Package 'tcpl'

Description

.buildAssayQ generates a query string to load assay information

Usage

.buildAssayQ(out, tblo, fld = NULL, val = NULL, add.fld = NULL)

Arguments

Value

A character containing the query to send to tcplQuery

Description

.convertNames converts the assay names as they appear in the tcpl database to their respective abbreviations

Usage

.convertNames(names)

Arguments

Value

The same character vector given with any name strings converted to the abbreviated version

Description

.load6DR loads dose-response data for tcpl6.

Usage

.load6DR(ae)

Arguments

Description

Plot plate heatmap, to be used with tcplPlotPlate

Usage

.plateHeat(vals, rowi, coli, wllt, wllq, rown, coln, main, arng)

Arguments

Note

Optimized for an output with height = 20/3, width = 10, and pointsize = 10

Description

Paste appropriate table name to field name

Usage

.prepField(fld, tbl, db)

Arguments

Details

The function loops through the given tables, and for each field i it assigns the last table containing i to i. ORDER OF FLD MATTERS!!

Description

blineShift Takes in dose-response data and shifts the baseline to 0 based on the window.

Usage

blineShift(resp, conc, wndw)

Arguments

Value

A numeric vector containing the shifted response values

Note

This function is not exported and is not intended to be used by the user.

See Also

mc3_mthds, mc3

Description

Function that checks if the most recent v3 table schema is used in the database schema

Usage

check_tcpl_db_schema()

Value

boolean TRUE if param tables are listed in schema FALSE otherwise

Examples

## Not run: 
#connect to database first with tcplConf
tcplConf(user=user,
 pass= pass,
 db=dbname,
 drvr='MySQL',
 host=hostname)
 
#check if it is part of the new schema
new_schema <- check_tcpl_db_schema()

## End(Not run)

Description

These functions are used to configure the tcpl settings.

Usage

tcplConf(drvr = NULL, user = NULL, pass = NULL, host = NULL, db = NULL, ...)

tcplConfDefault()

tcplConfList(show.pass = FALSE)

tcplConfLoad(list.new = TRUE)

tcplConfReset()

tcplConfSave()

Arguments

Details

Currently, the tcpl package supports the "MySQL", "example", and "API" database drivers.

The settings can be stored in a configuration file to make the using the package more user-friendly. To create the configuration file, the user must first create a system environment variable ('TCPL_CONF') that points to to the file. There is more information about system environment variables in Startup and Sys.getenv. Briefly, the user needs to modify the '.Renviron' file in their home directory. If the file does not exist, create it, and add the following line:

TCPL_CONF=path/to/confFile.conf

Here 'path/to/confFile.conf' can be any path to a file. One suggestion would be to include .tcplConf in the home directory, e.g. TCPL_CONF=~/.tcplConf. Note, '~' may not indicate the home directory on every operating system. Once the environment variable is added, the user can change the settings using tcplConf, then save the settings to the file given by the TCPL_CONF environment variable running tcplConfSave().

tcplConf changes options to set the tcpl-specific options, most importantly to configure the connection to the tcpl databases. tcplConf will only change non-null values, and can be used to change a single value if needed.

tcplConfSave modifies the configuration file to reflect the current tcpl settings.

tcplConfList lists the values assigned to the tcpl global options.

tcplConfLoad updates the tcpl settings to reflect the current configuration file.

tcplConfDefault changes the options to reflect the default settings for the API connection, but does not alter the configuration file.

tcplConfReset is used to generate the initial configuration script, and can be used to reset or regenerate the configuration script by the user.

Description

flareFunc calculates the weighted mean of square regions to detect plate flares.

Usage

flareFunc(val, coli, rowi, apid, r)

Arguments

See Also

MC6_Methods, Method functions, mc6

Description

These functions solve for Hill model parameters.

Usage

tcplHillACXX(XX, tp, ga, gw, bt = 0)

tcplHillConc(val, tp, ga, gw, bt = 0)

tcplHillVal(logc, tp, ga, gw, bt = 0)

Arguments

Details

tcplHillVal computes the value of the Hill model for a given log concentration.

tcplHillACXX computes the activity concentration for a Hill model for a given activity level.

tcplHillConc computes the Hill model concentration for a given value.

Examples

## The following code gives examples for a Hill model with a top of 50, 
## bottom of 0, AC50 of 1 and Hill coefficient of 1.
## tcplHillVal calculates activity value given a concentration. tcplHillVal
## will return the tp/2 when logc equals ga:
tcplHillVal(logc = 1, tp = 50, ga = 1, gw = 1, bt = 0)

## Here, tcplHillConc returns the concentration where the value equals 20
tcplHillConc(val = 20, tp = 50, ga = 1, gw = 1, bt = 0)

## Note how this differs from tcplHillACXX:
tcplHillACXX(XX = 20, tp = 50, ga = 1, gw = 1, bt = 0)

## tcplHillACXX is based on the top value and allows the user to calculate 
## specifc activity concentrations based on a percentage of the top value

## For example, we can calculate the value for the concentration 0.25, then
## use that value to check the other two functions.

value <- tcplHillVal(logc = 0.25, tp = 50, ga = 1, gw = 1, bt = 0)
c1 <- tcplHillConc(val = value, tp = 50, ga = 1, gw = 1, bt = 0)
c2 <- tcplHillACXX(XX = value/50*100, tp = 50, ga = 1, gw = 1, bt = 0)
all.equal(0.25, c1, c2)

## Notice, the value had to be transformed to a percentage of the top value
## when using tcplHillACXX

Description

interlaceFunc calculates the distance weighted mean of square regions from a 384-well plate that is interlaced onto a 1536 well plate to detect non-random signals coming from the source plate

Usage

interlaceFunc(val, intq, coli, rowi, apid, r)

Arguments

See Also

MC6_Methods, Method functions, mc6

Description

Short descriptions of fields for different tables are stored in a data dictionary.

Usage

invitrodb_dd

Format

A data frame with 44 rows and 3 variables:

invitrodb_table

Table of the data dictionary

invitrodb_field

Field of the data dictionary

description

Description

Source

ToxCast database

Description

is.odd takes an integer vector, x, and returns TRUE for odd integers.

Usage

is.odd(x)

Arguments

Value

TRUE for odd integers and FALSE for even integers.

See Also

Other tcpl abbreviations: lu(), lw(), sink.reset()

Description

These functions query the tcpl databases and returns a data.table with assay ID and name information. More information about the assay hierarchy is available in the overview vignette.

Usage

tcplLoadAcid(fld = NULL, val = NULL, add.fld = NULL)

tcplLoadAeid(fld = NULL, val = NULL, add.fld = NULL)

tcplLoadAid(fld = NULL, val = NULL, add.fld = NULL)

tcplLoadAsid(fld = NULL, val = NULL, add.fld = NULL)

Arguments

Details

Each element in the assay hierarchy has its own function, loading the ID and name for the given assay element. For example, tcplLoadAsid will return the assay source ID (asid) and assay source name (asnm).

Value

A data.table containing the ID, name, and any additional fields.

Examples

## Not run: 
## The load assay functions can be used without any parameters to list the 
## full list of registered assay elements:
tcplLoadAsid()
tcplLoadAeid()

## Similarly, the user can add fields without doing any element selection:
tcplLoadAeid(add.fld = c("asid", "aid", "acid"))

## Or, the user can look only at a subset:
tcplLoadAeid(fld = "aeid", val = 1, add.fld = "asid")

## The field can be any value in one of the corresponding assay element
## tables, but the functions also recognize the abbreviated version of
## the name fields.
tcplListFlds("assay")
a1 <- tcplLoadAeid(fld = "anm", val = "Steroidogenesis")
a2 <- tcplLoadAeid(fld = "assay_name", val = "Steroidogenesis")
identical(a1, a2)

## End(Not run)

Description

Lists of column names returned from tcplLoadData invitrodb v4.1 (same as CCTE Bioactivity API version).

Usage

load_data_columns

Format

A list with 12 items:

mc0

Column names returned requesting mc lvl 0 data

mc1

Column names returned requesting mc lvl 1 data

mc2

Column names returned requesting mc lvl 2 data

mc3

Column names returned requesting mc lvl 3 data

mc4

Column names returned requesting mc lvl 4 data

mc5

Column names returned requesting mc lvl 5 data

mc6

Column names returned requesting mc lvl 6 data

mcagg

Column names returned requesting mc lvl "agg" data

sc0

Column names returned requesting sc lvl 0 data

sc1

Column names returned requesting sc lvl 1 data

sc2

Column names returned requesting sc lvl 2 data

scagg

Column names returned requesting sc lvl "agg" data

Source

ToxCast database

Description

lu takes a logical vector, x, and returns length(unique(x)).

lu takes a logical vector, x, and returns length(unique(x)).

Usage

lu(x)

lu(x)

Arguments

Value

The unique of the TRUE values in x

The unique of the TRUE values in x

See Also

unique, which

unique, which

Other tcpl abbreviations: is.odd(), lw(), sink.reset()

Other tcpl abbreviations: is.odd(), lw(), sink.reset()

Description

lw takes a logical vector, x, and returns length(which(x)).

lw takes a logical vector, x, and returns length(which(x)).

Usage

lw(x)

lw(x)

Arguments

Value

The length of the TRUE values in x

The length of the TRUE values in x

See Also

length, which

length, which

Other tcpl abbreviations: is.odd(), lu(), sink.reset()

Other tcpl abbreviations: is.odd(), lu(), sink.reset()

Description

List of lists containing queries sent to tcplQuery associated with each test case. Each list also contains the associated ids with each case. Only meant to be used with automated testing with mocking for mc data.

Usage

mc_test

Format

A list with 30 items:

tcplConfQuery

Data table with 1 row and 2 columns used for each test case for establishing connection using tcplConf. This data table mocks the response one would get from connecting with invitrodb.

mc0_by_m0id

List containing the queries used for loading mc0 data by m0id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm0id' labeled item storing the id used to load the data, for use in tests.

mc0_by_acid

List containing the queries used for loading mc0 data by acid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'acid' labeled item storing the id used to load the data, for use in tests.

mc1_by_m1id

List containing the queries used for loading mc1 data by m1id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm1id' labeled item storing the id used to load the data, for use in tests.

mc1_by_acid

List containing the queries used for loading mc1 data by acid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'acid' labeled item storing the id used to load the data, for use in tests.

mc2_by_m2id

List containing the queries used for loading mc2 data by m2id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm2id' labeled item storing the id used to load the data, for use in tests.

mc2_by_acid

List containing the queries used for loading mc2 data by acid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'acid' labeled item storing the id used to load the data, for use in tests.

mc3_by_m3id

List containing the queries used for loading mc3 data by m3id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm3id' labeled item storing the id used to load the data, for use in tests.

mc3_by_aeid

List containing the queries used for loading mc3 data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

mc4_by_m4id

List containing the queries used for loading mc4 data by m4id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm4id' labeled item storing the id used to load the data, for use in tests.

mc4_by_aeid

List containing the queries used for loading mc4 data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

mc5_by_m5id

List containing the queries used for loading mc5 data by m5id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm5id' labeled item storing the id used to load the data, for use in tests.

mc5_by_aeid

List containing the queries used for loading mc5 data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

mc6_by_m6id

List containing the queries used for loading mc6 data by m6id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm6id' labeled item storing the id used to load the data, for use in tests.

mc6_by_aeid

List containing the queries used for loading mc6 data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

mc7_by_m7id

List containing the queries used for loading mc7 data by m7id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'm7id' labeled item storing the id used to load the data, for use in tests.

mc7_by_aeid

List containing the queries used for loading mc7 data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

mcagg_by_aeid

List containing the queries used for loading mc 'agg' data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

plot_single_m4id

List containing the queries used for loading and plotting mc data by m4id via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 'm4id' labeled item storing the id used to load the data, for use in tests.

plot_multiple_m4id

List containing the queries used for loading and plotting mc data by multiple m4ids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 'm4id' labeled item storing the ids used to load the data, for use in tests.

plot_single_aeid

List containing the queries used for loading and plotting mc data by aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

plot_multiple_aeid

List containing the queries used for loading and plotting mc data by multiple aeids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the ids used to load the data, for use in tests.

plot_single_spid

List containing the queries used for loading and plotting mc data by spid/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid' and 'aeid' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_spid

List containing the queries used for loading and plotting mc data by multiple spids/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid' and 'aeid' labeled items storing the ids used to load the data, for use in tests.

plot_single_m4id_compare

List containing the queries used for loading and plotting compared mc data by m4id via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'm4id' and 'compare.m4id' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_m4id_compare

List containing the queries used for loading and plotting compared mc data by multiple m4ids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'm4id' and 'compare.m4id' labeled items storing the ids used to load the data, for use in tests.

plot_single_aeid_compare

List containing the queries used for loading and plotting compared mc data by aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'aeid' and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_aeid_compare

List containing the queries used for loading and plotting compared mc data by multiple aeids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'aeid' and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

plot_single_spid_compare

List containing the queries used for loading and plotting compared mc data by spid/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid', 'compare.spid', 'aeid', and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_spid_compare

List containing the queries used for loading and plotting compared mc data by multiple spids/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid', 'compare.spid', 'aeid', and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

Source

ToxCast database

Description

This dataset is a list with 6 data.tables (mc0,mc1,mc2,mc3,mc4,mc5).

