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Tissue specificity based on the human RNA profile available in the HPA Tissue Atlas that is integrated in the Pig RNA Atlas for orthologes.
Low tissue specificity
Human diseasei
Human disease related information is integrated from the KEGG database, indicating disease association in human orthologes. The diseases are grouped into organ systems, based on what tissue type the disease is related to.
Cardiac diseases Other congenital malformations Primary immunodeficiency Vascular diseases
GENE CLASSIFICATIONi
To better understand the transcriptomics landscape of the pig, several gene classification strategies are used to summarize tissue specificity, distribution, regional variation, whole genome co-expression clustering and within tissue heterogeneity.
Tissue specificity categoryi
The tissue specificity categories are defined as follows: Tissue enriched; a single tissue has 4-fold or higher NX than any other tissue, Group enriched; 2-5 tissues have NX larger than a fourth of the maximum NX and their average NX is 4-fold higher than any other tissue, Tissue enhanced; the gene is neither tissue enriched nor group enriched and one or multiple tissues have an NX at least 4-fold higher than the average NX, Low tissue specificity; the gene is neither tissue enriched, group enriched, nor tissue enhanced and detected above cut of in at least one tissue.
The tissue type/s with elevated expression is indicated in parenthesis.
The pig gene expression distribution categories are defined as follows: Detected in all; NX > 1 for all tissues, Detected in many; NX > 1 for at least 31% (n=14) tissue types but not in all, Detected in some; NX > 1 for more than 1 tissue but less than 31% (n=14), Detected in single; NX > 1 for a single tissue. A gene was classified as Not detected if no tissue had NX > 1.
The brain regional expression classification is based on similar specificity rules (4-fold higher expression) used for the tissue specificity and is applied to the main 10 regions of the brain, further described in the HPA Brain Atlas.
Low region specificity
UMAP classificationi
UMAP gene classification shows which cluster the gene belongs to. The genome-wide UMAP visualization enable gene clustering based on expression in all 350 tissue samples, and is utilized for tissue and function annotation of the whole pig gene landscape.
Click on the cluster name and you will find all genes in that specific cluster.
Gene classification based on expression heterogeneity within grouped tissue types.
Within tissue variation highlights genes with a significant difference in expression level among the sub tissues included the tissue types used for whole body classification. If a certain tissue is mentioned as variable - take a closer look at the RNA expression below for more details related to which sub tissue is high vs low.
The transcriptomics data was normalized in a similar manner as done for the Human Protein Atlas . In brief, transcripts per million (TPM) values were calculated per each sample (n=350) for all protein coding genes, referred to as pTPM. Samples of the same tissue type (n=98) were then aggregated by using the average pTPM per gene, and resulting values were sample-wise corrected using trimmed mean of M values (TMM) and then gene-wise pareto scaled, resulting in an expression score referred to as NX.
The expression barplot can be sorted based on the selection to the right.
Expression values for grouped tissues were calculated as the maximum expression of sub-tissues. The 98 tissues are grouped into 44 tissue/organ types, which is used for the gene classification based on specificity and distribution.
Organ
Expression
Alphabetical
RNA tissue specificity: Low tissue specificity
Pig tissue dataseti
Here, all 98 different tissues are shown, enabling further exploration regarding expression heterogeneity within different tissue types.
Organ
Expression
Alphabetical
HUMAN ORTHOLOG GENESi
The expression profile for corresponding human orthologue, based on the expression data in HPA Tissue Atlas Only high confidence gene orthologues are included.
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