MC5_Methods | R Documentation |
mc5_mthds
returns a list of additional activity cutoff methods to be used during level 5
multiple-concentration processing.
mc5_mthds(ae)
ae |
Integer of length 1, the assay endpoint id |
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.
A list of functions
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."
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.
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.
Add a cutoff value of 3 multiplied by the baseline median absolute deviation (bmad) as defined at Level 4.
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.
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.
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.
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.
Add a cutoff value of 0.2. Typically for zero centered fold change data.
Add a cutoff value of 0.25. Typically for zero centered fold change data.
Add a cutoff value of 0.3. Typically for zero centered fold change data.
Add a cutoff value of 0.5. Typically for zero centered fold change data.
Log Base 2
Add a cutoff value of -\log_{2}{0.88}
.
Add a cutoff value of \log_{2}{1.2}
. Typically for fold
change data.
Add a cutoff value \log_{2}{2}
. Typically for fold change data.
Log Base 10
Add a cutoff value of \log_{10}{1.2}
. Typically for fold
change data.
Add a cutoff value of \log_{10}{2}
. Typically for fold change
data.
Add a cutoff value of 5. Typically for percent of control data.
Add a cutoff value of 10. Typically for percent of control data.
Add a cutoff value of 16. Typically for percent of control data.
Add a cutoff value of 20. Typically for percent of control data.
Add a cutoff value of 25. Typically for percent of control data.
Add a cutoff value of 30. Typically for percent of control data.
Add a cutoff value of 40. Typically for percent of control data.
Add a cutoff value of 50. Typically for percent of control data.
Add a cutoff value of 70. Typically for percent of control data.
Add a cutoff value of 95. Typically for percent of control data.
Add a cutoff value of 20 percent of the maximum of all endpoint maximal average response values (max_med).
Add a cutoff value of 2.32.
Method not yet updated for tcpl implementation. Identify the lowest observed effective concentration (loec) compared to baseline.
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.
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.
Overwrite the osd value so that bmr equals cutoff.
This function is not exported and is not intended to be used by the user.
mc5
, Method functions
to query what methods get applied to
each aeid.
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