIC = "Sample_Err", pen = "Sample_Err",
amr_profile = "Sample_Err", lw_allele_profile = "Sample_Err")
}else
{
##### ermA #####
lw_ermA <- as.character(Output.df[m, "ermA_result"])
lw_ermAp_allele <- NA
molec_profile_ermA <- NA
if (lw_ermA == "POS")
{
# ermA promoter analysis
lw_ermAp_result <- as.character(Output.df[m, "ermAp_result"]) #POS/NEG
lw_ermAp_allele <- as.character(Output.df[m, "ermAp_allele"]) #ID number
lw_ermAp_mutation <- as.character(Output.df[m, "ermAp_mutations"]) # differences from wildtype
lw_ermAp_comment <- as.character(Output.df[m, "ermAp_comments"]) #Susceptible(Inducible)/Resistant
lw_ermAp_motif <- as.character(Output.df[m, "ermAp_motifs"]) #amino acid substitutions from MasterBlastR
if (lw_ermAp_motif == "WT/WT") {lw_ermAp_motif <- ""}
if (lw_ermAp_result == "NEG")
{
molec_profile_ermA <- "ermA R"
lw_ermAp_allele <- "ermAp 0"
} else # susceptible promoter found, might have mutations for CLI-R
{
if (lw_ermAp_motif == "N90H/D97G")
{
molec_profile_ermA <- "ermA S N90H/D97G" # CLI-Inducible
lw_ermAp_allele <- paste0("ermAp ", lw_ermAp_allele)
}else
{
molec_profile_ermA <- "ermA R"
lw_ermAp_allele <-"ermA 0"
}
}
}
##### ermB #####
ermB <- posneg_gene(Output.df, "ermB", m)
ermBSNP <- allele1SNP(Output.df, "ermB", "motifs", m)
if(ermB$lw_ermB == "POS" & ermBSNP$lw_ermB == "N100S")
{
ermB$v_ermB <- 0
ermB$molec_profile_ermB <- "ermB N100S"
}
##### ermT #####
ermT <- posneg_gene(Output.df, "ermT", m)
##### mefAE #####
mefAE <- posneg_gene(Output.df, "mefAE", m)
##### msrD #####
msrD <- posneg_gene(Output.df, "msrD", m) ################################ Not included in labware export, but is included in molec_profile
##### gyrA #####
gyrA <- allele1SNP(Output.df, "gyrA", "motifs",m)
##### parC #####
parC <- allele3SNPs(Output.df, "parC", "parC", "motifs", "D78", "S79", "D83", m)
##### tetM #####
tetM <- posneg_gene(Output.df, "tetM", m)
##### tetO #####
tetO <- posneg_gene(Output.df, "tetO", m)
##### tetT #####
tetT <- posneg_gene(Output.df, "tetT", m)
##### tetA #####
tetA <- posneg_gene(Output.df, "tetA", m)
##### tetB #####
tetB <- posneg_gene(Output.df, "tetB", m)
##### cat #####
cat <- posneg_gene(Output.df, "cat", m)
##### catQ #####
catQ <- posneg_gene(Output.df, "catQ", m)
##### dfrF #####
dfrF <- posneg_gene(Output.df, "dfrF", m)
##### dfrG #####
dfrG <- posneg_gene(Output.df, "dfrG", m)
##### folA #####
folA <- allele1SNP(Output.df, "folA", "motifs", m)
##### folP #####
folP <- allele1SNP(Output.df, "folP", "motifs", m)
##### pbp2x #####
pbp2x <- allele1SNP(Output.df, "pbp2x", "motifs", m)
# -------------------- Vancomycin - Not included in LW export, but is included in molec_profile
##### vanA #####
vanA <- posneg_gene(Output.df, "vanA", m)
##### vanB #####
vanB <- posneg_gene(Output.df, "vanB", m)
##### vanC #####
vanC <- posneg_gene(Output.df, "vanC", m)
##### vanD #####
vanD <- posneg_gene(Output.df, "vanD", m)
##### vanE #####
vanE <- posneg_gene(Output.df, "vanE", m)
##### vanG #####
vanG <- posneg_gene(Output.df, "vanG", m)
######################## Now put them all together #######################
molec_profile <- paste(molec_profile_ermA, ermB$molec_profile_ermB,
ermT$molec_profile_ermT, mefAE$molec_profile_mefAE,
