R/allEndpoints.R
get_dataset_differential_expression_analyses.RdRetrieve annotations and surface level stats for a dataset's differential analyses
A numerical dataset identifier or a dataset short name
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 differential expression
analysis of the queried dataset. Note that this funciton does not return
differential expression values themselves. Use get_differential_expression_values
to get differential expression values (see examples).
The fields of the output data.table are:
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
probes.analyzed: Number of probesets represented in the contrast
genes.analyzed: Number of genes represented in the contrast
result <- get_dataset_differential_expression_analyses("GSE2872")
get_differential_expression_values(resultSet = result$result.ID[1])
#> $`570867`
#> Probe NCBIid GeneSymbol
#> <char> <char> <char>
#> 1: 1368058_at 64196 Safb
#> 2: 1386270_at
#> 3: 1368851_at 24356 Ets1
#> 4: 1395939_at
#> 5: 1381999_at 296580 Camsap1
#> ---
#> 31069: 1393379_a_at
#> 31070: 1377975_at
#> 31071: 1382534_at
#> 31072: 1389727_at 314848 Lrrc10
#> 31073: 1392428_at 360604 Spata20
#> GeneName pvalue
#> <char> <num>
#> 1: scaffold attachment factor B 0.9716
#> 2: 0.1520
#> 3: ETS proto-oncogene 1, transcription factor 0.6364
#> 4: 0.6581
#> 5: calmodulin regulated spectrin-associated protein 1 0.5268
#> ---
#> 31069: 0.4792
#> 31070: 0.3199
#> 31071: 0.5406
#> 31072: leucine-rich repeat-containing 10 0.5045
#> 31073: spermatogenesis associated 20 0.6297
#> corrected_pvalue rank contrast_134441_log2fc contrast_134441_tstat
#> <num> <num> <num> <num>
#> 1: 0.9999 0.9655 0.0434 0.3027
#> 2: 0.9999 0.0911 -0.0748 -0.9062
#> 3: 0.9999 0.5707 -0.0850 -0.5670
#> 4: 0.9999 0.5957 0.0411 0.4143
#> 5: 0.9999 0.4488 0.0158 0.0797
#> ---
#> 31069: 0.9999 0.3961 -0.0235 -0.1884
#> 31070: 0.9999 0.2388 0.0924 0.6052
#> 31071: 0.9999 0.4637 -0.0054 -0.0561
#> 31072: 0.9999 0.4249 -0.0593 -0.5642
#> 31073: 0.9999 0.5629 0.0828 0.5923
#> contrast_134441_pvalue
#> <num>
#> 1: 0.7676
#> 2: 0.3839
#> 3: 0.5819
#> 4: 0.6865
#> 5: 0.9379
#> ---
#> 31069: 0.8539
#> 31070: 0.5572
#> 31071: 0.9562
#> 31072: 0.5838
#> 31073: 0.5654
#>