## ----eval = TRUE, echo = FALSE, message = FALSE-------------------------------
library(htmlTable)
library(tcpl)
library(data.table)


## ----eval = TRUE, echo = FALSE, message = FALSE, results = "hide"-------------
## This chunk copies the tcplLite local directory to the temp directory used in installation
## to comply with CRAN policies on not writing to the installation directory
tmpdir <- tempdir()
#dbfile <- file.path(system.file(package = "tcpl"), "csv")
#file.copy(from = dbfile, tmpdir, recursive  = TRUE)
dbfile_temp <- file.path(tmpdir, "csv")
dir.create(dbfile_temp, showWarnings = F)
tcplConf(db = dbfile_temp, drvr = 'tcplLite')


## ----eval = TRUE, echo = FALSE, message = FALSE-------------------------------
tcplRegister(what='spid', flds = list(spid=c("Tox21_303655", "Tox21_110011", "Tox21_400081", "DMSO", "Tox21_400037"),
                                      chid=c("22364", "23463", "24102", "21735", "20283"),
                                      tested_conc_unit=c('uM', 'uM', 'uM', 'uM', 'uM')
                                      
                                      ) )
tcplRegister(what='chid', flds=list(chid = c("22364", "23463" , "24102", "21735","20283"),
                       casn = c("521-18-6", "150-30-1", "22224-92-6", "67-68-5", "95-83-0"),
                       chnm = c("5alpha-Dihydrotestosterone","Phenylalanine","Fenamiphos",
"Dimethyl sulfoxide","4-Chloro-1,2-diaminobenzene"),
                      dsstox_substance_id = c("DTXSID9022364",         "DTXSID9023463", "DTXSID3024102", "DTXSID2021735", "DTXSID5020283"),
                    code = c("C521186","C150301","C22224926", "C67685","C95830")))

## ----eval = TRUE, message = FALSE---------------------------------------------
## Add a new assay source, call it CTox,
## that produced the data
tcplRegister(what = "asid", flds = list(asid = 1, 
                      asnm = "CTox"))

## ----eval = TRUE--------------------------------------------------------------
tcplLoadAsid()

## ----eval = TRUE, message = FALSE---------------------------------------------
tcplRegister(what = "aid", 
             flds = list(asid = 1, 
                         anm = "TOX21_ERa_BLA_Agonist", 
                         assay_footprint = "1536 well"))

## ----eval = TRUE, message = FALSE---------------------------------------------
tcplRegister(what = "acid", 
             flds = list(aid = 1,
                         acnm = "TOX21_ERa_BLA_Agonist_ratio"))
tcplRegister(what = "aeid", 
             flds = list(acid = c(1, 1), 
                         aenm = c("TOX21_ERa_BLA_Agonist_ratio_gain", 
                                  "TOX21_ERa_BLA_Agonist_ratio_loss"),
                         normalized_data_type = 
                         rep("percent_activity", 1),
                         export_ready = c(1, 1),
                         burst_assay = c(0, 0),
                         fit_all = c(0, 0)))

## ----eval = TRUE--------------------------------------------------------------
data(chdat, package = "tcpl")
setDT(chdat)
head(chdat)

## ----eval = TRUE, message = FALSE---------------------------------------------
## Register the unique chemicals
cmap <- tcplLoadChem() # Chemicals already registered
chdat.register <- chdat[!(chdat$code %in% cmap$code)] # Chemicals in chdat that are not registered yet

tcplRegister(what = "chid", 
             flds = chdat.register[, 
                        unique(.SD), 
                        .SDcols = c("casn", "chnm", "dsstox_substance_id", "code", "chid")])

## ----eval = TRUE, message = FALSE---------------------------------------------
tcplRegister(what = "spid", 
             flds = merge(chdat[ , list(spid, casn)], 
                          chdat.register[ , list(casn, chid)], 
                          by = "casn")[ , list(spid, chid)])

## ----eval = FALSE-------------------------------------------------------------
#  grp1 <- cmap[chid %% 2 == 0, unique(chid)]
#  grp2 <- cmap[chid %% 2 == 1, unique(chid)]
#  tcplRegister(what = "clib",
#               flds = list(clib = "group_1", chid = grp1))

## ----eval = FALSE-------------------------------------------------------------
#  tcplRegister(what = "clib",
#               flds = list(clib = "group_2", chid = grp2))

## ----eval = FALSE-------------------------------------------------------------
#  tcplRegister(what = "clib",
#               flds = list(clib = "other", chid = 1:2))

