R Packages

# Primary Packages #
library(tcpl)
library(tcplfit2)
# Data Formatting Packages #
library(dplyr)
library(magrittr)
# Plotting Packages #
library(ggplot2)
library(RColorBrewer)
library(colorspace)
library(viridis)
# Table Packages #
library(htmlTable)
library(kableExtra)

Introduction

This vignette describes how the user can retrieve data from the ToxCast database, known as invitrodb, using tcpl. The MySQL version of the ToxCast database containing all the publicly available ToxCast data is available for download at: https://www.epa.gov/chemical-research/exploring-toxcast-data-downloadable-data.

NOTE:

Users must be connected to the ToxCast database (invitrodb), or a replicate of the database, to utilize many of these functions and execute the examples in this vignette. Please see the introductory vignette in this package for more details.

Overview of Key Functions

To support different data retrieval needs within tcpl, there are a number of functions which query the database and return information to the local R session.

Overview of Data Nomenclature

Throughout this vignette we will use abbreviated designations for data retrieved from the database or to refer to processing steps within tcpl. For data from single concentration assays we use ‘SC.’ ‘MC’ is used for assay data with multiple concentrations. A particular data or processing level is indicated by appending the level id/number to the end of the ‘SC’ or ‘MC’ designation. For example, if we are discussing single concentration data from level 2 processing, then we will use the abbreviation ‘SC2.’

Assay Elements

The tcplLoadAsid, tcplLoadAid, tcplLoadAcid, and tcplLoadAeid functions load relevant assay ids and names for the respective assay elements based on the user specified parameters.

# List all assay source IDs
tcplLoadAsid() 
# Create table of all assay endpoint ids (aeids) per assay source
aeids <- tcplLoadAeid(fld="asid", # field to query on
                      val=14, # value for each field
                              # values should match their corresponding 'fld'
                      add.fld = c("aid", "anm", "acid", "acnm")) # additional fields to return

Data

The tcplQuery function allows a user to provide an SQL query to load data from the MySQL database into the R session. In the following chunk we provide an example, but any valid SQL query can replace the one provided in our example.

# Load sample table using a MySQL query.
samples <- tcplQuery("SELECT * FROM sample;")

The tcplLoadData function can be used to load the data from the MySQL database into the R session. Further, the tcplPrepOtpt function can be used in combination with tcplLoadData to add useful chemical and assay annotation information, mapped to the retrieved data.

# Load single concentration data from level 1,
# and map only the chemical annotation information.
mc1_fmtd <- tcplPrepOtpt(
  tcplLoadData(
    lvl = 2, # data level
    fld = 'acid', # field to query on
    val = 49, # value for each field
             # values should match their corresponding 'fld'
    type = 'mc' # data type
  ),
  ids = 'spid' # additional annotation fields to add - just chemical info
               # - (Default): map assay and chemical annotation
               # - 'acid' OR 'aeid': map only assay annotation
               # - 'spid': map only chemical annotation
)
# Print the first 6 rows of 'sc1_fmtd'
head(mc1_fmtd)

When loading data, the user must indicate the applicable fields and ids for the corresponding data level of interest. Loading level 0 (SC0 and MC0), MC1, and MC2 data the assay component id (\(\mathit{acid}\)) will always be used. As described in Table 1 of the tcpl Data Processing vignette, SC1 and MC3 processing levels perform data normalization where assay component ids (\(\mathit{acid}\)) are converted to assay endpoint ids (\(\mathit{aeid}\)). Thus, the SC1 and MC3 data tables contain both \(\mathit{acid}\) and (\(\mathit{aeid}\)) ID’s. Data can be loaded using either id as long as it is properly specified. Loading SC2, MC4, and MC5 one should always use the assay endpoint id (\(\mathit{aeid}\)). Selected id(s) are based on the primary key within each table containing data. Examples of loading data are detailed in later sections.

Chemical Information

The tcplLoadChem function returns chemical information for user specified parameters, e.g. the chemical name (chnm) and chemical id (chid). The tcplLoadClib function provides more information about the ToxCast chemical library used for sample generation.

Methods

The tcplMthdList function returns methods available for processing at a specified level (i.e. step in the tcpl pipeline). The user defined function in the following code chunk utilizes the tcplMthdList function to retrieve and output all available methods for both the SC and MC data levels.