Usage

mc_vignette

Format

1. mc0 A data frame with 78 rows and 18 columns containing level 0 formatted raw data.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   acid

   Assay Component ID

   acnm

   Assay Component Name

   m0id

   Level 0 (mc0) ID

   apid

   Assay plate ID

   rowi

   Row Index

   coli

   Column Index

   wllt

   Well Type

   wllq

   Well Quality (0 or 1)

   conc

   Concentration in micromolar

   rval

   Raw assay component readout value

   srcf

   Source file containing the raw data

   conc_unit

   Concentration Units

2. mc1 A data frame with 78 rows and 21 columns containing level 1 replicate and concentration level indicated data.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   acid

   Assay Component ID

   acnm

   Assay Component Name

   m0id

   Level 0 (mc0) ID

   m1id

   Level 1 (mc1) ID

   apid

   Assay plate ID

   rowi

   Row Index

   coli

   Column Index

   wllt

   Well Type

   wllq

   Well Quality (0 or 1)

   conc

   Concentration in micromolar

   rval

   Raw assay component readout value

   cndx

   Concentration index defined by ranking the unique concentrations, with the lowest concentration starting at 1.

   repi

   Temporary replicate ID is defined, the data are scanned from top to bottom and increment the replicate index every time a replicate ID is duplicated

   srcf

   Source file containing the raw data

   conc_unit

   Concentration Units

3. mc2 A data frame with 78 rows and 20 columns containing level 2 assay component-specific corrections.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   acid

   Assay Component ID

   acnm

   Assay Component Name

   m0id

   Level 0 (mc0) ID

   m1id

   Level 1 (mc1) ID

   m2id

   Level 2 (mc2) ID

   apid

   Assay plate ID

   rowi

   Row Index

   coli

   Column Index

   wllt

   Well Type

   conc

   Concentration in micromolar

   cval

   Corrected Value

   cndx

   Concentration index defined by ranking the unique concentrations, with the lowest concentration starting at 1.

   repi

   Temporary replicate ID is defined, the data are scanned from top to bottom and increment the replicate index every time a replicate ID is duplicated

   conc_unit

   Concentration Units

4. mc3 A data frame with 78 rows and 22 columns containing level 3 assay endpoint normalized data.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   aeid

   Assay Component Endpoint ID

   aenm

   Assay endpoint name (i.e., assay_component_endpoint_name)

   m0id

   Level 0 (mc0) ID

   m1id

   Level 1 (mc1) ID

   m2id

   Level 2 (mc2) ID

   m3id

   Level 3 (mc3) ID

   logc

   Log base 10 concentration

   resp

   Normalized response value

   cndx

   Concentration index defined by ranking the unique concentrations, with the lowest concentration starting at 1.

   wllt

   Well Type

   apid

   Assay plate ID

   rowi

   Row Index

   coli

   Column Index

   repi

   Temporary replicate ID is defined, the data are scanned from top to bottom and increment the replicate index every time a replicate ID is duplicated

   resp_unit

   Response Units

   conc_unit

   Concentration Units

5. mc4 A data frame with 5 rows and 149 columns containing level 4 concentration-response fitting data (all fits).

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   aeid

   Assay Component Endpoint ID

   aenm

   Assay endpoint name (i.e., assay_component_endpoint_name)

   m4id

   Level 4 (mc4) ID

   bmad

   The median absolute deviation of all treatment wells (default option) or blank wells

   resp_max

   Maximum observed response

   resp_min

   Minimum observed response

   max_mean

   Maximum mean response

   max_mean_conc

   Concentration of the maximum mean response

   max_med

   Maximum median response

   max_med_conc

   Concentration of the maximum median response

   logc_max

   Maximum concentration on the log scale

   logc_min

   Minimum concentration on the log scale

   nconc

   The total number of concentration groups

   npts

   Total number of observed responses (i.e. data points in the concentration series)

   nrep

   Number of replicates in concentration groups

   nmed_gtbl

   The number of median responses greater than 3BMAD

   cnst_success

   Success indicator for the Constant model; 1 if the optimization was successful, otherwise 0

   cnst_aic

   Akaike Information Criteria (AIC) for the Constant model

   cnst_rme

   Root mean square error for the Constant model

   cnst_er

   Error term for the Constant model

   hill_success

   Success indicator for the Hill model; 1 if the optimization was successful, otherwise 0

   hill_aic

   Akaike Information Criteria (AIC) for the Hill model

   hill_cov

   Success indicator for the Hill model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   hill_rme

   Root mean square erro for the Hill model

   hill_tp

   The top parameter indicating the maximal estimated response

   hill_ga

   The gain parameter for the Hill model, gain AC50

   hill_p

   The power parameter for the Hill model

   hill_er

   Error term for the Hill model

   hill_tp_sd

   Standard deviation of the Hill model top parameter

   hill_ga_sd

   Standard deviation of the Hill model gain parameter

   hill_p_sd

   Standard deviation of the Hill model power parameter

   hill_er_sd

   Standard deviation of the Hill model error term

   hill_top

   The maximal response on the resulting Hill model fit

   hill_ac50

   Concentration at 50% of the maximal response on the Hill model fit

   gnls_success

   Success indicator for the Gain-loss model; 1 if the optimization was successful, otherwise 0

   gnls_aic

   Akaike Information Criteria (AIC) for the Gain-loss model

   gnls_cov

   Success indicator for the Gain-loss model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   gnls_rme

   Root mean square erro for the Gain-loss model

   gnls_tp

   The top parameter indicating the maximal estimated response

   gnls_ga

   The gain parameter for the Gain-loss model, gain AC50

   gnls_p

   The gain power parameter for the Gain-loss model

   gnls_la

   The loss parameter for the Gain-loss model, loss AC50

   gnls_q

   The loss power parameter for the Gain-loss model

   gnls_er

   Error term for the Gain-loss model

   gnls_tp_sd

   Standard deviation of the Gain-loss model top parameter

   gnls_ga_sd

   Standard deviation of the Gain-loss model gain parameter

   gnls_p_sd

   Standard deviation of the Gain-loss model gain power parameter

   gnls_la_sd

   Standard deviation of the Gain-loss model loss parameter

   gnls_q_sd

   Standard deviation of the Gain-loss model loss power parameter

   gnls_er_sd

   Standard deviation of the Gain-loss model error term

   gnls_top

   The maximal response on the resulting Gain-loss model fit

   gnls_ac50

   Concentration at 50% of the maximal response on the Gain-loss model fit, gain AC50

   gnls_ac50_loss

   Concentration at 50% of the maximal response on the Gain-loss model fit, loss AC50

   poly1_success

   Success indicator for the Polynomial 1 model; 1 if the optimization was successful, otherwise 0

   poly1_aic

   Akaike Information Criteria (AIC) for the Polynomial 1 model

   poly1_cov

   Success indicator for the Polynomial 1 model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   poly1_rme

   Root mean square erro for the Polynomial 1 model

   poly1_a

   The y-scale parameter for the Polynomial 1 model

   poly1_er

   Error term for the Polynomial 1 model

   poly1_a_sd

   Standard deviation of the Polynomial 1 model y-scale parameter

   poly1_er_sd

   Standard deviation of the Polynomial 1 model error term

   poly1_top

   The maximal response on the resulting Polynomial 1 model fit

   poly1_ac50

   Concentration at 50% of the maximal response on the Polynomial 1 model fit

   poly2_success

   Success indicator for the Polynomial 2 model; 1 if the optimization was successful, otherwise 0

   poly2_aic

   Akaike Information Criteria (AIC) for the Polynomial 2 model

   poly2_cov

   Success indicator for the Polynomial 2 model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   poly2_rme

   Root mean square erro for the Polynomial 2 model

   poly2_a

   The y-scale parameter for the Polynomial 2 model

   poly2_b

   The x-scale parameter for the Polynomial 2 model

   poly2_er

   Error term for the Polynomial 2 model

   poly2_a_sd

   Standard deviation of the Polynomial 2 model y-scale parameter

   poly2_b_sd

   Standard deviation of the Polynomial 2 model x-scale parameter

   poly2_er_sd

   Standard deviation of the Polynomial 2 model error term

   poly2_top

   The maximal response on the resulting Polynomial 2 model fit

   poly2_ac50

   Concentration at 50% of the maximal response on the Polynomial 2 model fit

   pow_success

   Success indicator for the Power model; 1 if the optimization was successful, otherwise 0

   pow_aic

   Akaike Information Criteria (AIC) for the Power model

   pow_cov

   Success indicator for the Power model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   pow_rme

   Root mean square erro for the Power model

   pow_a

   The y-scale parameter for the Power model

   pow_p

   The power parameter for the Power model

   pow_er

   Error term for the Power model

   pow_a_sd

   Standard deviation of the Power model y-scale parameter

   pow_p_sd

   Standard deviation of the Power model power parameter

   pow_er_sd

   Standard deviation of the Power model error term

   pow_top

   The maximal response on the resulting Power model fit

   pow_ac50

   Concentration at 50% of the maximal response on the Power model fit

   exp2_success

   Success indicator for the Exponential 2 model; 1 if the optimization was successful, otherwise 0

   exp2_aic

   Akaike Information Criteria (AIC) for the Exponential 2 model

   exp2_cov

   Success indicator for the Exponential 2 model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   exp2_rme

   Root mean square erro for the Exponential 2 model

   exp2_a

   The y-scale parameter for the Exponential 2 model

   exp2_b

   The x-scale parameter for the Exponential 2 model

   exp2_er

   Error term for the Exponential 2 model

   exp2_a_sd

   Standard deviation of the Exponential 2 model y-scale parameter

   exp2_b_sd

   Standard deviation of the Exponential 2 model x-scale parameter

   exp2_er_sd

   Standard deviation of the Exponential 2 model error term

   exp2_top

   The maximal response on the resulting Exponential 2 model fit

   exp2_ac50

   Concentration at 50% of the maximal response on the Exponential 2 model fit

   exp3_success

   Success indicator for the Exponential 3 model; 1 if the optimization was successful, otherwise 0

   exp3_aic

   Akaike Information Criteria (AIC) for the Exponential 3 model

   exp3_cov

   Success indicator for the Exponential 3 model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   exp3_rme

