MmapprParam: MmapprParam Class and Constructor
In kjohnsen/MMAPPR2: Mutation Mapping Analysis Pipeline for Pooled RNA-Seq

Description Usage Arguments Value Examples

MmapprParam stores parameters for running mmappr.

MmapprParam(refFasta, wtFiles, mutFiles, species, vepFlags = NULL,
  refGenome = NULL, outputFolder = NULL, distancePower = 4,
  peakIntervalWidth = 0.95, minDepth = 10, homozygoteCutoff = 0.95,
  minBaseQuality = 20, minMapQuality = 30,
  loessOptResolution = 0.001, loessOptCutFactor = 0.1, naCutoff = 0,
  fileAggregation = c("sum", "mean"))

`refFasta`	The path to the fasta file genome, which will be referenced in reading the BAM files.
`wtFiles`	Character vector, `BamFile`, or `BamFileList` containing BAM files for the wild-type pool to be analyzed.
`mutFiles`	Character vector, `BamFile`, or `BamFileList` containing BAM files for the mutant pool to be analyzed.
`species`	Length-one character vector of name of species under analysis. Used only in generating default `VEPFlags` object.
`vepFlags`	Optional `VEPFlags` object containing runtime options for Ensembl's Variant Effect Predictor. See vignette for details. Generated by default.
`refGenome`	`GmapGenome` object storing reference genome to be used in variant calling. Make sure it is the same genome aligned to and used installed with VEP. Generated by default.
`outputFolder`	Length-one character vector specifying where to save output, including a `MmapprData` stored as `mmappr_data.RDS`, `mmappr2.log`, a `.tsv` file for each peak chromosome containing candidate mutations, and PDF plots of both the entire genome and peak chromosomes. Defaults to an automatically generated `mmappr2_<timestamp>`.
`distancePower`	Length-one numeric vector determing to what power Euclidean distance values are raised before fitting. Higher powers tend to increase high values and decrease low values, exaggerating the variation in the data. Default of 4.
`peakIntervalWidth`	Length-one numeric vector between `0` and `1` specifying desired width of linkage region(s). The default value of `0.95`, for example, yields peak regions defined as including the top 95% of SNPs in the peak region, as determined by the peak resampling distribution.
`minDepth`	Length-one integer vector determining minimum depth required for a position to be considered in the analysis. Defaults to 10.
`homozygoteCutoff`	Length-one numeric vector between `0` and `1` specifying threshold for throwing out base pairs on account of homozygosity. Positions with high major allele frequency in the wild-type pool are unlikely to exhibit polymorphism and are thus thrown out when they exceed this cutoff. Defaults to `0.95`.
`minBaseQuality`	Length-one numeric vector indicating minimum base call quality to consider in analysis. Read positions with qualities below this score will be thrown out. Defaults to 20.
`minMapQuality`	Length-one numeric vector indicating minimum read mapping quality to consider in analysis. Reads with qualities below this score will be thrown out. Defaults to 30.
`loessOptResolution`	Length-one numeric vector between `0` and `1` specifying desired resolution for Loess fit optimization. The default of `0.001`, for example, indicates that the span ultimately chosen will perform better than its neighbor values at `+-0.001`.
`loessOptCutFactor`	Length-one numeric vector between `0` and `1` specifying how aggressively the Loess optimization algorithm proceeds. For example, with a default of `0.1` different spans at intervals of `0.001` would be evaluated after intervals of `0.01`.
`naCutoff`	Integer specifying the most NAs to accept at a given position–that is, the number of files without data for that position. Defaults to 0.
`fileAggregation`	A length-one character vector determining strategy for aggregating base calls when multiple wild-type or multiple mutant files are provided. When 'sum', average base call proportions are computed using the read counts from each file, effectively weighting files with higher counts at a given position. When equal to 'mean', the base call proportions as well as read depths, rather than the absolute count, are averaged across files, which is useful when you want to weight each replicate evenly without regards to differing depth.

A MmapprParam object.

if (requireNamespace('MMAPPR2data', quietly=TRUE)
        & all(Sys.which(c("samtools", "vep")) != "")) {
    mmappr_param <- MmapprParam(refFasta = MMAPPR2data::goldenFasta(),
                                wtFiles = MMAPPR2data::exampleWTbam(),
                                mutFiles = MMAPPR2data::exampleMutBam(),
                                species = "danio_rerio",
                                outputFolder = tempOutputFolder())
}