msrD$molec_profile_msrD, gyrA$molec_profile_gyrA,
parC$molec_profile_parC, tetM$molec_profile_tetM,
tetO$molec_profile_tetO, tetT$molec_profile_tetT,
tetA$molec_profile_tetA, tetB$molec_profile_tetB,
cat$molec_profile_cat, catQ$molec_profile_catQ,
dfrF$molec_profile_dfrF, dfrG$molec_profile_dfrG,
folA$molec_profile_folA, folP$molec_profile_folP,
pbp2x$molec_profile_pbp2x, 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(lw_ermAp_allele, ermB$allele_ermB, ermT$allele_ermT,
mefAE$allele_mefAE, msrD$allele_msrD, gyrA$allele_gyrA,
parC$allele_parC, tetM$allele_tetM, tetO$allele_tetO,
tetT$allele_tetT, tetA$allele_tetA, tetB$allele_tetB,
cat$allele_cat, catQ$allele_catQ, dfrF$allele_dfrF,
dfrG$allele_dfrG, folA$allele_folA, folP$allele_folP,
pbp2x$allele_pbp2x, vanA$allele_vanA, vanB$allele_vanB,
vanC$allele_vanC, vanD$allele_vanD, vanE$allele_vanE,
vanG$allele_vanG, sep = ":")
lw_allele_profile <- gsub("NA:", "", lw_allele_profile)
lw_allele_profile <- sub(":NA", "", lw_allele_profile)
##########################################################################
# Calculate MICs
##########################################################################
##### Erythromycin (ERY) #####
if(str_detect(molec_profile, paste(c("ermA", "ermB", "ermT", "mefAE"),collapse = '|')))
{
ery_MIC <- ">= 2 ug/ml"
ery <- "Resistant"
ery_AMR <- "ERY-R"
}else
{
ery_MIC <- "<= 0.25 ug/ml"
ery <- "Susceptible"
ery_AMR <- NA
}
##### Clindamycin (CLI) #####
if(str_detect(molec_profile, paste(c("ermB", "ermA R"), collapse = '|')))
{
cli_MIC <- ">= 1 ug/ml"
cli <- "Resistant"
cli_AMR <- "CLI-R"
}else
{
cli_MIC <- "<= 0.12 ug/ml"
cli <-"Susceptible"
cli_AMR <- NA
}
if(str_detect(molec_profile, "ermB N100S"))
{
cli_MIC <- "<= 0.12 ug/ml"
cli <- "Susceptible"
cli_AMR <- NA
}
if(str_detect(molec_profile, "ermA S"))
{
cli_MIC <- "<= 0.12 ug/ml"
cli <- "Inducible"
cli_AMR <- "CLI-Ind"
}
##### Chloramphenicol (CHL) #####
if(str_detect(molec_profile, paste(c("cat", "catQ"),collapse = '|')))
{
chl_MIC <- ">= 32 ug/ml"
chl <- "Resistant"
chl_AMR <- "CHL-R"
}else
{
chl_MIC <- "<= 4 ug/ml"
chl <- "Susceptible"
chl_AMR <- NA
}
##### Levofloxacin (LEV) #####
if(gyrA$lw_gyrA == "Err" | parC$lw_parC == "Err")
{
lev_MIC <- "Error"
lev <- "Error"
lev_AMR <- "LEV-Err"
}else if(str_detect(molec_profile, "gyrA S81"))
{
lev_MIC <- ">= 8 ug/ml"
lev <- "Resistant"
lev_AMR <- "LEV-R"
}else if((str_detect(molec_profile, "D78")))
{
lev_MIC <- "1 ug/ml"
lev <- "Susceptible"
lev_AMR <- NA
}else if((str_detect(molec_profile, paste(c("S79", "D83"), collapse = '|'))))
{
lev_MIC <- "2 ug/ml"
lev <- "Susceptible"
lev_AMR <- NA
}else
{
lev_MIC <- "<= 0.5 ug/ml"
lev <- "Susceptible"
lev_AMR <- NA
}
##### Tetracycline (TET) #####
if(str_detect(molec_profile, paste(c("tetM", "tetO", "tetT", "tetA", "tetB"), collapse = '|')))
{
tet_MIC <- ">= 8 ug/ml"
tet <- "Resistant"
tet_AMR <- "TET-R"
}else
{
tet_MIC <- "<= 1 ug/ml"
tet <- "Susceptible"
tet_AMR <- NA
}
if(str_detect(molec_profile, "tetM Disrupted"))
{
tet_MIC <- "<= 1 ug/ml"
tet <- "Susceptible"
tet_AMR <- NA
}
##### Trimethoprim/Sulfamethoxazole (SXT) #####
if(folA$lw_folA == "Err" | folP$lw_folP == "Err")
{
sxt_MIC <- "Error"
sxt <- "Error"