   Root mean square erro for the Exponential 3 model

   exp3_a

   The y-scale parameter for the Exponential 3 model

   exp3_b

   The x-scale parameter for the Exponential 3 model

   exp3_p

   The power parameter for the Exponential 3 model

   exp3_er

   Error term for the Exponential 3 model

   exp3_a_sd

   Standard deviation of the Exponential 3 model y-scale parameter

   exp3_b_sd

   Standard deviation of the Exponential 3 model x-scale parameter

   exp3_p_sd

   Standard deviation of the Exponential 3 model power parameter

   exp3_er_sd

   Standard deviation of the Exponential 3 model error term

   exp3_top

   The maximal response on the resulting Exponential 3 model fit

   exp3_ac50

   Concentration at 50% of the maximal response on the Exponential 3 model fit

   exp4_success

   Success indicator for the Exponential 4 model; 1 if the optimization was successful, otherwise 0

   exp4_aic

   Akaike Information Criteria (AIC) for the Exponential 4 model

   exp4_cov

   Success indicator for the Exponential 4 model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   exp4_rme

   Root mean square erro for the Exponential 4 model

   exp4_tp

   The top parameter indicating the maximal estimated response

   exp4_ga

   The gain parameter for the Exponential 4 model, gain AC50

   exp4_er

   Error term for the Exponential 4 model

   exp4_tp_sd

   Standard deviation of the Exponential 4 model top parameter

   exp4_ga_sd

   Standard deviation of the Exponential 4 model gain parameter

   exp4_er_sd

   Standard deviation of the Exponential 4 model error term

   exp4_top

   The maximal response on the resulting Exponential 4 model fit

   exp4_ac50

   Concentration at 50% of the maximal response on the Exponential 4 model fit

   exp5_success

   Success indicator for the Exponential 5 model; 1 if the optimization was successful, otherwise 0

   exp5_aic

   Akaike Information Criteria (AIC) for the Exponential 5 model

   exp5_cov

   Success indicator for the Exponential 5 model covariance calculation; 1 if the Hessian matrix inversion is successful, otherwise 0

   exp5_rme

   Root mean square erro for the Exponential 5 model

   exp5_tp

   The top parameter indicating the maximal estimated response

   exp5_ga

   The gain parameter for the Exponential 5 model, gain AC50

   exp5_p

   The power parameter for the Exponential 5 model

   exp5_er

   Error term for the Exponential 5 model

   exp5_tp_sd

   Standard deviation of the Exponential 5 model top parameter

   exp5_ga_sd

   Standard deviation of the Exponential 5 model gain parameter

   exp5_p_sd

   Standard deviation of the Exponential 5 model power parameter

   exp5_er_sd

   Standard deviation of the Exponential 5 model error term

   exp5_top

   The maximal response on the resulting Exponential 5 model fit

   exp5_ac50

   Concentration at 50% of the maximal response on the Exponential 5 model fit

   all_onesd

   Standard deviation of the baseline response for all models

   all_bmed

   Median noise estimation of the baseline response for all models

   resp_unit

   Response Units

   conc_unit

   Concentration Units

6. mc5 A data frame with 5 rows and 54 columns containing level 5 best curve-fit and hitcall data.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   aeid

   Assay Component Endpoint ID

   aenm

   Assay endpoint name (i.e., assay_component_endpoint_name)

   m5id

   Level 5 (mc5) ID

   m4id

   Level 4 (mc4) ID

   bmad

   The median absolute deviation of all treatment wells (default option) or blank wells

   resp_max

   Maximum observed response

   resp_min

   Minimum observed response

   max_mean

   Maximum mean response

   max_mean_conc

   Concentration of the maximum mean response

   max_med

   Maximum median response

   max_med_conc

   Concentration of the maximum median response

   logc_max

   Maximum concentration on the log scale

   logc_min

   Minimum concentration on the log scale

   nconc

   The total number of concentration groups

   npts

   Total number of observed responses (i.e. data points in the concentration series)

   nrep

   Number of replicates in concentration groups

   nmed_gtbl

   The number of median responses greater than 3BMAD

   hitc

   Hitcall

   modl

   Best model fit from tcplFit2 curve-fitting

   fitc

   Fit category

   coff

   Cutoff

   top_over_cutoff

   Ratio of the top of the best model fit curve and the cutoff

   rmse

   Root mean squared error

   a

   The y-scale parameter for poly1, poly2, pow, exp2, or exp3 model

   er

   Error term

   bmr

   Benchmark response

   bmdl

   Lower 95% confidence bound on the benchmark dose/concentration estimate

   caikwt

   Akaike Information Criteria weight of constant model relative to the best model fit

   mll

   Maximum log-likelihood of the best model fit

   hitcall

   Continuous hitcall

   ac50

   Concentration where 50% of the maximal response occurs - if 'modl' is the Hill or Gain-loss model this is for the "gain" side of the response

   top

   The maximal response on the best model curve fit - i.e. top of the curve fit

   ac5

   Concentration where 5% of the maximal response occurs

   ac10

   Concentration where 10% of the maximal response occurs

   ac20

   Concentration where 20% of the maximal response occurs

   acc

   Concentration where the efficacy cutoff response occurs

   ac1sd

   Concentration where one standard deviation of the background response occurs

   bmd

   Benchmark response/concentration estimate - concentration where the benchmark response occurs

   bmdu

   Upper 95% confidence bound on the benchmark dose/concentration estimate

   tp

   The top curve parameter for the exp4, exp5, hill, or gnls model

   ga

   The gain parameter for the hill or gnls model - gain AC50

   p

   The power parameter for the pow, exp3, exp5, gnls, or hill model - for gnls this is the gain power parameter

   q

   The loss power parameter for the gnls model

   la

   The loss parameter for the gnls model, loss AC50

   ac50_loss

   Concentration where 50% of the maximal response occurs - if 'modl' is the Hill or Gain-loss model this is for the "loss" side of the response

   b

   The x-scale parameter for poly2, exp2, or exp3 model

   resp_unit

   Response Units

   conc_unit

   Concentration Units

Description

mc1 loads level 0 data from the tcpl database for the given id and performs level 1 multiple-concentration processing. The processed data is then loaded into the mc1 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Usage

mc1(ac, wr = FALSE)

Arguments

Details

Level 1 processing includes defining the concentration and replicate index, cndx and repi, respectively.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

Other multiple-concentration: mc2(), mc3(), mc4(), mc5(), mc6()

Description

mc2 loads level 1 data from the tcpl database for the given id and performs level 2 multiple-concentration processing. The processed data is then loaded into the mc2 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Usage

mc2(ac, wr = FALSE)

Arguments

Details

Level 2 multiple-concentration processing includes defining the corrected value, cval, based on the correction methods listed in the mc2_acid and mc2_methods tables.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

Method functions, MC2_Methods

Other multiple-concentration: mc1(), mc3(), mc4(), mc5(), mc6()

Description

mc2_mthds returns a list of correction/transformation functions to be used during level 2 multiple-concentration processing.

Usage

mc2_mthds()

Details

The functions contained in the list returned by mc2_mthds return a list of expressions to be executed in the mc2 (not exported) function environment. The functions are described here for reference purposes, The mc2_mthds function is not exported, nor is it intended for use.

All available methods are described in the Available Methods section, listed by the function/method name.

Value

A list functions

Available Methods

More information about the level 2 multiple-concentration processing is available in the package vignette, "Data_processing."

Correction Methods

log2

Transform the corrected response value (cval) to log-scale (base 2).

log10

Transform the corrected response value (cval) to log-scale (base 10).

rmneg

Exclude wells with negative corrected response values (cval) and downgrading their well quality (wllq); if $cval<0, wllq=0$.

rmzero

Exclude wells with corrected response values (cval) equal to zero and downgrading their well quality (wllq); if $cval=0, wllq=0$.

mult25

Multiply corrected response value (cval) by 25; $25*cval$.

mult100

Multiply corrected response value (cval) by 100; $100*cval$.

negshift

Shift corrected response values (cval) by subtracting the minimum cval and adding 1, such that the new minimum is 1; $cval-min+1$.

mult2.5

Multiply corrected response value (cval) by 2.5; $2.5*cval$.

mult3

Multiply corrected response value (cval) by 3; $3*cval$.

mult6

Multiply corrected response value (cval) by 6; $6*cval$.

sub100

Center data around zero by subtracting the corrected response value (cval) from 100; $100-cval$. Typically used if data was pre-normalized around 100 with responses decreasing to 0.

zscore.npwlls

Convert the corrected response value (cval) to an absolute Z-Score based on the neutral and positive control wells (wllts = n and p), by assay plate ID (apid)

sub1

Center data around zero by subtracting the corrected response value (cval) from 1; $1-cval$. Typically used if data was pre-normalized around 1 with responses decreasing to 0.

Aggregation Methods

agg.mean.rep.apid

Aggregate technical test replicates (wllt=t) by taking the plate-wise mean per sample id (spid), assay plate (apid), and concentration index (cndx).

agg.median.rep.apid

Aggregate technical test replicates (wllt=t) by taking the plate-wise median per sample id (spid), assay plate (apid), and concentration index (cndx).

agg.percent.rep.spid

Use for binary data. Aggregate technical replicates as percentage by taking the sum of rval (raw values) relative to total replicates per sample id (spid) and concentration index (cndx); $cval = (sum(rval)/.N)*100$.

agg.percent.rep.spid.min1

Use for binary data with variable number of replicates. Aggregate technical replicates as percentage by taking the sum of rval (raw values) relative to total replicates per per sample id (spid) and concentration index (cndx), where there is more than one replicate; $cval=(sum(rval)/.N)*100,~where~.N>1$.

Rvals are collapsed to one value per cndx.

Note

This function is not exported and is not intended to be used by the user.

See Also

mc2, Method functions to query what methods get applied to each acid

Description

mc3 loads level 2 data from the tcpl database for the given id and performs level 3 multiple-concentration processing. The processed data is then loaded into the mc3 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Usage

mc3(ac, wr = FALSE)

Arguments

Details

Level 3 multiple-concentration processing includes mapping assay component to assay endpoint, duplicating the data when the assay component has multiple assay endpoints, and any normalization of the data. Data normalization based on methods listed in mc3_aeid and mc3_methods tables.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

Method functions, MC3_Methods

Other multiple-concentration: mc1(), mc2(), mc4(), mc5(), mc6()

Description

mc3_mthds returns a list of normalization methods to be used during level 3 multiple-concentration processing.

Usage

mc3_mthds()

Details

The functions contained in the list returned by mc3_mthds take aeids (a numeric vector of aeid values) and returns a list of expressions to be executed in the mc3 (not exported) function environment. The functions are described here for reference purposes, The mc3_mthds function is not exported, nor is it intended for use.

All available methods are described in the Available Methods section, listed by the type of function and the function/method name.

Value

A list of functions

Available Methods

The methods are broken into three types, based on what fields they define. Different methods are used to define "bval" (the baseline value), "pval" (the positive control value), and "resp" (the final response value).

Although it does not say so specifically in each description, all methods are applied by aeid.

