dream_pairwise: Set up a model matrix and set of contrasts for pairwise...

In elsayed-lab/hpgltools: A pile of (hopefully) useful R functions

Set up a model matrix and set of contrasts for pairwise comparisons using voom/limma.

Description

Creates the set of all possible contrasts and performs them using voom/limma.

Usage

dream_pairwise(
  input = NULL,
  conditions = NULL,
  batches = NULL,
  model_cond = TRUE,
  model_batch = TRUE,
  model_intercept = FALSE,
  alt_model = NULL,
  extra_contrasts = NULL,
  annot_df = NULL,
  libsize = NULL,
  limma_method = "ls",
  limma_robust = FALSE,
  voom_norm = "quantile",
  limma_trend = FALSE,
  force = FALSE,
  keepers = NULL,
  ...
)

Arguments

input	Dataframe/vector or expt class containing count tables, normalization state, etc.
conditions	Factor of conditions in the experiment.
batches	Factor of batches in the experiment.
model_cond	Include condition in the model?
model_batch	Include batch in the model? If this is a character instead of a logical, then it is passed to all_adjusers() to attempt to find model parameters which describe surrogate variables in the data.
model_intercept	Perform a cell-means or intercept model? A little more difficult for me to understand. I have tested and get the same answer either way.
alt_model	Specify another model.
extra_contrasts	Some extra contrasts to add to the list. This can be pretty neat, lets say one has conditions A,B,C,D,E and wants to do (C/B)/A and (E/D)/A or (E/D)/(C/B) then use this with a string like: "c_vs_b_ctrla = (C-B)-A, e_vs_d_ctrla = (E-D)-A, de_vs_cb = (E-D)-(C-B),"
annot_df	Data frame for annotations.
libsize	I've recently figured out that libsize is far more important than I previously realized. Play with it here.
limma_method	Choose one of limma's lm methods.
limma_robust	Make the significance estimation robust?
voom_norm	Use this method to normalize the voom inputs.
limma_trend	Add trend lines to limma's voom plot?
force	Force data which may not be appropriate for limma into it?
keepers	Perform an explicit set of contrasts instead of all.
...	Use the elipsis parameter to feed options to write_limma().

Value

List including the following information: macb = the mashing together of condition/batch so you can look at it macb_model = The result of calling model.matrix(~0 + macb) macb_fit = The result of calling lmFit(data, macb_model) voom_result = The result from voom() voom_design = The design from voom (redundant from voom_result, but convenient) macb_table = A table of the number of times each condition/batch pairing happens cond_table = A table of the number of times each condition appears (the denominator for the identities) batch_table = How many times each batch appears identities = The list of strings defining each condition by itself all_pairwise = The list of strings defining all the pairwise contrasts contrast_string = The string making up the makeContrasts() call pairwise_fits = The result from calling contrasts.fit() pairwise_comparisons = The result from eBayes() limma_result = The result from calling write_limma()

See Also

[limma] [Biobase] [deseq_pairwise()] [edger_pairwise()] [basic_pairwise()] DOI:10.1101/2023.03.17.533005

Examples

## Not run: 
 pretend <- dream_pairwise(expt)

## End(Not run)

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