## ----eval = FALSE-------------------------------------------------------------
#  tcplLoadClib(field = "chid", val = 1:2)

## ----eval = TRUE--------------------------------------------------------------
data(mcdat, package = 'tcpl')
setDT(mcdat)


## ----eval = TRUE, message = FALSE---------------------------------------------
tcplRegister(what = "acsn", 
             flds = list(acid = 1, acsn = "TCPL-MC-Demo"))

## ----eval = TRUE, message = FALSE---------------------------------------------
tcplWriteLvl0(dat = mcdat, type = "mc")


## ----eval = TRUE--------------------------------------------------------------
tcplLoadData(lvl = 0, fld = "acid", val = 1, type = "mc")

## ----warning = FALSE, echo = FALSE--------------------------------------------
Type <- c('SC', 'SC', 'MC', 'MC', 'MC', 'MC', 'MC', 'MC')
Level <- c('Lvl1', 'Lvl2', 'Lvl1', 'Lvl2', 'Lvl3', 'Lvl4', 'Lvl5', 'Lvl6')
InputID <- c('acid', 'aeid', 'acid', 'acid', 'acid', 'aeid', 'aeid', 'aeid')
MethodID <- c('aeid', 'aeid', 'N/A', 'acid', 'aeid', 'N/A', 'aeid', 'aeid')
Table <- data.frame(Type, Level, InputID, MethodID)
library(htmlTable)
htmlTable(Table,
         rnames = FALSE ,
        
          caption="Table 1: Processing checklist.",
          tfoot = " The Input ID column indicates the ID used for each processing step; Method ID indicates the ID used for assigning methods for data processing, when necessary. SC = single-concentration; MC = multiple-concentration.")
          

## ----eval= FALSE--------------------------------------------------------------
#  ## For illustrative purposes, assign level 2 MC methods to
#  ## ACIDs 97, 98, and 99. First check for available methods.
#  mthds <- tcplMthdList(lvl = 2, type = "mc")
#  mthds[1:2]
#  ## Assign some methods to ACID 97, 98, and 99
#  tcplMthdAssign(lvl = 2,
#                 id = 97:99,
#                 mthd_id = c(3, 4, 2),
#                 ordr = 1:3,
#                 type = "mc")
#  tcplMthdLoad(lvl = 2, id = 97:99, type = "mc")
#  ## Methods can be cleared one at a time for the given id(s)
#  tcplMthdClear(lvl = 2, id = 99, mthd_id = 2, type = "mc")
#  tcplMthdLoad(lvl = 2, id = 99, type = "mc")
#  ## Or all methods can be cleared for the given id(s)
#  tcplMthdClear(lvl = 2, id = 97:98, type = "mc")
#  tcplMthdLoad(lvl = 2, id = 97:98, type = "mc")

## ----warning = FALSE, echo = FALSE--------------------------------------------
output <- 
  matrix(c("1. bval.apid.nwlls.med", "2. resp.fc", "1. bval.apid.lowconc.med", "2. bval.apid.pwlls.med",
"3. resp.log2", "4. resp.mult.neg1", "3. resp.pc", "4. resp.multneg1 ", 
"1. bval.apid.lowconc.med", "2. resp.fc", "1. bval.spid.lowconc.med", "2. pval.apid.mwlls.med", 
"3. resp.log2", "4. \t", "3. resp.pc", "4. \t" ,
"1. none", "2. resp.log10", "1. none", "2. resp.multneg1",
"3. resp.blineshift.50.spid", "4. \t", "3. \t", "4. \t"), 
         ncol=4, byrow = TRUE)

library(htmlTable)
htmlTable(output,
         
          rnames = FALSE,
          rgroup = c("Scheme 1",
                     "Scheme 2", "Scheme3"),
          n.rgroup = c(2,2),
          cgroup = c("Fold-Change", "\\%Control"),
          n.cgroup = c(2,2), 
          caption="Table 2: Example normalization method assignments.")
          

