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Trait Data and Analysis for ITP_10002

Details and Links

Group Mouse: HET3-ITP group
Phenotype
Aging, metabolism: Body weight at 6 months of age on NIH-31 standard (for breeding cages, UM = Purina 5008, UT = Teklad 7912, TJL = Purina 5K52; for weanlings prior to 4 months of age, UM = Purina 5001, UT = Teklad 7912, TJL = Purina 5LG6; PMID: 17578509) [g]
Authors
Miller RA, Harrison DE, Astle CM, Floyd RA, Flurkey K, Hensley KL, Javors MA, Leeuwenburgh C, Nelson JF, Ongini E, Nadon NL, Warner HR, Strong R
Title
An Aging Interventions Testing Program: study design and interim report
Journal Aging Cell (2007)
Database HET3 ITP Longevity Phenotypes
Resource Links PubMed

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

R/qtl (version 1.44.9)
R/qtl maps using several models and uniquely support 4-way intercrosses such as the "Aging Mouse Lifespan Studies" (NIA UM-HET3). We will add support for R/qtl2 (PMID: 30591514) in the near future—a version that handles complex populations with admixture and many haplotypes.
Pair Scan (R/qtl v 1.44.9)
The Pair Scan mapping tool performs a search for joint effects of two separate loci that may influence a trait. This search typically requires large sample sizes. Pair Scans can included covariates such as age and sex. For more on this function by K. Broman and colleagues see www.rdocumentation.org/packages/qtl/versions/1.60/topics/scantwo
More information on R/qtl mapping models and methods can be found here.

Review and Edit Data



Show/Hide Columns: 


            
  # read into R
  trait <- read.csv("ITP_10002.csv", header = TRUE, comment.char = "#")

  # read into python
  import pandas as pd
  trait = pd.read_csv("ITP_10002.csv", header = 0, comment = "#")
Edit CaseAttributes

Samples


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