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Trait Data and Analysis for LSD_10011

Details and Links

Group Mouse: LGSM-AIG34_50-56-GBS group
Phenotype
Musculoskieletal system, morphology: Femur bone length [mm]
Authors
Hernandez Cordero AI, Gonzales NM, Parker CC, Sokoloff G, Vandenbergh DJ, Cheng R, Abney M, Skol A, Douglas A, Palmer AA, Gregory JS, Lionikas A
Title
Genome-wide associations reveal human-mouse genetic convergence and modifiers of myogenesis, CPNE1 and STC2
Journal None (2019)
Database LGSM AI G34/50-56 GBS Phenotypes

Statistics


More about Normal Probability Plots and more about interpreting these plots from the glossary

Transform and Filter Data

Edit or delete values in the Trait Data boxes, and use the Reset option as needed.



Outliers highlighted in orange can be hidden using the Hide Outliers button.

Samples with no value (x) can be hidden by clickingHide No Value button.

Calculate Correlations

Chr:     Mb:  to 
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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GEMMA
GEMMA maps with correction for kinship using a linear mixed model and can include covariates such as sex and age. Defaults include a minor allele frequency of 0.05 and the leave-one-chromosome-out method (PMID: 2453419, and GitHub code).
More information on R/qtl mapping models and methods can be found here.

Review and Edit Data 



            
  # read into R
  trait <- read.csv("LSD_10011.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("LSD_10011.csv", header = 0, comment = "#")
Edit CaseAttributes

Samples


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