modACDC (version 2.0.1)

GCTA_parPlot: 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)

Value

ggplot object

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

Author

Katelyn Queen,

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.

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
if (FALSE) par <- GCTA_par(df = geneExpressionData, 
          ILCincrement = 0.25, 
          fileLoc = "pathHere",
          gctaPath = "pathHere",
          summaryType = "coexpression",
          permute = TRUE,
          numNodes = 1)

# run function
if (FALSE) GCTA_parPlot(df=par, dataName = "Example Data", summaryType = "coexpression")