test_gene_rank {tidybulk} | R Documentation |
test_gene_rank() takes as input a 'tbl' formatted as | <SAMPLE> | <ENSEMBL_ID> | <COUNT> | <...> | and returns a 'tbl' with the GSEA statistics
test_gene_rank( .data, .entrez, .arrange_desc, species, .sample = NULL, gene_sets = NULL, gene_set = NULL ) ## S4 method for signature 'spec_tbl_df' test_gene_rank( .data, .entrez, .arrange_desc, species, .sample = NULL, gene_sets = NULL, gene_set = NULL ) ## S4 method for signature 'tbl_df' test_gene_rank( .data, .entrez, .arrange_desc, species, .sample = NULL, gene_sets = NULL, gene_set = NULL ) ## S4 method for signature 'tidybulk' test_gene_rank( .data, .entrez, .arrange_desc, species, .sample = NULL, gene_sets = NULL, gene_set = NULL ) ## S4 method for signature 'SummarizedExperiment' test_gene_rank( .data, .entrez, .arrange_desc, species, .sample = NULL, gene_sets = NULL, gene_set = NULL ) ## S4 method for signature 'RangedSummarizedExperiment' test_gene_rank( .data, .entrez, .arrange_desc, species, .sample = NULL, gene_sets = NULL, gene_set = NULL )
.data |
A 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | |
.entrez |
The ENTREZ ID of the transcripts/genes |
.arrange_desc |
A column name of the column to arrange in decreasing order |
species |
A character. For example, human or mouse. MSigDB uses the latin species names (e.g., \"Mus musculus\", \"Homo sapiens\") |
.sample |
The name of the sample column |
gene_sets |
A character vector. The subset of MSigDB datasets you want to test against (e.g. \"C2\"). If NULL all gene sets are used (suggested). This argument was added to avoid time overflow of the examples. |
gene_set |
DEPRECATED. Use gene_sets instead. |
This wrapper execute gene enrichment analyses of the dataset using a list of transcripts and GSEA. This wrapper uses clusterProfiler (DOI: doi.org/10.1089/omi.2011.0118) on the back-end.
Undelying method: # Get gene sets signatures msigdbr::msigdbr(species = species)
# Filter specific gene_sets if specified. This was introduced to speed up examples executionS when( !is.null(gene_sets ) ~ filter(., gs_cat ~ (.) )
# Execute calculation nest(data = -gs_cat) mutate(fit = map( data, ~ clusterProfiler::GSEA( my_entrez_rank, TERM2GENE=.x pvalueCutoff = 1 )
))
A 'tbl' object
A 'spec_tbl_df' object
A 'tbl_df' object
A 'tidybulk' object
A 'SummarizedExperiment' object
A 'RangedSummarizedExperiment' object
df_entrez = tidybulk::se_mini %>% tidybulk() %>% as_tibble() %>% symbol_to_entrez( .transcript = feature, .sample = sample) df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count) df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7")) df_entrez = df_entrez %>% test_differential_abundance(~ condition) test_gene_rank( df_entrez, .sample = sample, .entrez = entrez, species="Homo sapiens", gene_sets =c("C2"), .arrange_desc = logFC )