statmod (version 1.4.1)

elda: Extreme Limiting Dilution Analysis

Description

Fit single-hit model to a dilution series using complementary log-log binomial regression.

Usage

elda(response, dose, tested=rep(1,length(response)), group=rep(1,length(response)), observed=FALSE, confidence=0.95, test.unit.slope=FALSE)
limdil(response, dose, tested=rep(1,length(response)), group=rep(1,length(response)), observed=FALSE, confidence=0.95, test.unit.slope=FALSE)

Arguments

response
numeric of integer counts of positive cases, out of tested trials
dose
numeric vector of expected number of cells in assay
tested
numeric vector giving number of trials at each dose
group
vector or factor giving group to which the response belongs
observed
logical, is the actual number of cells observed?
confidence
numeric level for confidence interval
test.unit.slope
logical, should the adequacy of the single-hit model be tested?

Value

  • elda is the newer name for the limdil function. limdil produces an object of class limdil with the following components. There are print.limdil and plot.limdil methods for limdil objects.
  • CInumeric vector giving estimated frequency and lower and upper limits of Wald confidence interval of each group
  • test.differencenumeric vector giving chisquare likelihood ratio test statistic and p-value for testing the difference between groups
  • test.unit.slopenumeric vector giving chisquare likelihood ratio test statistic and p-value for testing the slope of the offset equal to one
  • responsenumeric of integer counts of positive cases, out of tested trials
  • testednumeric vector giving number of trials at each dose
  • dosenumeric vector of expected number of cells in assay
  • groupvector or factor giving group to which the response belongs
  • num.groupnumber of groups

Details

A binomial generalized linear model is fitted for each group with cloglog link and offset log(dose). If observed=FALSE, a classic Poisson single-hit model is assumed, and the Poisson frequency of the stem cells is the exp of the intercept. If observed=TRUE, the values of dose are treated as actual cell numbers rather than expected values. This doesn't changed the generalized linear model fit but changes how the frequencies are extracted from the estimated model coefficient. The confidence interval is a Wald confidence interval, unless all the responses are zero or at the maximum value, in which case Clopper-Pearson intervals are computed. If group takes several values, then separate confidence intervals are computed for each group. In this case it also possible to test for non-equality in frequency between the groups.

References

Bonnefoix T, Bonnefoix P, Verdiel P, Sotto JJ. (1996). Fitting limiting dilution experiments with generalized linear models results in a test of the single-hit Poisson assumption. J Immunol Methods 194, 113-119. Clopper, C. and Pearson, S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404-413. Shackleton, M., Vaillant, F., Simpson, K. J., Stingl, J., Smyth, G. K., Asselin-Labat, M.-L., Wu, L., Lindeman, G. J., and Visvader, J. E. (2006). Generation of a functional mammary gland from a single stem cell. Nature 439, 84-88. http://www.nature.com/nature/journal/v439/n7072/abs/nature04372.html Hu, Y, and Smyth, GK (2008). limdil Software for Limiting Dilution Analysis. Technical Report, The Walter and Eliza Hall Institute of Medical Research. http://bioinf.wehi.edu.au/software/elda/elda_main.pdf

Examples

Run this code
# When there is one group
Dose <- c(50,100,200,400,800)
Responses <- c(2,6,9,15,21)
Tested <- c(24,24,24,24,24)
out <- limdil(Responses,Dose,Tested,test.unit.slope=TRUE)
out
plot(out)

# When there are four groups
Dose <- c(30000,20000,4000,500,30000,20000,4000,500,30000,20000,4000,500,30000,20000,4000,500)
Responses <- c(2,3,2,1,6,5,6,1,2,3,4,2,6,6,6,1)
Tested <- c(6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6)
Group <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4)
limdil(Responses,Dose,Tested,Group,test.unit.slope=TRUE)

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