## ----warning = FALSE, echo = FALSE--------------------------------------------
Level <- c(" Lvl 0", "Lvl 1  ", "Lvl 2  ")
Description <- c("Pre-processing: Vendor/dataset-specific pre-processing to organize heterogeneous raw data to the uniform format for processing by the *tcpl* package&dagger;",
                 "Normalize: Apply assay endpoint-specific normalization listed in the \'sc1_aeid\' table to the raw data to define response",
                 "Activity Call: Collapse replicates by median response, define the response cutoff based on methods in the \'sc2_aeid\' table, and determine activity"
                 )

output <- 
  data.frame(Level, Description)

library(htmlTable)
htmlTable(output,
        
        rnames = FALSE  ,
        css.cell =  ' padding-bottom: 5px;  vertical-align:top; padding-right: 10px;min-width: 5em ',
      
        align = 'l',
        align.header = 'l',
        caption="Table 3: Summary of the tcpl single-concentration pipeline.",
        tfoot="&dagger; Level 0 pre-processing is outside the scope of this package")


## ----eval = FALSE-------------------------------------------------------------
#  tcplLoadAeid(fld = "acid", val = 2)

## ----eval = FALSE-------------------------------------------------------------
#  tcplMthdAssign(lvl = 1,
#                 id = 1:2,
#                 mthd_id = c(1, 11, 13),
#                 ordr = 1:3,
#                 type = "sc")
#  

## ----eval = FALSE-------------------------------------------------------------
#  tcplMthdAssign(lvl = 1,
#                 id = 2,
#                 mthd_id = 16,
#                 ordr = 1,
#                 type = "sc")

## ----eval = FALSE-------------------------------------------------------------
#  ## Do level 1 processing for acid 1
#  sc1_res <- tcplRun(id = 1, slvl = 1, elvl = 1, type = "sc")

## ----eval = FALSE-------------------------------------------------------------
#  ## Assign a cutoff value of log2(1.2)
#  tcplMthdAssign(lvl = 2,
#                 id = 1,
#                 mthd_id = 3,
#                 type = "sc")

## ----eval = FALSE-------------------------------------------------------------
#  ## Do level 2 processing for acid 1
#  sc2_res <- tcplRun(id = 1, slvl = 2, elvl = 2, type = "sc")

## ----warning = FALSE, echo = FALSE--------------------------------------------
Level <- c("Lvl 0 ", "Lvl 1", "Lvl 2", "Lvl 3", "Lvl 4", "Lvl 5", "Lvl 6")
Description <- c("Pre-processing: Vendor/dataset-specific pre-processing to organize heterogeneous raw data to the uniform format for processing by the *tcpl* package&dagger;",
                 "Index: Defne the replicate and concentration indices to facilitate
all subsequent processing",
                 "Transform: Apply assay component-specifc transformations
listed in the \'mc2_acid\' table to the raw data to defne the
corrected data",
"Normalize: Apply assay endpoint-specifc normalization listed in
the \'mc3_aeid\' table to the corrected data to define response",
"Fit: Model the concentration-response data utilizing three
objective functions: (1) constant, (2) hill, and (3) gain-loss",
"Model Selection/Acitivty Call: Select the winning model, define
the response cutoff based on methods in the \'mc5_aeid\' table, and
determine activity",
"Flag: Flag potential false positive and false negative endings based
on methods in the \'mc6_aeid\' table"
                 )

output <- 
  data.frame(Level, Description)

library(htmlTable)
htmlTable(output,
        
        rnames = FALSE  ,
        css.cell =  ' padding-bottom: 5px;  vertical-align:top; padding-right: 10px;min-width: 5em ',
        align = 'l',
        align.header = 'l',
        caption="Table 4: Summary of the tcpl multiple-concentration pipeline.",
        tfoot="&dagger; Level 0 pre-processing is outside the scope of this package")


## ----eval = TRUE, message = FALSE---------------------------------------------
## Do level 1 processing for acid 1
mc1_res <- tcplRun(id = 1, slvl = 1, elvl = 1, type = "mc")

## ----eval = TRUE--------------------------------------------------------------
## Load the level 1 data and look at the cndx and repi values
m1dat <- tcplLoadData(lvl = 1, 
                      fld = "acid", 
                      val = 1, 
                      type = "mc")
m1dat <- tcplPrepOtpt(m1dat)
setkeyv(m1dat, c("repi", "cndx"))
m1dat[chnm == "Bisphenol A", 
      list(chnm, conc, cndx, repi)]

## ----eval = TRUE, warning = FALSE, message = FALSE, fig.width = 30, fig.height= 20----
tcplPlotPlate(dat = m1dat, apid = "4009721")


## ----eval = TRUE, message = FALSE---------------------------------------------
tcplMthdAssign(lvl = 2,
                id =1,
                mthd_id = 1,
                ordr = 1, 
                type = "mc")

## ----eval = TRUE,  warning = FALSE, message = FALSE---------------------------
## Do level 2 processing for acid 1
mc2_res <- tcplRun(id = 1, slvl = 2, elvl = 2, type = "mc")

