get_clustered_median_junction_expression {gtexr} | R Documentation |
Get Clustered Median Junction Expression
Description
Find median junction expression data along with hierarchical clusters.
Returns median junction read counts in tissues of a given gene from all known transcripts along with the hierarchical clustering results of tissues and genes, based on junction 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_junction_expression(
gencodeIds,
datasetId = "gtex_v8",
tissueSiteDetailIds = NULL,
.return_raw = FALSE
)
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
|
.return_raw |
Logical. If |
Value
A tibble. Or a list if .return_raw = TRUE
.
See Also
Other Expression Data Endpoints:
get_clustered_median_exon_expression()
,
get_clustered_median_gene_expression()
,
get_clustered_median_transcript_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
## Not run:
get_clustered_median_junction_expression(gencodeIds = c(
"ENSG00000203782.5",
"ENSG00000132693.12"
))
# clustering data is stored as an attribute "clusters"
result <- get_clustered_median_junction_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)
## End(Not run)