This is a convenience function that runs multiple steps of the permutation process together: it runs COCOA permutations, converts these to null distributions, gets the empirical p value (which is limited by the number of permutations), gets z scores, and fits a gamma distribution to each null distribution to estimate p values (not limited by the number of permutations), Requires that the user has previously calculated the real COCOA scores. See these individual functions for more info on each step: runCOCOA, convertToFromNullDist, getPermStat, and getGammaPVal.
runCOCOAPerm( genomicSignal, signalCoord, GRList, rsScores, targetVar, signalCol = c("PC1", "PC2"), scoringMetric = "default", absVal = TRUE, olList = NULL, centerGenomicSignal = TRUE, centerTargetVar = TRUE, variationMetric = "cor", nPerm = 300, useSimpleCache = TRUE, cacheDir = getwd(), dataID = "", testType = "greater", gammaFitMethod = "mme", realScoreInDist = TRUE, force = FALSE, verbose = TRUE, returnCovInfo = FALSE, ... )
| genomicSignal | Matrix/data.frame. The genomic signal (e.g. DNA methylation levels) Columns of genomicSignal should be samples/patients. Rows should be individual signal/features (each row corresponds to one genomic coordinate/range) |
|---|---|
| signalCoord | A GRanges object or data frame with coordinates for the genomic signal/original epigenetic data. Coordinates should be in the same order as the original data and the feature contribution scores (each item/row in signalCoord corresponds to a row in signal). If a data.frame, must have chr and start columns (optionally can have end column, depending on the epigenetic data type). |
| GRList | GRangesList object. Each list item is a distinct region set to test (region set: regions that correspond to the same biological annotation). The region set database must be from the same reference genome as the coordinates for the actual data/samples (signalCoord). |
| rsScores | data.frame. A data.frame with region set scores. The output of the 'aggregateSignalGRList' function. Each row is a region set. One column for each sample variable of interest (e.g. PC or sample phenotype). Also can have columns with info on the overlap between the region set and the epigenetic data. Rows should be in the same order as the region sets in GRList (the list of region sets used to create rsScores.) |
| targetVar | Matrix or data.frame. Rows should be samples. Columns should be the target variables (whatever variable you want to test for association with the epigenetic signal: e.g. PC scores), |
| signalCol | A character vector with the names of the sample variables of interest/target variables (e.g. PCs or sample phenotypes). The columns in `sampleLabels` for which to calculate the variation related to the epigenetic data (e.g. correlation) and then to run COCOA on. |
| scoringMetric | A character object with the scoring metric. There are different methods available for signalCoordType="singleBase" vs signalCoordType="multiBase". For "singleBase", the available methods are "regionMean", "regionMedian", "simpleMean", and "simpleMedian". The default method is "regionMean". For "multiBase", the methods are "proportionWeightedMean", "simpleMean", and "simpleMedian". The default is "proportionWeightedMean". "regionMean" is a weighted average of the signal, weighted by region (absolute value of signal if absVal=TRUE). First the signal is averaged within each regionSet region, then all the regions are averaged. With "regionMean" method, be cautious in interpretation for region sets with low number of regions that overlap signalCoord. The "regionMedian" method is the same as "regionMean" but the median is taken at each step instead of the mean. The "simpleMean" method is just the unweighted average of all (absolute) signal values that overlap the given region set. For multiBase data, this includes signal regions that overlap a regionSet region at all (1 base overlap or more) and the signal for each overlapping region is given the same weight for the average regardless of how much it overlaps. The "simpleMedian" method is the same as "simpleMean" but takes the median instead of the mean. "proportionWeightedMean" is a weighted average of all signalCoord regions that overlap with regionSet regions. For each signalCoord region that overlaps with a regionSet region, we calculate what proportion of the regionSet region is covered. Then this proportion is used to weight the signal value when calculating the mean. The denominator of the mean is the sum of all the proportion overlaps. |
| absVal | Logical. If TRUE, take the absolute value of values in signal. Choose TRUE if you think there may be some genomic loci in a region set that will increase and others will decrease (if there may be anticorrelation between regions in a region set). Choose FALSE if you expect regions in a given region set to all change in the same direction (all be positively correlated with each other). |
| olList | list. Each list item should be a "SortedByQueryHits" object (output of findOverlaps function). Each hits object should have the overlap information between signalCoord and one item of GRList (one unique region set). The region sets from GRList must be the "subject" in findOverlaps and signalCoord must be the "query". E.g. findOverlaps(subject=regionSet, query=signalCoord). Providing this information can greatly improve permutation speed since the overlaps will not have to be calculated for each permutation. The "runCOCOAPerm" function calculates this information only once, internally, so this does not have to be provided when using that function. When using this parameter, signalCoord, genomicSignal, and each region set must be in the same order as they were when olList was created. Otherwise, the wrong genomic loci will be referenced (e.g. if epigenetic features were filtered out of genomicSignal after olList was created.) |
