AdjustVariance: Adjust feature variance to remove effect of varying feature...

In yanwu2014/swne: Similarity Weighted Nonnegative Embedding: A method for visualizing high dimensional datasets

Adjust feature variance to remove effect of varying feature means. Adapted from pagoda2: https://github.com/hms-dbmi/pagoda2

Description

Adjust feature variance to remove effect of varying feature means. Adapted from pagoda2: https://github.com/hms-dbmi/pagoda2

Usage

AdjustVariance(
  counts,
  gam.k = 10,
  plot = F,
  max.adjusted.variance = 1000,
  min.adjusted.variance = 0.001,
  verbose = T,
  q.val = 0.05
)

Arguments

counts	Input data matrix
gam.k	Number of additive models to use when fitting mean variance relationship
plot	Whether or not to plot the mean variance relationship
max.adjusted.variance	Maximum adjusted feature variance
min.adjusted.variance	Minimum adjusted feature variance
verbose	Whether or not to print output
q.val	Maximum adjusted p-value for a feature to qualify as overdispersed

Value

Dataframe with adjusted feature variances

yanwu2014/swne documentation built on Aug. 5, 2023, 4:42 a.m.