# Function to fit mixture models for series of markers and save the results to files This is the main function that calls fitOneMarker for a series of markers and saves the tabular, graphical and log output to files. Most of the arguments are identical to those of fitOneMarker and are directly passed through. ```r saveMarkerModels(ploidy, markers=NA, data, diplo=NULL, select=TRUE, diploselect=TRUE, pop.parents=NULL, population=NULL, parentalPriors=NULL, samplePriors=NULL, startmeans=NULL, maxiter=40, maxn.bin=200, nbin=200, sd.threshold=0.1, p.threshold=0.99, call.threshold=0.6, peak.threshold=0.85, try.HW=TRUE, dip.filter=1, sd.target=NA, filePrefix, rdaFiles=FALSE, allModelsFile=FALSE, plot="none", plot.type="png", ncores=1) ``` ## Arguments - `ploidy`: The ploidy level, 2 or higher: 2 for diploids, 3 for triploids etc. - `markers`: NA or a character or numeric vector specifying the markers to be fitted. If a character vector, names should match the MarkerName column of data; if numeric, the numbers index the markers based on the alphabetic order of the MarkerNames in data. - `data`: A data frame with the polyploid samples, with (at least) columns MarkerName, SampleName and ratio, where ratio is the Y-allele signal divided by the sum of the X- and Y-allele signals: ratio == Y/(X+Y) - `diplo`: NULL or a data frame like data, with the diploid samples and (a subset of) the same markers as in data. Genotypic scores for diploid samples are calculated according to the best-fitting model calculated for the polyploid samples and therefore may range from 0 (nulliplex) to , with the expected dosages 0 and for the homozygotes and for the heterozygotes. diplo can also be used for any other samples that need to be scored, but that should not affect the fitted models. - `select`: A logical vector, recycled if shorter than nrow(data): indicates which rows of data are to be used (default TRUE, i.e. keep all rows) - `diploselect`: A logical vector like select, matching diplo instead of data - `pop.parents`: NULL or a data.frame specifying the population structure. The data frame has 3 columns: the first containing population ID's, the 2nd and 3rd with the population ID's of the parents of these populations (if F1's) or NA (if not). The population ID's should match those in parameter population. If pop.parents is NULL all samples are considered to be in one population, and parameter population should be NULL (default). - `population`: NULL or a data.frame specifying to which population each sample belongs. The data frame has two columns, the first containing the SampleName (containing all SampleNames occurring in data), the second column containing population ID's that match pop.parents. In both columns NA values are not allowed. Parameters pop.parents and population should both be NULL (default) or both be specified. - `parentalPriors`: NULL or a data frame specifying the prior dosages for the parental populations. The data frame has one column MarkerName followed by one column for each F1 parental population. Column names (except first) are population ID's matching the parental populations in pop.parents. In case there is just one F1 population in pop.parents, it is possible to have two columns for both parental populations instead of one (allowing two specify two different prior dosages); in that case both columns for each parent have the same caption. Each row specifies the priors for one marker. The contents of the data frame are dosages, as integers from 0 to ; NA values are allowed. Note: when reading the data frame with read.table or read.csv, set check.names=FALSE so column names (population ID's) are not changed. - `samplePriors`: NULL or a data.frame specifying prior dosages for individual samples. The first column called MarkerName is followed by one column per sample; not all samples in data need to have a column here, only those samples for which prior dosages for one or more markers are available. Each row specifies the priors for one marker. The contents of the data frame are dosages, as integers from 0 to ; NA values are allowed. Note: when reading the data frame with read.table or read.csv, set check.names=FALSE so column names (population ID's) are not changed. - `startmeans`: NULL or a data.frame specifying the prior means of the mixture distributions. The data frame has one column MarkerName, followed by columns with the prior ratio means on the original (untransformed) scale. Each row specifies the means for one marker in strictly ascending order (all means NA is allowed, but markers without start means can also be omitted). - `maxiter`: A single integer, passed to CodomMarker, see there for explanation - `maxn.bin`: A single integer, passed to CodomMarker, see there for explanation - `nbin`: A single integer, passed to CodomMarker, see there for explanation - `sd.threshold`: The maximum value allowed for the (constant) standard deviation of each peak on the arcsine - square root transformed scale, default 0.1. If the optimal model has a larger standard deviation the marker is rejected. Set to a large value (e.g. 1) to disable this filter. - `p.threshold`: The minimum P-value required to assign a genotype (dosage) to a sample; default 0.99. If the P-value for all possible genotypes is less than p.threshold the sample is assigned genotype NA. Set to 1 to disable this filter. - `call.threshold`: The minimum fraction of samples to have genotypes assigned ("called"); default 0.6. If under the optimal model the fraction of "called" samples is less than call.threshold the marker is rejected. Set to 0 to disable this filter. - `peak.threshold`: The maximum allowed fraction of the scored samples that are in one peak; default 0.85. If any of the possible genotypes (peaks in the ratio histogram) contains more than peak.threshold of the samples the marker is rejected (because the remaining samples offers too little information for reliable model fitting). - `try.HW`: Logical: if TRUE (default), try models with and without a constraint on the mixing proportions according to Hardy-Weinberg equilibrium ratios. If FALSE, only try models without this