
Get Clustered Median Transcript Expression
Source:R/get_clustered_median_transcript_expression.R
get_clustered_median_transcript_expression.Rd
Find median transcript expression data of all known transcripts of a gene along with hierarchical clusters.
Returns median normalized expression in tissues of all known transcripts of a given gene along with the hierarchical clustering results of tissues and genes, based on expression, in Newick format.
Results may be filtered by dataset, gene or tissue, but at least one gene must be provided.
The hierarchical clustering is performed by calculating Euclidean distances and using the average linkage method.
This endpoint is not paginated.
By default, this service queries the latest GTEx release.
Usage
get_clustered_median_transcript_expression(
gencodeIds,
datasetId = "gtex_v8",
tissueSiteDetailIds = NULL
)
Arguments
- gencodeIds
A character vector of Versioned GENCODE IDs, e.g. c("ENSG00000132693.12", "ENSG00000203782.5").
- datasetId
String. Unique identifier of a dataset. Usually includes a data source and data release. Options: "gtex_v8", "gtex_snrnaseq_pilot".
- tissueSiteDetailIds
Character vector of IDs for tissues of interest. Can be GTEx specific IDs (e.g. "Whole_Blood"; use
get_tissue_site_detail()
to see valid values) or Ontology IDs.
See also
Other Expression Data Endpoints:
get_clustered_median_exon_expression()
,
get_clustered_median_gene_expression()
,
get_clustered_median_junction_expression()
,
get_expression_pca()
,
get_gene_expression()
,
get_median_exon_expression()
,
get_median_gene_expression()
,
get_median_junction_expression()
,
get_median_transcript_expression()
,
get_single_nucleus_gex()
,
get_single_nucleus_gex_summary()
,
get_top_expressed_genes()
Examples
if (FALSE) { # \dontrun{
get_clustered_median_transcript_expression(gencodeIds = c("ENSG00000203782.5",
"ENSG00000132693.12"))
# clustering data is stored as an attribute "clusters"
result <- get_clustered_median_transcript_expression(c("ENSG00000203782.5",
"ENSG00000132693.12"))
attr(result, "clusters")
# process clustering data with the ape package
# install.packages("ape")
# phylo_tree <- ape::read.tree(text = attr(result, "clusters")$tissue)
# plot(phylo_tree)
# print(phylo_tree)
} # }