| GCTA_parPlot {modACDC} | R Documentation |
GCTA_parPlot
Description
GCTA_parPlot creates a graph of the output from the GCTA_par function, plotting average heritability of the first principal component of either co-expression or covariance of gene modules against information lost/percent reduction for both observed and permuted data.
Usage
GCTA_parPlot(df, dataName = "", summaryType)
Arguments
df |
output from GCTA_par function with permutations |
dataName |
string of name of data for graph labels; default is blank |
summaryType |
one of "coexpression" or "covariance"; how modules were summarized for GCTA calculations |
Details
Genome-wide Complex Trait Analysis (GCTA) is a suite of C++ functions. In order to use the GCTA functions, the user must specify the absolute path to the GCTA software, which can be downloaded from the Yang Lab website here.
In GCTA_par, we use GCTA's Genomics REstricted Maximum Likelihood (GREML) method to estimate the average heritability of the first principal component of either co-expression or covariance of gene modules. The produced plot shows these heritability estimates at varying levels of dataset reduction, calculated for observed data in blue and permuted data in red. An information loss value of 0 represents the unreduced dataset, and an information loss level of 100 represents the data being reduced to the average expression of all features.
Value
ggplot object
Author(s)
Katelyn Queen, kjqueen@usc.edu
References
Millstein J, Battaglin F, Barrett M, Cao S, Zhang W, Stintzing S, et al. Partition: a surjective mapping approach for dimensionality reduction. Bioinformatics 36 (2019) 676–681. doi:10.1093/bioinformatics/ btz661.
Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011 Jan 7;88(1):76-82. doi: 10.1016/j.ajhg.2010.11.011. Epub 2010 Dec 17. PMID: 21167468; PMCID: PMC3014363.
See Also
GCTA software - https://yanglab.westlake.edu.cn/software/gcta/#Overview
Examples
# run OSCA_par and save output; input absolute path to OSCA software before running
## Not run: par <- GCTA_par(df = geneExpressionData,
ILCincrement = 0.25,
fileLoc = "pathHere",
gctaPath = "pathHere",
summaryType = "coexpression",
permute = TRUE,
numNodes = 1)
## End(Not run)
# run function
## Not run: GCTA_parPlot(df=par, dataName = "Example Data", summaryType = "coexpression")