findHVGMatrix: Find highly variable genes
In BPijuanSala/proSeq: Tools to preprocess RNA-seq data

Description Usage Arguments Value Author(s)

It finds highly variable genes using the normalised data stored in the countsNorm slot within the RNAseq object provided. It follows Brennecke et al., Nature Methods, 2013. object@SpikeIn vector is required. If any entry is true, it will take spike-ins to calculate the fit. If none is true, it will take biological genes to calculate the fit.

findHVGMatrix(mat, signThres = 0.1, outputPlots = "./",
  plotting = "pdf", colVarGenes = "deeppink", UseSpike = TRUE,
  spikeins = NULL, minQuantCv2 = 0.2, maxQuantCv2 = 0.8,
  minQuantMeans = 0.2, maxQuantMeans = 0.8)

`mat`	Matrix to use for finding HVG.
`signThres`	Threshold to adjust for multiple testing with the Benjamini-Hochberg method. Default: 0.1 (cut at 10 percent).
`outputPlots`	state directory where you want to output the plots. Default: Current directory.
`plotting`	It states whether plots are generated or not and you should specify the type. Options: "pdf", "tiff", "no". Default: "pdf". Default: TRUE
`colVarGenes`	Color for variable genes. Default: deeppink.
`UseSpike`	Logical vector stating whether to use Spike-ins to calculate the fitting curve or not. Default: NULL (it will depend on whether the SpikeIn slot has spike ins).
`spikeins`	Logical vector stating whether each gene is spike or not. Only used when UseSpike=TRUE.
`minQuantCv2`	Lower quantile of cv2 values to discard to fit the data. Default: 0.2. This means that those genes with cv2 (across cells) in the lower 0.2 quantile will be excluded for the fit.
`maxQuantCv2`	Upper quantile of cv2 values to discard to fit the data. Default: 0.8. This means that those genes with cv2 (across cells) in the top 0.8 quantile will be discarded for the fit.
`minQuantMeans`	Lower quantile of means to discard to fit the data. Default: 0.2. This means that those genes with mean (across cells) in the 0.2 lower quantile will be discarded for the fit.
`maxQuantMeans`	Upper quantile of means to discard to fit the data. Default: 0.8. This means that those genes with mean (across cells) in the 0.8 upper quantile will be discarded for the fit.