basic_pairwise | R Documentation |
Perform a pairwise comparison among conditions which takes nothing into account. It _only_ takes the conditions, a mean value/variance among them, divides by condition, and returns the result. No fancy nomalizations, no statistical models, no nothing. It should be the very worst method possible. But, it should also provide a baseline to compare the other tools against, they should all do better than this, always.
basic_pairwise(
input = NULL,
design = NULL,
conditions = NULL,
batches = NULL,
model_cond = TRUE,
model_intercept = FALSE,
alt_model = NULL,
model_batch = FALSE,
force = FALSE,
keepers = NULL,
fx = "mean",
...
)
input |
Count table by sample. |
design |
Data frame of samples and conditions. |
conditions |
Not currently used, but passed from all_pairwise() |
batches |
Not currently used, but passed from all_pairwise() |
model_cond |
Not currently used, but passed from all_pairwise() |
model_intercept |
Not currently used, but passed from all_pairwise() |
alt_model |
Not currently used, but passed from all_pairwise() |
model_batch |
Not currently used, but passed from all_pairwise() |
force |
Force as input non-normalized data? |
keepers |
Set of specific contrasts to perform instead of all. |
fx |
What function to use for mean/median? |
... |
Extra options passed to arglist. |
Tested in test_27de_basic.R This function was written after the corresponding functions in de_deseq.R, de_edger.R, and de_limma.R. Like those, it performs the full set of pairwise comparisons and returns a list of the results. As mentioned above, unlike those, it is purposefully stupid.
Df of pseudo-logFC, p-values, numerators, and denominators.
[deseq_pairwise()] [limma_pairwise()] [edger_pairwise()] [ebseq_pairwise()]
## Not run:
expt <- create_expt(metadata = "sample_sheet.xlsx", gene_info = "annotations")
basic_de <- basic_pairwise(expt)
basic_tables <- combine_de_tables(basic_de)
## End(Not run)
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