data_example: Simulated effects on quantitative incidence and prognosis...

In Osmahmoud/SlopeHunter: Slope-Hunter: A ROBUST METHOD FOR COLLIDER BIAS CORRECTION IN CONDITIONAL GENOME-WIDE ASSOCIATION STUDIES

Simulated effects on quantitative incidence and prognosis traits

Description

A simulated dataset for 10,000 independent variables (e.g. SNPs) consisting of regression coefficients on incidence and prognosis, with their standard errors. Among all the SNPs, 5% (500 variables) have effects on incidence only, 5% (500 variables) on prognosis only, and 5% have correlated effects on both with a correlation coeficient of '-0.5'. The estimates are obtained from linear regression in a simulated dataset of 20,000 individuals.

Usage

data_example

Format

A data frame with 10,000 rows and 5 variables:

xbeta

Regression coefficient on incidence

xse

Standard error of xbeta

ybeta

Regression coefficient on prognosis

yse

Standard error of ybeta

yp

P-value of the association with prognosis

Examples

# Load the \code{SlopeHunter} package
require(SlopeHunter)

# Load the input data set
data(data_example, package = "SlopeHunter")
head(data_example)

# Implement the Slope-Hunter method
Sh.Model <- hunt(dat = data_example, xbeta_col="xbeta", xse_col="xse",
                        ybeta_col="ybeta", yse_col="yse", yp_col="yp",
                        xp_thresh = 0.001, Bootstrapping = TRUE, show_adjustments = TRUE, seed=2021)

# [1] "Estimated slope: -0.274120383700514"
# [1] "SE of the slope: 0.0229566376478153"
# [1] "95% CI: -0.319115393490232, -0.229125373910796"

# Display the estimated slope (adjustment factor)
Sh.Model$b
# [1] -0.2741204

# Extract information about cluster memberships of SNPs included in the analysis
Adj <- Sh.Model$Fit

# Show the first 6 values of the unadjusted estimated effects on prognosis
head(data_example$ybeta)
# [1] -0.0092889266  0.0005575032  0.0112203795 -0.0095533069  0.0082635203  0.0026550045


# Show results of the first 6 corrected variants:
head(Sh.Model$est)

#   xbeta  xse   ybeta  yse   yp    xp    SNP  ybeta_adj   yse_adj yp_adj
# 1 -0.007 0.007 -0.009 0.006 0.136 0.300 snp1 -0.011      0.006   0.083
# 2  0.014 0.007  0.000 0.006 0.928 0.042 snp2  0.004      0.006   0.492
# 3 -0.011 0.007  0.011 0.006 0.072 0.097 snp3  0.008      0.006   0.220
# 4  0.004 0.007 -0.009 0.006 0.125 0.493 snp4 -0.008      0.006   0.208
# 5 -0.025 0.007  0.008 0.006 0.185 0.000 snp5  0.001      0.006   0.851
# 6  0.013 0.007  0.002 0.006 0.670 0.054 snp6  0.006      0.006   0.329

# Generate an interactive plot for the estimated clusters (hover on the data points to view info)
require(ggplot2)
require(plotly)
ggplotly(Sh.Model$plot)

Osmahmoud/SlopeHunter documentation built on Oct. 7, 2022, 4:38 p.m.