OSCA_singleValue {modACDC}

OSCA_singleValue

Description

Function to return the percent variance explained in an external phenotype for a single dataset

Usage

OSCA_singleValue(
  df,
  externalVar,
  oscaPath,
  remlAlg = 0,
  maxRemlIt = 100,
  numCovars = NULL,
  catCovars = NULL
)

Arguments

df

n x p dataframe or matrix of numeric -omics values with no ID column

externalVar

vector of length n of external variable values with no ID column

oscaPath

absolute path to OSCA software

remlAlg

which algorithm to run REML iterations in GCTA; 0 = average information (AI), 1 = Fisher-scoring, 2 = EM; default is 0 (AI)

maxRemlIt

the maximum number of REML iterations; default is 100

numCovars

n x c_n matrix of numerical covariates to adjust heritability model for; must be in same person order as fam file; default is NULL

catCovars

n x c_c matrix of categorical covariates to adjust heritability model for; must be in same person order as fam file; default is NULL

Details

OmicS-data-based Complex trait Analysis (OSCA) is a suite of C++ functions. In order to use the OSCA functions, the user must specify the absolute path to the OSCA software, which can be downloaded from the Yang Lab website here.

Here, we use OSCA's Omics Restricted Maximum Likelihood (OREML) method to estimate the percent of variance in an external phenotype that can be explained by an omics profile, akin to heritability estimates in GWAS.

Value

Row of OREML output containing percent variance explained in external data and standard error

Author(s)

Katelyn Queen, [e-mail]

References

Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57 (1995) 289–300.

Martin P, et al. Novel aspects of PPARalpha-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study. Hepatology, in press, 2007.

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.

Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. ACDC: a general approach for detecting phenotype or exposure associated co-expression. Frontiers in Medicine (2023) 10. doi:10.3389/fmed.2023.1118824.

See Also

OSCA software - https://yanglab.westlake.edu.cn/software/osca/

Examples

#load CCA package for example dataset
library(CCA)

# load dataset
data("nutrimouse")

# run function; input absolute path to OSCA software before running
## Not run: OSCA_singleValue(df = nutrimouse$gene, 
                  externalVar = as.numeric(nutrimouse$diet),
                  oscaPath = "pathHere")
## End(Not run)