| centerGenomicSignal | Logical. Should rows in genomicSignal be centered based on their means? (subtracting row mean from each row) |
| centerTargetVar | Logical. Should columns in targetVar be centered based on their means? (subtract column mean from each column) |
| variationMetric | Character. The metric to use to quantify the association between each feature in genomicSignal and each target variable in sampleLabels. Either "cor" (Pearson correlation), "cov" (covariation), or "spearmanCor" (Spearman correlation). |
| nPerm | Numeric. The number of permutations to do. |
| useSimpleCache | Logical. Whether to use save caches. Caches will be created for each permutation so that if the function is disrupted it can restart where it left off. The final results are also saved as a cache. See simpleCache package for more details. |
| cacheDir | Character. The path for the directory in which the caches should be saved. |
| dataID | Character. A unique identifier for this dataset (for saving results with simpleCache) |
| testType | Character. Parameter for `getPermStat`. Whether to create p values based on one a two sided test or a lesser/greater one sided test. Options are: "greater", "lesser", "two-sided" |
| gammaFitMethod | Character. method to use for fitting the gamma distribution to null distribution. Options are "mme" (moment matching estimation), "mle" (maximum likelihood estimation), "qme" (quantile matching estimation), and "mge" (maximum goodness-of-fit estimation). See ?COCOA::getGammaPVal and ?fitdistrplus::fitdist() for more info. |
| realScoreInDist | Logical. Should the actual score (from test with no permutations) be included in the null distribution when fitting the gamma distribution. realScoreInDist=TRUE is recommended. |
| force | Logical. If force=TRUE, when fitting the gamma distribution returns an error (as may happen when a method other than "mme" is used) then allow the error. If force=FALSE, when fitting the gamma distribution returns an error then don't return an error but instead use the "mme" method for fitting that specific gamma distribution. |
| verbose | A "logical" object. Whether progress of the function should be shown. One bar indicates the region set is completed. |
| returnCovInfo | logical. If TRUE, the following coverage and region set info will be calculated and included in function output: regionSetCoverage, signalCoverage, totalRegionNumber, and meanRegionSize. For the proportionWeightedMean scoring method, sumProportionOverlap will also be calculated. |
| ... | Character. Optional additional arguments for simpleCache. |
Returns a list with the following 4 items: 1. a list of length nPerm where each item is a data.frame of the COCOA scores from a single permutation. Each data.frame is the output of `runCOCOA()` 2. a data.table/data.frame of empirical p-values (the output of `getPermStat`) 3. a data.table/data.frame of z-scores (the output of `getPermStat`. 4. a data.frame of p-values based on the gamma approximation (the output of getGammaPVal().
For reproducibility, set seed with 'set.seed()' function before running.
data("esr1_chr1") data("nrf1_chr1") data("brcaMethylData1") data("brcaMCoord1") pcScores <- prcomp(t(brcaMethylData1))$x targetVarCols <- c("PC1", "PC2") targetVar <- pcScores[, targetVarCols] # give the actual order of samples to `runCOCOA` to get the real scores correctSampleOrder=1:nrow(targetVar) realRSScores <- runCOCOA(genomicSignal=brcaMethylData1, signalCoord=brcaMCoord1, GRList=GRangesList(esr1_chr1, nrf1_chr1), signalCol=c("PC1", "PC2"), targetVar=targetVar, sampleOrder=correctSampleOrder, variationMetric="cor")#>#># give random order of samples to get random COCOA scores # so you start building a null distribution for each region set # (see vignette for example of building a null distribution with `runCOCOA`) randomOrder <- sample(1:nrow(targetVar), size=nrow(targetVar), replace=FALSE) randomRSScores <- runCOCOA(genomicSignal=brcaMethylData1, signalCoord=brcaMCoord1, GRList=GRangesList(esr1_chr1, nrf1_chr1), signalCol=c("PC1", "PC2"), targetVar=targetVar, sampleOrder=randomOrder, variationMetric="cor")#>#># runCOCOAPerm permResults <- runCOCOAPerm(genomicSignal=brcaMethylData1, signalCoord=brcaMCoord1, GRList=GRangesList(esr1_chr1, nrf1_chr1), rsScores=realRSScores, targetVar=targetVar, signalCol=c("PC1", "PC2"), variationMetric="cor", nPerm = 10, useSimpleCache=FALSE)#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>permResults#> $permRSScores #> $permRSScores[[1]] #> PC1 PC2 #> 1 0.05636227 0.03165399 #> 2 0.04149646 0.03508087 #> #> $permRSScores[[2]] #> PC1 PC2 #> 1 0.03615834 0.05189248 #> 2 0.05450995 0.05190256 #> #> $permRSScores[[3]] #> PC1 PC2 #> 1 0.05001772 0.03527180 #> 2 0.05470416 0.03706698 #> #> $permRSScores[[4]] #> PC1 PC2 #> 1 0.04029578 0.03725218 #> 2 0.04062850 0.04369218 #> #> $permRSScores[[5]] #> PC1 PC2 #> 1 0.03722493 0.04010779 #> 2 0.04244374 0.04716641 #> #> $permRSScores[[6]] #> PC1 PC2 #> 1 0.03442951 0.03844721 #> 2 0.04877966 0.04163513 #> #> $permRSScores[[7]] #> PC1 PC2 #> 1 0.05260004 0.06984914 #> 2 0.03722491 0.04460508 #> #> $permRSScores[[8]] #> PC1 PC2 #> 1 0.04164834 0.04310962 #> 2 0.05188697 0.05634488 #> #> $permRSScores[[9]] #> PC1 PC2 #> 1 0.03935936 0.05299381 #> 2 0.06200376 0.03838110 #> #> $permRSScores[[10]] #> PC1 PC2 #> 1 0.03817994 0.03958822 #> 2 0.04206345 0.03821802 #> #> #> $empiricalPVals #> PC1 PC2 #> 1: 0 0 #> 2: 0 0 #> #> $zScores #> PC1 PC2 #> 1: 47.940310 20.777707 #> 2: 2.770509 9.182187 #> #> $gammaPVal #> PC1 PC2 #> 1 0.01922850 0.016659306 #> 2 0.02729004 0.008258537 #>