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Get Clustered Median Exon Expression

Source: R/get_clustered_median_exon_expression.R
get_clustered_median_exon_expression.Rd

Find median transcript expression data along with hierarchical clusters.

  • Returns median normalized transcript expression in tissues of all known transcripts of a given gene along with the hierarchical clustering results of tissues and transcripts, based on exon expression, in Newick format.

  • The hierarchical clustering is performed by calculating Euclidean distances and using the average linkage method.

  • This endpoint is not paginated.

By default, this endpoint queries the latest GTEx release.

GTEx Portal API documentation

Usage

get_clustered_median_exon_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_gene_expression(), get_clustered_median_junction_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_exon_expression(c("ENSG00000203782.5",
                                       "ENSG00000132693.12"))
#> ℹ Retrieve clustering data with `attr(<df>, 'clusters')`
#> # A tibble: 216 × 8
#>     median exonId   tissueSiteDetailId ontologyId datasetId gencodeId geneSymbol
#>      <dbl> <chr>    <chr>              <chr>      <chr>     <chr>     <chr>     
#>  1  0.789  ENSG000… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000… CRP       
#>  2 16.8    ENSG000… Adipose_Subcutane… UBERON:00… gtex_v8   ENSG0000… CRP       
#>  3  0.0921 ENSG000… Adipose_Visceral_… UBERON:00… gtex_v8   ENSG0000… CRP       
#>  4 12      ENSG000… Adipose_Visceral_… UBERON:00… gtex_v8   ENSG0000… CRP       
#>  5  1      ENSG000… Adrenal_Gland      UBERON:00… gtex_v8   ENSG0000… CRP       
#>  6 22.7    ENSG000… Adrenal_Gland      UBERON:00… gtex_v8   ENSG0000… CRP       
#>  7  0      ENSG000… Artery_Aorta       UBERON:00… gtex_v8   ENSG0000… CRP       
#>  8  8      ENSG000… Artery_Aorta       UBERON:00… gtex_v8   ENSG0000… CRP       
#>  9  0      ENSG000… Artery_Coronary    UBERON:00… gtex_v8   ENSG0000… CRP       
#> 10 14      ENSG000… Artery_Coronary    UBERON:00… gtex_v8   ENSG0000… CRP       
#> # ℹ 206 more rows
#> # ℹ 1 more variable: unit <chr>

# clustering data is stored as an attribute "clusters"
result <- get_clustered_median_exon_expression(c("ENSG00000203782.5",
                                                 "ENSG00000132693.12"))
#> ℹ Retrieve clustering data with `attr(<df>, 'clusters')`
attr(result, "clusters")
#> $exon
#> [1] "((ENSG00000203782.5_2:8.51,ENSG00000132693.12_2:8.51):0.79,(ENSG00000203782.5_1:5.92,ENSG00000132693.12_1:5.92):3.38);"
#> 
#> $tissue
#> [1] "(((((((((((Uterus:0.02,Stomach:0.02):0.04,(Pituitary:0.02,Brain_Cerebellum:0.02):0.04):0.01,((((Thyroid:0.03,Colon_Transverse:0.03):0.00,((Artery_Coronary:0.02,Adipose_Visceral_Omentum:0.02):0.00,(Esophagus_Muscularis:0.01,Esophagus_Gastroesophageal_Junction:0.01):0.01):0.01):0.01,Small_Intestine_Terminal_Ileum:0.04):0.00,Artery_Aorta:0.04):0.03):0.04,Kidney_Cortex:0.11):0.00,Brain_Cerebellar_Hemisphere:0.11):0.02,(((((((Ovary:0.01,Nerve_Tibial:0.01):0.00,Adipose_Subcutaneous:0.01):0.01,Esophagus_Mucosa:0.02):0.01,(Spleen:0.01,Breast_Mammary_Tissue:0.01):0.02):0.01,Minor_Salivary_Gland:0.03):0.01,(Prostate:0.02,Lung:0.02):0.02):0.04,Testis:0.09):0.04):0.02,((Fallopian_Tube:0.09,Cervix_Ectocervix:0.09):0.01,((Kidney_Medulla:0.06,Bladder:0.06):0.03,((Cells_Cultured_fibroblasts:0.01,Adrenal_Gland:0.01):0.04,Cervix_Endocervix:0.05):0.03):0.02):0.05):0.03,((Vagina:0.09,Brain_Hypothalamus:0.09):0.03,((((((Brain_Hippocampus:0.01,Brain_Amygdala:0.01):0.01,Cells_EBV-transformed_lymphocytes:0.02):0.01,Brain_Frontal_Cortex_BA9:0.02):0.01,((Brain_Nucleus_accumbens_basal_ganglia:0.00,Brain_Caudate_basal_ganglia:0.00):0.00,Brain_Putamen_basal_ganglia:0.01):0.03):0.02,(Brain_Cortex:0.02,Brain_Anterior_cingulate_cortex_BA24:0.02):0.03):0.02,(((((Brain_Substantia_nigra:0.01,Artery_Tibial:0.01):0.00,(Heart_Atrial_Appendage:0.00,Brain_Spinal_cord_cervical_c-1:0.00):0.00):0.00,Muscle_Skeletal:0.01):0.01,Colon_Sigmoid:0.02):0.01,(Whole_Blood:0.02,Heart_Left_Ventricle:0.02):0.02):0.04):0.05):0.06):0.20,Pancreas:0.39):0.34,(Skin_Sun_Exposed_Lower_leg:0.02,Skin_Not_Sun_Exposed_Suprapubic:0.02):0.71):0.12,Liver:0.85);"
#> 

# 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)
# }