sxt_AMR <- "SXT-Err"
}else if(str_detect(molec_profile, paste(c("dfrG", "dfrF", "folA", "folP"), collapse = '|')))
{
if(str_detect(molec_profile, paste(c("dfrG", "dfrF"), collapse = '|')))
{
sxt_MIC <- ">= 4 ug/ml" #=4/76
sxt <- "Resistant"
sxt_AMR <- "SXT-R"
} else if(str_detect(molec_profile, paste(c("folA", "folP"), collapse = '|')))
{
sxt_MIC <- "<= 0.5 ug/ml" #=0.5/9.5
sxt <- "Susceptible"
sxt_AMR <- NA
if((str_detect(molec_profile, "folA")) & (str_detect(molec_profile, "folP")))
{
sxt_MIC <- ">= 4 ug/ml"
sxt <- "Resistant"
sxt_AMR <- "SXT-R"
}
}
}else
{
sxt_MIC <- "<= 0.5 ug/ml"
sxt <- "Susceptible"
sxt_AMR <- NA
}
##### Penicillin (PEN) #####
if(pbp2x$lw_pbp2x == "Err")
{
pen_MIC <- "Error"
pen <- "Error"
pen_AMR <- "PEN-Err"
}else
{
if(str_detect(molec_profile, "pbp2x"))
{
pen_MIC <- "> 0.12 ug/ml"
pen <- "Unknown"
pen_AMR <- "pen-NS"
}else
{
pen_MIC <- "<= 0.12 ug/ml"
pen <- "Susceptible"
pen_AMR <- NA
}
}
##### Vancomycin (VAN) #####
if(str_detect(molec_profile, paste(c(
"vanA", "vanB", "vanC", "vanD", "vanE", "vanG"), collapse = '|')))
{
van_MIC <- ">= 2 ug/ml"
van <- "Resistant"
van_AMR <- "VAN-R"
}else
{
van_MIC <- "<= 1 ug/ml"
van <- "Susceptible"
van_AMR <- NA
}
###################### Combine them for AMR profile ######################
amr_profile <- paste(ery_AMR, cli_AMR, chl_AMR, lev_AMR, tet_AMR, sxt_AMR,
pen_AMR, van_AMR, sep = "/")
amr_profile <- gsub("NA/", "", amr_profile)
amr_profile <- sub("/NA", "", amr_profile)
if(amr_profile == "NA") {amr_profile <- "Susceptible"}
##########################################################################
# Put all data together
##########################################################################
# combine cat and catQ results into single column for labware. Specified will be in molecular profile.
lw_cat <- ifelse(str_detect(molec_profile, paste(c("cat", "catQ"),collapse = '|')), "POS", "NEG")
sample_data.df <- tibble(lw_CurrSampleNo, lw_ermA, ermB$lw_ermB,
ermT$lw_ermT,mefAE$lw_mefAE, gyrA$lw_gyrA,
parC$lw_parC, tetM$lw_tetM, tetO$lw_tetO,
tetT$lw_tetT, tetA$lw_tetA, tetB$lw_tetB,
cat$lw_cat, dfrF$lw_dfrF, dfrG$lw_dfrG,
folA$lw_folA, folP$lw_folP, pbp2x$lw_pbp2x,
molec_profile, ery_MIC, ery, chl_MIC, chl,
lev_MIC, lev, cli_MIC, cli, tet_MIC, tet,
sxt_MIC, sxt, pen_MIC, pen, amr_profile,
lw_allele_profile)
sample_data.df <- sample_data.df %>% rename_at(vars(contains("$")), ~sub(".*\\$","",.))
}
if(m==1) #if first sample make one row profile table, otherwise add new row to table
{
lw_Output.df <- tibble(sample_data.df)
}else
{
lw_Output.df <- rbind(lw_Output.df, sample_data.df)
}
} # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> End of sample loop
lw_output_bad.df <- filter(lw_Output.df, amr_profile == "Error")
write.csv(lw_Output.df, paste(directorylist$output_dir, "LabWareUpload_GAS_AMR.csv", sep = ""), quote = FALSE, row.names = FALSE)
write.csv(lw_output_bad.df, paste(directorylist$output_dir, "LabWareUpload_GAS_AMR_bad.csv", sep = ""), quote = FALSE, row.names = FALSE)
cat("\n\nDone! ", directorylist$output_dir, "LabWareUpload_GAS_AMR.csv is ready in output folder", "\n\n\n", sep = "")
return(lw_Output.df)
}
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