cosine.similarity.iterative {OmicsQC}

Tests the accumulated quality scores for outliers using cosine similarity

Description

This function takes quality.scores, trims it and fits it to the distribution given. It then iteratively tests the largest datapoint compared a null distribution of size no.simulations. If the largest datapoint has a significant p-value it tests the 2nd largest one and so on. The function supports the following distributions:

• 'weibull'

• 'norm'

• 'gamma'

• 'exp'

• 'lnorm'

• 'cauchy'

• 'logis'

Usage

cosine.similarity.iterative(
  quality.scores,
  no.simulations,
  distribution = c("lnorm", "weibull", "norm", "gamma", "exp", "cauchy", "logis"),
  trim.factor = 0.05,
  alpha.significant = 0.05
)

Arguments

quality.scores

A dataframe with columns 'Sum' (of scores) and 'Sample', i.e. the output of accumulate.zscores

no.simulations

The number of datasets to simulate

distribution

A distribution to test, will default to 'lnorm'

trim.factor

What fraction of values of each to trim to get parameters without using extremes

alpha.significant

Alpha value for significance

Value

Results in the form of a named list

no.outliers

Number of nominated outliers

outlier.labels

Outlier IDs, corresponding to Sample column of quality.scores