More information about the level 3 multiple-concentration processing is available in the package vignette, "Data_processing."

bval Methods

bval.apid.nwlls.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for neutral control wells (wllt = n).

bval.apid.lowconc.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for test compound wells (wllt = t) with a concentration index (cndx) of 1 or 2.

bval.apid.twlls.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) of test compound wells (wllt = t).

bval.apid.tn.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for test compound wells (wllt = t) and neutral control wells (wllt = n).

bval.apid.nwllslowconc.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) of test compound wells (wllt = t) with a concentration index (cndx) of 1 or 2 or neutral control wells (wllt = n).

bval.spid.lowconc.med

Calculate the baseline value (bval) as the sample-wise median, by sample ID (spid), of the corrected values (cval) of the three lowest concentration test compound wells (wllt = t and cndx = 1, 2, & 3).

bval.apid.nwllstcwllslowconc.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for neutral control wells (wllt = n) or wells with a concentration index (cndx) of 1 or 2 and well type of test compound (wllt = t) or gain-of-signal control in multiple concentrations (wllt = c).

bval.aeid.nwlls.med

Calculate the baseline value (bval) as the endpoint-wise median, by assay component endpoint ID (aeid), corrected value (cval) for neutral control wells (wllt = n).

pval Methods

pval.apid.pwlls.med

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for single-concentration gain-of-signal positive control wells (wllt = p).

pval.apid.mwlls.med

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for multiple-concentration loss-of-signal negative control wells (wllt = m).

pval.apid.medpcbyconc.max

Calculate the positive control value (pval) as the plate-wise maximum, by assay plate ID (apid), of the medians of the corrected values (cval) for gain-of-signal single- or multiple-concentration negative control wells (wllt = m or o) by apid, well type, and concentration.

pval.apid.medpcbyconc.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of corrected value (cval) of gain-of-signal single- or multiple-concentration positive control wells (wllt = p or c) by apid, well type, and concentration.

pval.apid.medncbyconc.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of the corrected values (cval) for gain-of-signal single- or multiple-concentration negative control wells (wllt = m or o) by apid, well type, and concentration.

pval.apid.pmv.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of the corrected values (cval) for single-concentration gain-of-signal, multiple-concentration loss-of-signal, or viability control wells (wllt = p, m, or v) by apid, well type, and concentration.

pval.apid.pmv.max

Calculate the positive control value (pval) as the plate-wise maximum, by assay plate ID (apid), of the medians of the corrected values (cval) for single-concentration gain-of-signal, multiple-concentration loss-of-signal, or viability control wells (wllt = p, m, or v) by apid, well type, and concentration.

pval.apid.f.max

Calculate the positive control value (pval) as the plate-wise maximum, by assay plate ID (apid), of the medians of important reference wells (wllt = f) values by apid and concentration.

pval.apid.f.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of important reference wells (wllt = f) values by apid and concentration.

pval.apid.p.max

Calculate the positive control value (pval) as the plate-wise maximum, by assay plate ID (apid), of the medians of the corrected values (cval) for single-concentration gain-of-signal control wells (wllt = p) by apid.

pval.apid.p.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of corrected values (cval) for single-concentration gain-of-signal control wells (wllt = p) by apid.

pval.apid.v.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of the corrected values (cval) for viability control wells (wllt = v) by apid and concentration.

pval.zero

Set the positive control value (pval) to 0; $pval = 0$.

pval.apid.owlls.med

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for single-concentration negative control wells (wllt = o).

pval.2bval

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for neutral control wells (wllt = n) multiplied by 2.

pval.maxp

Calculate the positive control value (pval) as the endpoint-wise maximum, by assay component ID (aeid), of the corrected values for single-concentration gain-of-signal wells (wllt = p).

pval.apid.bwlls.med

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the corrected values (cval) for blank wells (wllt= b).

pval.twlls.99pct

Calculate positive control value (pval) as the 99th percentile of all corrected value (cvals) of the test compound wells (wllt = t).

pval.neg.100

Calculate positive control value (pval) as -100 for endpoints in the down direction; $pval = -100$.

resp Methods

resp.pc

Calculate the normalized response (resp) as a percent of control, i.e. the ratio of the difference between the corrected (cval) and baseline (bval) values divided the difference between the positive control (pval) and baseline (bval) values multiplied by 100; $resp = (cval-bval)/(pval-bval)*100$.

resp.pc.pval.cor

Calculate the normalized response (resp) as a percent of control, i.e. the ratio of the difference between the corrected (cval) and baseline (bval) values divided the positive control (pval) value multiplied by 100; $resp = (cval-bval)/pval*100$.

resp.fc

Calculate the normalized response (resp) as the fold change, i.e. the ratio of the corrected (cval) and baseline (bval) values; $resp = cval/bal$.

resp.logfc

Calculate the normalized response (resp) as the fold change of logged, i.e. the difference between corrected (cval) and baseline (bval) log-scale values.

resp.log2

Transform the response values to log-scale (base 2).

resp.mult25

Multiply the normalized response value (resp) by 25; $25*resp$.

resp.scale.mad.log2fc

Scale the normalized response value (resp) by the ratio of log2(1.2) and 3 multiplied by the baseline median absolute deviation (bmad) of the unscaled normalized response values (resp); $(\log_{2}{1.2})/3*bmad*resp$.

resp.scale.quant.log2fc

Scale the normalized response value (resp). First, determine the maximum difference (md) by finding the maximum between the absolute difference of the 1st percentile minus the 50th percentile and the absolute difference of the 99th percentile minus the 50th percentile. Then multiply resp by log2(1.2) divided by 20 percent of md; $(\log_{2}{1.2})/0.2*md*resp$.

resp.multneg1

Multiply the normalized response value (resp) by -1; $-1*resp$.

resp.shiftneg.3bmad

Shift all the normalized response values (resp) less than -3 multiplied by the baseline median absolute deviation (bmad) to 0; if $resp < -3*bmad,~resp = 0$.

resp.shiftneg.6bmad

Shift all the normalized response values (resp) less than -6 multiplied by the baseline median absolute deviation (bmad) to 0; if $resp < -6*bmad,~resp = 0$.

resp.shiftneg.10bmad

Shift all the normalized response values (resp) less than 10 multiplied by the baseline median absolute deviation (bmad) to 0; if $resp < -10*bmad,~resp = 0$.

resp.blineshift.3bmad.repi

Shift the normalized response value (resp) with a baseline correction, by replicate index (repi), with a window of 3 multiplied by the baseline median absolute deviation (bmad).

resp.blineshift.50.repi

Shift the normalized response value (resp) with a baseline correction, by replicate index (repi), with a window of 50.

resp.blineshift.3bmad.spid

Shift the normalized response value (resp) with a baseline correction, by sample ID (spid), with a window of 3 multiplied by the baseline median absolute deviation (bmad).

resp.blineshift.50.spid

Shift the normalized response value (resp) with a baseline correction, by sample ID (spid), with a window of 50.

none

Set the corrected response value (cval) as the normalized response value (resp); $cval = resp$. No additional mc3 methods needed for endpoint-specific normalization.

resp.zerocenter.fc

Calculate the normalized response (resp) as a zero center fold change, i.e. 1 minus the ratio of corrected (cval) and baseline (bval) values; $resp=1-cval/bval$. Typically used for increasing responses.

resp.incr.zerocenter.fc

Calculate the normalized response (resp) as a zero center fold change, i.e. the ratio of the the corrected (cval) and baseline (bval) values minus 1; $resp=cval/bval-1$. Typically used for increasing responses.

resp.mult100

Multiply the normalized response value (resp) by 100; $100*resp$.

'

resp.censormed.neg25

Censor (remove) response values from concentrations which median falls below -25.

Note

This function is not exported and is not intended to be used by the user.

See Also

mc3, Method functions to query what methods get applied to each aeid

Description

mc4 loads level 3 data from the tcpl database for the given id and performs level 4 multiple-concentration processing. The processed data is then loaded into the mc4 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Usage

mc4(ae, wr = FALSE)

Arguments

Details

Level 4 multiple-concentration modeling takes the dose-response data for chemical-assay pairs, and fits three models to the data: constant, hill, and gain-loss. For more information about the models see Models. When a chemical has more than one sample, the function fits each sample separately.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

tcplFit, Models

Other multiple-concentration: mc1(), mc2(), mc3(), mc5(), mc6()

Description

mc4_mthds returns a list of methods to be used during level 4 multiple-concentration processing for calculating bmad

Usage

mc4_mthds()

Details

The functions contained in the list returned by mc4_mthds take aeids (a numeric vector of aeid values) and returns a list of expressions to be executed in the mc4 (not exported) function environment. The functions are described here for reference purposes, The mc4_mthds function is not exported, nor is it intended for use.

All available methods are described in the Available Methods section, listed by the type of function and the function/method name.

Value

A list of functions

Available Methods

Although it does not say so specifically in each description, all methods are applied by aeid.

More information about the level 4 multiple-concentration processing is available in the package vignette, "Data_processing."

bmad.aeid.lowconc.twells

Calculate the baseline median absolute value (bmad) as the median absolute deviation of normalized response values (rep) for test compound wells (wllt = t) with concentration index (cndx) equal to 1 or 2. Calculate one standard deviation of the normalized response for test compound wells (wllt = t) with a concentration index (cndx) of 1 or 2; onesd = sqrt(sum((resp - mean resp)^2)/sample size - 1). Onesd is used to establish BMR and therefore required for tcplfit2 processing.

bmad.aeid.lowconc.nwells

Calculate the baseline median absolute value (bmad) as the median absolute deviation of normalized response values (resp) for neutral control wells (wllt = n). Calculate one standard deviation of the normalized response for neutral control wells (wllt = n); onesd = sqrt(sum((resp - mean resp)^2)/sample size - 1). Onesd is used to establish BMR and therefore required for tcplfit2 processing.

bidirectional.false

Limits bidirectional fitting and processes data in positive analysis direction only. Use for gain-of-signal or inverted data.

bmad5.onesd16.static

Replace baseline median absolute deviation (bmad) with 5 and one standard deviation (osd) of the normalized response for test compound wells (wllt = t) with a concentration index (cndx) of 1 or 2 with 16. Typically used for binary data where values would otherwise be 0; non-zero values are required for tcplfit2 processing.

no.unbounded.models

Exclude unbounded models and only fit data to bounded models (hill, gnls, exp4 and exp5).

Note

This function is not exported and is not intended to be used by the user.

See Also

mc4, Method functions to query what methods get applied to each aeid

Description

mc5 loads level 4 data from the tcpl database for the given id and performs level 5 multiple-concentration processing. The processed data is then loaded into the mc5 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Arguments

Details

Level 5 multiple-concentration hit-calling uses the fit parameters and the activity cutoff methods from mc5_aeid and mc5_methods to make an activity call and identify the winning model for each fit.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

Method functions, MC5_Methods

Other multiple-concentration: mc1(), mc2(), mc3(), mc4(), mc6()

Description

mc5_mthds returns a list of additional activity cutoff methods to be used during level 5 multiple-concentration processing.

Usage

mc5_mthds(ae)

Arguments

Details

The functions contained in the list returned by mc5_mthds take aeids (a numeric vector of aeid values) and returns a list of expressions to be executed in the mc5 (not exported) function environment. The functions are described here for reference purposes, The mc5_mthds function is not exported, nor is it intended for use.

All available methods are described in the "Available Methods" section, listed by the cutoff type in ascending order of cutoff value.

Value

A list of functions

Available Methods

The methods are broken down into five categories based on the type of cutoff they assign. Different methods are used to define cutoffs for "bmad" (baseline median absolute value), "fc" (fold change), "log" ($\log_{2}$ or $\log_{10}$), "pc" (percent of control), and "other" (uncategorized cutoffs).

All methods are applied by aeid.

Although there are method exceptions (notably within the “other” category), only highest calculated cutoff value based on assigned methods will be selected for hitcalling. Therefore, only the largest cutoff method per method type should be assigned.

More information about the level 5 multiple-concentration processing is available in the package vignette, "Data_processing."

BMAD Methods

bmad1

Add a cutoff value of 1 multiplied by baseline median absolute value (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad2

Add a cutoff value of 2 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad3

Add a cutoff value of 3 multiplied by the baseline median absolute deviation (bmad) as defined at Level 4.

bmad4

Add a cutoff value of 4 multiplied the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad5

Add a cutoff value of 5 multiplied the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad6

Add a cutoff value of 6 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad10

Add a cutoff value of 10 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

Fold Change Methods

fc0.2

Add a cutoff value of 0.2. Typically for zero centered fold change data.

fc0.25

Add a cutoff value of 0.25. Typically for zero centered fold change data.

fc0.3

Add a cutoff value of 0.3. Typically for zero centered fold change data.

fc0.5

Add a cutoff value of 0.5. Typically for zero centered fold change data.

