This functions takes a meta-data from METAL (tbl) and data from contributing studies (all) for forest plot. It also takes a SNPID-rsid mapping (rsid) as contributing studies often involve discrepancies in rsid so it is appropriate to use SNPID, i.e., chr:pos_A1_A2 (A1<=A2).
METAL_forestplot(tbl, all, rsid, package = "meta", split = FALSE, ...)It will generate a forest plot specified by pdf for direction-adjusted effect sizes.
Meta-anslysis summary statistics.
statistics from all contributing studies.
SNPID-rsid mapping file.
style of plot as in meta, rmeta or forestplot.
when TRUE, individual prot-MarkerName.pdf will be generated.
Additional arguments to meta::forest.
Jing Hua Zhao
The study-specific and total sample sizes (N) can be customised from METAL commands. By default, the input triplets each contain a `MarkerName` variable which is the unique SNP identifier (e.g., chr:pos:a1:a2) and the `tbl` argument has variables `A1` and `A2` as produced by METAL while the `all` argument has `EFFECT_ALLELE` and `REFERENCE_ALLELE` as with a `study` variable indicating study name. Another variable common the `tbl` and `all` is `prot` variable as the function was developed in a protein based meta-analysis. From these all information is in place for generation of a list of Forest plots through a batch run.
CUSTOMVARIABLE N
LABEL N as N
WEIGHTLABEL N
Scharzer G. (2007). meta: An R package for meta-analysis. R News, 7:40-5, https://cran.r-project.org/doc/Rnews/Rnews_2007-3.pdf, https://CRAN.R-project.org/package=meta.
Willer CJ, Li Y, Abecasis GR. (2010). METAL: fast and efficient meta-analysis of genomewideassociation scans. Bioinformations. 26:2190-1, https://github.com/statgen/METAL, https://genome.sph.umich.edu/wiki/METAL.
METAL_forestplot
if (FALSE) {
require(gap.datasets)
data(OPG)
METAL_forestplot(OPGtbl,OPGall,OPGrsid,width=8.75,height=5,digits.TE=2,digits.se=2)
}
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