## ----eval = TRUE--------------------------------------------------------------
## Look at the assay endpoints for acid 1
tcplLoadAeid(fld = "acid", val = 1)

## ----eval = TRUE, message = FALSE---------------------------------------------
tcplMthdAssign(lvl = 3, 
               id = 1:2,
               mthd_id = c(17, 9, 7),
               ordr = 1:3, type = "mc")


## ----eval = TRUE,warning = FALSE, message = FALSE-----------------------------
## Do level 3 processing for acid 1
mc3_res <- tcplRun(id = 1, slvl = 3, elvl = 3, type = "mc")

## ----eval = TRUE, message = FALSE, warning = FALSE----------------------------
## Do level 4 processing for aeids 1&2 and load the data
tcplMthdAssign(lvl = 4, id = 1:2, mthd_id = c(1,2), type = "mc" )
mc4_res <- tcplRun(id = 1:2, slvl = 4, elvl = 4, type = "mc")

## ----eval = TRUE--------------------------------------------------------------
## Load the level 4 data
m4dat <- tcplPrepOtpt(tcplLoadData(lvl = 4, type = "mc"))
## List the first m4ids where the hill model converged
## for AEID 1
m4dat[hill == 1 & aeid == 1, head(m4id)]

## ----eval = TRUE, fig.width = 15, fig.height= 10------------------------------
## Plot a fit for m4id 7
tcplPlotM4ID(m4id = 7, lvl = 4)


## ----eval = TRUE, warning = FALSE---------------------------------------------
## Assign a cutoff value of 6*bmad
tcplMthdAssign(lvl = 5,
               id = 1:2,
               mthd_id = 6,
               ordr = 1,
               type = "mc")

## ----eval = TRUE--------------------------------------------------------------

par(family = "mono", mar = rep(1, 4), pty = "m")
plot.new()
plot.window(xlim = c(0, 30), ylim = c(-30, 100))
axis(side = 2, lwd = 2, col = "gray35")
rect(xleft = par()$usr[1],
     xright = par()$usr[2], 
     ybottom = -15, 
     ytop = 15,
     border = NA, 
     col = "gray45",
     density = 15, 
     angle = 45)
abline(h = 26, lwd = 3, lty = "dashed", col = "gray30")
tmp <- list(modl = "gnls", gnls_ga = 12, gnls_tp = 80, 
            gnls_gw = 0.18, gnls_lw = 0.7, gnls_la = 25)
tcplAddModel(pars = tmp, lwd = 3, col = "dodgerblue2")

abline(v = 8.46, lwd = 3, lty = "solid", col = "firebrick")
text(x = 8.46, y = par()$usr[4]*0.9, 
     font = 2, labels = "ACB", cex = 2, pos = 2, srt = 90)
abline(v = 10.24, lwd = 3, lty = "solid", col = "yellow2")
text(x = 10.24, y = par()$usr[4]*0.9, 
     font = 2, labels = "ACC", cex = 2, pos = 2, srt = 90)
abline(v = 12, lwd = 3, lty = "solid", col = "dodgerblue2")
text(x = 12, y = par()$usr[4]*0.9, 
     font = 2, labels = "AC50", cex = 2, pos = 2, srt = 90)

points(x = c(8.46, 10.24, 12), y = c(15, 26, 40),
       pch = 21, cex = 2, col = "gray30", lwd = 2,
       bg = c("firebrick", "yellow2", "dodgerblue2"))


## ----eval = TRUE--------------------------------------------------------------
mc5_fit_categories <- fread(system.file("/example/mc5_fit_categories.csv",
  package = "tcpl"), 
  sep = ",", 
  header = TRUE)
tcplPlotFitc(mc5_fit_categories)

## ----eval = TRUE, warning = FALSE, message = FALSE----------------------------
## Do level 5 processing for aeids 1&2 and load the data
mc5_res <- tcplRun(id = 1:2, slvl = 5, elvl = 5, type = "mc")

## ----eval = TRUE, fig.width = 15, fig.height= 10------------------------------
tcplPlotM4ID(m4id = 4, lvl = 5)

## ----eval = TRUE--------------------------------------------------------------
m5dat <- tcplLoadData(lvl = 5, type = "mc")
tcplPlotFitc(fitc = m5dat$fitc)

## ----eval = TRUE--------------------------------------------------------------
head(m5dat[fitc == 21, 
           list(m4id, hill_tp, gnls_tp, 
                max_med, coff, hitc)])