Log Methods

Log Base 2

neglog2_0.88

Add a cutoff value of $-\log_{2}{0.88}$.

log2_1.2

Add a cutoff value of $\log_{2}{1.2}$. Typically for fold change data.

log2_2

Add a cutoff value $\log_{2}{2}$. Typically for fold change data.

Log Base 10

log10_1.2

Add a cutoff value of $\log_{10}{1.2}$. Typically for fold change data.

log10_2

Add a cutoff value of $\log_{10}{2}$. Typically for fold change data.

Percent of Control Methods

pc05

Add a cutoff value of 5. Typically for percent of control data.

pc10

Add a cutoff value of 10. Typically for percent of control data.

pc16

Add a cutoff value of 16. Typically for percent of control data.

pc20

Add a cutoff value of 20. Typically for percent of control data.

pc25

Add a cutoff value of 25. Typically for percent of control data.

pc30

Add a cutoff value of 30. Typically for percent of control data.

pc40

Add a cutoff value of 40. Typically for percent of control data.

pc50

Add a cutoff value of 50. Typically for percent of control data.

pc70

Add a cutoff value of 70. Typically for percent of control data.

pc95

Add a cutoff value of 95. Typically for percent of control data.

Other Methods

maxmed20pct

Add a cutoff value of 20 percent of the maximum of all endpoint maximal average response values (max_med).

coff_2.32

Add a cutoff value of 2.32.

loec.coff

Method not yet updated for tcpl implementation. Identify the lowest observed effective concentration (loec) compared to baseline.

ow_bidirectional_loss

Multiply winning model hitcall (hitc) by -1 for models fit in the positive analysis direction. Typically used for endpoints where only negative responses are biologically relevant.

ow_bidirectional_gain

Multiply winning model hitcall (hitc) by -1 for models fit in the negative analysis direction. Typically used for endpoints where only positive responses are biologically relevant.

osd_coff_bmr

Overwrite the osd value so that bmr equals cutoff.

Note

This function is not exported and is not intended to be used by the user.

See Also

mc5, Method functions to query what methods get applied to each aeid.

Description

mc6 loads level 5 data from the tcpl database for the given id and performs level 6 multiple-concentration processing. The processed data is then loaded into the mc6 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Usage

mc6(ae, wr = FALSE)

Arguments

Details

Level 6 multiple-concentration flagging uses both the plate level concentration-response data and the modeled parameters to flag potential false positives and false negative results.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

Method functions, MC6_Methods

Other multiple-concentration: mc1(), mc2(), mc3(), mc4(), mc5()

Description

mc6_mthds returns a list of flag methods to be used during level 6 multiple-concentration processing.

Usage

mc6_mthds()

Value

A list functions

Available Methods

More information about the level 6 multiple-concentration processing is available in the package vignette, "Data_processing."

modl.directionality.fail

Flag series if model directionality is questionable, i.e. if the winning model direction was opposite, more responses (resp) would have exceeded the cutoff (coff). If loss was winning directionality (top < 0), flag if $count(resp<-1*coff)<2*count(resp>coff)$. If gain was winning directionality (top > 0), flag if $count(resp>coff)<2*count(resp<-1*coff)$.

low.nrep

Flag series if the average number of replicates per concentration is less than 2; $nrep < 2$.

low.nconc

Flag series if 4 concentrations or less were tested; $nconc<=4$.

bmd.high

Flag series if modeled benchmark dose (BMD) is greater than AC50 (concentration at 50 percent maximal response). This is indicates high variability in baseline response in excess of more than half of the maximal response.

singlept.hit.high

Flag single-point hit that's only at the highest conc tested, where series is an active hit call (hitc >= 0.9) with the median response observed above baseline occurring only at the highest tested concentration tested.

singlept.hit.mid

Flag single-point hit that's not at the highest conc tested, where series is an active hit call (hitc >= 0.9) with the median response observed above baseline occurring only at one concentration and not the highest concentration tested.

multipoint.neg

Flag multi-point miss, where series is an inactive hit call (hitc < 0.9) with multiple median responses observed above baseline.

gnls.lowconc

Flag series where winning model is gain-loss (gnls) and the gain AC50 is less than the minimum tested concentration, and the loss AC50 is less than the mean tested concentration.

noise

Flag series as noisy if the quality of fit as calculated by the root mean square error (rmse) for the series is greater than the cutoff (coff); $rmse > coff$.

border

Flag series if borderline activity is suspected based on modeled top parameter (top) relative to cutoff (coff); $|top|<=1.2*coff~or~|top|>=0.8*coff$.

overfit.hit

Method not yet updated for tcpl implementation. Flag hit-calls that would get changed after doing the small N correction to the aic values.

efficacy.50

Flag low efficacy hits if series has an active hit call (hitc >= 0.9) and efficacy values (e.g. top and maximum median response) less than 50 percent; intended for biochemical assays. If $hitc>=0.9$ and $coff>=5$, then flag when $top<50$ or $maxmed < 50$. If $hitc>=0.9$ and $coff<5$, then flag when $top<\log_{2}{1.5}$ or $maxmed<\log_{2}{1.5}$.

ac50.lowconc

Flag series with an active hit call (hitc >= 0.9) if AC50 (concentration at 50 percent maximal response) is less than the lowest concentration tested; if $hitc>=0.9$ and $ac50<10^{log_{c}{min}}$, then flag.

viability.gnls

Flag series with an active hit call (hitc >= 0.9) if denoted as cell viability assay with winning model is gain-loss (gnls); if hitc >= 0.9, modl = "gnls" and cell_viability_assay = 1, then flag.

no.med.gt.3bmad

Flag series where no median response values are greater than baseline as defined by 3 times the baseline median absolute deviation (bmad); nmed_gtbl_pos and nmed_gtbl_neg both = 0, where nmed_gtbl_pos/_neg is the number of medians greater than 3 * bmad/less than -3 * bmad.

Note

This function is not exported and is not intended to be used by the user.

See Also

mc6, Method functions to query what methods get applied to each aeid.

Description

The example dataset is used to illustrate how the user can pipeline multiple-concentration data from chemical screening using tcpl.

Usage

mcdat

Format

A data frame with 14183 rows and 10 variables:

spid

sample ID

apid

assay plate ID

rowi

well-plate row number

coli

well-plate column number

wllt

well type

wllq

well quality

conc

concentration in micromolar

rval

raw assay component readout value

srcf

source file containing the data

acsn

assay component source name

Source

ToxCast database

Description

These functions are used to manage which methods are used to process data. They include methods for assigning, clearing, and loading the assigned methods. Also, tcplMthdList lists the available methods.

Usage

tcplMthdAssign(lvl, id, mthd_id, ordr = NULL, type)

tcplMthdClear(lvl, id, mthd_id = NULL, type)

tcplMthdList(lvl, type = "mc")

tcplMthdLoad(lvl, id = NULL, type = "mc")

Arguments

Details

tcplMthdLoad loads the assigned methods for the given level and ID(s). Similarly, tcplMthdList displays the available methods for the given level. These two functions do not make any changes to the database.

Unlike the -Load and -List functions, the -Assign and -Clear functions alter the database and trigger a delete cascade. tcplMthdAssign assigns methods to the given ID(s), and tcplMthdClear removes methods. In addition to the method ID ('mthd_id'), assigning methods at some levels require an order ('ordr'). The 'ordr' parameter is necessary to allow progression of methods at level one for single-concentration processing, and levels two and three for multiple-concentration processing. More information about method assignments and the delete cascade are available in the package vignette.

Examples

## Not run: 
## tcplListMthd allows the user to display the available methods for 
## a given level and data type
head(tcplMthdList(lvl = 2, type = "mc"))

## tcplLoadMthd shows which methods are assigned for the given ID, level,
## and data type. Here we will show how to register, load, and clear methods
## using an acid not in the example database. Note: There is no check for
## whether an ID exists before assigning/clearing methods. 
tcplMthdLoad(lvl = 2, id = 55, type = "mc")

## ACID 55 does not have any methods. Assign methods from the list above. 
tcplMthdAssign(lvl = 2, 
               id = 55, 
               mthd_id = c(3, 4, 2), 
               ordr = 1:3, 
               type = "mc")
## Method assignment can be done for multiple assays, too. 
tcplMthdAssign(lvl = 2, 
               id = 53:54, 
               mthd_id = c(3, 4, 2), 
               ordr = 1:3, 
               type = "mc")
               
## Cleanup example method assigments
tcplMthdClear(lvl = 2, id = 53:55, type = "mc")

## End(Not run)

Description

These functions take in the dose-response data and the model parameters, and return a likelihood value. They are intended to be optimized using constrOptim in the tcplFit function.

Usage

tcplObjCnst(p, resp)

tcplObjGnls(p, lconc, resp)

tcplObjHill(p, lconc, resp)

tcplObjCnst(p, resp)

tcplObjGnls(p, lconc, resp)

tcplObjHill(p, lconc, resp)

Arguments

Details

These functions produce an estimated value based on the model and given parameters for each observation. Those estimated values are then used with the observed values and a scale term to calculate the log-likelihood.

Let $t(z,\nu)$ be the Student's t-distribution with $\nu$ degrees of freedom, $y_{i}$ be the observed response at the $i^{th}$ observation, and $\mu_{i}$ be the estimated response at the $i^{th}$ observation. We calculate $z_{i}$ as:

$z_{i} = \frac{y_{i} - \mu_{i}}{e^\sigma}$

where $\sigma$ is the scale term. Then the log-likelihood is:

$\sum_{i=1}^{n} [ln(t(z_{i}, 4)) - \sigma]$

Where $n$ is the number of observations.

Value

The log-likelihood.

Constant Model (cnst)

tcplObjCnst calculates the likelyhood for a constant model at 0. The only parameter passed to tcplObjCnst by p is the scale term $\sigma$. The constant model value $\mu_{i}$ for the $i^{th}$ observation is given by:

$\mu_{i} = 0$

tcplObjCnst calculates the likelyhood for a constant model at 0. The only parameter passed to tcplObjCnst by p is the scale term $\sigma$. The constant model value $\mu_{i}$ for the $i^{th}$ observation is given by:

$\mu_{i} = 0$

Gain-Loss Model (gnls)

tcplObjGnls calculates the likelyhood for a 5 parameter model as the product of two Hill models with the same top and both bottoms equal to 0. The parameters passed to tcplObjGnls by p are (in order) top ($\mathit{tp}$), gain log AC50 ($\mathit{ga}$), gain hill coefficient ($gw$), loss log AC50 $\mathit{la}$, loss hill coefficient $\mathit{lw}$, and the scale term ($\sigma$). The gain-loss model value $\mu_{i}$ for the $i^{th}$ observation is given by:

$g_{i} = \frac{1}{1 + 10^{(\mathit{ga} - x_{i})\mathit{gw}}}$

$l_{i} = \frac{1}{1 + 10^{(x_{i} - \mathit{la})\mathit{lw}}}$

$\mu_{i} = \mathit{tp}(g_{i})(l_{i})$

where $x_{i}$ is the log concentration for the $i^{th}$ observation.

tcplObjGnls calculates the likelyhood for a 5 parameter model as the product of two Hill models with the same top and both bottoms equal to 0. The parameters passed to tcplObjGnls by p are (in order) top ($\mathit{tp}$), gain log AC50 ($\mathit{ga}$), gain hill coefficient ($gw$), loss log AC50 $\mathit{la}$, loss hill coefficient $\mathit{lw}$, and the scale term ($\sigma$). The gain-loss model value $\mu_{i}$ for the $i^{th}$ observation is given by:

$g_{i} = \frac{1}{1 + 10^{(\mathit{ga} - x_{i})\mathit{gw}}}$

$l_{i} = \frac{1}{1 + 10^{(x_{i} - \mathit{la})\mathit{lw}}}$

$\mu_{i} = \mathit{tp}(g_{i})(l_{i})$

where $x_{i}$ is the log concentration for the $i^{th}$ observation.

