Get Clustered Median Junction Expression

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.

GTEx Portal API documentation

Usage

get_clustered_median_junction_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.

Value

A tibble, with clustering data stored as an attribute, "clusters".

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

# \dontrun{
get_clustered_median_junction_expression(gencodeIds = c("ENSG00000203782.5",
                                                    "ENSG00000132693.12"))
#> ℹ Retrieve clustering data with `attr(<df>, 'clusters')`
#> # A tibble: 432 × 8
#>    median junctionId           tissueSiteDetailId ontologyId datasetId gencodeId
#>     <dbl> <chr>                <chr>              <chr>      <chr>     <chr>    
#>  1      0 chr1_159712581_1597… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000…
#>  2      0 chr1_159712795_1597… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000…
#>  3      0 chr1_159712795_1597… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000…
#>  4      0 chr1_159713604_1597… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000…
#>  5      0 chr1_159713641_1597… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000…
#>  6      0 chr1_159713973_1597… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000…
#>  7      0 chr1_159714139_1597… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000…
#>  8      0 chr1_159712581_1597… Adipose_Visceral_… UBERON:00… gtex_v8   ENSG0000…
#>  9      0 chr1_159712795_1597… Adipose_Visceral_… UBERON:00… gtex_v8   ENSG0000…
#> 10      0 chr1_159712795_1597… Adipose_Visceral_… UBERON:00… gtex_v8   ENSG0000…
#> # ℹ 422 more rows
#> # ℹ 2 more variables: geneSymbol <chr>, unit <chr>

# clustering data is stored as an attribute "clusters"
result <- get_clustered_median_junction_expression(c("ENSG00000203782.5",
                                                 "ENSG00000132693.12"))
#> ℹ Retrieve clustering data with `attr(<df>, 'clusters')`
attr(result, "clusters")
#> $junction
#> [1] "((((((chr1_159713641_159714006:0.03,chr1_159713604_159714002:0.03):0.67,(chr1_159712795_159714006:0.01,chr1_159712581_159713502:0.01):0.69):0.56,chr1_159712795_159713502:1.26):0.90,chr1_159713973_159714498:2.16):0.54,chr1_159714139_159714424:2.70):2.61,chr1_153259734_153260926:5.31);"
#> 
#> $tissue
#> [1] "(((((((((((((((((Adipose_Visceral_Omentum:0.00,Adipose_Subcutaneous:0.00):0.00,Artery_Aorta:0.00):0.00,Artery_Coronary:0.00):0.00,Artery_Tibial:0.00):0.00,Brain_Cortex:0.00):0.00,Breast_Mammary_Tissue:0.00):0.00,Colon_Sigmoid:0.00):0.00,Heart_Atrial_Appendage:0.00):0.00,Muscle_Skeletal:0.00):0.00,Nerve_Tibial:0.00):0.00,Vagina:0.00):0.08,Kidney_Medulla:0.08):0.03,(((((((((((((((((((((Brain_Amygdala:0.00,Bladder:0.00):0.00,Brain_Anterior_cingulate_cortex_BA24:0.00):0.00,Brain_Caudate_basal_ganglia:0.00):0.00,Brain_Cerebellar_Hemisphere:0.00):0.00,Brain_Cerebellum:0.00):0.00,Brain_Frontal_Cortex_BA9:0.00):0.00,Brain_Hippocampus:0.00):0.00,Brain_Hypothalamus:0.00):0.00,Brain_Nucleus_accumbens_basal_ganglia:0.00):0.00,Brain_Putamen_basal_ganglia:0.00):0.00,Brain_Spinal_cord_cervical_c-1:0.00):0.00,Brain_Substantia_nigra:0.00):0.00,Cells_EBV-transformed_lymphocytes:0.00):0.00,Colon_Transverse:0.00):0.00,Esophagus_Gastroesophageal_Junction:0.00):0.00,Esophagus_Muscularis:0.00):0.00,Heart_Left_Ventricle:0.00):0.00,Kidney_Cortex:0.00):0.00,Pituitary:0.00):0.00,Thyroid:0.00):0.00,Whole_Blood:0.00):0.11):0.03,((((((((((((Cells_Cultured_fibroblasts:0.00,Adrenal_Gland:0.00):0.00,Esophagus_Mucosa:0.00):0.00,Fallopian_Tube:0.00):0.00,Lung:0.00):0.00,Minor_Salivary_Gland:0.00):0.00,Ovary:0.00):0.00,Prostate:0.00):0.00,Small_Intestine_Terminal_Ileum:0.00):0.00,Spleen:0.00):0.00,Stomach:0.00):0.00,Uterus:0.00):0.06,((Cervix_Endocervix:0.00,Cervix_Ectocervix:0.00):0.00,Testis:0.00):0.06):0.09):0.19,Pancreas:0.33):0.27,(Skin_Sun_Exposed_Lower_leg:0.02,Skin_Not_Sun_Exposed_Suprapubic:0.02):0.59):0.68,Liver:1.29);"
#> 

# 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)
# }