all_adjusters: Combine all surrogate estimators and batch correctors into...

View source: R/normalize_batch.R

all_adjustersR Documentation

Combine all surrogate estimators and batch correctors into one function.

Description

For a long time, I have mostly kept my surrogate estimators and batch correctors separate. However, that separation was not complete, and it really did not make sense. This function brings them together. This now contains all the logic from the freshly deprecated get_model_adjust().

Usage

all_adjusters(
  input,
  design = NULL,
  estimate_type = "sva",
  batch1 = "batch",
  batch2 = NULL,
  surrogates = "be",
  low_to_zero = FALSE,
  cpus = 4,
  na_to_zero = TRUE,
  expt_state = NULL,
  confounders = NULL,
  chosen_surrogates = NULL,
  adjust_method = "ruv",
  filter = "raw",
  thresh = 1,
  noscale = FALSE,
  prior_plots = FALSE
)

Arguments

input

Dataframe or expt or whatever as the data to analyze/modify.

design

If the data is not an expt, then put the design here.

estimate_type

Name of the estimator.

batch1

Column in the experimental design for the first known batch.

batch2

Only used by the limma method, a second batch column.

surrogates

Either a number of surrogates or a method to search for them.

low_to_zero

Move elements which are <0 to 0?

cpus

Use parallel and split intensive operations?

na_to_zero

Set any NA entries to 0?

expt_state

If this is not an expt, provide the state of the data here.

confounders

List of confounded factors for smartSVA/iSVA.

chosen_surrogates

Somewhat redundant with surrogates above, but provides a second place to enter because of the way I use ... in normalize_expt().

adjust_method

Choose the method for applying the estimates to the data.

filter

Filter the data?

thresh

If filtering, use this threshold.

noscale

If using combat, scale the data?

prior_plots

Plot the priors?

Details

This applies the methodologies very nicely explained by Jeff Leek at https://github.com/jtleek/svaseq/blob/master/recount.Rmd and attempts to use them to acquire estimates which may be applied to an experimental model by either EdgeR, DESeq2, or limma. In addition, it modifies the count tables using these estimates so that one may play with the modified counts and view the changes (with PCA or heatmaps or whatever). Finally, it prints a couple of the plots shown by Leek in his document. In other words, this is entirely derivative of someone much smarter than me.

Value

List containing surrogate estimates, new counts, the models, and some plots, as available.

See Also

[all_adjuster()] [isva] [sva] [limma::removeBatchEffect()] [corpcor] [edgeR] [RUVSeq] [SmartSVA] [variancePartition] [counts_from_surrogates()]


elsayed-lab/hpgltools documentation built on May 9, 2024, 5:02 a.m.