getspres: Exploring Heterogeneity in Meta-Analysis with _SPRE_...

Description Usage Arguments Details Value Methods (by class) See Also Examples

View source: R/driver_compute_spre_statistics.R

Description

getspres computes SPRE (standardised predicted random-effects) statistics to identify outlier studies in genetic association meta-analyses which might have undue influence on the average genetic effect leading to inflated genetic signals.

Usage

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getspres(beta_in, se_in, study_names_in, variant_names_in, ...)

## Default S3 method:
getspres(beta_in, se_in, study_names_in,
  variant_names_in, tau2_method = "DL", verbose_output = FALSE, ...)

Arguments

beta_in

A numeric vector of study effect-sizes e.g. log odds-ratios.

se_in

A numeric vector of standard errors, genomically corrected at study-level.

study_names_in

A character vector of study names.

variant_names_in

A character vector of variant names e.g. rsIDs.

...

other arguments.

tau2_method

A character scalar, specifying the method that should be used to estimate heterogeneity either through DerSimonian and Laird's moment-based estimate "DL" or restricted maximum likelihood "REML". Note: The REML method uses the iterative Fisher scoring algorithm (step length = 0.5, maximum iterations = 10000) to estimate tau2. Default is "DL".

verbose_output

An optional boolean to display intermediate output. (Default is FALSE).

Details

SPRE statistics are precision-weighted residuals that summarise the direction and extent with which observed study effects in a meta-analysis differ from the summary (or average genetic) effect. See the getspres website for more information, documentation and examples.

getspres takes as input study effect-size estimates and their corresponding standard errors (i.e. summary data). Study effect estimates could be in the form of linear regression coefficients or log-transformed regression coefficients (per-allele log odds ratios) from logistic regression.

getspres uses inverse-variance weighted meta-analysis models in the metafor R package to calculate SPRE statistics.

Value

Returns a list containing:

Methods (by class)

See Also

https://magosil86.github.io/getspres/ to the visit getspres website.

Examples

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library(getspres)


# Calculate SPRE statistics for a subset of variants in the heartgenes214 dataset.
# heartgenes214 is a case-control GWAS meta-analysis of coronary artery disease.
# To learn more about the heartgenes214 dataset ?heartgenes214

# Calculating SPRE statistics for 3 variants in heartgenes214

heartgenes3 <- subset(heartgenes214, 
    variants %in% c("rs10139550", "rs10168194", "rs11191416")) 

getspres_results <- getspres(beta_in = heartgenes3$beta_flipped, 
                               se_in = heartgenes3$gcse, 
                      study_names_in = heartgenes3$studies, 
                    variant_names_in = heartgenes3$variants)


# Explore results generated by the getspres function
str(getspres_results)

# Retrieve number of studies and variants
getspres_results$number_variants
getspres_results$number_studies

# Retrieve SPRE dataset
df_spres <- getspres_results$spre_dataset
head(df_spres)

# Extract SPREs from SPRE dataset
head(spres <- df_spres[, "spre"])



# Exploring available options in the getspres function:
#     1. Estimate heterogeneity using "REML", default is "DL"
#     2. Calculate SPRE statistics verbosely
getspres_results <- getspres(beta_in = heartgenes3$beta_flipped, 
                               se_in = heartgenes3$gcse, 
                      study_names_in = heartgenes3$studies, 
                    variant_names_in = heartgenes3$variants,
                         tau2_method = "REML",
                      verbose_output = TRUE)

str(getspres_results)

magosil86/getspres documentation built on April 6, 2020, 9:40 a.m.