Returns queried result set
get_result_sets(
datasets = NA_character_,
resultSets = NA_character_,
filter = NA_character_,
offset = 0,
limit = 20,
sort = "+id",
raw = getOption("gemma.raw", FALSE),
memoised = getOption("gemma.memoised", FALSE),
file = getOption("gemma.file", NA_character_),
overwrite = getOption("gemma.overwrite", FALSE)
)A vector of dataset IDs or short names
A resultSet identifier. Note that result set identifiers
are not static and can change when Gemma re-runs analyses internally. Whem
using these as inputs, try to make sure you access a currently existing
result set ID by basing them on result sets returned for a particular dataset or
filter used in get_result_sets
Filter results by matching expression. Use filter_properties
function to get a list of all available parameters. These properties can be
combined using "and" "or" clauses and may contain common operators such as "=", "<" or "in".
(e.g. "taxon.commonName = human", "taxon.commonName in (human,mouse), "id < 1000")
The offset of the first retrieved result.
Defaults to 20. Limits the result to specified amount
of objects. Has a maximum value of 100. Use together with offset and
the totalElements attribute in the output to
compile all data if needed.
Order results by the given property and direction. The '+' sign indicate ascending order whereas the '-' indicate descending.
TRUE to receive results as-is from Gemma, or FALSE to enable
parsing. Raw results usually contain additional fields and flags that are
omitted in the parsed results.
Whether or not to save to cache for future calls with the
same inputs and use the result saved in cache if a result is already saved.
Doing options(gemma.memoised = TRUE) will ensure that the cache is always
used. Use forget_gemma_memoised to clear the cache.
The name of a file to save the results to, or NULL to not write
results to a file. If raw == TRUE, the output will be the raw endpoint from the
API, likely a JSON or a gzip file. Otherwise, it will be a RDS file.
Whether or not to overwrite if a file exists at the specified filename.
A data table with information about the queried result sets. Note that this function does not return
differential expression values themselves. Use get_differential_expression_values
to get differential expression values
result.ID: Result set ID of the differential expression analysis.
May represent multiple factors in a single model.
contrast.ID: Id of the specific contrast factor. Together with the result.ID
they uniquely represent a given contrast.
experiment.ID: Id of the source experiment
factor.category: Category for the contrast
factor.category.URI: URI for the contrast category
factor.ID: ID of the factor
baseline.factors: Characteristics of the baseline. This field is a data.table
experimental.factors: Characteristics of the experimental group. This field is a data.table
isSubset: TRUE if the result set belong to a subset, FALSE if not. Subsets are created when performing differential expression to avoid unhelpful comparisons.
subsetFactor: Characteristics of the subset. This field is a data.table
Output and usage of this function is mostly identical to get_dataset_differential_expression_analyses.
The principal difference being the ability to restrict your result sets, being able to
query across multiple datasets and being able to use the filter argument
to search based on result set properties.
get_result_sets(dataset = 1)
#> result.ID contrast.ID experiment.ID factor.category
#> <int> <int> <int> <char>
#> 1: 573187 1 1 disease
#> factor.category.URI factor.ID baseline.factors
#> <char> <char> <list>
#> 1: http://www.ebi.ac.uk/efo/EFO_0000408 1 disease,....
#> experimental.factors isSubset subsetFactor
#> <list> <lgcl> <list>
#> 1: disease,.... FALSE logical(....
# get all contrasts comparing disease states. use filter_properties to see avaialble options
get_result_sets(filter = "baselineGroup.characteristics.value = disease")
#> result.ID contrast.ID experiment.ID factor.category
#> <int> <int> <int> <char>
#> 1: 576360 255896 34545 phenotype
#> 2: 576360 255897 34545 phenotype
#> 3: 576360 255895 34545 phenotype
#> factor.category.URI factor.ID baseline.factors
#> <char> <char> <list>
#> 1: http://www.ebi.ac.uk/efo/EFO_0000651 54513 phenotyp....
#> 2: http://www.ebi.ac.uk/efo/EFO_0000651 54513 phenotyp....
#> 3: http://www.ebi.ac.uk/efo/EFO_0000651 54513 phenotyp....
#> experimental.factors isSubset subsetFactor
#> <list> <lgcl> <list>
#> 1: phenotyp.... FALSE logical(....
#> 2: phenotyp.... FALSE logical(....
#> 3: phenotyp.... FALSE logical(....