# Create a function to list all available methods function (SC & MC).
method_list <- function() {
  # Single Concentration
  ## Level 1
  sc1 <- tcplMthdList(1, 'sc')
  sc1[, lvl := "sc1"]
  setnames(sc1, c("sc1_mthd", "sc1_mthd_id"), c("mthd", "mthd_id"))
  ## Level 2
  sc2 <- tcplMthdList(2, 'sc')
  sc2[, lvl := "sc2"]
  setnames(sc2, c("sc2_mthd", "sc2_mthd_id"), c("mthd", "mthd_id"))
  
  # Multiple Concentration
  ## Level 2
  mc2 <- tcplMthdList(2, 'mc')
  mc2[, lvl := "mc2"]
  setnames(mc2, c("mc2_mthd", "mc2_mthd_id"), c("mthd", "mthd_id"))
  ## Level 3
  mc3 <- tcplMthdList(3, 'mc')
  mc3[, lvl := "mc3"]
  setnames(mc3, c("mc3_mthd", "mc3_mthd_id"), c("mthd", "mthd_id"))
  ## Level 4
  mc4 <- tcplMthdList(4, 'mc')
  mc4[, lvl := "mc4"]
  setnames(mc4, c("mc4_mthd", "mc4_mthd_id"), c("mthd", "mthd_id"))
  ## Level 5
  mc5 <- tcplMthdList(5, 'mc')
  mc5[, lvl := "mc5"]
  setnames(mc5, c("mc5_mthd", "mc5_mthd_id"), c("mthd", "mthd_id"))
  # Compile the Output
  mthd.list <- rbind(sc1, sc2, mc2, mc3, mc4, mc5)
  mthd.list <- mthd.list[, c("lvl", "mthd_id", "mthd", "desc")]
  # Return the Results
  return(mthd.list)
}

# Run the 'method_list' functions and store output.
amthds <- method_list()
# Print the available methods list.
amthds

The tcplMthdLoad function returns the method assignments for specified id(s). Later sections provide more detailed examples for utilizing the tcplMthdLoad function for individuals ids.

Retrieving Level 0 Data

Prior to the pipeline processing provided in this package, all the data must go through pre-processing, i.e. raw data to database level 0 data. Pre-processing the data should transform data from heterogeneous assays into a uniform format. This is executed using dataset specific R scripts. After pre-processing is complete and the formatted data matches the level 0 format, it can be loaded into the database using tcplWriteLvl0, as described in the tcpl Data Processing vignette. The standard level 0 format is identical for both testing paradigms, SC or MC. Users can inspect the level 0 data and calculate assay quality metrics prior to running the processing pipeline.

Load SC0 Data

# Load Level 0 single concentration data for a single acid to R.
sc0 <- tcplLoadData(lvl=0, # data level
                    fld="acid", # field to query on
                    val=1, # value for each field
                           # values should match their corresponding 'fld'
                    type = "sc") # data type - single concentration

# Alternatively, load data in and format with tcplPrepOtpt.
sc0 <- tcplPrepOtpt(tcplLoadData(lvl=0, fld="acid", val=1, type = "sc"))

Since we are not able to connect to the database directly in this vignette, we have provided a sample dataset in the package to illustrate what the results should look like.

# Load the example data from the package.
data(sc_vignette,package = 'tcpl')
# Save the single concentration level 0 data in the 'sc0' object.
sc0 <- sc_vignette[["sc0"]]
# Print the first 6 rows of the data.
head(sc0) %>%
  # format output into a table
  kbl() %>%
  # format the output rendering to allow horizontal scrolling
  scroll_box(width = "100%") %>% 
  # reduce the size of the table text to improve readability
  kable_styling(font_size = 10)