Hill Model (hill)

tcplObjHill calculates the likelyhood for a 3 parameter Hill model with the bottom equal to 0. The parameters passed to tcplObjHill by p are (in order) top ($\mathit{tp}$), log AC50 ($\mathit{ga}$), hill coefficient ($\mathit{gw}$), and the scale term ($\sigma$). The hill model value $\mu_{i}$ for the $i^{th}$ observation is given by:

$\mu_{i} = \frac{tp}{1 + 10^{(\mathit{ga} - x_{i})\mathit{gw}}}$

where $x_{i}$ is the log concentration for the $i^{th}$ observation.

tcplObjHill calculates the likelyhood for a 3 parameter Hill model with the bottom equal to 0. The parameters passed to tcplObjHill by p are (in order) top ($\mathit{tp}$), log AC50 ($\mathit{ga}$), hill coefficient ($\mathit{gw}$), and the scale term ($\sigma$). The hill model value $\mu_{i}$ for the $i^{th}$ observation is given by:

$\mu_{i} = \frac{tp}{1 + 10^{(\mathit{ga} - x_{i})\mathit{gw}}}$

where $x_{i}$ is the log concentration for the $i^{th}$ observation.

Description

Lists of data frames returned from tcplMthdList invitrodb v4.2

Usage

mthd_list_defaults

Format

A list with 7 items:

mc2

displays the available methods for mc lvl 2 data

mc3

displays the available methods for mc lvl 3 data

mc4

displays the available methods for mc lvl 4 data

mc5

displays the available methods for mc lvl 5 data

mc6

displays the available methods for mc lvl 6 data

sc1

displays the available methods for sc lvl 1 data

sc2

displays the available methods for sc lvl 2 data

Source

ToxCast database

Description

These functions send a query to the given database, and are the access point for all tcpl functions that query or update the tcpl database.

Usage

tcplQuery(
  query,
  db = getOption("TCPL_DB"),
  drvr = getOption("TCPL_DRVR"),
  tbl = NULL
)

tcplQueryAPI(resource = "data", fld = NULL, val = NULL, return_flds = NULL)

tcplSendQuery(
  query,
  db = getOption("TCPL_DB"),
  drvr = getOption("TCPL_DRVR"),
  tbl = NULL,
  delete = F
)

Arguments

Details

Currently, the tcpl package supports the "MySQL", "example", and "API" database drivers.

tcplQuery returns a data.table object with the query results. tcplSendQuery sends a query, but does not fetch any results, and returns 'TRUE' or the error message given by the database. tcplQueryAPI returns a data.table object with the query results when connected using "API" as driver.

Examples

## Not run: 
# only with MySQL driver
tcplQuery("SELECT 'Hello World';")

# only with API driver
tcplConfDefault()
tcplQueryAPI(resource = "data", fld = "aeid", val = 2)

## End(Not run)

Description

These functions are used to register and update the chemical and assay annotation information.

Usage

tcplRegister(what, flds)

tcplUpdate(what, id, flds)

Arguments

Details

These functions are used to populate the tcpl database with the necessary annotation information to complete the processing. As shown in the package vignette, the package requires some information about the samples and assays before data can be loaded into the tcpl database.

Depending on what is being registered, different information is required. The following table lists the fields that can be registered/updated by these functions, and the minimal fields required for registering a new ID. (The database table affected is in parentheses.)

• asid (assay_source): assay_source_name

• aid (assay): asid, assay_name, assay_footprint

• acid (assay_component): aid, assay_component_name

• aeid (assay_component_endpoint): acid, assay_component_endpoint_name, normalized_data_type

• acsn (assay_component_map): acid, acsn

• spid (sample): spid, chid

• chid (chemical): chid, casn

• clib (chemical_library): chid, clib

Note: The functions accept the abbreviated forms of the names, ie. "aenm" rather than the full "assay_component_endpoint_name." More information about the registration process and all of the fields is available in the vignette.

Examples

## Not run: 
## Load current ASID information
tcplLoadAsid()

## Register a new assay source
tcplRegister(what = "asid", flds = list(asnm = "example_asid"))

## Show the newly registered ASID
tcplLoadAsid(add.fld = "assay_source_desc")

## Notice that the newly created ASID does not have an assay_source_desc.
## The field could have been defined during the registration process, but
## can also be updated using tcplUpdate
i1 <- tcplLoadAsid()[asnm == "example_asid", asid]
tcplUpdate(what = "asid", 
           id = i1, 
           flds = list(assay_source_desc = "example asid description"))
tcplLoadAsid(add.fld = "assay_source_desc")

## Remove the created ASID. Note: Manually deleting primary keys can cause
## serious database problems and should not generally be done. 

## End(Not run)

Description

registerMthd registers a new analysis method to the tcpl databases.

Usage

registerMthd(lvl, mthd, desc, nddr = 0L, type)

Arguments

Details

'mthd' must match a corresponding function name in the functions that load the methods, ie. mc2_mthds. 'nddr' only applies to level 6 methods.

Description

List of lists containing queries sent to tcplQuery associated with each test case. Each list also contains the associated ids with each case. Only meant to be used with automated testing with mocking for sc data.

Usage

sc_test

Format

A list with 20 items:

tcplConfQuery

Data table with 1 row and 2 columns used for each test case for establishing connection using tcplConf. This data table mocks the response one would get from connecting with invitrodb.

sc0_by_s0id

List containing the queries used for loading sc0 data by s0id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 's0id' labeled item storing the id used to load the data, for use in tests.

sc0_by_acid

List containing the queries used for loading sc0 data by acid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'acid' labeled item storing the id used to load the data, for use in tests.

sc1_by_s1id

List containing the queries used for loading sc1 data by s1id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 's1id' labeled item storing the id used to load the data, for use in tests.

sc1_by_acid

List containing the queries used for loading sc1 data by acid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'acid' labeled item storing the id used to load the data, for use in tests.

sc2_by_s2id

List containing the queries used for loading sc2 data by s2id via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 's2id' labeled item storing the id used to load the data, for use in tests.

sc2_by_aeid

List containing the queries used for loading sc2 data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

scagg_by_aeid

List containing the queries used for loading sc 'agg' data by aeid via tcplLoadData. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

plot_single_s2id

List containing the queries used for loading and plotting sc data by s2id via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 's2id' labeled item storing the id used to load the data, for use in tests.

plot_multiple_s2id

List containing the queries used for loading and plotting sc data by multiple s2ids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 's2id' labeled item storing the ids used to load the data, for use in tests.

plot_single_aeid

List containing the queries used for loading and plotting sc data by aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the id used to load the data, for use in tests.

plot_multiple_aeid

List containing the queries used for loading and plotting sc data by multiple aeids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains one 'aeid' labeled item storing the ids used to load the data, for use in tests.

plot_single_spid

List containing the queries used for loading and plotting sc data by spid/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid' and 'aeid' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_spid

List containing the queries used for loading and plotting sc data by multiple spids/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid' and 'aeid' labeled items storing the ids used to load the data, for use in tests.

plot_single_s2id_compare

List containing the queries used for loading and plotting compared sc data by s2id via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 's2id' and 'compare.s2id' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_s2id_compare

List containing the queries used for loading and plotting compared sc data by multiple s2ids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 's2id' and 'compare.s2id' labeled items storing the ids used to load the data, for use in tests.

plot_single_aeid_compare

List containing the queries used for loading and plotting compared sc data by aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'aeid' and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_aeid_compare

List containing the queries used for loading and plotting compared sc data by multiple aeids via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'aeid' and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

plot_single_spid_compare

List containing the queries used for loading and plotting compared sc data by spid/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid', 'compare.spid', 'aeid', and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

plot_multiple_spid_compare

List containing the queries used for loading and plotting compared sc data by multiple spids/aeid via tcplPlot. Each query has an associated data table response for mocking an actual connection. Contains 'spid', 'compare.spid', 'aeid', and 'compare.aeid' labeled items storing the ids used to load the data, for use in tests.

Source

ToxCast database

Description

This dataset is a list with 3 data.tables (sc0,sc1,sc2).

Usage

sc_vignette

Format

1. sc0 A data frame with 10 rows and 18 columns containing level 0 formatted raw data.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   acid

   Assay Component ID

   acnm

   Assay Component Name

   s0id

   Level 0 (sc0) ID

   apid

   Assay plate ID

   rowi

   Row Index

   coli

   Column Index

   wllt

   Well Type

   wllq

   Well Quality (0 or 1)

   conc

   Concentration in micromolar

   rval

   Raw assay component readout value

   srcf

   Source file containing the raw data

   conc_unit

   Concentration Units

2. sc1 A data frame with 10 rows and 20 columns containing level 1 normalized data.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   aeid

   Assay Component Endpoint ID

   aenm

   Assay endpoint name (i.e., assay_component_endpoint_name)

   acid

   Assay Component ID

   acnm

   Assay Component Name

   s0id

   Level 0 (sc0) ID

   s1id

   Level 1 (sc1) ID

   apid

   Assay plate ID

   rowi

   Row Index

   coli

   Column Index

   wllt

   Well Type

   logc

   Log base 10 concentration

   resp

   Normalized response value

   resp_unit

   Response Units

   conc_unit

   Concentration Units

3. sc2 A data frame with 10 rows and 15 columns containing level 2 efficacy/hit designation data.

   spid

   Sample ID

   chid

   Unique chemical ID number for tcpl

   casn

   Chemical Abstract Service(CAS) number

   chnm

   Chemical name

   dsstox_substance_id

   Chemical-specific DTXSID

   code

   CAS number compressed into numeric string

   aeid

   Assay Component Endpoint ID

   aenm

   Assay endpoint name (i.e., assay_component_endpoint_name)

   s2id

   Level 2 (sc2) ID

   bmad

   The median absolute deviation of all treatment wells (default option) or blank wells

   max_med

   Maximum median response

   hitc

   Hitcall

   coff

   Cutoff

   resp_unit

   Response Units

   conc_unit

   Concentration Units

Description

sc1 loads level 0 data from the tcpl database for the given id and performs level 1 single-concentration processing. The processed data is then loaded into the sc1 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Usage

sc1(ac, wr = FALSE)

Arguments

Details

Level 1 single-concentration processing includes mapping assay component to assay endpoint, duplicating the data when the assay component has multiple assay endpoints, and any normalization of the data. Data normalization based on methods listed in sc1_aeid and sc1_methods tables.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

Method functions, SC1_Methods

Other single-concentration: sc2()

Description

sc1_mthds returns a list of functions to be used during level 1 single-concentration processing.

Usage

sc1_mthds()

Details

The functions contained in the list returned by sc1_mthds return a list of expressions to be executed in the sc2 (not exported) function environment. The functions are described here for reference purposes, The sc1_mthds function is not exported, nor is it intended for use.

All available methods are described in the Available Methods section, listed by the function/method name.

Value

A list functions

Available Methods

The methods are broken into three types, based on what fields they define. Different methods are used to define "bval" (the baseline value), "pval" (the positive control value), and "resp" (the final response value).

Although it does not say so specifically in each description, all methods are applied by acid.