## ----eval = TRUE, fig.width = 15, fig.height= 10------------------------------
tcplPlotM4ID(m4id = 3, lvl = 5)

## ----eval = TRUE, warning = FALSE, message = FALSE, error = TRUE--------------
## Clear old methods
tcplMthdClear(lvl = 6, id = 1:2, type = "mc")
tcplMthdAssign(lvl = 6, id = 1:2,
               mthd_id = c(6:8, 10:12, 15:16),
               type = "mc")
tcplMthdLoad(lvl = 6, id = 1, type = "mc")

## ----eval = TRUE, warning = FALSE, error = TRUE-------------------------------

## Do level 6 processing
mc6_res <- tcplRun(id = 1:2, slvl = 5, elvl = 6, type = "mc")

## ----eval = TRUE, warning = FALSE---------------------------------------------
m6dat <- tcplLoadData(lvl = 6, type = "mc")

## ----eval = TRUE--------------------------------------------------------------
m6dat[m4id == 6]

## ----eval = TRUE, fig.width = 15, fig.height= 10------------------------------
tcplPlotM4ID(m4id = 6, lvl = 6)

## ----eval = FALSE, echo = FALSE, message = FALSE------------------------------
#  rm(list=ls())
#  library(htmlTable)
#  library(tcpl)
#  library(data.table)

## ----eval = TRUE, echo = FALSE, message = FALSE-------------------------------


sample <- data.table (spid = c("Tox21_400088", "Tox21_303655", "Tox21_110011", "Tox21_400081",
                               "DMSO", "Tox21_400037"),
                      chid= c("20182", "22364", "23463", "24102", "21735", "20283"),
                      stkc = c("", "", "", "", "", ""),
                      stkc_unit = c("", "", "", "", "", ""),
                      tested_conc_unit= c("", "", "", "", "", ""),
                      spid_legacy = c("", "", "", "", "", ""))
        
assay <- data.table(aid=numeric(),	asid=numeric(),	assay_name= character(),	assay_desc= character(),	timepoint_hr=numeric(),	organism_id=numeric(),	organism= character(),	tissue= character(),	cell_format= character(),	cell_free_component_source= character(),	cell_short_name= character(),	cell_growth_mode= character(),	assay_footprint= character(),	assay_format_type= character(),	assay_format_type_sub= character(),	content_readout_type= character(),	dilution_solvent= character(),	dilution_solvent_percent_max= character()
)

assay_component <- data.table(acid=numeric(),	aid=numeric(),	assay_component_name= character(),	assay_component_desc= character(),	assay_component_target_desc= character(),	parameter_readout_type= character(),	assay_design_type= character(),	assay_design_type_sub	= character(),biological_process_target	= character(),detection_technology_type	= character(),detection_technology_type_sub= character(),	 detection_technology= character(),	signal_direction_type= character(),key_assay_reagent_type= character(),
                      key_assay_reagent	= character(),technological_target_type= character(),	technological_target_type_sub= character() )

assay_component_endpoint <- data.table(aeid=numeric(),	acid=numeric(),	assay_component_endpoint_name= character(),	export_ready=numeric(),	internal_ready= character(),	assay_component_endpoint_desc= character(),	assay_function_type= character(),	normalized_data_type= character(),	analysis_direction= character(),	burst_assay= character(),	key_positive_control= character(),	signal_direction= character(),	intended_target_type= character(),	intended_target_type_sub= character(),	intended_target_family= character(),	intended_target_family_sub= character(),	fit_all=numeric())

assay_component_map <- data.table(acid =numeric(), acsn = character())



assay_source <- data.table(asid=numeric(),	assay_source_name= character(),	assay_source_long_name= character(),	assay_source_desc= character())

chemical <- data.table(chid = c("20182", "22364", "23463" , "24102", "21735","20283"),
                       casn = c("80-05-7", "521-18-6", "150-30-1", "22224-92-6", "67-68-5", "95-83-0"),
                       chnm = c("Bisphenol A", "5alpha-Dihydrotestosterone","Phenylalanine","Fenamiphos",
"Dimethyl sulfoxide","4-Chloro-1,2-diaminobenzene"),
                      dsstox_substance_id = c("DTXSID7020182", "DTXSID9022364",         "DTXSID9023463", "DTXSID3024102", "DTXSID2021735", "DTXSID5020283"),
                    code = c("C80057", "C521186","C150301","C22224926", "C67685","C95830"))