spid	chid	casn	chnm	dsstox_substance_id	code	acid	acnm	s0id	apid	rowi	coli	wllt	wllq	conc	rval	srcf	conc_unit
TP0000073D03	34212	118134-30-8	Spiroxamine	DTXSID1034212	C118134308	111	ATG_RXRb_TRANS	9940119	NA	NA	NA	t	1	45.8	1.0599632	CLIN11 PlateTP0000049.xls	uM
TP0000073G09	20122	86-50-0	Azinphos-methyl	DTXSID3020122	C86500	111	ATG_RXRb_TRANS	9970455	NA	NA	NA	t	1	50.0	0.9581900	CLIN11 PlateTP0000049.xls	uM
TP0000075H04	21166	51-03-6	Piperonyl butoxide	DTXSID1021166	C51036	111	ATG_RXRb_TRANS	10045157	NA	NA	NA	t	1	50.0	0.9531718	CLIN11 PlateTP0000050.xls	uM
TP0000077B04	24102	22224-92-6	Fenamiphos	DTXSID3024102	C22224926	111	ATG_RXRb_TRANS	10062416	NA	NA	NA	t	1	50.0	1.1310499	CLIN11 PlateTP0000051.xls	uM
TP0000077B09	24195	94-74-6	2-(4-Chloro-2-methylphenoxy)acetic acid	DTXSID4024195	C94746	111	ATG_RXRb_TRANS	10066027	NA	NA	NA	t	1	50.0	0.8759538	CLIN11 PlateTP0000051.xls	uM
TP0000077B10	32398	131341-86-1	Fludioxonil	DTXSID2032398	C131341861	111	ATG_RXRb_TRANS	10066756	NA	NA	NA	t	1	19.3	12.7624120	CLIN11 PlateTP0000051.xls	uM

Load MC0 Data

# Load Level 0 multiple concentration data.
mc0 <- tcplPrepOtpt(
  tcplLoadData(lvl=0, # data level
               fld="acid", # field to query on
               val=1, # value for each field
                      # values should match their corresponding 'fld'
               type = "mc") # data type - multiple concentrations
)

We again can use one of the provided datasets in this package to demonstrate what the above results should look like.

# Load the example data from the package.
data(mc_vignette,package = 'tcpl')
# Save the multiple concentration level 0 data in the 'mc0' object.
mc0 <- mc_vignette[["mc0"]]
# Print the first 6 rows of the data.
head(mc0) %>%
  # format output into a table
  kbl() %>%
  # format the output rendering to allow horizontal scrolling
  scroll_box(width = "100%") %>% 
  # reduce the size of the table text to improve readability
  kable_styling(font_size = 10)

spid	chid	casn	chnm	dsstox_substance_id	code	acid	acnm	m0id	apid	rowi	coli	wllt	wllq	conc	rval	srcf	conc_unit
01504209	379721	2264-01-9	1H,1H,6H,6H-Perfluorohexane-1,6-diol diacrylate	DTXSID80379721	C2264019	49	ATG_GLI_CIS	626391856	TO-17-1CD	NA	NA	t	1	0.412	1.0183150	EPA-TO17-Part2-ATTAGENE-CIS-FACTORIAL-DATA-May-10-2019.xlsx	uM
01504209	379721	2264-01-9	1H,1H,6H,6H-Perfluorohexane-1,6-diol diacrylate	DTXSID80379721	C2264019	49	ATG_GLI_CIS	626391857	TO-17-1CD	NA	NA	t	1	11.100	0.9848485	EPA-TO17-Part2-ATTAGENE-CIS-FACTORIAL-DATA-May-10-2019.xlsx	uM
01504209	379721	2264-01-9	1H,1H,6H,6H-Perfluorohexane-1,6-diol diacrylate	DTXSID80379721	C2264019	49	ATG_GLI_CIS	626391850	TO-17-1CD	NA	NA	t	1	33.300	1.0134680	EPA-TO17-Part2-ATTAGENE-CIS-FACTORIAL-DATA-May-10-2019.xlsx	uM
01504209	379721	2264-01-9	1H,1H,6H,6H-Perfluorohexane-1,6-diol diacrylate	DTXSID80379721	C2264019	49	ATG_GLI_CIS	626391858	TO-17-1CD	NA	NA	t	1	1.230	0.9882155	EPA-TO17-Part2-ATTAGENE-CIS-FACTORIAL-DATA-May-10-2019.xlsx	uM
01504209	379721	2264-01-9	1H,1H,6H,6H-Perfluorohexane-1,6-diol diacrylate	DTXSID80379721	C2264019	49	ATG_GLI_CIS	626391851	TO-17-1CD	NA	NA	t	1	0.412	1.0860806	EPA-TO17-Part2-ATTAGENE-CIS-FACTORIAL-DATA-May-10-2019.xlsx	uM
01504209	379721	2264-01-9	1H,1H,6H,6H-Perfluorohexane-1,6-diol diacrylate	DTXSID80379721	C2264019	49	ATG_GLI_CIS	626391859	TO-17-1CD	NA	NA	t	1	11.100	1.0858586	EPA-TO17-Part2-ATTAGENE-CIS-FACTORIAL-DATA-May-10-2019.xlsx	uM