More information about the level 3 single-concentration processing is available in the package vignette, "Data_processing."

bval Methods

bval.apid.nwlls.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the raw values (rval) for neutral control wells (wllt = n).

bval.apid.twlls.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the raw values (rval) for test compound wells (wllt = t).

bval.apid.tn.med

Calculate the baseline value (bval) as the plate-wise median, by assay plate ID (apid), of the raw values (rval) for test compound wells (wllt = t) and neutral control wells (wllt = n).

bval.nwlls.med

Calculate the baseline value (bval) as the median of the raw values (rval) for neutral control wells (wllt = n) by assay endpoint id (aeid).

pval Methods

pval.apid.pwlls.med

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the raw values (rval) for single-concentration gain-of-signal positive control wells (wllt = p).

pval.apid.mwlls.med

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the raw values (rval) for multiple-concentration loss-of-signal negative control wells (wllt = m).

pval.apid.medpcbyconc.max

Calculate the positive control value (pval) as the plate-wise maximum, by assay plate ID (apid), of the medians of the raw values (rval) for gain-of-signal single- or multiple-concentration positive control wells (wllt = p or c) by apid, well type, and concentration.

pval.apid.medpcbyconc.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of the raw values (rval) for gain-of-signal single- or multiple-concentration positive control wells (wllt = p or c) by apid, well type, and concentration.

pval.apid.medncbyconc.min

Calculate the positive control value (pval) as the plate-wise minimum, by assay plate ID (apid), of the medians of the raw values (rval) for gain-of-signal single- or multiple-concentration negative control wells (wllt = m or o) by apid, well type, and concentration.

pval.zero

Set the positive control value (pval) to 0; pval = 0.

pval.apid.or.aeid.pwlls.med

Calculate the positive control value (pval) as the plate-wise median, by assay plate ID (apid), of the raw values (rval) for single-concentration gain-of-signal positive control wells (wllt = p). For plates without p wells, set the pval as the median pval calculated from all plates.

resp Methods

resp.pc

Calculate the normalized response (resp) as a percent of control, i.e. the ratio of the difference between the raw (rval) and baseline (bval) values divided by the difference between positive control (pval) and baseline (bval) values multiplied by 100; $resp=(rval-bval)/(pval-bval)*100$.

resp.fc

Calculate the normalized response (resp) as fold change, i.e. the ratio of the raw (rval) and baseline (bval) values; $resp = rval/bval$.

resp.logfc

Calculate the normalized response (resp) as the fold change of logged, i.e. the difference between raw (rval) and baseline (bval) log-scale values.

resp.log2

Transform the response values to log-scale (base 2).

resp.multneg1

Multiply the normalized response value (resp) by -1; $-1*resp$.

none

Use raw value (rval) as is. This may be necessary for additional endpoint-specific adjustments, or where no additional sc1 methods are needed.

resp.incr.zerocenter.fc

Calculate the normalized response (resp) as a zero center fold change, i.e. the ratio of the raw (rval) and baseline (bval) values minus 1; $resp=rval/bval-1$. Typically used for increasing responses.

Note

This function is not exported and is not intended to be used by the user.

See Also

sc1, Method functions to query what methods get applied to each acid

Description

sc2 loads level 1 data from the tcpl database for the given id and performs level 2 single-concentration processing. The processed data is then loaded into the sc2 table and all subsequent data is deleted with tcplCascade. See details for more information.

The individual processing functions are no longer exported, as it is typically more convenient and suggested to use the tcplRun wrapper function.

Usage

sc2(ae, wr = FALSE)

Arguments

Details

Level 2 single-concentration processing defines the bmad value, and uses the activity cutoff methods from sc2_aeid and sc2_methods to make an activity call.

Value

A boolean of length 1, indicating the success of the processing, or when 'wr' is FALSE, a list where the first element is a boolean indicating the success of processing and the second element is a data.table containing the processed data

See Also

Method functions, SC2_Methods

Other single-concentration: sc1()

Description

sc2_mthds returns a list of functions to be used during level 2 single-concentration processing.

Usage

sc2_mthds()

Details

The functions contained in the list returned by sc2_mthds return a list of expressions to be executed in the sc2 (not exported) function environment. The functions are described here for reference purposes, The sc2_mthds function is not exported, nor is it intended for use.

All available methods are described in the Available Methods section, listed by the function/method name.

Value

A list functions

Available Methods

The methods are broken down into four categories based on the type of cutoff they assign. Different methods are used to define cutoffs for "bmad" (baseline median absolute value), "pc" (percent of control), "pc or bmad", "log" ($\log_{2}$ or $\log_{10}$), and "other" (uncategorized methods).

All methods are applied by aeid.

Although there are method exceptions (notably within the “other” category), only highest calculated cutoff value based on assigned methods will be selected for hitcalling. Therefore, only the largest cutoff method per method type should be assigned.

More information about the level 2 single-concentration processing is available in the package vignette, "Data_processing."

BMAD Methods

bmad1

Add a cutoff value of 1 multiplied by baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad1.5

Add a cutoff value of 1.5 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad2

Add a cutoff value of 2 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad3

Add a cutoff value of 3 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad5

Add a cutoff value of 5 multiplied the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad6

Add a cutoff value of 6 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

bmad10

Add a cutoff value of 10 multiplied by the baseline median absolute deviation (bmad). By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

Percent of Control Methods

pc0.88

Add a cutoff value of 0.88. Typically for percent of control data.

pc16

Add a cutoff value of 16. Typically for percent of control data.

pc20

Add a cutoff value of 20. Typically for percent of control data.

pc25

Add a cutoff value of 25. Typically for percent of control data.

pc30

Add a cutoff value of 30. Typically for percent of control data.

Percent of Control or BMAD Methods

pc30orbmad3

Add a cutoff value of either 30 or 3 multiplied by the baseline median absolute deviation (bmad), whichever is less. By default, bmad is calculated using test compound wells (wllt = t) for the endpoint.

Log Methods

Log Base 2

log2_0.76

Add a cutoff value of 0.76 for log2-transformed data. This was a custom threshold value set for endpoint id 1690 (formerly aeid 1691).

log2_1.2

Add a cutoff value of $log_{2}{1.2}$. Typically for fold change data.

log2_1.5

Add a cutoff value of $log_{2}{1.5}$. Typically for fold change data.

Log Base 10

log10_1.2

Add a cutoff value of $log_{10}{1.2}$. Typically for fold change data.

Other Methods

ow_bmad_nwells

Overwrite the default baseline median absolute value (bmad) with a bmad calculated using neutral control wells (wllt = n).

ow_bidirectional_gain

Where responses only in the positive direction are biologically relevant, overwrite the max_med and max_tmp values, which were calculated using absolute value, to a calculation using a true maximum for uni-directional data.

ow_bidirectional_loss

Where responses only in the negative direction are biologically relevant, overwrite the max_med and max_tmp values, which were calculated using absolute value, to a calculation using a true minimum for uni-directional data.

Note

This function is not exported and is not intended to be used by the user.

See Also

sc2, Method functions to query what methods get applied to each acid

Description

The example dataset is used to illustrate how the user can pipeline single-concentration data from chemical screening using tcpl.

Usage

scdat

Format

A data frame with 320 rows and 10 variables:

spid

sample ID

apid

assay plate ID

rowi

well-plate row number (N/A)

coli

well-plate column number (N/A)

wllt

well type (N/A)

wllq

well quality (N/A)

conc

concentration in micromolar

rval

raw assay component readout value

srcf

source file containing the data

acsn

assay component source name

Source

ToxCast database

Description

sink.reset resets all sinks and returns all output to the console.

Usage

sink.reset()

Details

sink.reset identifies all sinks with sink.number then returns all output and messages back to the console.

See Also

sink, sink.number

Other tcpl abbreviations: is.odd(), lu(), lw()

Description

tcplAddModel draws a a line for one of the tcpl Models (see Models for more information) onto an existing plot.

Usage

tcplAddModel(pars, modl = NULL, adj = NULL, ...)

Arguments

Details

tcplAddModel draws the model line assuming the x-axis represents log base 10 concentration.

If modl is NULL, the function checks pars$modl and will return an error if pars$modl is also NULL.

adj is intended to scale the models, so that models with different response units can be visualized on a single plot. The recommended value for adl is 1/(3*bmad) for level 4 data and 1/coff for level 5 data. If adj is NULL the function will check pars$adj and set adj to 1 if pars$adj is also NULL.

See Also

Models, tcplPlotFits

Examples

## Create some dummy data to plot
logc <- 1:10
r1 <- sapply(logc, tcplHillVal, ga = 5, tp = 50, gw = 0.5)
r2 <- log2(sapply(logc, tcplHillVal, ga = 4, tp = 30, gw = 0.5))
p1 <- tcplFit(logc = logc, resp = r1, bmad = 10)
p2 <- tcplFit(logc = logc, resp = r2, bmad = log2(1.5))

## In the dummy data above, the two plots are on very different scales
plot(r1 ~ logc, pch = 16, ylab = "raw response")
tcplAddModel(pars = p1, modl = "hill")
points(r2 ~ logc)
tcplAddModel(pars = p2, modl = "hill", lty = "dashed")

## To visualize the two curves on the same plot for comparison, we can 
## scale the values to the bmad, such that a scaled response of 1 will equal
## the bmad for each curve.
plot(r1/10 ~ logc, pch = 16, ylab = "scaled response")
tcplAddModel(pars = p1, modl = "hill", adj = 1/10)
points(r2/log2(5) ~ logc)
tcplAddModel(pars = p2, modl = "hill", adj = 1/log2(5), lty = "dashed")

Description

tcplAICProb Calculates the probability that the model best represents the data based on the AIC value for each model.

Usage

tcplAICProb(...)

Arguments

Details

The function takes vectors of AIC values. Each vector represents the model AIC values for multiple observation sets. Each vector must contain the same number and order of observation sets. The calculation assumes every possible model is accounted for, and the results should be interpreted accordingly.

Value

A vector of probability values for each model given, as a list.

See Also

tcplFit, AIC for more information about AIC values.

Examples

## Returns the probability for each model, given models with AIC values 
## ranging from 80 to 100
tcplAICProb(80, 85, 90, 95, 100)

## Also works for vectors
m1 <- c(95, 195, 300) ## model 1 for three different observations
m2 <- c(100, 200, 295) ## model 2 for three different observations
tcplAICProb(m1, m2)

Description

tcplAppend takes a data.table (dat) and appends the data.table into a database table.

Usage

tcplAppend(dat, tbl, db, lvl = NULL)

Arguments

Note

This function is not exported and not intended to be used by the user.

Description

tcplCascade deletes the data for the given id(s) starting at the processing level given. The delete will cascade through all subsequent tables.

Usage

tcplCascade(lvl, type, id)

Arguments

Details

The data type can be either 'mc' for multiple concentration data, or 'sc' for single concentration data. Multiple concentration data will be loaded into the level tables, whereas the single concentration will be loaded into the single tables.

If lvl is less than 3, id is interpreted as acid(s) and if lvl is greater than or equal to 3, id is interpreted as aeid(s).

Note

This function is not exported and not intended to be used by the user.

Description

tcplCode2CASN takes a code and converts it CAS Registry Number.

Usage

tcplCode2CASN(code)

Arguments

Details

The function checks for the validity of the CAS Registry Number. Also, the ToxCast data includes chemicals for which there is no CASRN. The convention for these chemicals is to give them a CASRN as NOCAS_chid; the code for these compounds is CNOCASchid. The function handles the NOCAS compounds as they are stored in the database, as shown in the example below.

Value

A CAS Registry Number.

Examples

tcplCode2CASN("C80057")
tcplCode2CASN("C09812420") ## Invalid CASRN will give a warning
tcplCode2CASN("CNOCAS0015") ## The underscore is reinserted for NOCAS codes

Description

tcplCytoPt calculates the cytotoxicity point and average cytotoxicity distribution based on the activity in the "burst" assay endpoints.

Usage

tcplCytoPt(
  chid = NULL,
  aeid = NULL,
  flag = TRUE,
  min.test = TRUE,
  default.pt = 3
)

Arguments

Details

tcplCytoPt provides estimates for chemical-specific cytotoxicity distributions (more information available in the vignette.) Before calculating the cytotoxicity distributions, the level 5 data is subsetted by the tcplSubsetChid function.