mc0 <- data.table(m0id =numeric(), acid =numeric(), spid = character(), apid = character(), rowi =numeric(),
                  coli =numeric(), wllt = character(), wllq =numeric(), conc = character(), rval = character(),
                  srcf =character(), created_date = character(), modified_date = character(), 
                  modified_by = character())
mc1 <- data.table(m1id = character(),m0id =numeric(), acid = character(), cndx = character(), repi = character(), created_date = character(),
                  modified_date = character(), modified_by = character())
mc2 <- data.table(m2id = character(),m0id =numeric(), acid = character(),m1id =numeric(), cval = character(), created_date = character(),
                  modified_date = character(), modified_by = character())
mc3 <- data.table(m3id =numeric(), aeid =numeric(),m0id =numeric(), acid = character(),m1id =numeric(),m2id =numeric(), bval = character(), pval = character(),
                  logc = character(), resp = character(), created_date = character(), modified_date = character(), modified_by = character())

mc4 <- data.table(m4id =numeric(), aeid =numeric(),	spid = character(),	bmad = character(),	resp_max = character(),	resp_min = character(),	max_mean = character(),	max_mean_conc = character(),	max_med = character(),	max_med_conc = character(),	logc_max = character(),	logc_min = character(),	cnst = character(),	hill = character(),	hcov = character(),	gnls = character(),	gcov = character(),	cnst_er = character(),	cnst_aic = character(),	cnst_rmse = character(),	cnst_prob = character(),	hill_tp = character(),	hill_tp_sd = character(),	hill_ga = character(),	hill_ga_sd = character(),	hill_gw = character(),	hill_gw_sd = character(),	hill_er = character(),	hill_er_sd = character(),	hill_aic = character(),	hill_rmse = character(),	hill_prob = character(),	gnls_tp = character(),	gnls_tp_sd = character(),	gnls_ga = character(),	gnls_ga_sd = character(),	gnls_gw = character(),	gnls_gw_sd = character(),	gnls_la = character(),	gnls_la_sd = character(),	gnls_lw = character(),	gnls_lw_sd = character(),	gnls_er = character(),	gnls_er_sd = character(),	gnls_aic = character(),	gnls_rmse = character(),	gnls_prob = character(),	nconc = character(),	npts = character(),	nrep = character(),	nmed_gtbl = character(),	tmpi = character(),	created_date = character(),	modified_date = character(),	modified_by = character())

mc5 <- data.table(m5id =numeric(),	m4id =numeric(),	aeid =numeric(),	modl = character(),	hitc = character(),	fitc = character(),	coff = character(),	actp = character(),	modl_er = character(),	modl_tp = character(),	modl_ga = character(),	modl_gw = character(),	modl_la = character(),	modl_lw = character(),	modl_prob = character(),	modl_rmse = character(),	modl_acc = character(),	modl_acb = character(),	modl_ac10 = character(),	created_date = character(),	modified_date = character(),	modified_by = character())

mc6 <- data.table (m6id =numeric(),	m5id =numeric(),	m4id =numeric(),	aeid =numeric(),	mc6_mthd_id = character(),	flag = character(),	fval = character(),	fval_unit = character(),	created_date = character(),	modified_date = character(),	modified_by = character())



dir <- dbfile_temp
write.csv(mc0, file = file.path(dir,"mc0.csv"), row.names = F)
write.csv(mc1, file = file.path(dir,"mc1.csv"), row.names = F)
write.csv(mc2, file = file.path(dir,"mc2.csv"), row.names = F)
write.csv(mc3, file = file.path(dir,"mc3.csv"), row.names = F)
write.csv(mc4, file = file.path(dir,"mc4.csv"), row.names = F)
write.csv(mc5, file = file.path(dir,"mc5.csv"), row.names = F)
write.csv(mc6, file = file.path(dir,"mc6.csv"), row.names = F)
write.csv(sample, file = file.path(dir,"sample.csv"), row.names = F)
write.csv(assay, file = file.path(dir,"assay.csv"), row.names = F)
write.csv(assay_component, file = file.path(dir,"assay_component.csv"), row.names = F)
write.csv(assay_component_map, file = file.path(dir,"assay_component_map.csv"), row.names = F)
write.csv(assay_component_endpoint, file = file.path(dir,"assay_component_endpoint.csv"), row.names = F)
write.csv(assay_source, file = file.path(dir,"assay_source.csv"), row.names = F)
write.csv(chemical, file = file.path(dir,"chemical.csv"), row.names = F)