Review MC assay quality

# Create a function to review assay quality metrics using indexed Level 0 data.
aq <- function(ac){
  # obtain level 1 multiple concentration data for specified acids
  dat <- tcplPrepOtpt(tcplLoadData(1L, "acid", aeids$acid, type="mc"))
  
  # keep only observations with good well quality (wllq = 1)
  dat <- dat[wllq==1]
  
  # obtain summary values for data and remove missing data (i.e. NA's)
  agg <- dat[ ,
              list(
                # median response values (rval) of neutral wells (wllt = n)
                nmed = median(rval[wllt=="n"], na.rm=TRUE), 
                # median absolute deviation (mad) of neutral wells (wllt = n)
                nmad = mad(rval[wllt=="n"], na.rm=TRUE), 
                # median response values of positive control wells (wllt = p)
                pmed = median(rval[wllt=="p"], na.rm=TRUE),
                # median absolute deviation of positive control wells (wllt = p)
                pmad = mad(rval[wllt=="p"], na.rm=TRUE),
                # median response values of negative control wells (wllt = m)
                mmed = median(rval[wllt=="m"], na.rm=TRUE),
                # median absolute deviation of negative control wells (wllt = m)
                mmad = mad(rval[wllt=="m"], na.rm=TRUE)
                ),
              # aggregate on assay component id, assay component name,
              # and assay plate id
              by = list(acid, acnm, apid)]
  
  # Z prime factor: separation between positive and negative controls,
  # indicative of likelihood of false positives or negatives. 
  # - Between 0.5 - 1 are excellent,
  # - Between 0 and 0.5 may be acceptable,
  # - Less than 0 not good
  # obtain the z-prime factor for positive controls and neutral
  agg[ , zprm.p := 1 - ((3 * (pmad + nmad)) / abs(pmed - nmed))]  
  # obtain the z-prime factor for negative controls and neutral
  agg[ , zprm.m := 1 - ((3 * (mmad + nmad)) / abs(mmed - nmed))]
  
  agg[ , ssmd.p := (pmed - nmed) / sqrt(pmad^2 + nmad^2 )]
  agg[ , ssmd.m := (mmed - nmed) / sqrt(mmad^2 + nmad^2 )]
  
  # Coefficient of Variation (cv) of neutral control
  # - Ideally should be under 25%
  agg[ , cv     := nmad / nmed] 
  
  agg[ , sn.p :=  (pmed - nmed) / nmad]
  agg[ , sn.m :=  (mmed - nmed) / nmad]
  agg[ , sb.p :=  pmed / nmed]
  agg[ , sb.m :=  mmed / nmed]
  
  agg[zprm.p<0, zprm.p := 0]
  agg[zprm.m<0, zprm.m := 0]
  
  acqu <- agg[ , list( nmed   = signif(median(nmed, na.rm = TRUE)),
                       nmad   = signif(median(nmad, na.rm = TRUE)),
                       pmed   = signif(median(pmed, na.rm = TRUE)),
                       pmad   = signif(median(pmad, na.rm = TRUE)),
                       mmed   = signif(median(mmed, na.rm = TRUE)),
                       mmad   = signif(median(mmad, na.rm = TRUE)),
                       zprm.p = round(median(zprm.p, na.rm=TRUE),2),
                       zprm.m = round(median(zprm.m, na.rm=TRUE),2),
                       ssmd.p = round(median(ssmd.p, na.rm=TRUE),0),
                       ssmd.m = round(median(ssmd.m, na.rm=TRUE),0),
                       cv = round(median(cv, na.rm=TRUE),2),
                       sn.p = round(median(sn.p, na.rm=TRUE),2),
                       sn.m = round(median(sn.m, na.rm=TRUE),2),
                       sb.p = round(median(sb.p, na.rm=TRUE),2),
                       sb.m = round(median(sb.m, na.rm=TRUE),2)
  ), by = list(acid, acnm)]
  # Return the Results.
  return(acqu)
} #per acid 

# Run the 'aq' function & store the output. 
assayq <- aq(ac)
# Print the first 6 rows of the assay quality results.
head(assayq)

Retrieving Processed Single-Concentration (SC) Data and Methods

The goal of SC processing is to identify potentially active compounds from a large screen at a single concentration. After processing, users can inspect SC activity hit calls and the applied methods.

