elda: Extreme Limiting Dilution Analysis

In statmod: Statistical Modeling

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)
eldaOneGroup(response, dose, tested, observed=FALSE, confidence=0.95,
     tol=1e-8, maxit=100, trace=FALSE)

Arguments

response	numeric vector giving number of positive cases out of tested trials. Should take non-negative integer values.
dose	numeric vector of expected number of cells in assay. Values must be positive.
tested	numeric vector giving number of trials at each dose. Should take integer values.
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. Should be strictly between 0 and 1.
test.unit.slope	logical, should the adequacy of the single-hit model be tested?
tol	convergence tolerance.
maxit	maximum number of Newton iterations to perform.
trace	logical, if TRUE then iterim results are output at each iteration.

Details

elda and limdil are alternative names for the same function. (limdil was the older name before the 2009 paper by Hu and Smyth.) eldaOneGroup is a lower-level function that does the computations when there is just one group, using a globally convergent Newton iteration. It is called by the other functions.

These functions implement maximum likelihood analysis of limiting dilution data using methods proposed by Hu and Smyth (2009). The functions gracefully accommodate situations where 0% or 100% of the assays give positive results, which is why we call it "extreme" limiting dilution analysis. The functions provide the ability to test for differences in stem cell frequencies between groups, and to test goodness of fit in a number of ways. The methodology has been applied to the analysis of stem cell assays (Shackleton et al, 2006).

The statistical method is explained by Hu and Smyth (2009). 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 change the generalized linear model fit, but it does change how the frequencies are extracted from the estimated model coefficient (Hu and Smyth, 2009).

The confidence interval is a Wald confidence interval, unless the responses are all negative or all positive, 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 a likelihood ratio test is conducted for differences in active cell frequencies between the groups.

These functions compute a number of different tests of goodness of fit. One test is based on the coefficient for log(dose) in the generalized linear model. The nominal slope is 1. A slope greater than one suggests a multi-hit model in which two or more cells are synergistically required to produce a positive response. A slope less than 1 suggests some sort of cell interference. Slopes less than 1 can also be due to heterogeneity of the stem cell frequency between assays. elda conducts likelihood ratio and score tests that the slope is equal to one.

Another test is based on the coefficient for dose. This idea is motivated by a suggestion of Gart and Weiss (1967), who suggest that heterogeneity effects are more likely to be linear in dose than log(dose). These functions conducts score tests that the coefficient for dose is non-zero. Negative values for this test suggest heterogeneity.

These functions produce objects of class "limdil". There are print and plot methods for "limdil" objects.

Value

elda and limdil produce an object of class "limdil". This is a list with the following components:

CI	numeric matrix giving estimated stem cell frequency and lower and upper limits of Wald confidence interval for each group
test.difference	numeric vector giving chisquare likelihood ratio test statistic and p-value for testing the difference between groups
test.slope.wald	numeric vector giving wald test statistics and p-value for testing the slope of the offset equal to one
test.slope.lr	numeric vector giving chisquare likelihood ratio test statistics and p-value for testing the slope of the offset equal to one
test.slope.score.logdose	numeric vector giving score test statistics and p-value for testing multi-hit alternatives
test.slope.score.dose	numeric vector giving score test statistics and p-value for testing heterogeneity
response	numeric of integer counts of positive cases, out of tested trials
tested	numeric vector giving number of trials at each dose
dose	numeric vector of expected number of cells in assay
group	vector or factor giving group to which the response belongs
num.group	number of groups

Author(s)

Yifang Hu and Gordon Smyth

References

Hu, Y, and Smyth, GK (2009). ELDA: Extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. Journal of Immunological Methods 347, 70-78. doi: 10.1016/j.jim.2009.06.008 http://www.statsci.org/smyth/pubs/ELDAPreprint.pdf

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. doi: 10.1038/nature04372

Gart, JJ, and Weiss, GH (1967). Graphically oriented tests for host variability in dilution experiments. Biometrics 23, 269-284.

See Also

plot.limdil and print.limdil are methods for limdil class objects.

A web interface to this function is available at https://bioinf.wehi.edu.au/software/elda/.

Examples

# 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 <- elda(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)
elda(Responses,Dose,Tested,Group,test.unit.slope=TRUE)

statmod documentation built on Jan. 6, 2023, 5:14 p.m.