Trait Data and Analysis for ENSRNOG00000012874

Repulsive guidance molecule family member A

Details and Links

Group Rat: HSNIH-Palmer group
Tissue Lateral Habenula mRNA
Gene Symbol Rgma
Aliases Wikidata: RGM; BC059072; C230063O06
GeneNetwork: RGM; BC059072; C230063O06
Location Chr 1 @ 127.128934 Mb on the plus strand
Summary Predicted to enable coreceptor activity and transferrin receptor binding activity. Involved in negative regulation of axon regeneration; negative regulation of collateral sprouting; and positive regulation of neuron projection development. Located in neuron projection. Used to study middle cerebral artery infarction. Biomarker of middle cerebral artery infarction; sciatic neuropathy; and temporal lobe epilepsy. Human ortholog(s) of this gene implicated in multiple sclerosis. Orthologous to human RGMA (repulsive guidance molecule BMP co-receptor a). [provided by Alliance of Genome Resources, Apr 2022]
Database HSNIH-Palmer Lateral Habenula RNA-Seq (Aug18) rlog
Resource Links Gene    GeneMANIA    Protein Atlas    Rat Genome DB    GTEx Portal    PhenoGen   
BioGPS    STRING    PANTHER    Gemma    EBI GWAS   

Statistics


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Calculate Correlations

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Sample Correlation
The Sample Correlation is computed between trait data and any other traits in the sample database selected above. Use Spearman Rank when the sample size is small (<20) or when there are influential outliers.
Literature Correlation
The Literature Correlation (Lit r) between this gene and all other genes is computed
using the Semantic Gene Organizer and human, rat, and mouse data from PubMed. Values are ranked by Lit r, but Sample r and Tissue r are also displayed.
More on using Lit r
Tissue Correlation
The Tissue Correlation (Tissue r) estimates the similarity of expression of two genes or transcripts across different cells, tissues, or organs (glossary). Tissue correlations are generated by analyzing expression in multiple samples usually taken from single cases.
Pearson and Spearman Rank correlations have been computed for all pairs of genes using data from mouse samples.

Mapping Tools

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Review and Edit Data

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  # read into R
  trait <- read.csv("ENSRNOG00000012874.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("ENSRNOG00000012874.csv", header = 0, comment = "#")
            
          
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Samples


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