detect.ladder {Fragman}

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, making sure the 'dev' argument is passed to the new functions. If using the correlation method ("cor"), don't worry about it.

Usage

detect.ladder(stored, ind=1, ladder, channel.ladder=dim(stored[[1]])[2],
              ci.upp=1.96, ci.low=1.96, draw=TRUE, dev=50, warn=TRUE, 
              init.thresh=250,sep.index=8, method="cor", avoid=1500, who="sample")

Arguments

stored

Lis of dataframes obtained by using the storing.inds function.

ind

The individual that you wish to analyze for assessing that the ladder was correctly detected.

ladder

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

channel.ladder

A scalar value indicating in which channel or color the ladder was read

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, could be really important for the correlation method

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 for peaks meeting the conditions specified in the previous arguments

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 which is usually related to noisy area in some ladders.

Details

The peaks are detected by default using a correlation method bu the user can use confidence intervals if desired.

Value

If parameters are indicated correctly the function returns:

$pos

the index positions for the intensities

$hei

the intensities for the fragments found

$wei

the putative weights in base pairs based on the ladder provided

References

Covarrubias-Pazaran G, Diaz-Garcia L, Schlautman B, Salazar W, Zalapa J. Fragman: An R package for fragment analysis. 2016. BMC Genetics 17(62):1-8.

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)
# looking at the first individual
detect.ladder(stored=my.plants, ind=1, ladder=my.ladder)