waltlabtools.core.lod

lod(blank_signal, inverse_fun=None, sds=3, corr='c4')[source]

Compute the limit of detection (LOD).

Parameters
  • blank_signal (array-like) -- Signal (e.g., average number of enzymes per bead, AEB) of the zero calibrator. Must have at least two elements.

  • inverse_fun (function or CalCurve) -- The functional form used for the calibration curve. If a function, it should accept the measurement reading (y, e.g., fluorescence) as its only argument and return the value (x, e.g., concentration). If inverse_fun is a CalCurve object, the LOD will be calculated from its inverse method.

  • sds (numeric, optional) -- How many standard deviations above the mean should the background should the limit of detection be calculated at? Common values include 2.5 (Quanterix), 3 (Walt Lab), and 10 (lower limit of quantification, LLOQ).

  • corr ({"n", "n-1", "n-1.5", "c4"} or numeric, default "c4") -- The sample standard deviation under-estimates the population standard deviation for a normally distributed variable. Specifies how this should be addressed. Options:

    • "n" : Divide by the number of samples to yield the uncorrected sample standard deviation.

    • "n-1" : Divide by the number of samples minus one to yield the square root of the unbiased sample variance.

    • "n-1.5" : Divide by the number of samples minus 1.5 to yield the approximate unbiased sample standard deviation.

    • "c4" : Divide by the correction factor to yield the exact unbiased sample standard deviation.

    • If numeric, gives the delta degrees of freedom.

Returns

lod_x (numeric) -- The limit of detection, in units of x (e.g., concentration).

See also

c4

unbiased estimation of the population standard deviation

numpy.std

standard deviation