Load SC2 Data

# Load Level 2 single concentration data for a single aeid.
sc2 <- tcplPrepOtpt(
  tcplLoadData(lvl=2, # data level
               fld="aeid", # id field to query on
               val=3, # value for the id field
               type = "sc") # data type - single concentration
)
# Alternatively, data for a set of aeids can be loaded with a vector of ids.
sc2 <- tcplPrepOtpt(
  tcplLoadData(lvl=2, fld="aeid", val=aeids$aeid, type = "sc")
)

Load SC Methods

# Create a function to load methods for single concentration data processing
# steps for given aeids.
sc_methods <- function(aeids) {
  # load the level 1 methods assigned for the single concentration aeid's
  sc1_mthds <- tcplMthdLoad(lvl=1, type ="sc", id=aeids$aeid)
  # aggregate the method id's by aeid
  sc1_mthds<- aggregate(mthd_id ~ aeid, sc1_mthds, toString)
  # reset the names of the sc1_mthds object
  setnames(sc1_mthds, "mthd_id", "sc1_mthd_id")
  
  # load the level 2 methods assigned for the single concentration aeid's
  sc2_mthds <- tcplMthdLoad(lvl=2, type ="sc", id=aeids$aeid)
  # aggregate the method id's by aeid
  sc2_mthds<- aggregate(mthd_id ~ aeid, sc2_mthds, toString)
  # reset the names of the sc2_mthds object
  setnames(sc2_mthds, "mthd_id", "sc2_mthd_id")
  
  # Compile the Output 
  methods <- merge( merge(aeids, sc1_mthds,  by = "aeid", all = TRUE), 
                  sc2_mthds, by = "aeid", all = TRUE )
  # Return the Results
  return(methods)
}

# Run the 'sc_methods' function and store the output.
smthds <- sc_methods(aeids)

# Print the assigned sc methods.
smthds

Retrieving Processed Multi-Concentration (MC) Data, Methods, and Plots

The goal of MC processing is to estimate the hitcall, potency, efficacy, and other curve-fitting parameters for sample-assay endpoint pairs. After processing, users can inspect the activity hitcalls, model parameters, concentration-response plots, and the applied methods for the multiple concentration data.

Load MC5 Data

# Load Level 5 MC data summary values for a set of aeids.
# (NOTE: As before, the user can obtain data for individual aeids.)
mc5 <- tcplPrepOtpt(
  tcplLoadData(lvl=5, # data level
               fld="aeid", # fields to query on
               val=aeids$aeid, # value for each field
                               # values should match their corresponding 'fld'
               type = "mc") # data type - MC
)

# For tcpl v3.0.0 and future releases, to output mc5_param information with
# the default mc5 results then 'add.fld' must be set to TRUE.
# (NOTE: Default for add.fld is FALSE, unless otherwise specified.)
mc5 <- tcplPrepOtpt(
  tcplLoadData(lvl=5, # data level
               fld="aeid", # fields to query on
               val=aeids$aeid, # value for each field
                               # values should match their corresponding 'fld'
               type = "mc", # data type - multiple concentration
               add.fld=TRUE) # return additional parameters from mc5_param 
  )

Load MC Methods

# Create a function to load methods for MC data processing
# for select aeids.
mc_methods <- function(aeids) {
  # acid
  ## load the methods assigned to level 2 for given acids
  mc2_mthds <- tcplMthdLoad(2,aeids$acid)
  ## aggregate the assigned methods by acid
  mc2_mthds<- aggregate(mthd_id ~ acid, mc2_mthds, toString)
  ## rename the columns for the 'mc2_mthds' object
  setnames(mc2_mthds, "mthd_id", "mc2_mthd_id")
  
  # aeid
  ## load the methods assigned to level 3 for given aeids
  mc3_mthds <- tcplMthdLoad(3,aeids$aeid)
  ## aggregate the assigned methods by aeid
  mc3_mthds<- aggregate(mthd_id ~ aeid, mc3_mthds, toString)
  ## rename the columns for the 'mc3_mthds' object
  setnames(mc3_mthds, "mthd_id", "mc3_mthd_id")
  ## load the methods assigned to level 4 for given aeids
  mc4_mthds <- tcplMthdLoad(4,aeids$aeid)
  ## aggregate the assigned methods by aeid
  mc4_mthds<- aggregate(mthd_id ~ aeid, mc4_mthds, toString) 
  ## rename the columns for 'mc4_mthds' object
  setnames(mc4_mthds, "mthd_id", "mc4_mthd_id")
  ## load the methods assigned to level 5 for given aeids
  mc5_mthds <- tcplMthdLoad(5,aeids$aeid)
  ## aggregate the assigned methods by aeid
  mc5_mthds<- aggregate(mthd_id ~ aeid, mc5_mthds, toString)
  ## rename the columns for 'mc5_mthds' object
  setnames(mc5_mthds, "mthd_id", "mc5_mthd_id")

