R/compute_nsize.R
compute_nsize.Rd
Check for N column if not present and user wants, impute N based on user's sample size. NOTE this will be the same value for each SNP which is not necessarily correct and may cause issues down the line. N can also be inputted with "ldsc", "sum", "giant" or "metal" by passing one or multiple of these.
compute_nsize(
sumstats_dt,
imputation_ind = FALSE,
compute_n = c("ldsc", "giant", "metal", "sum"),
standardise_headers = FALSE,
force_new = FALSE,
return_list = TRUE
)
data table obj of the summary statistics file for the GWAS.
Binary Should a column be added for each imputation step to show what SNPs have imputed values for differing fields. This includes a field denoting SNP allele flipping (flipped). Note these columns will be in the formatted summary statistics returned. Default is FALSE.
How to compute per-SNP sample size (new column "N").
0
: N will not be computed.
>0
: If any number >0 is provided,
that value will be set as N for every row.
Note: Computing N this way is incorrect and should be avoided
if at all possible.
"sum"
: N will be computed as:
cases (N_CAS) + controls (N_CON), so long as both columns are present.
"ldsc"
: N will be computed as effective sample size:
Neff =(N_CAS+N_CON)*(N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))(N_CAS+N_CON)==max(N_CAS+N_CON)).
"giant"
: N will be computed as effective sample size:
Neff = 2 / (1/N_CAS + 1/N_CON).
"metal"
: N will be computed as effective sample size:
Neff = 4 / (1/N_CAS + 1/N_CON).
Standardise headers first.
If "Neff" (or "N") already exists in sumstats_dt
,
replace it with the recomputed version.
Return the sumstats_dt
within a named list
(default: TRUE
).
list("sumstats_dt"=sumstats_dt)
sumstats_dt <- MungeSumstats::formatted_example()
#> Standardising column headers.
#> First line of summary statistics file:
#> MarkerName CHR POS A1 A2 EAF Beta SE Pval
#> Sorting coordinates.
sumstats_dt2 <- MungeSumstats::compute_nsize(sumstats_dt=sumstats_dt,
compute_n=10000)
#> Assigning N=10000 for all SNPs.