constraint. Even when the HW assumption is not applicable, setting try.HW to TRUE often still leads to a better model. For more details on how try.HW is used see the Details section of function fitOneMarker. - `dip.filter`: if 1 (default), select best model only from models that do not have a dip (a lower peak surrounded by higher peaks: these are not expected under Hardy-Weinberg equilibrium or in cross progenies). If all fitted models have a dip still the best of these is selected. If 2, similar, but if all fitted models have a dip the marker is rejected. If 0, select best model among all fitted models, including those with a dip. - `sd.target`: If the fitted standard deviation of the peaks on the transformed scale is larger than sd.target a penalty is given (see Details section of function fitOneMarker); default NA i.e. no penalty is given. - `filePrefix`: partial file name, possibly including an absolute or relative file path. filePrefix must always be specified. All output files will have filePrefix prefixed to their name so it is clear they are all derived from the same call to saveMarkerModels. If filePrefix includes a file path all output files will be saved there; if a filePrefix is specified that does not include a a path the output will be saved in the working directory. - `rdaFiles`: logical, default FALSE. The tabular output (scorefile, diploscorefile, modelfile, allmodelsfile) is saved as tab-separated text files with extension .dat or as an .RData file if this parameter is FALSE or TRUE respectively. - `allModelsFile`: logical, default FALSE. If TRUE an allmodelsfile is saved with all models that have been tried for each marker; also the log file will contain a few lines for each marker. This information is mostly useful for debugging and locating problems. - `plot`: String, "none" (default), "fitted" or "all". If "fitted" a plot of the best fitting model and the assigned genotypes is saved with filename ., preceded by "rejected_" if the marker was rejected. If "all", small plots of all models are saved to files (8 per file) with filename <"plots">. in addition to the plot of the best fitting model. - `plot.type`: String, "png" (default), "emf", "svg" or "pdf". Indicates format for saving the plots. - `ncores`: The number of processor cores to use for parallel processing, default 1. Specifying more cores than available may cause problems. Note that the implementation under Windows involves duplicating the input data (under Linux that does not happen, nor under Windows if ncores=1), so if under Windows memory size is a problem it would be better to run several R instances simultaneously, each with ncores=1, each processing part of the data. ## Returns NULL. The result of saveMarkerModels is a set of output files. ## Details saveMarkerModels calls fitOneMarker for all markers specified by parameter markers. The markers are processed in batches; the number of markers per batch is printed to the console when saveMarkerModels is started. If multiple cores are used the batches are processed in parallel. During the processing a series of RData files (2 for each batch) is saved in the directory specified in filePrefix. At the end these are combined into the required output files and then deleted. If something goes wrong at any stage, the files for the completed batches are still available and can be combined manually, avoiding the need to re-run the process for the completed batches. The output files consist of: * \.log: a logfile containing several lines listing the input parameters. If parameter allModelsFile is TRUE the logfile also contains several text lines per marker, corresponding to component "log" in the result of fitOneMarker * \_scores.dat (or .RData) a file containing one line per polyploid sample for every marker that could be fitted, corresponding to component "scores" in the result of fitOneMarker * \_diploscores.dat (or .RData) a file containing one line per diploid sample for every marker that could be fitted, corresponding to component "diploscores" in the result of fitOneMarker. This file is only produced if parameter diplo is not missing * \_models.dat (or .RData) a file containing one line per marker, corresponding to component "modeldata" in the result of fitOneMarker: the selected model for each marker, with several statistics * \_allmodels.dat (or .RData) as the models file, but containing all models fitted for each marker, not only the selected model, marker, corresponding to component "allmodeldata" in the result of fitOneMarker. This file is only produced if parameter allModelsFile is TRUE Additionally, if plot != "none", plot files are generated in directory _plots ## Examples ```r # These examples run for a total of about 55 sec. # All output files are saved in tempdir() and subdirectories of it. data(fitPoly_data) # tetraploid, with no populations and with sample prior dosages saveMarkerModels(ploidy=4, data=fitPoly_data$ploidy4$dat4x, samplePriors=fitPoly_data$ploidy4$sampPriors4x, filePrefix=paste0(tempdir(),"/4xA"), allModelsFile=TRUE, plot="fitted") # tetraploid, with specified populations and parental and sample prior dosages saveMarkerModels(ploidy=4, data=fitPoly_data$ploidy4$dat4x, population=fitPoly_data$ploidy4$pop4x, pop.parents=fitPoly_data$ploidy4$pop.par4x, parentalPriors=fitPoly_data$ploidy4$parPriors4x, samplePriors=fitPoly_data$ploidy4$sampPriors4x, filePrefix=paste0(tempdir(),"/4xB"), allModelsFile=TRUE, plot="fitted") # hexaploid, no populations or prior information saveMarkerModels(ploidy=6, data=fitPoly_data$ploidy6$dat6x, filePrefix=paste0(tempdir(),"/6xA"), allModelsFile=TRUE, plot="fitted") # hexaploid, with specified populations, prior dosages of parents and other samples # and prior means of the mixture components saveMarkerModels(ploidy=6, data=fitPoly_data$ploidy6$dat6x, population=fitPoly_data$ploidy6$pop6x, pop.parents=fitPoly_data$ploidy6$pop.par6x, startmeans=fitPoly_data$ploidy6$startmeans6x, parentalPriors=fitPoly_data$ploidy6$parPriors6x, samplePriors=fitPoly_data$ploidy6$sampPriors6x, filePrefix=paste0(tempdir(),"/6xB"), plot="fitted") ```

saveMarkerModels function