Fragman (version 1.0.1)

find.ladder: Ladder detection by correlation or confidence intervals

Description

This function takes a vector of color heights/intensities from the fragment analysis containing the ladder/standard channel, and detects the biggest peaks where the derivative is equal zero and uses the information from the expected weights for the ladder to construct confidence intervals in order to detect the ladder peaks.

Please! if using the confidence interval method ("ci"), which is not the default, once you have found the best parameters for the arguments to match your ladder using this function, please pass those values to all the posterior functions, please make sure the 'dev' argument is passed to the new functions.

Usage

find.ladder(x, ladder, ci.upp=1.96, ci.low=1.96, 
            draw=TRUE, dev=50, warn=TRUE, init.thresh=250, 
            sep.index=8, method="red", avoid=1500, who="sample")

Arguments

x

Vector of heights from the ladder channel. See example to see how to access to it.

ladder

Vector containing the expected weights of the dna fragments of the ladder in use

ci.upp

A scalar value indicating how many standar errors will be used to detect peaks when checking the height of the ladder peaks(upper bound). To be used in the find.ladder function

ci.low

A scalar value indicating how many standar errors will be used to detect peaks when checking the height of the ladder peaks(lower bound). To be used in the 'find.ladder' function

draw

A TRUE/FALSE value indicating if the plot for the ladder found should be printed or not

dev

A scalar value indicating the number of indexes to be used as peak separation when deciding the ladder peaks. Some ladders contain dna fragments of very closed weights and modifying this parameter helps to detect them correctly

warn

A TRUE/FALSE value indicating if warnings should be provided when detecting the ladder

init.thresh

An initial value of color intensity to be used when detecting the ladder

sep.index

A scalar value indicating how many indexes should be allowed to considered a true peak from noisy peaks

method

An argument indicating one of the 2 methods available; "cor" makes all possible combination of peaks and searches exhaustive correlations to find the right peaks corresponsding to the expected DNA weights, or "ci" constructing confidence intervals to look

who

A name to indicate which sample is being analyzed

avoid

A scalar value indicating how many indexes should be avoided when the method of correlation fails to find peaks and a random sample will be drawn from the existing peaks. The default is 1500 indexes which will samples peaks avoiding the first 1500 indexes

Value

• If parameters are indicated correctly the function returns: [object Object],[object Object],[object Object]

Details

We have implemented 3 methods for sizing the ladder, each with their advantages and disadvantages. The default method named "red" which stands for "reduction" detect the region where peaks exist (in indexes) in the ladder channel and assumes that your ladder should have some equivalence in indexes and creates an 'expected ladder', then the putative ladder moves along the peak region and correlations and squared distances to the closest peaks are calculated. We have define the coefficient of similarity (CS) as cor(x,y)/var(z), where:

cor(x,y) are the correlations between expected and observed peaks, and var(z) is the sum of squares between the differences of expected and observed peaks.

This value usually let us identify the most likely peaks and then all possible combinations for those peaks are computed followed by exhaustive correlations of those combinations with the actual ladder. The highest correlation usually points to the right peaks, which is selected.

In addition the method "cor" is the previous version to "red" which doesn't reduce the search of peaks and computes all possible combinations of peaks from the beggining, with the drawback that slows down the detection process especially when the ladder intensities are low and noisy peaks exist in abundance.

The last method that has been superseded by the previous 2 is the "ci" method based on confidence intervals, which assumes that real ladder peaks have more or less the same intensity and a they can be found by finding the median intensity and computing a 90 percent confidence interval to find the rest of the peaks. This method has been proved to fail when the first condition is broken and ladder have real peaks with intensities greater than the expected.

References

Robert J. Henry. 2013. Molecular Markers in Plants. Wiley-Blackwell. ISBN 978-0-470-95951-0.

Ben Hui Liu. 1998. Statistical Genomics. CRC Press LLC. ISBN 0-8493-3166-8.

Examples

data(my.plants)
my.ladder <- c(120, 125, 129, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375)
find.ladder(my.plants[[1]][,4], ladder=my.ladder)