Use a loess fit to estimate error rates from transition counts.
This function accepts a matrix of observed transitions, with each transition
corresponding to a row (eg. row 2 = A->C) and each column to a quality score
(eg. col 31 = Q30). It returns a matrix of estimated error
rates of the same shape. Error rates are estimates by a loess fit
of the observed rates of each transition as a function of the quality score.
Self-transitions (i.e. A->A) are taken to be the left-over probability.
loessErrfun(trans)
trans |
(Required). A matrix of the observed transition counts. Must be 16 rows, with the rows named "A2A", "A2C", ... |
A numeric matrix with 16 rows and the same number of columns as trans.
The estimated error rates for each transition (row, eg. "A2C") and quality score
(column, eg. 31), as determined by loess smoothing over the quality
scores within each transition category.
derep1 <- derepFastq(system.file("extdata", "sam1F.fastq.gz", package="dada2"))
dada1 <- dada(derep1, err=tperr1)
err.new <- loessErrfun(dada1$trans)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.