The 'chid' parameter specifies a subset of chemicals to use in the calculations, given by chemical ID (chid). The 'aeid' parameter specifies which assays to use in calculating the cytotoxicity point and distribution. By default tcplCytoPt will use all available chemicals and the assay endpoints defined by the 'burst_assay' field in the "assay_component_endpoint" table. The examples show how to identify the "burst" endpoints.

tcplCytoPt returns the cytotoxicity point (the AC50 values of the active "burst" endpoints), the corresponding MAD, and the global MAD (median of the calculated MAD values). Not every chemical must be tested in every "burst" endpoint. The 'min.test' parameter allows the user to specify a minimum number of tested assay endpoints as a requirement for MAD values to be included in the global MAD calculation. For example, suppose the user supplies 10 "burst" assays. The user can choose to require a chemical to be tested in at least 5 of those assays for it's MAD value to be included in the global MAD calculation. Having chemicals with many less "burst" endpoints tested may inflate or deflate the global MAD calculation. By default (values of TRUE or NULL), tcplCytoPt requires a chemical to be tested in at least 80% of the given "burst" assays. The user can also provide 'min.test' values of FALSE (indicating to include all MAD values), or a number (indicating a specific number of endpoints).

Chemicals without at least 2 active "burst" assays do not have a MAD value, and the cytotoxicity point is defined by the 'default.pt' parameter. The default value for 'default.pt' is 3.

The resulting data.table has the following fields:

1. "chid" – The chemical ID.

2. "code" – The chemical code.

3. "chnm" – The chemical name.

4. "casn" – The chemical CASRN.

5. "med" – The median of the "burst" endpoint log(AC50)

6. "mad" – The MAD of the "burst" endpoint log(AC50) values.

7. "ntst" – The number of "burst" endpoints tested.

8. "nhit" – The number of active "burst" endpoints.

9. "used_in_global_mad_calc" – TRUE/FALSE, whether the mad value was used in the global MAD calculation.

10. "global_mad" – The median of the "mad" values where "used_in_global_mad_calc" is TRUE.

11. "cyto_pt" – The cytotoxicity point, or the value in "med" when "nhit" is at least 2.

12. "cyto_pt_um" – $10^\mathit{cyto\_pt}$

13. "lower_bnd_um" – $10^{\mathit{cyto\_pt} - 3\mathit{global\_mad}}$

Value

A data.table with the cytotoxicity distribution for each chemical. The definition of the field names are listed under "details."

Examples

## Not run: 
## Can only calculate the cytotox burst if using the MySQL database and
## TCPL_DRVR == 'MySQL'

if (getOption("TCPL_DRVR") == "MySQL") {

## Load the "burst" endpoints -- none are defined in the example dataset
tcplLoadAeid(fld = "burst_assay", val = 1)

## Calculate the cytotoxicity distributions using both example endpoints
tcplCytoPt(aeid = 1:2)

## The above example does not calculate a global MAD, because no chemical
## hit both endpoints. (This makes sense, because both endpoints are 
## derived from one component, where one endpoint is acitivity in the
## up direction, and the other is activity in the down direction.)
## Note, the cyto_pt is also 3 for all chemicals, because the function
## requires at least two endpoints to calculate a cytotoxicity point. If 
## the user wishes to use one assay, this function is not necessary. 

## Changing 'default.pt' will change cyto_pt in the resulting data.table
tcplCytoPt(aeid = 1:2, default.pt = 6)
}

## End(Not run)

Description

tcpldbStats takes a string(type) and an optional parameter(val) to return the summary statistics on the entire tcpl database When type = "all" the val is ignored. the function returns the number of distinct spid and aeids in the database at each level When type = "aeid", the val parameter has to be a valid aeid in the database. The function returns a table consisting of the number of distinct spids at each level of processing for the aeid given in 'val' When type = "spid", the val parameter has to be a valid spid in the database. The function returns a table consisting of the number of distinct aeids at each level of processing for the given spid in 'val'

Usage

tcpldbStats(type = "all", val = NULL)

Arguments

Description

tcplDefine queries the tcpl databases and returns field descriptions from the data dictionary.

Usage

tcplDefine(val = NULL)

Arguments

Details

Short descriptions of fields for different tables are stored in a data dictionary. Query by table name to retrieve descriptions of each field in the given table, and/or query by field name to retrieve descriptions on every field with the given name, regardless of which table they belong to.

Value

A data.table with the data dictionary information for the given parameters.

Examples

## Store the current config settings, so they can be reloaded at the end 
## of the examples
conf_store <- tcplConfList()
tcplConf(drvr = "example")

## Passing no parameters returns all of the fields described in the data
## dictionary
tcplDefine()

## Specifying table names of 'chemical' and 'sample' yields all of the
## fields from the 'chemical' and 'sample' tables
tcplDefine(c("chemical", "sample"))

## Specifying a field of 'wllt' yields all of the fields from any table that
## contains 'wllt' as a field
tcplDefine("wllt")

## Specifying a combination of table and field names results in all of the
## fields which are contained in the given tables and all of the given fields
## found in any table
tcplDefine(c("chemical", "spid", "wllt"))

## Reset configuration
options(conf_store)

Description

tcplDelete deletes rows from the given table and database.

Usage

tcplDelete(tbl, fld, val, db)

Arguments

Note

This function is not exported and not intended to be used by the user.

See Also

tcplSendQuery

Description

tcplFit fits the constant, hill, and gain-loss models to the given data and returns some summary statistics and the fit parameters in a list.

Usage

tcplFit(
  logc,
  resp,
  bmad,
  force.fit = FALSE,
  bidirectional = FALSE,
  verbose = FALSE,
  ...
)

Arguments

Details

when at least one median value is greater than 3*bmad.

Value

List of summary values and fit parameters for the given data.

See Also

tcplObjCnst, tcplObjHill, tcplObjGnls, constrOptim

Examples

logc <- 1:10
resp <- sapply(1:10, tcplHillVal, ga = 5, tp = 50, gw = 0.5)
params <- tcplFit(logc = logc, resp = resp, bmad = 10)
plot(resp ~ logc)
tcplAddModel(pars = params, modl = "hill")

Description

tcpl Wrapper for tcplfit2_core including additional calculations to fit into new schema

Usage

tcplFit2(
  dat,
  fitmodels = c("cnst", "hill", "gnls", "poly1", "poly2", "pow", "exp2", "exp3", "exp4",
    "exp5"),
  bmed = NULL
)

Arguments

Value

Data.table with an additional column fitparams that includes all of the fitting parameters

Description

Nest dataframe into a list that is readable by tcplfit2

Usage

tcplFit2_nest(dat)

Arguments

Value

a list of fitting parameters that can be consumed by tcplfit2

Description

Unnest tcplfit2 parameters into a dataframe

Usage

tcplFit2_unnest(output)

Arguments

Value

list of parameters unnested and compiled into a dataframe

Description

tcplGetAeid takes a string(name) and finds the assay component endpoint names that match the string and the aeids associated with those names.The function performs a regular expression like matching for strings in the assay component endpoint name column in the assay component endpoint table.

Usage

tcplGetAeid(name)

Arguments

Examples

## Not run: 
## Search for aenm (assay name) case insensitive
tcplGetAeid("TOX21")
tcplGetAeid("tox21")

## End(Not run)

Description

tcplggplot

Usage

tcplggplot(dat, lvl = 5, verbose = FALSE, flags = FALSE, yrange = c(NA, NA))

Arguments

Value

A ggplot object or grob with accompanied table depending on verbose option

Description

tcplggplotCompare

Usage

tcplggplotCompare(
  dat,
  compare.dat,
  lvl = 5,
  verbose = FALSE,
  flags = FALSE,
  yrange = c(NA, NA)
)

Arguments

Value

A ggplot object or grob with accompanied table depending on verbose option

Description

Hitcalling with tcplfit2

Usage

tcplHit2(mc4, coff)

Arguments

Value

Data.table with key value pairs of hitcalling parameters

Description

tcplLegacyPlot

Usage

tcplLegacyPlot()

Value

a ggplot based on old plotting methodology

Description

tcplListFlds loads the column names for the given table and database.

Usage

tcplListFlds(tbl, db = getOption("TCPL_DB"))

Arguments

Details

This function can be particularly useful in defining the 'fld' param in the tcplLoad- functions.

Value

A string of field names for the given table.

Examples

## Not run: 
## Gives the fields in the mc1 table
tcplListFlds("mc1")

## End(Not run)

Description

tcplLoadChem queries the tcpl database and returns the chemical information for the given field and values.

Usage

tcplLoadChem(field = NULL, val = NULL, exact = TRUE, include.spid = TRUE)

Arguments

Details

The 'field' parameter is named differently from the 'fld' parameter seen in other functions because it only takes one input.

In the MySQL environment the user should be able to give partial chemical name strings, to find chemicals with similar names. For example, setting 'val' to "phenol" when 'field' is "chnm" and 'exact' is FALSE might pull up the chemicals "Bisphenol A" and "4-Butylphenol". More technically, setting 'exact' to FALSE passes the string in 'val' to an RLIKE statement within the MySQL query.

Value

A data.table with the chemical information for the given parameters

Examples

## Not run: 
## Passing no parameters gives all of the registered chemicals with their
## sample IDs
tcplLoadChem()

## Or the user can exclude spid and get a unique list of chemicals
tcplLoadChem(include.spid = FALSE)

## In addition, the user can retrieve only the registered chemicals from the chemical table
tcplLoadChem(field = 'chem.only')

## Other examples:
tcplLoadChem(field = "chnm", val = "Bisphenol A")
tcplLoadChem(field = "chid", val = 20182)

## End(Not run)

Description

tcplLoadChemList queries the tcpl databases and returns information about the chemical lists.

Usage

tcplLoadChemList(field = NULL, val = NULL)

Arguments

Details

Chemicals are stored in different lists by chemical ID. Therefore, it is not possible to delineate samples with the same chemical ID into two distinct chemical lists. However, it is possible for a chemical ID to belong to more than one (or no) chemical lists.

When chemicals belong to more than one list, the chemical is listed multiple times (one for each distinct list).

Value

A data.table with the chemical list information for the given parameters.

Examples

## Not run: 
## Passing no parameters gives all of the chemical IDs that have a chemical
## list registered
clist <- tcplLoadChemList()

## Notice there are different number of rows in tcplLoadChemList than in tcplLoadChem, 
## indicating some chemicals must belong to more than list (or no lists).
chem <- tcplLoadChem(include.spid = TRUE)
nrow(chem)
nrow(clist)


## Show the unique chemical lists
clist[ , unique(list_acronym)]

## Specifying a chemical list will not show what other libraries a 
## chemical might belong to.
tcplLoadChemList(field = "list_acronym", val = "CPDBAS")
tcplLoadChemList(field = "chid", val = 20182)
tcplLoadChemList(field = "dsstox_substance_id", val = "DTXSID7020182")

## End(Not run)

Description

tcplLoadUnit queries the tcpl databases and returns a data.table with the concentration units for the given assay endpoint ids (spid).

Usage

tcplLoadConcUnit(spid)

Arguments

Value

A data.table containing level 3 correction methods for the given spids.

See Also

tcplQuery, data.table

Description

tcplLoadData queries the tcpl databases and returns a data.table with data for the given level and data type.

Usage

tcplLoadData(lvl, fld = NULL, val = NULL, type = "mc", add.fld = TRUE)

Arguments

Details

The data type can be either 'mc' for mutliple concentration data, or 'sc' for single concentration data. Multiple concentration data will be loaded into the 'mc' tables, whereas the single concentration will be loaded into the 'sc' tables.

Setting 'lvl' to "agg" will return an aggregate table containing the m4id with the concentration-response data and m3id to map back to well-level information.

If tcplConf() was set with "API" as the driver, then tcplLoadData will return data from the CCTE Bioactivity API. API data is available for type = 'mc' and lvl = c(3,4,5,6) and 'agg'. Only fields relating to the requested level are returned, but not all fields that usually return from invitrodb are available from the API. To have all fields available from the API return, regardless of what lvl is set to, set add.fld to TRUE. API query-able fields include "aeid", "spid", "m4id", and "dtxsid".

Leaving fld NULL will return all data.

Valid fld inputs are based on the data level and type: