Description

On this page, users can explore the scARE database to discover highlighted retrotransposable elements (RTEs) across various cell types and disease conditions.

Highly expressed RTEs

Within this section, users have the option to select conditions for exploring highly expressed retrotransposable elements (RTEs).


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Differential expressed RTEs (disease v.s. control)

In this section, users can select conditions to explore differentially expressed retrotransposable elements (RTEs).


PoE: Percent of Expressing.
Log2(FC): Log2 of fold change of normalized read count.
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Differential expressed RTEs (cell type v.s. cell type)

In this section, users have the option to select conditions for exploring differentially expressed retrotransposable elements (RTEs).


PoE: Percent of Expressing.
Log2(FC): Log2 of fold change of normalized read count.
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Expression correlation

It could take a few minutes to load/refresh the data.

Expression correlation (gene list)

In this section, users can compare the expression of a gene/RTE to the mean expression of a list of genes/RTEs. The mean expression is calculated by normalizing the read count of all genes in the list and then dividing by the number of genes in the list. The inputted gene list will be validated, and only genes present in our database will be retained for analysis.
It could take a few minutes to load/refresh the data.

Co-expression network

In this section, we built co-expression network between RTE and genes as follow:
  1. Cell type : AD vs control in specific cell type.
  2. Diagnosis: Cell type vs cell type in AD
We extracted the differentially expressed (DE) genes from both conditions and computed their positive Spearman correlation using the MEGENA package in R, specifically the calculate.rho.signed function, with an FDR cutoff of 0.05. To construct the coexpression networks, we considered correlations between DE genes and retrotransposable elements (RTEs) with correlation coefficients greater than or equal to 0.5. In the coexpression networks, pink diamonds represent the RTEs, orange circles represent the DE genes, and edges denote correlations between them. Multiple edges indicate evidence from multiple datasets.

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