ebseq_pairwise | R Documentation |
Invoking EBSeq is confusing, this should help.
ebseq_pairwise(
input = NULL,
patterns = NULL,
conditions = NULL,
batches = NULL,
model_cond = NULL,
model_intercept = NULL,
alt_model = NULL,
model_batch = NULL,
keepers = NULL,
ng_vector = NULL,
rounds = 10,
target_fdr = 0.05,
method = "pairwise_subset",
norm = "median",
force = FALSE,
...
)
input |
Dataframe/vector or expt class containing data, normalization state, etc. |
patterns |
Set of expression patterns to query. |
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() |
keepers |
Perform a specific set of contrasts instead of all? |
ng_vector |
I think this is for isoform quantification, but am not yet certain. |
rounds |
Number of iterations for doing the multi-test |
target_fdr |
Definition of 'significant' |
method |
The default ebseq methodology is to create the set of all possible 'patterns' in the data; for data sets which are more than trivially complex, this is not tenable, so this defaults to subsetting the data into pairs of conditions. |
norm |
Normalization method to use. |
force |
Force ebseq to accept bad data (notably NA containing stuff from proteomics. |
... |
Extra arguments currently unused. |
List containing tables from ebseq, the conditions tested, and the ebseq table of conditions.
[limma_pairwise()] [deseq_pairwise()] [edger_pairwise()] [basic_pairwise()]
## Not run:
expt <- create_expt(metadata = "sample_sheet.xlsx", gene_info = annotations)
ebseq_de <- ebseq_pairwise(input = expt)
## End(Not run)
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