  # Compile the Results.
  ## merge the aeid information with the level 2 methods by acid
  acid.methods <- merge(aeids, mc2_mthds,by.x = "acid", by.y = "acid")
  ## merge the level 3, 4, and 5 methods by aeid
  mthd35 <- merge(
    merge(mc3_mthds, mc4_mthds, by = "aeid", all = TRUE),
    mc5_mthds, by = "aeid", all = TRUE
    )
  ## merge all methods information by aeid
  methods <- merge(acid.methods, mthd35,by.x = "aeid", by.y = "aeid")
  # Print the Results.
  print(methods)
  # Return the Results.
  return(methods)
}

# Run the 'methods' function and store the output.
mmthds <- mc_methods(aeids)

# Print the assigned mc methods.
mmthds

Plot MC Data

The tcplPlot function is the generic plotting function that returns a plot for the given sample-assay endpoint combination(s), level, and parameters.

# Create Level 4 plot for a single m4id.
tcplPlot(lvl = 4,  # data level
         fld = "m4id", # field to query on 
         val = c(18609966), # value for each field
                            # values should match their corresponding 'fld'
         multi = FALSE, # single plot per page - output 6 per page if TRUE
         verbose = FALSE, # output all details if TRUE
         output = "console") # output in R console

# Plot Level 5 MC data for select aeids.
tcplPlot(lvl = 5, # data level
         fld = "aeid", # field to query on
         val = 3157:3159, # value for each field
                          # values should match their corresponding 'fld'
         by = "aeid", # parameter to divide files
         multi = TRUE, # multiple plots per page - output 6 per page if TRUE
         verbose = TRUE, # output all details if TRUE
         output = "pdf", # output as pdf
         fileprefix = "output/") # prefix of the filename

Additional Examples

Below are a few case examples for retrieving various bits of information from the database.

Load Data for a Specific Chemical

In this example, we illustrate the necessary steps for extracting information about the compound Bisphenol A found within the database. The user will define the chemical of interest, isolate all associated sample ids (\(\mathit{spids}\)), and then load all data for the given chemical.

# Provide the chemical name and assign to 'chnm'.
chnm <- 'Bisphenol A'
# Load the chemical data from the database.
chem <- tcplLoadChem(field = 'chnm',val = chnm)
# Load mc5 data from the database for the specified chemical.
BPA.mc5 <- tcplLoadData(lvl = 5, # data level 
                        fld = 'spid', # field to query on
                        val = chem[,spid], # value for each field (fld)
                        type = 'mc') # data type - MC

Plot Sample Subset

In this example, we illustrate how to plot by endpoint for a sample subset, as opposed to plotting all samples tested within an endpoint. The user will load data for the select endpoints, isolate the samples of interest, and then plot by endpoint for the sample subset.

# Load Level 5 multiple concentration data summary values for select aeids.
mc5 <- tcplPrepOtpt(
  tcplLoadData(lvl=5, # data level
               fld='aeid', # id field to query on
               val=tcplLoadAeid(fld="asid",val = 25)$aeid, # value for each field
               type='mc', # data type - MC
               add.fld=TRUE) # return additional parameters from mc5_param
  )

# Identify sample subset.
spid.mc5 <- mc5[spid %in% c("EPAPLT0018N08", "EPAPLT0023A16", "EPAPLT0020C11",  
                            "EPAPLT0018B13","EPAPLT0018B14","EPAPLT0018B15"),]

# Plot by endpoint for sample subset.
tcplPlot(lvl = 5, # data level
         fld = c("spid","aeid"), # fields to query on
         val = list( # value for each field, must be same order as 'fld'
           spid.mc5$spid, # sample id's
           spid.mc5$aeid  # assay endpoint id's
           ),
         by = "aeid", # parameter to divide files
         multi = TRUE, # multiple plots per page - output 6 per page if TRUE
         verbose = TRUE, # output all details if TRUE
         output = "pdf", # output as pdf
         fileprefix = "output/upitt") # prefix of the filename

tcpl v3.0
Data Retrieval

Center for Computational Toxicology and Exposure, US EPA