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Fit concentration-response data using Curve Class2 approach

Source: R/curveclass2_base.R
fit_cc2_modl.Rd

Curve Class2 uses 4-parameter Hill model to fit the data. The algorithm assumes the responses are in percentile. Curve Class2 classifies the curves based on fit quality and response magnitude.

Usage

fit_cc2_modl(Conc, Resp, classSD = 5, minYrange = 20, ...)

Arguments

Conc

A vector of log10 concentrations.

Resp

A vector of numeric responses.

classSD

A standard deviation (SD) derived from the responses in the vehicle control. it is used for classification of the curves. Default = 5%.

minYrange

A minimum response range (max activity - min activity) required to apply curve fitting. Curve fitting will not be attempted if the response range is less than the cutoff. Default = 20%.

...

for additional curve class2 parameters (currently none)

Value

A list of output parameters from Curve Class2 model fit. If the data are fit or not fittable (fit = 0), the default value for tp, ga, gw, bt pvalue, masks, nmasks is NA. For cc2 = 4, it is still possible to have fit parameters.

  • modl: model type, i.e., cc2

  • fit: fittable, 1 (yes) or 0 (no)

  • aic: NA, it is not calculated for this model. The parameter is kept for compatability.

  • cc2: curve class2, default = 4

  • tp: model top, <0 means the fit for decreasing direction is preferred

  • ga: ac50 (log10 scale)

  • gw: Hill coefficient

  • bt: model bottom

  • pvalue: from F-test, for fit quality

  • r2: fitness

  • masks: a string to indicate at which positions of response are masked

  • nmasks: number of masked responses

Details

cc2 = 1.1

2-asymptote curve, pvalue < 0.05, emax > 6\*classSD

cc2 = 1.2

2-asymptote curve, pvalue < 0.05, emax <= 6\*classSD & emax > 3\*classSD

cc2 = 1.3

2-asymptote curve, pvalue >= 0.05, emax > 6\*classSD

cc2 = 1.4

2-asymptote curve, pvalue >= 0.05, emax <= 6\*classSD & emax > 3\*classSD

cc2 = 2.1

1-asymptote curve, pvalue < 0.05, emax > 6\*classSD

cc2 = 2.2

1-asymptote curve, pvalue < 0.05, emax <= 6\*classSD & emax > 3\*classSD

cc2 = 2.3

1-asymptote curve, pvalue >= 0.05, emax > 6\*classSD

cc2 = 2.4

1-asymptote curve, pvalue >= 0.05, emax <= 6\*classSD & emax > 3\*classSD

cc2 = 3

single point activity, pvalue = NA, emax > 3\*classSD

cc2 = 4

inactive, pvalue >= 0.05, emax <= 3\*classSD

cc2 = 5

inconclusive, high bt, further investigation is needed

References

Huang R (2022). “A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling.” Methods in molecular biology (Clifton, N.J.), 2474, 133—145. ISSN 1064-3745, doi:10.1007/978-1-0716-2213-1_13 .

See also

fit_modls()

Examples


fit_cc2_modl(c(-9, -8, -7, -6, -5, -4), c(0, 2, 30, 40, 50, 60))
#> $aic
#> [1] NA
#> 
#> $fit
#> [1] 1
#> 
#> $modl
#> [1] "cc2"
#> 
#> $cc2
#> [1] 1.1
#> 
#> $gw
#> [1] 0.6
#> 
#> $tp
#> [1] 57.4
#> 
#> $bt
#> [1] -4
#> 
#> $ga
#> [1] -6.9
#> 
#> $masks
#> [1] "0 0 0 0 0 0"
#> 
#> $nMasks
#> [1] 0
#> 
#> $pvalue
#> [1] 0.01651874
#> 
#> $r2
#> [1] 0.9699059
#>