pvalueAdjustment: Perform independent filtering in differential expression...
In zinbwave: Zero-Inflated Negative Binomial Model for RNA-Seq Data

This function performs independent filtering to increase detection power in high throughput gene expression studies.

pvalueAdjustment(
  baseMean,
  filter,
  pValue,
  theta,
  alpha = 0.05,
  pAdjustMethod = "BH"
)

`baseMean`	A vector of mean values.
`filter`	A vector of stage-one filter statistics.
`pValue`	A vector of unadjusted p-values, or a function which is able to compute this vector from the filtered portion of data, if data is supplied. The option to supply a function is useful when the value of the test statistic depends on which hypotheses are filtered out at stage one. (The limma t-statistic is an example.)
`theta`	A vector with one or more filtering fractions to consider. Actual cutoffs are then computed internally by applying quantile to the filter statistics contained in (or produced by) the filter argument.
`alpha`	A cutoff to which p-values, possibly adjusted for multiple testing, will be compared. Default is 0.05.
`pAdjustMethod`	The unadjusted p-values contained in (or produced by) test will be adjusted for multiple testing after filtering. Default is "BH".

a list with pvalues, filtering threshold, theta, number of rejections, and alpha.

This function is an adapted version of the pvalueAdjustment function that was originally written by Michael I. Love as part of the DESeq2 package. Koen Van den Berge adapted the function.

zinbwave documentation built on Nov. 8, 2020, 8:11 p.m.